Patent Application
20160050670
The present invention relates to the field of network communications, and, in particular embodiments, to a system and method for a super bin concept for radio resource requirement assessment for user equipments (UEs).
Network operators are tasked with equitably distributing finite shared resources (e.g., bandwidth, spectral efficiency, forwarding paths, radio nodes, radio channels, or other network resources) amongst multiple users in a manner that satisfies the users' collective quality of service (QoS) requirements. Conventional techniques allocate network resources in an ad hoc manner (e.g., on a case-by-case basis), which satisfies QoS requirements at the expense of overall resource utilization efficiency. For example, in wireless environments, spectrum bandwidth may be allocated to satisfy an individual service request without considering how interference resulting from increased traffic load will reduce spectral efficiency over nearby interferences. Accordingly, mechanisms and techniques for more efficiently allocating resources in a network are needed in order to satisfy ever increasing demands of next generation networks.
In accordance with an embodiment of the disclosure, a method by a network component for assessing radio resource requirements using bin virtualization includes receiving a service request from a user equipment (UE) in a geographical bin. The method further includes obtaining resource requirements for a serving radio node and neighbor radio nodes associated with the geographic bin of the UE from a lookup table (LUT). The LUT comprises a plurality of entries that map combinations of path losses of wireless links for each of the serving radio node and neighbor radio nodes to corresponding combinations of resource requirements for the serving radio node and neighbor radio nodes. Each distinct combination of path losses in one or more entries of the LUT represents a virtual bin. The obtained resource requirements are assessed, including deciding whether to serve the UE according to the obtained resource requirements and to resource availability.
In accordance with another embodiment of the disclosure, a network component for assessing radio resource requirements using bin virtualization includes at least one processor and a non-transitory computer readable storage medium storing programming for execution by the at least one processor. The programming includes instructions to receive a service request from a UE in a geographical bin. The instructions further include instructions to obtain resource requirements for a serving radio node and neighbor radio nodes associated with the geographic bin of the UE from a LUT. The LUT comprises a plurality of entries that map combinations of path losses of wireless links for each of the serving radio node and neighbor radio nodes to corresponding combinations of resource requirements for the serving radio node and neighbor radio nodes. Each distinct combination of path losses in one or more entries of the LUT represents a virtual bin. The network component is further configured to assess the resource requirements.
In accordance with another embodiment of the disclosure, a method by a network component for bin virtualization for radio resources assessment includes, for each considered service type, collecting information indicating multiple combinations of path losses of wireless links for a plurality of radio nodes. The combinations of path losses are then mapped to corresponding combinations of resource requirements for each considered serving node and corresponding neighbor nodes. The combinations of path losses and the corresponding resource requirements are maintained in entries in a LUT for assessing radio resource requirements. Each distinct combination of path losses in one or more entries of the LUT represents a virtual bin.
In accordance with yet another embodiment of the disclosure, a network component for bin virtualization for radio resources assessment includes at least one processor and a non-transitory computer readable storage medium storing programming for execution by the at least one processor. The programming includes instructions to, for each considered service type, collect information indicating multiple combinations of path losses of wireless links with service specific and network node parameters for a plurality of radio nodes. The programming includes further instructions to map the combinations of path losses to corresponding combinations of resource requirements for each considered serving node and corresponding neighbor nodes. The instructions further maintain the combinations of path losses with service specific and network node parameters and the corresponding resource requirements in entries in a LUT for assessing radio resource requirements. Each distinct combination of path losses in one or more entries of the LUT represents a virtual bin.
The foregoing has outlined rather broadly the features of an embodiment of the present invention in order that the detailed description of the invention that follows may be better understood. Additional features and advantages of embodiments of the invention will be described hereinafter, which form the subject of the claims of the invention. It should be appreciated by those skilled in the art that the conception and specific embodiments disclosed may be readily utilized as a basis for modifying or designing other structures or processes for carrying out the same purposes of the present invention. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention as set forth in the appended claims.
For a more complete understanding of the present invention, and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawing, in which:
Corresponding numerals and symbols in the different figures generally refer to corresponding parts unless otherwise indicated. The figures are drawn to clearly illustrate the relevant aspects of the embodiments and are not necessarily drawn to scale.
The making and using of the presently preferred embodiments are discussed in detail below. It should be appreciated, however, that the present invention provides many applicable inventive concepts that can be embodied in a wide variety of specific contexts. The specific embodiments discussed are merely illustrative of specific ways to make and use the invention, and do not limit the scope of the invention.
Embodiments are provided herein for assessing radio resource requirement of user equipments (UEs) using a super bin concept. The embodiments includes a method for abstracting radio access node (RAN) characteristics for estimating the resource requirement for a given service for a user at a particular location (e.g., at a defined bin area). The resource requirement is estimated or assessed for the purpose of administration and traffic management purposes (e.g., admission control, traffic engineering, cost control). The embodiments allow network operators to compute the cost of providing resources for a user at a specific location using a lookup table (LUT) designed for this purpose, without having to recomputed LUT information for each new UE location associated with a given network deployment. The same unchanged LUT can be used even if there is a change in the network deployment (e.g., adding a new radio node) for a UE anywhere in the network.
The information in the abstraction model 200 is linked to position and context. To train the abstraction model 200, the function f( ) are obtained using online data collection, off-line data collection, using emulated traffic, or using simulations. All relevant combinations of parameter values should be swept for each geographical bin. Further, depending on the accuracy required by the algorithm using the abstraction, a quantizer should be applied to restrict the LUT containing the information to a manageable size. This is a bin based abstraction that couples physical bin (location) and a specific infrastructure topology. When a new infra-structure node is introduced, the resource requirements has to be re-assessed again for all the positions (bins) and possible context combinations, which increases computations time and cost and thus reduces efficiency.
In an embodiment, a super bin concept is introduced to make the bin abstraction cover a generic infrastructure, e.g., independent of network infrastructure topology and changes to the infrastructure topology. Specifically, each super bin is a virtual bin that corresponds to a functional mapping from a particular combination of input variables (multidimensional function of quantized inputs), to make the abstraction independent of any specific infrastructure. Each virtual bin corresponds to a specific combination of path losses of wireless links as seen by a UE, irrespective of the geographical location. Similar to the abstraction model 200, the super bin abstraction model builds the LUT using online data collection, off-line data collection, emulated traffic, or simulations, and by quantizing the data. However, unlike the model 200, the LUT is build and trained over all physical geographical bins and can then be used irrespective of the actual geographical bin of interest. The information in the super bin abstraction model is linked to the context considering all geographical bins regardless of location or network architecture. Since any geographical bin has a context, any bin can be mapped to a super bin regardless of location. Additionally, a database is established to map the positions of a UE to a virtual bin (combination of path losses as seen by the UE). Thus, if the network infrastructure topology is changed, e.g., a base station is added or removed, the mapping is modified accordingly without changing the LUT.
In addition to path loss and the service and network node parameters, the speed (speed) and direction of the user may also be used as dependent parameters in some scenarios. In addition to direct resource utilization and resource cost, the probability of assignment to a cell may also be provided as an output. The probability of assignment can be applied to the resource cost for a cell to assess resources. For instance, an expected resource requirement for each of the cells may be evaluated using a product of the two outputs, such as P_assign(cell)×Resource_cost(cell). In other embodiments, other suitable input parameters and output metrics for assessing resources can be added as entries to the LUT.
The LUT inputs (e.g., L1 to Ln, PL1 to PLn, P1 to Pn, SI1 to SIn) are quantized. The quantization requirements depend on the requirements of the application. For example, for admission control (AC), loading quantization for low loads can be very rough (coarse quantization) in comparison to traffic engineering (TE) requirement. There may also be practical limitations in possible ranges of each variable. For example, multi-dimensional non-uniform quantization could be done based on probability of occurrence of certain combination of parameters. The actual location of each station (or RAN) does not matter directly in the mapping. For instance, the stations can be sorted according to a given parameter. In one implementation, the neighbour stations are ordered according to the signal strength of each station assuming the maximum transmit power of that station. The loading and other characteristics are then provided in the same order.
The path losses and service/network parameters are quantized, e.g., into discrete numbers to facilitate processing and handling (e.g., sorting, comparing) the data in the table. The service/network parameters include mean transmission powers (Power) from the cells/stations, loading information (Load) from the cells/stations, and total interference power from nodes other than neighbors. Other embodiments can include different information, additional information such as SI, or combinations thereof. The path losses and different parameters are arranged in columns for the serving station/cell and n neighbor stations/cells. Each row includes a different combination of quantized values for the path losses and the considered parameters. Further, in each row, the quantized values for the serving and neighbor cells are sorted (e.g., in ascending order). This facilitates searching and comparison of entries in the table, which can speed processing time and decision making at the controller.
The corresponding mapping of resource requirements/cost to the different combinations of path losses and parameters can be obtained by training schemes (e.g., using actual data and feedback from the network over time) and/or simulations. Thus, each entry corresponding to a combination is mapped to a corresponding combination of resource requirement/cost values for the serving/neighbor cells. The LUT 400 may be static, e.g. obtained once via simulations considering all possible or practical combinations of values. A combination of resource requirements/cost in an entry can correspond to different scenarios of user location and/or network topology. In the LUT, each combination of path losses represents a virtual or super bin. For each combination of path losses or virtual bins, the LUT can include multiple entries, each corresponding to a different combination of the service/network parameters.
Although, the LUT 400 shows service specific and network node parameters associated with the path losses in the rows, this is not a requirement. In other scenarios, path losses for serving/neighbor nodes can be mapped to resource requirements/cost without including service specific and network node parameters. In other embodiments, any suitable service specific and network node parameters can be included, for example other than the parameters shown in LUT 400. The LUT 400 can also be updated over time, e.g., via training or feedback. However, the updates are not on a dynamic or relatively fast scale with network changes since there is no need to change entries each time network architecture or topology is changed (e.g., due to adding/removing stations or other resources). The entries in the LUT 400 are expected to already account for such changes. For example, the combinations of values in the LUT 400 cover such changes or all possibilities of network topology that affect the considered parameters.
In an embodiment, a database is established to map the current position of a UE to a virtual bin (combination of path losses). This database can be modified if network deployment is changed (e.g., a new base station is deployed). The LUT above, in which there may be multiple rows for each virtual bin (combination of path losses) representing all relevant possible combinations of service specific and network node parameters for that virtual bin, would not change with the deployment change. The LUT may be the same no matter where the LUT is used in the world, as long as the rows still span all combinations of service specific and network node parameters.
The CPU 610 may comprise any type of electronic data processor. The memory 620 may comprise any type of system memory such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), read-only memory (ROM), a combination thereof, or the like. In an embodiment, the memory 620 may include ROM for use at boot-up, and DRAM for program and data storage for use while executing programs. In embodiments, the memory 620 is non-transitory. The mass storage device 630 may comprise any type of storage device configured to store data, programs, and other information and to make the data, programs, and other information accessible via the bus. The mass storage device 630 may comprise, for example, one or more of a solid state drive, hard disk drive, a magnetic disk drive, an optical disk drive, or the like.
The processing unit 601 also includes one or more network interfaces 650, which may comprise wired links, such as an Ethernet cable or the like, and/or wireless links to access nodes or one or more networks 680. The network interface 650 allows the processing unit 601 to communicate with remote units via the networks 680. For example, the network interface 650 may provide wireless communication via one or more transmitters/transmit antennas and one or more receivers/receive antennas. In an embodiment, the processing unit 601 is coupled to a local-area network or a wide-area network for data processing and communications with remote devices, such as other processing units, the Internet, remote storage facilities, or the like.
While several embodiments have been provided in the present disclosure, it should be understood that the disclosed systems and methods might be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The present examples are to be considered as illustrative and not restrictive, and the intention is not to be limited to the details given herein. For example, the various elements or components may be combined or integrated in another system or certain features may be omitted, or not implemented.
In addition, techniques, systems, subsystems, and methods described and illustrated in the various embodiments as discrete or separate may be combined or integrated with other systems, modules, techniques, or methods without departing from the scope of the present disclosure. Other items shown or discussed as coupled or directly coupled or communicating with each other may be indirectly coupled or communicating through some interface, device, or intermediate component whether electrically, mechanically, or otherwise. Other examples of changes, substitutions, and alterations are ascertainable by one skilled in the art and could be made without departing from the spirit and scope disclosed herein.
