Patent Application
20250142413
The present disclosure relates to scheduling of sensing signals for two or more wireless devices.
Wireless communication has been advancing over several decades now. Exemplary notable standards organizations include the 3rd Generation Partnership Project (3GPP) and IEEE 802.11, commonly referred to as Wi-Fi.
Cognitive radio is one of the emerging technologies for exploiting the system spectrum. Cognitive radio devices are supposed to dynamically use the best wireless channels in their vicinity to improve spectrum efficiency. In order to achieve this, spectrum occupancy information may be desirable to help modeling and predicting the spectrum availability for efficient dynamic spectrum access. Spectrum occupancy prediction may be based on using the information on previous spectrum occupancy to predict future occupancy. Such a prediction is based on exploiting the inherent correlation between past and future occupancies. Some approaches exploit time-domain correlation and thus cast spectrum prediction as a time-series prediction. Some approaches additionally consider exploiting the correlation along the frequency axis, and thus exploit time-frequency correlation. Correlation may also exist in the spatial domain. Thus, exploiting the correlation in all mentioned domains may be desirable.
Future wireless devices are expected to be sensing capable, or at times, solely wireless sensors, to support communication applications and/or provide a wide range of other applications, such as fully immersive extended reality, improving quality of life by enabling smart environments, improving health-related applications through non-invasive tests and vital signs monitoring.
The present disclosure relates to methods and apparatuses for scheduling of sensing signals for two or more wireless devices.
According to an embodiment, a method is provided for scheduling sensing signals for two or more wireless devices, comprising: determining requirements of sensing performance from the two or more wireless devices; determining existing sensing signals being transmitted by the two or more wireless devices; determining one or more groups of wireless devices having similar requirements of sensing performance, each group comprising one or more wireless devices of the two or more wireless devices; for each group, comparing features of existing sensing signals with respective requirements of sensing performance; providing information about the existing sensing signals to a group out of the one or more groups of wireless devices, if the predetermined requirements for the group are fulfilled.
According to an embodiment, a wireless device is provided for scheduling sensing signals, comprising: processing circuitry configured to determine requirements of sensing performance from two or more wireless devices; determine existing sensing signals being transmitted; determine one or more groups of wireless devices having similar requirements of sensing performance, each group comprising one or more wireless devices of the two or more wireless devices; for each group, compare features of existing sensing signals with respective requirements of sensing performance; a transceiver configured to transmit information about the existing sensing signals to a group out of the one or more groups of wireless devices, if the predetermined requirements for the group are fulfilled.
These and other features and characteristics of the presently disclosed subject matter, as well as the methods of operation and functions of the related elements of structures and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the disclosed subject matter. As used in the specification and the claims, the singular form of “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.
An understanding of the nature and advantages of various embodiments may be realized by reference to the following figures.
For purposes of the description hereinafter, the terms “end,” “upper,” “lower,” “right,” “left,” “vertical,” “horizontal,” “top,” “bottom,” “lateral,” “longitudinal,” and derivatives thereof shall relate to the disclosed subject matter as it is oriented in the drawing figures. However, it is to be understood that the disclosed subject matter may assume various alternative variations and step sequences, except where expressly specified to the contrary. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary embodiments or aspects of the disclosed subject matter. Hence, specific dimensions and other physical characteristics related to the embodiments or aspects disclosed herein are not to be considered as limiting unless otherwise indicated.
No aspect, component, element, structure, act, step, function, instruction, and/or the like used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items and may be used interchangeably with “one or more” and “at least one.” Furthermore, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, and/or the like) and may be used interchangeably with “one or more” or “at least one.” Where only one item is intended, the term “one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise.
Although the aforementioned techniques may be successful in some cases, they do not provide full information or awareness about the future spectrum or its usage. Meanwhile, wireless sensing is gaining popularity in commercial devices, for environment monitoring, health monitoring, and numerous other applications. Military use of wireless sensing such as radar has always been popular. Sensing applications generate signals, which may typically have a pattern different from those of some communication applications. For instance, most wireless sensing applications generate periodic signal transmissions of varying periodicity. Effectively scheduling and managing these transmissions would result in less spectrum and power wastage. Therefore, if a device can determine which sensing application is utilizing the spectrum, it can either plan its own transmissions such that they fall in the empty slots or utilize these transmissions for its own sensing, if they are suitable.
The present disclosure is not limited to any particular transmitter Tx, receiver Rx and/or interface Itf implementation. However, it may be applied readily to some existing communication systems as well as to the extensions of such systems, or to new communication systems. Exemplary existing communication systems may be, for instance the 5G New Radio (NR) in its current or future releases, and/or the IEEE 802.11 based systems such as the recently studied IEEE 802.11be or the like. The wireless signal is not necessarily a communication signal in the sense that it does not necessarily carry out human or machine communication. It may be, in particular, a sensing signal such as a radar signal or sounding a signal or any other kind of wireless signal from a sensing device such as some signal reporting sensing results to another device(s).
For instance, the amendment IEEE 802.11bf—Wireless Local Area Network (WLAN) Sensing—may include support for wireless sensing in WLAN networks. Some embodiments may be used to enhance the performance of devices complying with this standard, e.g., to reduce the amount of redundant sensing signals in an area or a network. The fifth-generation (5G) New Radio (NR) standard, 6G standards or other future standards may also apply wireless sensing as its part of future cellular communications networks. Some embodiments of the present disclosure may help to predict the empty spaces in the licensed-exempt spectrum during opportunistic spectrum usage, where most wireless sensing is expected to take place. The present disclosure is also applicable to other communication technologies such as 3G or communication technologies under long-term evolution (LTE)/LTE Unlicensed (LTE-U).
As mentioned above, spectrum awareness is an important part of cognitive radio (CR). Some embodiments of the present disclosure may facilitate the identification and prediction of sensing transmissions. The IEEE 802.22 and IEEE 802.15 standard support CR and may thus profit from the present disclosure. The present disclosure is also applicable to low-power wide-area network (LPWAN) technologies, as it aids in increasing power efficiency through reducing the number of redundant sensing transmissions. Thus, it is related to LPWAN standards such as Wize, ZigBee, NarrowBand loT, and LoRaWAN. In general, some embodiments can be used in high frequencies—or millimeter waves (mm-waves)—as the spectrum availability and propagation characteristics are suitable for high-resolution wireless sensing. It can be used for managing resources for wireless sensing.
There may be separate devices including the functionality of the Rx and Tx, respectively. The transmitter Tx and receiver Rx may be implemented in any device such as a base station (eNB, AP) or terminal (UE, STA), or in any other entity of the wireless system WiS. A device such as a base station, access point, or terminal may implement both Rx and Tx. The present disclosure is not limited to any particular transmitter Tx, receiver Rx and/or interface Itf implementation. However, it may be applied readily to some existing communication systems as well as to the extensions of such systems, or to new communication systems. Exemplary existing communication systems may be, for instance, the 5G New Radio (NR) in its current or future releases, and/or the IEEE 802.11 based systems such as the recently studied IEEE 802.11be or the like. Sensing applications signals may also be embedded within resources provided by one or more or the known systems such as some IEEE 802.11 standards or their possible specific extensions for supporting sensing applications.
Future wireless devices are expected to be sensing capable, or at times, solely wireless sensors, to support communication applications and/or provide a wide range of other applications, such as fully immersive extended reality, improving quality of life by enabling smart environments, improving health-related applications through non-invasive tests and vital signs monitoring, and much more. Wireless sensing applications may require periodic or continuous sensing transmissions. However, allowing all sensing/sensing capable devices to transmit their own sensing transmissions may reduce spectral efficiency and degrade the performance of networks operating in the license-exempt bands. Additionally, because sensing transmissions are periodic, there is a strong likelihood that they will cause interference to communication transmissions if they are scheduled opportunistically, or if they have opportunistic channel access mechanisms. This problem can be solved by including sensing-aware channel access and sensing coordination protocols in the standards. However, these would only enable communication and coordination for sensing and devices within the same network. At the same time, this would increase control signalization overhead and complexity. Wireless communication trends are heading towards decentralized and minimum-coordination networks, with the coexistence of a larger number of wireless networks in the same area. As such, methods to identify and predict, or the act of identifying and predicting, future sensing transmissions before transmitting are required in the standards. This would allow devices to better allocate their resources and schedule their transmissions.
Wireless sensing is a process of obtaining information or awareness of the environment through measurements on received (e.g., reflected or directly received) electromagnetic signals. In this definition, processes such as spectrum/channel sensing, radar, joint radar and communication, WLAN sensing, and other methods can be considered as wireless sensing methods. Most wireless sensing methods have either periodic or continuous transmission patterns. An example could be a radar, where a continuous signal or periodic pulses are transmitted. Another example could be channel state information (CSI) based WLAN sensing, where packets are transmitted with some periodicity.
For example, important signal characteristics for radar-based sensing may include periodicity, bandwidth, frequency, number of antennas and/or training sequences. In the CSI-based sensing, important signal characteristics may include, for example, periodicity, bandwidth, frequency and/or number of antennas.
The sensing transmissions may have a specific frame design or transmission mechanism, which is specific for some sensing application(s) and may vary based on the sensing application requirements and environment conditions. Periodicity is an important condition for sensing applications, as disruptions in the periodicity of transmitted/received signals due to interference from other devices, the inability to schedule transmissions, or access the channel using channel access protocols may cause a disruption of measurements. This may cause false alarms, missed detections, reduced resolution of sensed information, overall performance degradation of the sensing application. Depending on the application, this could have severe monetary consequences or life risks.
Signal identification allows devices to identify some features of a signal, such as wireless technology (LTE, 5G, etc.), waveform, modulation type, etc., based on some characteristics of the signals, such as bandwidth, spectrogram image, etc. There are some applications in which certain characteristics are identified for the purpose of synchronization or authentication. Also, spectrum sensing is known, which is used to identify primary users' spectrum occupancy status. However, it may require continuous spectrum sensing. Alternatively, spectrum prediction techniques can be used to save time, energy, and computation overheads required by spectrum sensing.
In future wireless communications, there may be several wireless sensing applications. Generally, sensing applications use continuous signals (with some periodicity), which may degrade the spectral efficiency drastically. This is especially the case where numerous sensing applications/devices are used at the same time and/or in the same area.
Some embodiments and exemplary implementations provide methods and apparatuses for scheduling of sensing signals for two or more wireless devices. Transmissions may be scheduled accordingly without interfering with other applications or utilize the present sensing transmissions for their own sensing applications. Thereby, the spectrum, power, and other resources can be used efficiently.
Scheduling of transmissions may be performed by a scheduling device 20, as shown in
Sensing devices have wireless sensing functionality. They are configured to run a sensing application. These devices may be also configurable or configured to perform wireless communication to transmit their sensed measurements, which is typically a small amount of data compared to amounts of data transmitted by usual communication applications or devices. In the sensing, measurements are taken as the parameters (features) which can be extracted from the wireless signal received, whether directly or after some processing. Some non-limiting examples of measured parameters include received signal strength indicator (RSSI), channel state information (CSI), range, velocity, or the like.
JSC devices are configured to run both the communication application(s) and the sensing application(s). For example, the main function of the JSC devices may be communication, meaning they may have a large amount of data to transmit, but they can perform wireless sensing as well, to improve communication performance or for a user application, such as navigation, or the like. For example, the main function of the JSC devices may be sensing, but they can perform communication as well. The importance of sensing and communication may be equal.
Some non-limiting examples of sensing devices include smart bands, non-invasive medical sensors, such as heart rate monitors, body mass monitors, or the like. Non-limiting examples of applications supported (implemented) by JSC devices include object tracking and/or user tracking for beam management, physical layer security through physical user (human) identification, or the like. Non-limiting exemplary devices include cellphones, laptops, tablets, access points (APs), or the like.
A sensing session may be composed of one or more of the following phases: setup phase, measurement phase, reporting phase, and termination phase. In the setup phase of a sensing session, a sensing session is established, and operational parameters associated with the sensing session are determined and may be exchanged between STAs. In the measurement phase of a sensing session, sensing measurements are performed. In the reporting phase of a sensing session, sensing measurement results are reported. In the termination phase of a sensing session, STAs stop performing measurements and terminate the sensing session.
When more than one independent device is involved in the sensing process (i.e. collaborative wireless sensing), sensing may be performed after some planning by the involved devices. An initiating station (ISTA) is the device which may initiates the wireless sensing process, generally by requesting some resources (transmissions, measurements, etc.) from other devices. A responding station (RSTA) may respond to the ISTA by transmitting sensing transmissions and/or making measurements on sensing signals transmitted by other RSTA and/or making spectrum measurements. These measurements may be communicated to the ISTA or some processor (which in turn will communicate the results of the sensing to the ISTA or sensing requesting application associated with the ISTA).
As shown in the figures, the sensing signals may have different periodicity (indicated by different density of the concentric circle portions illustrating the sensing signal). In this case, identifying the sensing applications without direct communication or coordination with another wireless device may be beneficial. In response to the detection of a sensing signal, the devices can use signals, which are suitable for them rather than transmitting their own sensing signals. For example, once the STA1 detects that WS Tx is transmitting a sensing signal, it may use the sensing signal in addition or alternatively to the sensing signal from the AP1. It is even possible that AP1 detects sensing signal from WS Tx and stops transmitting own sensing signal, since one sensing signal may be sufficient. Or, vice versa, WS Tx detects that AP1 transmits a sensing signals and stops own transmission of the sensing signal. As is clear to those skilled in the art, various implementations of coordination and adaption of the sensing environment may be provided once the sensing applications (signals from sensing applications) have been detected in an area.
In the above description, some particular examples were given. However, the present disclosure is not limited to those examples. Rather, variations and modifications may be advantageous for some scenarios. For example, any features that are different for sensing and communication signals can be used to differentiate them, such as frame structure, periodicity, resolution, RSS/RSSI values, or some features of the sensing that will be defined in the future standards, such as periodic channel access mechanisms, back-off behavior, special sensing sequences or waveforms, or the like. The RSS/RSSI can be used instead of PSD for detecting spectrum occupancy and/or for measurement of the signal to determine whether it is a communication or a sensing signal or to determine the sensing application which originated said signal. For example, the RSS/RSSI measurement is available effortlessly in most communication devices and can give a rough quantification of user activity, i.e., spectrum usage. The term “user” here refers more broadly to a particular application running on a device.
Some embodiments of the present disclosure may be used for applications such as home surveillance or home appliances or entertainment.
Different sensing applications may require different characteristics of the sensing signal. Some characteristics of the signal are, for example, bandwidth (BW), sensing duration, sensing start time, sensing end time, waveform, periodicity, carrier frequency, power, beam width, beam sweep rate, training sequences, pilot placement, or the like. Exemplary frame structures such as communication transmission frame structures 310, 311, empty communication frame structures 320, 321, sensing frame structures 330 and joint sensing and communication frame structures 340 are shown in
There may be numerous communicating devices in all frequency bands. To summarize, the sensing elements and example parameters for an exemplary scenario may be as listed below:
As can be seen in the above-mentioned examples, there are one or more features, which enable distinction between the applications. It is noted that the above example is fictional, and that the measurement values may vary. It is noted that these five features (number of transmitters and/or receivers, frame structure/waveform, carrier frequency, bandwidth, and periodicity) are only exemplary here. In general, depending on the desired resolution for the sensing application identification, one or more of these five features and/or any other feature capable of distinguishing (or contributing to the distinction) between sensing applications may be used. In exemplary and non-limiting implementations, the features may be required as mentioned above.
Signal characteristics and or sensing applications may be identified based on (blind signal analysis) BSA techniques and/or using predefined tags and/or a set of rules or the like.
Features of a signal out of the existing sensing signals may be identified by machine learning (ML) algorithms.
The ML-based approach may include two stages, which are referred to as training and testing. In the training stage, a dataset may be collected, and the ML model may be configured and trained. Then, in the testing stage, the features of existing sensing signals may be learned. These stages are detailed below.
In the training stage, a set of signals may be transmitted by a transmitter 600 via a wireless communication channel 610 and received signals may be captured at the receiver 620. Afterwards, signal features of the received signals, may be estimated by conventional algorithms (models) 650. Then, these estimated features may be stored as outputs in vector format. Correspondingly, the received signals of which these features may be obtained are stored as the input. These processes may be repeated until a sufficient dataset may be generated. The size of the dataset may be determined according to the system requirements, for example, in terms of system performance, complexity, and memory. Then, the ML model 630 may be trained with the created dataset in order to obtain a trained machine learning model 640. These processes are illustrated exemplarily in
In the testing stage, a signal is captured in the receiver 620. Then, this signal is fed to the trained ML algorithm 645. Afterward, the trained ML algorithm estimates the signal features 665. These processes are illustrated exemplarily in
It is noted that the transmission over the channel 610 may be simulated. In such simulation, the channel may be represented by a certain mathematical model, or obtained by simulating actual transmission conditions. In these simulations since the features of the transmitted signals are known, there will be no need to estimate features by conventional algorithms in the training stage. The provision of data from the real system, however, may help training the ML model more efficiently for practical use.
For example, instead or in combination with the trained (e.g. ML/DL) module, other kind of methods such as statistical methods or deterministic methods may be employed for the estimation. For example, if it is observed that a signal is repeating periodically, ML or DL methods may not be necessary to detect presence of such sensing signal. It can be determined deterministically whether sensing is taking place or not. In a blind signal analysis (BSA) a received signal may be analyzed regarding its characteristics such as frequency, bandwidth, periodicity or the like. A blind signal analysis may take into account, for example, time domain related features such as received signal strength indication, complementary cumulative distribution function (CCDF), peak to average power ratio (PAPR), duty cycle, frame/burst length. A blind signal analysis may further take into account, for example, frequency domain related features such as bandwidth or carrier frequency. Further characteristics used in a BSA may be cyclostationarity-based features of the signal such as spectral correlation and cyclic features, statistical properties such as autocorrelation function properties, variance, mean, cumulants, and moments (2nd, 3rd, etc.) or multi-carrier parameters of the signal in the time domain (cyclic prefix (CP) duration, symbol duration) and the frequency domain (number of subcarriers, subcarrier spacing). Characteristics used in BSA may further include chip rates, symbol rates, the angle of arrival, a distinction between single-carrier or multi-carrier, a distinction between spread spectrum or narrowband, a hopping sequence or a type of modulation and its order.
Similarly, for sensing application identification, given a predefined set of features and their values for particular applications, identification may be performed. Still further, where sensing applications use specific header information for detecting network types, this or other header information may be detected and used deterministically to determine the identification of an application. There may be the drawback of defining the parameters (features and their values) and application sets for each environment. Thus, depending on the deployment scenario, trained modules may provide better results, for example in more complex scenarios, where deterministic or stochastic distinction is difficult or complex.
Identification or prediction may be made with MAC Layer Protocols or PHY Layer (like with BSA methods), or using some tags (like coding). Identification or prediction may be made in Network Layer or even maybe in upper layers of the Open Systems Interconnection (OSI) model. Also, there may be no need to identify applications. Application identification may not be enough to understand signal characteristics since the environment characteristics may be changed and devices that receive the signal may be different. It may be impossible to have a complete list for all sensing applications. Thus, applications themselves or applications and environments may be grouped/classified based on their similarities, or requirements of the application may be determined by the request of the corresponding wireless system in the setup phase.
Previous methods do not use coordination between multiple devices, such as APs to utilize existing sensing signals.
In
The non-collaborative sensing signal sharing is illustrated exemplarily in
A scheduling of sensing signals for two or more wireless devices 920, 921, 922, which is exemplarily shown in
A wireless device may be any device such as a base station (eNB, AP) or terminal (UE, STA), or any other entity of the wireless system that is capable of transmitting and/or utilizing sensing transmissions.
The one or more existing sensing signals 910 being transmitted are determined. Such a determination may include receiving information on existing sensing signals from an external source. The determination of sensing signals may include, for example, a signal identification and/or feature extraction S810 as explained above.
One or more groups of wireless devices having similar requirements of sensing performance are determined. Each group comprises one or more of the two or more wireless devices. Similar requirements may be defined by an identical subset of requirements out of a set of performance requirements. Similar requirements may, for example, be defined by a predetermined permissible range and/or a predetermined minimum value and/or a predetermined maximum value for one or more requirements out of the set of requirements. Different set of groups of sensing applications may be defined for different groups of environments.
For each group the features of existing sensing signals are compared with the respective requirements of sensing performance. If the predetermined requirements for a group are fulfilled (“Yes” in S820), the information about existing sensing signals are provided to the group of the one or more wireless devices. In other words, if the received signals meet the minimum requirements of the sensing application, there may not be generated new signals. Instead, the available signals are used S830.
However, note that in the present disclosure the sensing signal may not be limited to a signal used solely for sensing. Joint communication and sensing signals may also be utilized.
If the predetermined requirements for the group are not fulfilled (“No” in S820), generation of new signals 970 may be initiated S840. A new signal may be generated based on minimum signal requirements of said group.
Initiating generation of new sensing signals may comprise starting to transmit a new sensing signal. A wireless device out of the two or more wireless devices may be instructed to start transmitting a new sensing signal. The instruction may be performed by the device that is scheduling the sensing transmissions.
If the predetermined requirements for the group are not fulfilled (the received signals may not meet the minimum requirements), some of the signal characteristics may be changed by a request to the corresponding transmitter. This process may be made with the consideration of complexity, reliability, and generalizability. In some cases, it may be difficult to adjust signal characteristics, for example, when the wireless devices are mobile or the environment changes rapidly, or some devices may not be capable to adjust the signal because of their power and/or computational complexity requirements. An adjustment of a signal out of the existing sensing signals may be initiated such that the predetermined requirements for the group are fulfilled. In other words, an existing sensing signal is adjusted (reoptimized) in order to serve the requirements for additional sensing applications. More specifically, signals or applications that require maximum signal requirements (among the current ongoing sensing applications of the two or more wireless devices) may be identified, and then based on their respective minimum requirements the signals may be generated. In this way, other applications may also be supported while maintaining the minimum performance requirements of critical/other applications. For example, a new sensing application may require a higher resolution than offered by existing sensing signals. Said higher resolution, which is defined by the minimum requirements of the new sensing application, may be a maximum signal requirement of the present sensing applications. Then an existing sensing signal may be adjusted to provide such a required resolution. Said adjusted signal may still support a sensing application whose minimum requirements are fulfilled by a lover resolution of the sensing signal.
Initiating an adjustment of an existing sensing signal may comprise starting to adjust a sensing signal. Such an adjustment may include one or more of: i) changing parameters of one or more existing sensing signals, ii) generating one or more additional sensing signals or iii) replacing one or more existing sensing signals. One or more wireless devices out of the two or more wireless devices may be instructed to start transmitting a new sensing signal. The wireless device to be instructed may be the wireless device transmitting the sensing signal that is to be adjusted. The instructions may include the minimum performance requirements of the sensing signal to be adjusted and/or generated.
For example, the scheduling may be performed by an AP. If the existing signal to be adjusted is generated by the scheduling AP, the scheduling AP may perform the adjustment of its own signal. If the existing signal to be adjusted is not generated by the scheduling AP or additional signals are required, the scheduling AP may instruct other APs and/or STAs to perform the adjustment on their respective sensing transmissions and/or to perform the generation of additional signals.
Signal features that may be identified in the previous step may be compared with the desired signal of the corresponding sensing application and device property. To decide this, e.g. one or more of periodicity, bandwidth, frame duration, training/sensing sequence, carrier frequency, power, etc. can be considered. Each of the features of a sensing signal may affect one or more of the requirements of a sensing application such as resolution, max/min delay, max/min range, detection rate, false alarm rate, etc. The features of the sensing signal may include, for example, one or more of the following characteristics: frame structure, bandwidth, sensing duration, sensing start time, sensing end time, waveform, periodicity, carrier frequency, power, beam width, beam sweep rate, training sequences and/or pilot placement. However, the present disclosure is not limited to said exemplary features of the sensing signal.
The predetermined requirements may comprise one or more out of the characteristics: frame structure, bandwidth, sensing duration, sensing start time, sensing end time, waveform, periodicity, carrier frequency, power, beam width, beam sweep rate, training sequences and/or pilot placement. Said one or more characteristics may differ for at least two among the predetermined requirements. The mentioned characteristics are explained in detail in the following.
One or more of these characteristics may be changed from user to user. Therefore, personal characteristics may be also used to have better system performance. For example, the duration of sensing applications such as home monitoring or sleep monitoring may depend on personal preferences of a user.
The predetermined requirements may include one or more of the above-mentioned requirements. However, note that the present disclosure is not limited to those exemplary characteristics.
A first wireless device out of the two or more wireless devices may be associated with a first network and a second wireless device out of the two or more wireless devices may be associated with a second wireless network.
The first and second network may a same network, e.g., a network including multiple APs (like in an enterprise network) where each AP coordinates its own devices but they belong to the same overall network.
The first and the second network may be of the same type, e.g., WLAN networks or cellular networks, or the like. The two networks of the same type may operate independently from each other.
The first and the second network may be two different types of networks. For example, the first network may be a WLAN network and the second network may be a cellular network.
However, note that the present disclosure is not limited to these exemplary networks. For instance, the amendment IEEE 802.11bf—Wireless Local Area Network (WLAN) Sensing—may include support for wireless sensing in WLAN networks. The fifth-generation (5G) New Radio (NR) standard, 6G standards or other future standards may also apply wireless sensing as its part of future cellular communications networks. The present disclosure is also applicable to other communication technologies such as 3G or communication technologies under long-term evolution (LTE)/LTE Unlicensed (LTE-U). The present disclosure may be applied to any future type of a wireless network that may support wireless sensing.
Any signals that can support the required features of the sensing application may be used. For this purpose, first, existing sensing applications/signals may be identified. The features of a signal out of the existing sensing signals may be identified using one or more of the following techniques.
A sensing signal may be identified by an identification information that is provided by the wireless device generating the signal. For example, a predefined tag may be provided by the generating wireless device. Said information or tag may be mapped to a set of signal characteristics by the receiving device. Such a predefined mapping may, for example, be set by a standard or be configured by a control protocol of a device as an access point or a station or the like.
If the sensing signals are generated by the wireless devices that may share the information about existing sensing signals with each other, said identification information may be provided directly to other wireless devices.
The identification information may be provided by the generating wireless device on request and/or the identification information may be added to the sensing signal.
The features of an existing sensing signal may be determined by a blind signal analysis as explained above.
The features of an existing sensing signal may be determined by machine learning techniques as explained above with reference to
Wireless devices may not need to identify and predict the existing signals. Signal type information may be available in future wireless systems, for example in future WLAN standards.
The information about a sensing application of a wireless device out of the two or more wireless devices may be stored in a first database. For example, the first database may be the sensing application registry (SAR), which is explained in detail in the section “Multi-AP sensing signal sharing framework”. The sensing capabilities of the two or more wireless devices may be stored in a second database. For example, the second data base may be a device sensing capability registry (DSCR), which is explained in detail in the section “Multi-AP sensing signal sharing framework” below.
The entries of the first database may be transmitted to a wireless device out of the two or more wireless devices. The entries of the second database may be transmitted to a wireless device out of the two or more wireless devices. For example, the sharing of database entries with other wireless devices may be performed by a sensing transmission control (STC) unit 1201, which is explained in detail in the section “Multi-AP sensing signal sharing framework”.
If a wireless device is mobile, periodic control of the best resource allocation may be made.
In the future standards, sensing applications may be categorized into small groups based on their requirements similar to the communication services in 5G (eMBB (enhanced Mobile Broadband), URLLC (Ultra-Reliable Low Latency Communications), mMTC (massive Machine Type Communications)). In this case, sensing signals may be utilized based on the sensing application (and their requirements) categories.
This present disclosure can be used in any kind of device that is used for wireless sensing. For instance, health monitoring, activity classification, gesture recognition, people counting, through the wall sensing, emotion recognition, attention monitoring, keystrokes recognition, drawing in the air, imaging, step counting, speed estimation, sleep detection, traffic monitoring, smoking detection, metal detection, sign language recognition, humidity estimation, wheat moisture detection, fruit ripeness detection, sneeze sensing, etc. Besides these applications, the embodiments of the present disclosure can be used in JSC technologies. This disclosure can also be used for sensing applications to support communication applications, like obstacle tracking for beam management. Therefore, devices that can utilize the disclosed subject matter could be smart homes/offices/cities/factories/etc. devices, like electrical kitchen appliances, television sets, smart bus stops, smart office equipment (printers, etc.), lighting systems, phones, computers, WLAN and Wi-Fi devices, etc. Other devices could be stand-alone wireless sensors, such as heart-rate monitors, motion detectors, smart watches, etc. Besides these applications, the disclosed subject matter can be used for military services such as enemy sensors, the existence of enemy devices and what they are sensing can be learned and some precaution can be taken.
This disclosure can especially be used in network controllers and managing devices, such as Aps, BSs, edge nodes, enhanced nodes, etc. for technologies such as CR, reconfigurable radio systems, etc.
In some embodiments, the sensing signal is a continuous or periodic radar signal. In some embodiments, the sensing signal is a signal generated by a sensing application supporting wireless sensing, wireless local area sensing, and/or non-invasive medical sensing.
In general, wireless sensing is performed by measuring some features of a received signal. On the other hand, communication is performed by detecting from the received signal information encoded therein at the transmitter. In communication, some features of the received signal are used to perform the detection (such as demodulation and decoding).
There are numerous frame designs, waveforms, and transmission schemes that are being used for wireless sensing and JSC. Their use depends on the method of wireless sensing that is being used. For example, wireless sensing may be done in one of the following ways:
In general, the present disclosure is not limited to the above-mentioned three types of sensing.
The features of the received signal measured by the wireless sensing may include, but are not limited to, time-of-flight, RSSI, CSI, or the like. Transmitted signal parameters affecting the performance of the wireless sensing are, but not limited to, the transmitted frequency, bandwidth, waveform, power, training or sensing sequence, auto-correlation capabilities, pilots, beam angle and width (if beamforming is taking place), duty cycle, transmission rate, or the like.
It is desired that wireless sensing, communication, and JSC devices operate/coexist peacefully in the same (or at least partially overlapping) frequency bands with a maximum efficiency, in terms of spectrum usage, power, or the like, and sensing and communication performance, in terms of throughput, reliability, sensing accuracy, or the like. This may be facilitated by detecting and/or identifying the sensing signals as described herein.
In an exemplary scenario the transmissions are scheduled by an AP. The scheduling may performed by the sensing transmission control architecture 1200 including a sensing transmission control unit 1201. However, note that that the present disclosure is not limited to this exemplary implementation. The STC architecture may be included, for example in a STA, a base station, a terminal or any other wireless device.
The coordination between APs may be initiated by sharing the Sensing Application Registry (SAR), which contains information on the sensing applications occurring in the network controlled by the AP. An exemplary illustration of the SAR in a standalone network during multi-AP coordination for sensing signal sharing is given in
An exemplary architecture for the sensing transmission control and optimization between multiple APs or STAs is given in
In the following, the SAR, DSCR and STC are explained in detail.
The Sensing Application Registry (SAR) is a database containing information on the sensing applications/sessions in the environment. It may be further divided into Network SAR (N-SAR), which contains the sensing applications/sessions in the devices' own network, and External SAR (E-SAR), which contains information on sensing applications/sessions taking place in other/neighboring networks.
The SAR database may contain the Application ID, which is unique for each sensing session and may be used to identify the sensing session and associated information. It may also contain some sequence which may be used to determine the type of sensing application.
The SAR may further contain an Associated Station (ASTA). These devices may not be involved in the sensing session initiated by a particular ISTA, but may utilize the transmissions of the sensing session opportunistically for their own sensing application. An ASTA may also be indirectly involved in the sensing parameter optimization by providing their own sensing parameter requirements.
The SAR may further contain Tx/Frame Parameters. These parameters may indicate the frame design parameters used for the sensing signal transmissions in the sensing session. Examples may be bandwidth, training sequence, security measures, power, waveform, or the like.
The SAR may further contain Performance Requirements, which may indicate the performance requirements of the sensing session. The values may be specific to certain dimensions, such as range and velocity, or may be the overall detection accuracy. An exemplary entry may be <0.1 m@90%, which indicates an error of less than 10 cm for 90% of the results.
The SAR may further contain a Timeout Duration, which may indicate the duration of the sensing session.
The SAR may further contain a Priority. This may indicate the priority level of the sensing application. The priority level may be over other sensing applications or over other wireless transmissions in general. For example, a home security oriented sensing application may have priority over gaming oriented sensing applications and therefore, when allocating radio resources or optimizing sensing signals for joint sensing, the requirements of the home security oriented sensing application may be prioritized. For example, health critical sensing applications may have priority over even communication transmissions.
Sometimes, sensing may be more important than communication. For example, at night time there will be no communication but people's breath rate may be detected. In that case, priority may be given to breath rate surveillance based on coordination between wireless devices. On the other hand, sometimes, communication may be more critical. At that time, priority may be given to communication. Sometimes joint communication and sensing may be made with the same priority.
The Device Sensing Capability Registry (DSCR) is a database on the sensing capabilities of all devices in the network (e.g. all STAs linked to the AP controlling the network). The information contained in this database may be the transmission capabilities of the devices (Tx freq, BW, MIMO, beam related parameters or the like), their willingness to participate in others' sensing sessions (as RSTA), their maximum participation duration, how much power they may allocate to others' sensing sessions, or the like.
The Sensing Transmission Control (STC) Architecture may include components for controlling, coordinating, and optimizing the sensing parameters related to the transmission of sensing signals. The STC Architecture includes, for example, Sensing Registries (SR), which may include the E-SAR, N-SAR and DSCR databases.
Further an STC Architecture may include a Sensing Transmission Scheduler (STS). In an exemplary implementation a STS may be realized as processor. The STS may be tasked with optimizing and scheduling the sensing signals. Further tasks like negotiating the sensing session parameters based on the sensing application requirements or sensing activities in the environments, available resources and available devices may be performed by the STS.
Further an STC Architecture may include an Interface. The interface may collect and direct the communication between other components of the STC and external devices, such as, for example, the AP, a neighboring AP, and/or STAs.
Exemplary coordination processes in the Multi-AP (or device) framework to utilize Wi-Fi signals efficiently for Wi-Fi sensing is described in the following with reference to
As exemplarily shown in
An exemplary case during status monitoring is described with reference to
In both cases the STC may share the received information with other Aps S1430. The STS may compare the requirements with existing sensing signals S1440. If the existing sensing signals are suitable (“Yes” in S1461) the STA may be directed to utilize the existing sensing signals S1461. The entry “Associated STA” in the SAR database may be updated S1471. In the following, other Aps may be notified of changes in the SAR S1480. If the existing signals are not suitable (“No” in S1450), generation of suitable sensing signals may be initiated S1460. In order to obtain a suitable sensing signal, the scheduling device may initiate the generation of a new sensing signal or initiate the adaption of an existing sensing signal as explained above. A record of the new or adapted sensing signal may be added to the SAR S1470. In the following, other aPs may be notified of changes in the SAR S1480.
In such cases, before changing the parameters of the sensing session of the primary application, coordination between the STAs may be done to prevent a drop in performance of the other sensing sessions. The same may apply for an exemplary scenario where the requirements or priority of the primary sensing application increases.
A sensing session may time-out or terminate during status monitoring S1510. Such a process is exemplarily described with reference to
During status monitoring a new station may enter the network S1610. Such a process is exemplarily described with reference to
When there is a new sensing session request the resource allocation strategy may be changed. There may already be existing signals transmitted by an AP with similar requirements as what is desired by the new application. Thus, the existing signals may be adjusted (reoptimized) to optimally cater to both the new application and the existing ones.
A new STA entering an environment of coordinating aPs is exemplarily shown in
In
A station may leave the network S1710 during status monitoring. Such a process is exemplarily described with reference to
If multiple STAs leave the environment or multiple sensing sessions are terminated, the resource allocation and signal generation strategies may be changed as described above for one station.
A new AP may enter the environment S1810 as it is exemplarily described in
In
In
APs may turn off due to many reasons—power requirements, power cuts, hardware damage/issues, or the like. Additionally, in future wireless communication systems APs may be mobile and may physically leave the environment. In such cases, before an AP loses its connectivity, it may share its workload with other APs due to the existing coordination between multiple APs and de-associate any STA from other APs in its sensing sessions.
If a new AP enters the environment S1810 or an AP updates its status S1811, the STC may update the ESAR and DSCR databases S1820. The STS may adjust (reoptimize) sensing signals S1830. The STC may share the information with other APs and update the SAR database S1840.
Multiple STAs with multiple distinct sensing session requests may enter the environment simultaneously. When the number of applications or requirements is decreasing in an exemplary scenario, the characteristic of the signal that will be generated may be change. In this exemplary case, first the APs may check whether existing signals from sensing sessions in the network or sensing sessions in other networks (with other APs) may support the sensing requirements of these STAs. This may be done by checking the SARs. If existing signals may support the applications, then there is no need to generate a new signal. If existing signals cannot support these new STAs, it may be determined whether an adjustment of the existing sensing session parameters may support the requested applications. If this is not possible, then the multiple APs may coordinate to decide on the sensing session parameters which may serve all or a maximum number of applications.
The present disclosure is not limited to the described exemplary scenarios. An environment in which signal sharing is performed may include one or more wireless devices. A wireless device may be an AP or a STA or the like. In the above described exemplary scenarios, the sensing signals are transmitted by APs. However, the present disclosure is not limited to this. Currently, STAs are not powerful devices. Most of them have power and complexity constraints. In the future, STAs may be more powerful. For example, a fridge, television, or the like may be used as STA.
In that case, a coordination of APs as described in the exemplary scenarios may also be performed by STAs.
In an example, two different sensing applications/sessions may be active and these applications/sessions may be initially unaware of each other. After a certain period of time, the applications may become aware of each other. If there is no coordination between these sessions, both applications may stop transmitting the sensing signals they need at the same time (because both applications assume that they have found a sensing signal they can opportunistically use, i.e. the others' signal). Coordination is required to prevent such a thing. In this scenario, one signal may be stopped and the other may continue to be transmitted or one may be stopped and the values of the other may be optimized to support both applications simultaneously. Or the parameters of both may be separately optimized and the signals may be used for other sensing applications/sessions as well.
It is noted that although embodiments and examples of the present disclosure were provided in terms of a method above, the corresponding device providing the functionality described by the methods are also provided. For example, a device is provided for scheduling sensing signals. The device may comprise a processing circuitry which is configured to perform step according to any of the above mentioned methods. The device may further comprise a transceiver for performing wireless reception, transmission or sensing. Alternatively to the transceiver, the processing circuitry may control an external transceiver to perform wireless reception, transmission or sensing.
The wireless device for scheduling of sensing signals may further comprise a memory, which stores information about a sensing application of a wireless device out of the two or more wireless devices in a first database and/or stores sensing capabilities of the two or more wireless devices in a second database.
The memory 2110 may store the program, which may be executed by the processing circuitry 2120 to perform steps of any of the above-mentioned methods. The processing circuitry may comprise one or more processors and/or other dedicated or programmable hardware. The wireless transceiver 2140 may be configured to receive and/or transmit wireless signals. The transceiver 2140 may include also baseband processing which may detect, decode and interpret the data according to some standard or predefined convention. However, this is not necessary and devices with only sensing applications may implement only the lower one or two protocol layers. For example, the transceiver may be used to perform measurement, communicate with other devices such as base stations and/or terminals. The device 2100 may further include a user interface 2130 for displaying messages or status of the device, or the like and/or for receiving a user's input. A bus 2101 interconnects the memory, the processing circuitry, the wireless transceiver, and the user interface.
The above examples are not to limit the present disclosure. There are many modifications and configurations which may be used in addition or alternatively, as will be briefly described below.
Similarly, a device is provided for training a module for identifying a sensing application, the device comprising a processing circuitry configured to: input to the module: a representation of a received wireless signal and one of or both a desired indication of presence or absence of a sensing signal in the representation and a desired indication of a sensing application generating the sensing signal; and modify at least one parameter of the module in accordance with the input.
This present disclosure can be used in any kind of device that is used for wireless sensing. For instance, health monitoring, activity classification, gesture recognition, people counting, through the wall sensing, emotion recognition, attention monitoring, keystrokes recognition, drawing in the air, imaging, step counting, speed estimation, sleep detection, traffic monitoring, smoking detection, metal detection, sign language recognition, humidity estimation, wheat moisture detection, fruit ripeness detection, sneeze sensing, etc. Besides these applications, the embodiments of the present disclosure can be used in JSC technologies. This disclosure can also be used for sensing applications to support communication applications, like obstacle tracking for beam management. Therefore, devices that can utilize the disclosed subject matter could be smart homes/offices/cities/factories/etc. devices, like electrical kitchen appliances, television sets, smart bus stops, smart office equipment (printers, etc.), lighting systems, WLAN and Wi-Fi devices, etc. Other devices could be stand-alone wireless sensors, such as heart-rate monitors, motion detectors, smart watches, etc. Besides these applications, the disclosed subject matter can be used for military services such as enemy sensors, the existence of enemy devices and what they are sensing can be learned and some precaution can be taken. This disclosure can especially be used in network controllers and managing devices, such as APs, BSs, edge nodes, enhanced nodes, etc. for technologies such as CR, reconfigurable radio systems, etc.
The methodologies described herein may be implemented by various means depending upon the application. For example, these methodologies may be implemented in hardware, operation system, firmware, software, or any combination of two or all of them. For a hardware implementation, any processing circuitry 2120 may be used, which may include one or more processors. For example, the hardware may include one or more of application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, any electronic devices, or other electronic circuitry units or elements designed to perform the functions described above.
If implemented as program code, the functions performed by the transmitting apparatus (device) may be stored as one or more instructions or code on a non-transitory computer readable storage medium such as the memory 2110 or any other type of storage. The computer-readable media includes physical computer storage media, which may be any available medium that can be accessed by the computer, or, in general by the processing circuitry 2120. Such computer-readable media may comprise RAM, ROM, EEPROM, optical disk storage, magnetic disk storage, semiconductor storage, or other storage devices. Some particular and non-limiting examples include compact disc (CD), CD-ROM, laser disc, optical disc, digital versatile disc (DVD), Blu-ray (BD) disc or the like. Combinations of different storage media are also possible—in other words, distributed and heterogeneous storage may be employed.
The embodiments and exemplary implementations mentioned above show some non-limiting examples. It is understood that various modifications may be made without departing from the disclosed subject matter. For example, modifications may be made to adapt the examples to new systems and scenarios without departing from the central concept described herein.
Summarizing, some embodiments in the present disclosure relate to scheduling sensing signals for two or more wireless devices. Requirements of sensing applications of the wireless devices are determined. Existing sensing signals are received and the characteristics of the existing sensing signals may be determined. The wireless devices having similar requirements are grouped. Each group is provided with information about existing sensing signals if the predetermined requirements for the group are fulfilled in a comparison with features of the existing sensing signals.
According to an embodiment, a method is provided for scheduling sensing signals for two or more wireless devices, comprising: determining requirements of sensing performance from the two or more wireless devices; determining existing sensing signals being transmitted by the two or more wireless devices; determining one or more groups of wireless devices having similar requirements of sensing performance, each group comprising one or more wireless devices of the two or more wireless devices; for each group, comparing features of existing sensing signals with respective requirements of sensing performance; providing information about the existing sensing signals to a group out of the one or more groups of wireless devices, if the predetermined requirements for the group are fulfilled.
In an exemplary implementation, the method is further comprising, if the predetermined requirements for the group are not fulfilled, initiating generation of a new sensing signal.
For example, initiating generation of a new sensing signal comprises at least one of starting to transmit a new sensing signal, or instructing a wireless device out of the two or more wireless devices to start transmitting a new sensing signal.
In an exemplary implementation, the method is further comprising, if the predetermined requirements for the group are not fulfilled, initiating an adjustment of a signal out of the existing sensing signals such that the predetermined requirements for the group are fulfilled.
For example, initiating an adjustment of the signal out of the existing sensing signals comprises at least one of starting to adjust the signal, or instructing a wireless device out of the two or more wireless devices to start adjusting the signal.
In an exemplary implementation, the predetermined requirements comprise on one or more out of the characteristics: frame structure, bandwidth, sensing duration, sensing start time, sensing end time, waveform, periodicity, carrier frequency, power, beam width, beam sweep rate, training sequences, pilot placement; and said one or more characteristics differ for at least two among the predetermined requirements.
For example, a first wireless device out of the two or more wireless devices is associated with a first wireless network and a second wireless device out of the two or more wireless devices is associated with a second wireless network, wherein the first wireless network and the second wireless network are either a same wireless network or different wireless networks.
In an exemplary implementation, features of a signal out of the existing sensing signals are identified using one or more of an identification information that is provided by a wireless device out of the two or more wireless devices, the wireless device generating the signal, blind signal analysis (BSA), machine learning techniques.
For example, the method is further comprising storing information about a sensing application of a wireless device out of the two or more wireless devices in a first database and/or storing sensing capabilities of the two or more wireless devices in a second database.
In an exemplary implementation, the method is further comprising transmitting entries of the first database to a wireless device out of the two or more wireless devices and/or transmitting entries of the second database to a wireless device out of the two or more wireless devices.
In an exemplary implementation, a computer program stored in a non-transitory, computer-readable medium, the program comprising code instructions which, when executed on one or more processors, cause the one or more processors to perform steps of any of the methods described above.
According to an embodiment, a wireless device is provided for scheduling sensing signals, comprising: processing circuitry configured to determine requirements of sensing performance from two or more wireless devices; determine existing sensing signals being transmitted; determine one or more groups of wireless devices having similar requirements of sensing performance, each group comprising one or more wireless devices of the two or more wireless devices; for each group, compare features of existing sensing signals with respective requirements of sensing performance; a transceiver configured to transmit information about the existing sensing signals to a group out of the one or more groups of wireless devices, if the predetermined requirements for the group are fulfilled.
In an exemplary implementation, the processing circuitry is further configured to, if the predetermined requirements for the group are not fulfilled, initiate generation of a new sensing signal.
For example, initiating generation of a new sensing signal comprises at least one of starting to transmit a new sensing signal, or instructing a wireless device out of the two or more wireless devices to start transmitting a new sensing signal.
In an exemplary implementation, the processing circuitry is further configured to, if the predetermined requirements for the group are not fulfilled, initiate an adjustment of a signal out of the existing sensing signals such that the predetermined requirements for the group are fulfilled.
For example, initiating an adjustment of the signal out of the existing sensing signals comprises at least one of starting to adjust the signal, or instructing a wireless device out of the two or more wireless devices to start adjusting the signal.
In an exemplary implementation, the predetermined requirements comprise on one or more out of the characteristics: frame structure, bandwidth, sensing duration, sensing start time, sensing end time, waveform, periodicity, carrier frequency, power, beam width, beam sweep rate, training sequences, pilot placement; and said one or more characteristics differ for at least two among the predetermined requirements.
For example, a first wireless device out of the two or more wireless devices is associated with a first wireless network and a second wireless device out of the two or more wireless devices is associated with a second wireless network, wherein the first wireless network and the second wireless network are either a same wireless network or different wireless networks.
In an exemplary implementation, features of a signal out of the existing sensing signals are identified using one or more of an identification information that is provided by a wireless device generating the signal, blind signal analysis (BSA), machine learning techniques.
For example, the wireless device is further comprising: a memory configured to store information about a sensing application of a wireless device out of the two or more wireless devices in a first database and/or store sensing capabilities of the two or more wireless devices in a second database.
In an exemplary implementation, the transceiver is further configured to transmit entries of the first database to a wireless device out of the two or more wireless devices and/or transmit entries of the second database to a wireless device out of the two or more wireless devices.
Moreover, the corresponding methods are provided including steps performed by any of the above-mentioned processing circuitry implementations.
Still further, a computer program is provided, stored on a non-transitory medium, and comprising code instructions which when executed by a computer or by a processing circuitry, performs steps of any of the above-mentioned methods.
According to some embodiments, the processing circuitry and/or the transceiver is embedded in an integrated circuit, IC.
Any of the apparatuses of the present disclosure may be embodied on an integrated chip.
Any of the above-mentioned embodiments and exemplary implementations may be combined.
Although the disclosed subject matter has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the disclosed subject matter is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the presently disclosed subject matter contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2021/012978 | Aug 2021 | TR | national |
This application is the United States national phase of International Application No. PCT/EP2022/057028, filed Mar. 17, 2022, and claims priority to Turkish Patent Application No. 2021/012978, filed Aug. 17, 2021, the disclosures of each of which are hereby incorporated by reference in their entireties.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2022/057028 | 3/17/2022 | WO |
