The present disclosure relates to intelligent reconfigurable surfaces (IRSs) for wireless communications, such as radio frequency (RF) communications.
Wireless communications systems in fifth generation (5G) and beyond use multi-antenna technologies such as multiple-input-multiple-output (MIMO) and massive MIMO with higher frequency signals than previous systems (e.g., in the millimeter wave (mmWave) band in 5G and sub-terahertz bands in sixth generation (6G) and beyond). The large bandwidth available at these high frequencies enables the communication systems to send data with very high data rates. However, a major challenge that these systems face is network coverage. This is because these high frequencies do not penetrate well in most objects, making them more susceptible to blockages of wireless communication links.
To overcome this challenge, the concept of intelligent reconfigurable surfaces (IRS) has been recently proposed and attracted massive interest from academia, industry, and defense. IRSs are devices that comprise large numbers of controllable nearly-passive reflecting elements. These low-cost devices reflect and focus incident signals towards intended receivers to enhance the network coverage and provide a way to avoid or mitigate blockages of wireless communication links. A major challenge for current IRS systems, however, is that they need a massive number of elements to meet their power gain promises. The large numbers of elements consequently require extremely large channel estimation/beam training overhead (to find the best direction to point very narrow beams). Further, since the beams of these systems are extremely narrow, the users may easily go out of coverage with any small movements. The above issues can render real deployment of these systems infeasible.
A more widely accepted method for adapting wireless communication environment is by using relay stations, which may also generate additional wireless routes toward a destination. While both relays and intelligent surfaces are relatively similar, a relay plays the role of receiving and retransmitting the signal with amplification. Comparisons between intelligent surfaces and decode-and-forward (DF)/amplify-and-forward (AF) relays have reached the conclusion that an IRS needs hundreds of reconfigurable elements to be competitive against relays. However, conventional relays lack the ability to focus a signal, which limits their application for wireless coverage and increases interference to unintended receivers. Further, MIMO relays are costly and bulky with high power consumption.
Relay-aided intelligent reconfigurable surfaces (IRSs) are provided. A novel relay-aided intelligent surface architecture is described herein that has the potential of achieving the promising gains of IRSs with a much smaller number of elements, opening the door for realizing these surfaces in practice. A half-duplex or full-duplex relay is connected to one or more IRSs. This merges the gains of relays and reconfigurable surfaces and splits the required signal-to-noise ratio (SNR) gain between them. This architecture can then significantly reduce the required number of reconfigurable elements in the IRS(s) while achieving the same spectral efficiencies. Consequently, the proposed relay-aided intelligent surface architecture needs far less channel estimation/beam training overhead and provides enhanced robustness compared to traditional IRS solutions. In one aspect, the proposed architecture splits the reflection process over two intelligent surfaces connected wired or wirelessly by a relay. This allows leveraging full-duplex relays with practical isolation. Further, this enables the proposed architecture to be deployed in very flexible ways by optimizing the position and orientation of the two surfaces, which leads to much better coverage. Other examples embed the relay within one or multiple IRSs to (e.g., via wired connection to one or each of multiple IRSs) to provide amplification in addition to the beamforming of the IRS(s).
After describing the proposed architecture, this disclosure develops an accurate mixed near-far field channel model that describes the composite channel between a transmitter/receiver pair and the relay through the IRS surfaces. Further, the disclosure derives closed-form expressions for the achievable rates using the proposed relay-aided intelligent surface architecture with decode-and-forward (DF) and amplify-and-forward (AF) relays. Finally, these rates are evaluated using numerical simulations which further highlight the promising gains of the proposed architecture.
An exemplary embodiment provides a relay for an intelligent surface device. The relay includes a first antenna port configured to receive a first signal from a first IRS; amplification circuitry configured to amplify the first signal; and a second antenna port configured to send the amplified first signal to be transmitted from a second IRS.
Another exemplary embodiment provides a method for providing amplified signal reflection. The method includes receiving a first signal at a first IRS, the first IRS comprising a first array of reconfigurable elements; beamforming and reflecting the first signal from the first IRS toward a relay; and retransmitting the first signal from the relay to a second IRS, the second IRS comprising a second array of reconfigurable elements.
Another exemplary embodiment provides a wireless communications system. The wireless communications system includes a first IRS comprising a first array of reconfigurable elements and a relay. The relay is configured to: amplify and relay a first signal from the first IRS to a second IRS; and amplify and relay a second signal from the second IRS to the first IRS.
Those skilled in the art will appreciate the scope of the present disclosure and realize additional aspects thereof after reading the following detailed description of the preferred embodiments in association with the accompanying drawing figures.
The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.
The embodiments set forth below represent the necessary information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims.
It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
It will be understood that when an element such as a layer, region, or substrate is referred to as being “on” or extending “onto” another element, it can be directly on or extend directly onto the other element or intervening elements may also be present. In contrast, when an element is referred to as being “directly on” or extending “directly onto” another element, there are no intervening elements present. Likewise, it will be understood that when an element such as a layer, region, or substrate is referred to as being “over” or extending “over” another element, it can be directly over or extend directly over the other element or intervening elements may also be present. In contrast, when an element is referred to as being “directly over” or extending “directly over” another element, there are no intervening elements present. It will also be understood that when an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being “directly connected” or “directly coupled” to another element, there are no intervening elements present.
Relative terms such as “below” or “above” or “upper” or “lower” or “horizontal” or “vertical” may be used herein to describe a relationship of one element, layer, or region to another element, layer, or region as illustrated in the Figures. It will be understood that these terms and those discussed above are intended to encompass different orientations of the device in addition to the orientation depicted in the Figures.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including” when used herein specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Relay-aided intelligent reconfigurable surfaces (IRSs) are provided. A novel relay-aided intelligent surface architecture is described herein that has the potential of achieving the promising gains of IRSs with a much smaller number of elements, opening the door for realizing these surfaces in practice. A half-duplex or full-duplex relay is connected to one or more IRSs. This merges the gains of relays and reconfigurable surfaces and splits the required signal-to-noise ratio (SNR) gain between them. This architecture can then significantly reduce the required number of reconfigurable elements in the IRS(s) while achieving the same spectral efficiencies. Consequently, the proposed relay-aided intelligent surface architecture needs far less channel estimation/beam training overhead and provides enhanced robustness compared to traditional IRS solutions.
In one aspect, the proposed architecture splits the reflection process over two intelligent surfaces connected wired or wirelessly by a relay. This allows leveraging full-duplex relays with practical isolation. Further, this enables the proposed architecture to be deployed in very flexible ways by optimizing the position and orientation of the two surfaces, which leads to much better coverage. Other examples embed the relay within one or multiple IRSs to (e.g., via wired connection to one or each of multiple IRSs) to provide amplification in addition to the beamforming of the IRS(s).
After describing the proposed architecture, this disclosure develops an accurate mixed near-far field channel model that describes the composite channel between a transmitter/receiver pair and the relay through the IRS surfaces. Further, the disclosure derives closed-form expressions for the achievable rates using the proposed relay-aided intelligent surface architecture with decode-and-forward (DF) and amplify-and-forward (AF) relays. Finally, these rates are evaluated using numerical simulations which further highlight the promising gains of the proposed architecture.
IRSs have the potential of enhancing the coverage and data rates of future wireless communications systems. This is particularly important for millimeter wave (mmWave) and terahertz (THz) systems where network coverage is a critical problem. The current approach in realizing these surfaces is through using massive numbers of nearly-passive elements that focus the incident signals towards a desired direction. In order to achieve sufficient receive power, however, these surfaces will typically need to deploy tens of thousands of antenna elements (as described further below in Section VI). Having IRSs with that many antennas carries fundamental problems that may render these surfaces infeasible. In addition to a high production cost, these surfaces have extremely narrow beams which incur massive training overhead with which supporting even low-mobility applications is questioned. Further, narrow beams constitute a critical challenge for the robustness of the communication links as even very small movements may result in a sudden large drop in the receive power. With the motivation of overcoming these challenges and enabling the potential gains of IRSs in practice, a novel architecture is proposed herein based on merging these surfaces with half- or full-duplex relays. Next, the proposed architecture is briefly described and its potential gains are highlighted.
A. Architecture Description
To achieve this goal, the architecture of
When the relay 18 is a full-duplex relay, the two IRSs 14, 16 switch their roles as the direction of communication switches. Note that in an exemplary aspect, the proposed architecture has two different IRSs 14, 16 doing different (transmit/receive) functions at any point in time. This allows employing a full-duplex relay 18 (with reasonable isolation) and enables the proposed relay-aided intelligent surface architecture to continuously reflect the incident signals. In another aspect, the first IRS 14 and the second IRS 16 represent a single IRS 14 with a connected or embedded relay 18 to provide amplification of a signal received from the transmitter 20, while other components of the IRS 14 beamform the amplified signal toward the receiver 22.
Embodiments of the relay-aided intelligent surface device 12 can be implemented with one or multiple IRSs 14, 16. In addition, communication between the relay 18 and the IRSs 14, 16 can be wireless, wired, or a combination of wired and wireless.
Each of the first IRS 14 and the second IRS 16 includes an array of reconfigurable antenna elements 26. As illustrated in
Each of the antennas 28 can be a horn antenna, a phased antenna array, or another appropriate antenna for sending and receiving signals reflected from the IRSs 14, 16. As described further below, in some embodiments the first IRS 14 and the second IRS 16 can be separated from one another and may further be oriented in different directions. In some embodiments the first IRS 14 is collocated and oriented substantially parallel with the second IRS 16.
In some embodiments, the relay 18 further includes multiple antennas 28 coupled to multiple antenna ports ANT1 , ANT1A, . . . ANT1 N, each of which is aimed toward a portion of the array of antenna elements 26 in the first IRS 14. In some embodiments, the relay 18 further includes multiple antennas 28 coupled to multiple antenna ports ANT2, ANT2A, . . . ANT2N, each of which is aimed toward a portion of the array of antenna elements 26 in the second IRS 16. As such, one or multiple amplification paths may be provided through the relay 18. Where multiple amplification paths are provided, the number of amplification paths between the first IRS 14 and the second IRS 16 may be equal, or the number of amplification paths from the first IRS 14 to the second IRS 16 may be different from the number of amplification paths from the second IRS 16 to the first IRS 14.
In some embodiments, multiple amplification paths through the relay 18 may provide for communications with multiple devices or between multiple locations of moving devices. In some embodiments, multiple amplification paths through the relay 18 may provide for communications at different frequency bands. In some embodiments, the wireless relay-aided intelligent surface device 12 includes multiple relays 18 to similarly provide multiple amplification paths (e.g., for different groups of antenna elements and/or at different frequency bands). In some embodiments, the multiple relays 18 can share at least some signal processing circuitry (e.g., amplification circuitry, logic circuitry, etc.).
In this regard, the first IRS 14 receives the first signal (e.g., from the transmitter 20 of
In some embodiments, the relay 18 further includes multiple wired connections with the first IRS 14 at multiple antenna ports ANT1 , ANT1 A, . . . ANT1 N. In some embodiments, the relay 18 further includes multiple antennas 28 coupled to multiple antenna ports ANT2, ANT2A, . . . ANT2N, each of which is aimed toward a portion of the array of antenna elements 26 in the second IRS 16. As such, one or multiple amplification paths may be provided through the relay 18 in a manner similar to the embodiment of
In this regard, the first IRS 14 includes or is the same as the second IRS 16. The first IRS 14 receives the first signal (e.g., from the transmitter 20 of
In some embodiments, the relay 18 further includes multiple wired connections with the first IRS 14 at multiple antenna ports ANT1 , ANT1 A, . . . ANT1 N. In some embodiments, the relay 18 further includes multiple wired connections with the first IRS 14 at multiple antenna ports ANT2, ANT2A, . . . ANT2N. As such, one or multiple amplification paths may be provided through the relay 18 in a manner similar to the embodiments of
The proposed relay-aided intelligent surface architecture has several potential gains compared to the classical intelligent surface architecture that has a single surface. Next, these gains are briefly highlighted.
To achieve a sufficient SNR gain, the proposed architecture has the possibility to split this required gain between the power amplification gain of the relay 18 and the focusing gain of the IRS(s) 14, 16. This can considerably reduce the required number of elements at the IRS(s) 14, 16.
To realize the potential beamforming gain, the reconfigurable antenna elements 26 of the IRSs 14, 16 need to be configured based on the channels between these surfaces and the transmitters/receivers. Acquiring this channel knowledge (or equivalently finding the best beam), however, requires huge training overhead in classical intelligent surfaces that employ massive numbers of elements. This imposes a critical challenge for the feasibility of these surfaces in practical deployments. Given that the proposed architecture has the potential of achieving the same SNR gains with a much smaller number of reconfigurable antenna elements 26 (and hence much less training overhead), it presents an interesting path for realizing these systems in practice.
Another critical challenge that follows from employing a massive number of elements in classical intelligent surfaces is the very small beamwidth of the focusing beams. These laser-like beams highly affect the robustness of these systems as the links can be abruptly disconnected with any small movement by the transmitter or the receiver. In contrast, and thanks to requiring a smaller number of reconfigurable antenna elements 26, the proposed relay-aided intelligent surface architecture employs wider beams, which enhances the robustness of the system.
Consider the wireless communications system 10 shown in
When the transmitter 20 sends the signal s, this signal is first reflected by the receive reflecting surface (the first IRS 14 in
y
RIR=√{square root over (pt)}gtTΨ1hts+n1 Equation 1
where pt denotes the transmit power at the transmitter, s is the transmit symbol with unit average power, and n1˜N(0,σ12) is the receive noise at the relay. The M×M diagonal matrix Ψ1 is the interaction matrix of the first intelligent surface (first IRS 14).
If ψ1 denotes the diagonal vector of Ψ1, i.e., Ψ1=diag(ψ1), then Equation 1 can be rewritten as
y
RIR=√{square root over (pr)}(ht⊙gt)Tψ1s+n1 Equation 2
where ⊙ is the Hadamard product. This disclosure focuses on the case when the intelligent surfaces interact with the incident signals via phase shifters, i.e., ψl=√{square root over (κ1)}[ejϕ
For AF relays: An amplification gain β will be applied to the receive signals before retransmitting it towards the second intelligent surface (second IRS 16). This surface will then reflect the signal to the receiver 22 using its interaction matrix Ψ2, defined similarly to Ψ1. If gr∈CN×1 and bt∈CM×1 represent the channels between the second IRS 16 and the transmit antennas of the relay 18 and between the second IRS 16 and the receiver 22, then the receive signal at the receiver 22 can then be written as
y
r=√{square root over (β)}(hr⊙gr)Tψ2(√{square root over (pt)}(ht⊙gt)Tψ1s+n1)+n2 Equation 3
where n2˜N(0,σ22) is the receive noise at the receiver.
For DF relays: The receive signals will be decoded and retransmitted with power pr to the second intelligent surface (second IRS 16), which reflects the signal towards the receiver 22 using its interaction matrix Ψ2. In this case, the receive signal at the receiver can be written as
y
r=√{square root over (pr)}(hr⊙gr)Tψ2s+n2 Equation 4
An important note on the transmit and receive side composite channels, (ht⊙gt) and (hr⊙gr) is that they combine far-field channels ht, hr and near-field channels gt, gr. In the next section, an accurate model is developed for these channels.
One important characteristic of the proposed relay-aided intelligent surface architecture is that the channels between intelligent surfaces and the transmitter/receiver can be modeled as far-field channels while the channels between the surfaces and the relay need to adopt near-field modeling. This section describes in detail the composite channel model for the transmit side, which is denoted h°t=ht⊙gt. The receive-side composite channel h°r=hr⊙gr can be similarly defined.
Given the description of the relay-aided intelligent surface architecture in Section II, the transmit-side composite channel can be written as
h°
t
=h
t⊙çt⊙Θt Equation 5
where çt and Θt are the magnitude and phase vectors of the near-field IRS-relay channel gt, i.e., gt=t⊙Θt.
First, the far-field channel vectors, ht, are described using a geometric channel model. In this model, the signal propagating between the transmitter 20 and the first IRS 14 experiences L clusters, and each cluster contributes with one ray via a complex coefficient ∈ and azimuth/elevation angles of arrival, ,∈[0,2π). Hence, the channel ht can be written by
h
t==1√{square root over (pt)}a(,) Equation 6
where pt denotes the path loss between the transmitter 20 and the first IRS 14, and a(.)∈M×1 represents the array response vector of the first intelligent surface (the first IRS 14).
For the channel between the intelligent surface and the antenna (e.g., a horn antenna), given the small distance between them, near-field and spherical propagation models need to be considered. Near-field effects are reflected on both the magnitude and phase of the channel entries and magnitude depends on the free-space path-loss, the polarization mismatch and the effective aperture area of the antenna. For reconfigurable antenna elements 26 of side-length
the magnitude of the channel between element m of the first IRS 14 and the antenna 28 of the relay 18, [t]m, can be approximated as
where
with c and f denoting the speed of light and carrier frequency. The height of the relay antenna is denoted by d=|zm−z0| and the gain of the horn antenna over the isotropic antenna is represented by Gt.
Finally, following the spherical wave equations, the phase factor of the channel between the mth element of the first IRS 14 and the antenna 28 of the relay 18, which is captured in the mth element of Θt, can be written as
where λ is the wavelength.
This section investigates the achievable spectral efficiency using the proposed relay-aided intelligent surface architecture. First, the spectral efficiency achieved by the standard intelligent surfaces and AF/DF relays is briefly reviewed. Then, the spectral efficiency of the proposed relay-aided intelligent surface architecture is derived with both AF and DF relays, respectively. In the following derivations, it is assumed that perfect channel state information is available at standard intelligent surfaces, relays, and relay-aided IRSs.
A. Standard Intelligent Surfaces
First, spectral efficiency of standard intelligent surfaces is derived for comparison purposes. By adopting the same channel definitions for the transmitter-intelligent surface and intelligent surface-receiver channels, i.e., ht and hr, the received signal is formulated as
y
r=√{square root over (pt)}hrTΨhts+n2 Equation 10
where n2 is the receiver noise as defined previously, and Ψ=diag(ψ) is the interaction matrix of the intelligent surface with ψ=√{square root over (κ)}[ejϕ
Note that Equation 11 is obtained by a transformation of Equation 10 similar to Equation 1 and Equation 2. In Equation 12, the intelligent surface is configured to maximize the gain via applying inverse phase shift of combined receive and transmit channels such that ϕm3=−[ht]m[hr]m. The results in Equation 13 are in a compact form by defining
Moreover, it can be upper-bounded with Cauchy-Schwarz inequality as given in Equation 14 with the definitions
Line-of-sight (LOS) scenario: The expression in Equation 13 can be further simplified in the case where only LOS path is available. In this case, the channel between the transmitter and the intelligent surface follows Equation 6 for L=1 and α1=1 resulting in ht=√{square root over (ρt)}a(,). Hence, ξt,r=ρtρr and
which is a similar expression to the upper-bound defined in Equation 14, however, the equality is exactly satisfied with the scalar channel gain values σt and σr.
B. Standard Relays
A standard relay with a single antenna in each direction is also considered, again adopting the same channel definitions ht, hr for M=1. The spectral efficiency of the relay models follows the derivations of a classical work with trivial changes due to (i) the absence of LOS channel between the transmitter and source, and (ii) the full-duplex operation without any interference.
1) DF Relay: With the given definitions, spectral efficiency of the DF relay can be written by
which simply selects the minimum rate of two channels utilized in the transmission.
2) AF Relay: For AF operation, the relay amplifies the received signal with the amplifying coefficient β, leading to
Note that the relay is subject to a power constraint pr, resulting in constraint
For the equality where full power is applied by the relay, the expression can be further simplified to
C. Relay-Aided Intelligent Surface
Recall that relay-aided intelligent surfaces can adopt either DF or AF operations depending on application. For instance, a DF relay is preferable for frequency selective fading channels, while an AF relay is favored when less transmission latency between a base station and a user is required. Gains of the IRSs 14, 16 are taken to be equal as they are identical, i.e., κ1=κ2=κ. To derive spectral efficiency of relay-aided intelligent surfaces, the transmitter-relay direction is written as
where Equation 22 is obtained by setting ϕM1=−[h°t]m maximizing the expression, and defining
By applying the same operations in Equations 19-22, the spectral efficiency of relay-receiver direction can be written as
with the phase shift values of the second IRS 16 being selected as ϕM2=[h°r]m and
1) DF Relay Operation: In a similar way to Equation 16, a DF-relay-aided intelligent surface can support the spectral efficiency
where Rt in Equation 24 shows the maximum rate at which the relay can reliably decode, while Rf is the maximum rate at which the relay can reliably transmit to the receiver.
LOS scenario: For the LOS case, the channels follow Equation 6 with L=1 and α1=1 leading to ht=√{square root over (ρt)}a(,). Moreover, ξ°t=ρtηt can be expanded with the definition
The spectral efficiency becomes
In addition, the near-field gain can be bounded by
due to the conservation of energy, resulting in
Note that this expression clearly indicates the proposed relay-aided intelligent surface model can offer κM gain on SNR of DF-relay with a LOS path as can be seen by setting =p in Equation 16.
2) AF Relay Operation: In a similar way to Equation 17, for the AF-relay-aided intelligent surface, the spectral efficiency can be formulated by
for a given gain constraint
Moreover, with the equality of Equation 28, similarly to Equation 18, the expression can be simplified to
LOS scenario: The same channel simplifications following the LOS scenario of the DF-Relay allow forming
with
and Equation 28. Also, maximum near-field gain
can bound the spectral efficiency as
since log(x), and
for a, b, c, x≥0 are strictly increasing functions. In the case of equality of gain constraint, similar expressions to Equation 29 for only LOS path can readily be obtained.
In addition to the achievable rates, the number of antennas needed for providing a given gain Rlim over a fixed distance are investigated. To this end, the expressions for the standard intelligent surface, AF- and DF-relay-aided intelligent surface are derived from the corresponding spectral efficiency. For ease of notation, γlim=2R
A. Standard Intelligent Surface
For the sake of a fair comparison, the standard intelligent surface is considered to have 2M antennas. Therefore, the inverse function of Equation 13 for 2M antennas with respect to M can be obtained as follows:
Note that this is a lower bound on M for an IRS with 2M antennas providing the rate Rlim.
B. Relay-Aided Intelligent Surface
1) DF Relay Operation: With DF-relay-aided intelligent surfaces, the number of antennas needed to provide the gain Rlim can be derived as
using Equation 24.
2) AF Relay Operation: The number of antennas needed for AF-relay-aided intelligent surfaces depends on the gain and power limitation of the relay. Recall the gain constraint of Equation 28, which depends on M. For a given amplifier coefficient β, the positive solution {tilde over (M)}RIRAF to the quadratic equation of M2 is found and given by
p
tβκ2ξ°t,rM4−γlimβκξ°rσ12M2γlimσ22 Equation 34
If corresponding {tilde over (M)}RIRAF holds for Equation 28, then the maximum gain does not violate the power constraint and MRIRAF={tilde over (M)}RIRAF. Otherwise, the system applies maximum power instead of the maximum relay gain through Equation 29 and the number of antennas needed in this case can be formulated as the positive solution of the following quadratic equation of M2:
This section evaluates the performance of the proposed relay-aided intelligent surface architecture using numerical simulations.
A. Simulation Setup
In this setup, the channel gains are generated by using the 3GPP Urban Micro (UMi)—street canyon model given as
P
loss=32.4+21 log10(d3D)+20 log10(fc)
where d3D and fc denote the 3D LOS path distance in meters and carrier frequency in gigahertz (GHz), respectively. In the following simulations, the LOS scenario with the near-field upper-bounds is considered. The LOS channel gains pr and pt are computed with the UMi model and utilized in the achievable rates of standard intelligent surfaces, DF and AF relays, and the upper-bounds for DF- and AF-relay-aided intelligent surfaces through the equations derived in Sections IV and V.
As detailed earlier, the standard intelligent surface considers twice the size of reflection elements 2M for comparison as there are two IRSs 14, 16 adopted in the relay-aided intelligent surface. A transmitter power of pt=20 decibels per milliwatt (dBm) and a relay maximum power of pr=20 dBm are considered for all scenarios. Two different carrier frequency values 60 GHz and 3.5 GHz are considered, representing mmWave and sub-6 GHz channels. The noise figure is set at 8 decibels (dB) and the bandwidth is assumed to be 100 megahertz (MHz) at the 3.5 GHz band and 1 GHz at the 60 GHz band. A unitary reflection coefficient, α=1, is adopted assuming perfect reflection at all the relay-aided intelligent surfaces and standard intelligent surfaces. For the simulations where AF relay gain is given by β, the relays apply the minimum of β amplification gain using maximum power.
B. Achievable Rates
C. How Many Elements Are Needed? Next, the number of reconfigurable antenna elements 26 needed to provide a fixed rate for varying distances between the transmitter 20 and receiver 22 is examined. Note that distance between the first IRS 14 and the second IRS 16 also increases with the increasing distance as shown in
With the traditional intelligent surface architecture, the required number of elements easily exceeds 100,000 over 25 m. On the other hand,
Although the operations of
The exemplary computer system 1000 in this embodiment includes a processing device 1002 or processor, a system memory 1004, and a system bus 1006. The system memory 1004 may include non-volatile memory 1008 and volatile memory 1010. The non-volatile memory 1008 may include read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and the like. The volatile memory 1010 generally includes random-access memory (RAM) (e.g., dynamic random-access memory (DRAM), such as synchronous DRAM (SDRAM)). A basic input/output system (BIOS) 1012 may be stored in the non-volatile memory 1008 and can include the basic routines that help to transfer information between elements within the computer system 1000.
The system bus 1006 provides an interface for system components including, but not limited to, the system memory 1004 and the processing device 1002. The system bus 1006 may be any of several types of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and/or a local bus using any of a variety of commercially available bus architectures.
The processing device 1002 represents one or more commercially available or proprietary general-purpose processing devices, such as a microprocessor, central processing unit (CPU), or the like. More particularly, the processing device 1002 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, or other processors implementing a combination of instruction sets. The processing device 1002 is configured to execute processing logic instructions for performing the operations and steps discussed herein.
In this regard, the various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with the processing device 1002, which may be a microprocessor, field programmable gate array (FPGA), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or other programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Furthermore, the processing device 1002 may be a microprocessor, or may be any conventional processor, controller, microcontroller, or state machine. The processing device 1002 may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).
The computer system 1000 may further include or be coupled to a non-transitory computer-readable storage medium, such as a storage device 1014, which may represent an internal or external hard disk drive (HDD), flash memory, or the like. The storage device 1014 and other drives associated with computer-readable media and computer-usable media may provide non-volatile storage of data, data structures, computer-executable instructions, and the like. Although the description of computer-readable media above refers to an HDD, it should be appreciated that other types of media that are readable by a computer, such as optical disks, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the operating environment, and, further, that any such media may contain computer-executable instructions for performing novel methods of the disclosed embodiments.
An operating system 1016 and any number of program modules 1018 or other applications can be stored in the volatile memory 1010, wherein the program modules 1018 represent a wide array of computer-executable instructions corresponding to programs, applications, functions, and the like that may implement the functionality described herein in whole or in part, such as through instructions 1020 on the processing device 1002. The program modules 1018 may also reside on the storage mechanism provided by the storage device 1014. As such, all or a portion of the functionality described herein may be implemented as a computer program product stored on a transitory or non-transitory computer-usable or computer-readable storage medium, such as the storage device 1014, volatile memory 1010, non-volatile memory 1008, instructions 1020, and the like. The computer program product includes complex programming instructions, such as complex computer-readable program code, to cause the processing device 1002 to carry out the steps necessary to implement the functions described herein.
An operator, such as the user, may also be able to enter one or more configuration commands to the computer system 1000 through a keyboard, a pointing device such as a mouse, or a touch-sensitive surface, such as the display device, via an input device interface 1022 or remotely through a web interface, terminal program, or the like via a communication interface 1024. The communication interface 1024 may be wired or wireless and facilitate communications with any number of devices via a communications network in a direct or indirect fashion. An output device, such as a display device, can be coupled to the system bus 1006 and driven by a video port 1026. Additional inputs and outputs to the computer system 1000 may be provided through the system bus 1006 as appropriate to implement embodiments described herein.
The operational steps described in any of the exemplary embodiments herein are described to provide examples and discussion. The operations described may be performed in numerous different sequences other than the illustrated sequences. Furthermore, operations described in a single operational step may actually be performed in a number of different steps. Additionally, one or more operational steps discussed in the exemplary embodiments may be combined.
Those skilled in the art will recognize improvements and modifications to the preferred embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow.
This application claims the benefit of provisional patent application Ser. No. 63/038,070, filed June 11, 2020, the disclosure of which is hereby incorporated herein by reference in its entirety.
Filing Document | Filing Date | Country | Kind |
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PCT/US2021/036953 | 6/11/2021 | WO |
Number | Date | Country | |
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63038070 | Jun 2020 | US |