Maximum Ratio Transmission Research Articles

Sum rate optimization in STAR-RIS assisted multiuser massive MIMO-OFDM VLC systems

Visible Light Communication (VLC) offers a promising solution for future networks, leveraging existing lighting infrastructure in indoor environments. However, VLC requires a direct line of sight to function which can be limiting. Reconfigurable Intelligent Surface (RIS) is a new technology that can bend light and radio waves, addressing this limitation in VLC. RIS come in three types: passive which reflects signals, active that boosts and reflects signals, and Simultaneous Transmission and Reflection (STAR)-RIS which can both reflect and transmit signals simultaneously. STAR-RIS offers the most control over the signal. In this paper, we propose a new multi-user VLC system with a massive Multiple-Input, Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) architecture, leveraging STAR-RIS to optimize data rates and improve coverage, particularly in non-line-of-sight scenarios. We formulate a system model and solve a convex optimization problem to determine the optimal transmission and reflection coefficients for STAR-RIS elements, aiming to maximize the total sum rate for all users. By employing maximum ratio transmission precoding, we minimize interference among users and demonstrate significant performance gains. Simulation results show that our proposed energy splitting-based STAR-RIS configuration outperforms traditional mode selection and time switching approaches with fixed or random coefficients, yielding substantial improvements in data rates and user experience. This study offers the first detailed exploration of STAR-RIS in VLC systems, highlighting its potential for future high-speed, multi-user communication networks. Our findings set the stage for further research into optimizing VLC systems using STAR-RIS, particularly in complex environments with non-line-of-sight users and massive MIMO-OFDM configurations.

Secure communication with a multiple-antennas untrusted relay in presence of randomly distributed adversary jammers

In this article, we examine the achievable secrecy rate of a cooperative network including one source, one destination, and one untrusted amplify-and-forward relay, where randomly located adversary jammers attempt to disrupt the wireless communications. The relay has multiple antennas, while the other nodes have a single antenna. The adversary jammers are placed following a Poisson Point Process (PPP) and aim to interfere with the untrusted relay's channel. The relay employs maximal-ratio combining (MRC) to mitigate the jammers' impact and retransmits the signal to the destination using maximum ratio transmission (MRT). To prevent the relay from capturing the message, the destination injects pre-known artificial noise, known as destination-assisted cooperative jamming. We present a closed-form solution for the Ergodic secrecy rate (ESR) of the system with Rayleigh fading channels. An optimization problem for maximizing the secrecy rate is also designed, resulting in a closed-form formula for optimal power allocation (OPA) between the source and destination.

Evaluating the effectiveness of various diversity and combining techniques on an RF-FSO link

Abstract Diversity and combining is one of the robust techniques to mitigate multi-path fading in RF links and turbulence-induced scintillation in FSO links. This article evaluates the effect of different diversity and combining techniques on outage and BER performance of decode and forward RF-FSO link. We have utilized diversity and combining techniques over source to relay RF link and/or relay to destination FSO link. The selection combining, transmit antenna selection, maximal ratio combining, and maximum ratio transmission schemes are considered. The Monte-Carlo simulation results of outage probability and bit error rate are compared by considering different parameters affecting link quality like the strength of atmospheric turbulence, pointing error, FSO link detection scheme, RF link fading strength, diversity order, etc. It is found that configurations like SIMO-SISO, MISO-SISO, SISO-SIMO, and SISO-MISO, where diversity is applied in either source to relay link or relay to destination link, are improving the overall link performance provided that diversity is applied at the link experiencing stronger fading compared to another link. The link configurations SIMO-MISO or MISO-SIMO also improve the link performance and it can achieve BER of 10−5 at less than 15 dB SNR in favourable link conditions.

Joint beamforming and power splitting design for MISO downlink communication with SWIPT: a comparison between cell-free massive MIMO and small-cell deployments

Simultaneous wireless information and power transfer (SWIPT) has been advocated as a highly promising technology for enhancing the capabilities of 5G and 6G devices. However, the challenge of dealing with large propagation path loss poses a significant hurdle. To address this issue, massive multiple-input multiple-output (MIMO) is employed to enhance the efficiency of SWIPT in cellular-based networks with multiple small cells, and especially increase the energy for cell-edge users. In addition, by leveraging a large set of spatially distributed base stations to collaboratively serve SWIPT-enabled user equipment, the cell-free massive MIMO has the potential to provide even better performance than the conventional small-cell systems. In this work, we extend the investigation to include the application of SWIPT technology with alternating current (AC) logic in the cell-free networks and the small-cell networks and propose joint beamforming and power splitting optimization frameworks to maximize the system sum-rate, subject to the constraints on harvested energy, AC logic energy supply, and total transmit power. The optimization problem is shown to be non-convex, posing a significant challenge. To address this challenge, we resort to a two-stage decomposition approach. Specifically, we first introduce quadratic transform-based fractional programming (FP) algorithms to iteratively solve the non-convex optimization problems in the first stage, achieving near-optimal solutions with low time complexities. To further reduce the complexities, we also incorporate conventional schemes such as zero forcing, maximum ratio transmission, and signal-to-leakage-and-noise ratio for the design of beamforming vectors. Second, to determine the optimal power splitting ratio within the framework, we develop a one-dimensional (1-D) search algorithm to tackle the single variable optimization problem reduced in the second stage. These algorithms are then evaluated in the context of cell-free MIMO and small-cell networks with numerical experiments. The results show that the FP-based algorithms can consistently outperform those utilizing the conventional beamforming schemes, and the solutions of this work can achieve up to fivefold improvement in the system sum-rate than the small-cell counterpart while providing different but comparable performance trends in energy harvesting (EH).

Contextual beamforming: Exploiting location and AI for enhanced wireless telecommunication performance

Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users’ location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages, and implications. Notably, we demonstrate an impressive 53% improvement in the signal-to-interference-plus-noise ratio by implementing the adaptive beamforming maximum ratio transmission (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence (AI) schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and zero-forcing techniques, alongside deep neural networks employing Bayesian optimization, represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino—an innovative switch developed by Barefoot Networks (now a part of Intel)—in enabling location-aware beamforming. This paper highlights the significance of contextual beamforming for improving wireless telecommunications performance. By capitalizing on location information and employing advanced AI techniques, the field can overcome challenges and unlock new possibilities for delivering reliable and efficient mobile networks.

Joint mode selection and resource allocation to flexibly guarantee quality of service for downlink MISO-NOMA systems

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) detection, knownas SIC NOMA, enjoys higher spectral efficiency (SE), but suffers increased processing complexity and additional error propagation compared to Non-SIC NOMA,in whichthe receivers directly detect their own signalsfrom thesuperposed signalwithout SIC detection. We propose a joint Mode Selection and Power Allocation (MSPA) scheme that can flexibly realize different kinds of tradeoffs while guaranteeing minimum rate requirements (MRRs) for two-user MISO-NOMA downlink. Specifically, we first obtain rate region boundaries that guarantee two users’ MRRs for two candidatemodes: maximum-ratio transmission (MRT)/SIC NOMA and minimum mean square error beamforming (MMSE-BF)/Non-SIC NOMA. We then construct an expression of power allocation factors (transmit power factors) that can satisfy the two users’ MRRs for MRT/SIC NOMA (MMSE-BF/Non-SIC NOMA) by introducing a parameter λ. Finally, we achieve outer boundary of the union ofthe two modes' rate regionsby mode selection and derive expected users' rates on it. Numerical results demonstrate that the proposed MSPA can improve SE and flexibly achieve different kinds of tradeoffs by adjusting λ, while guaranteeing the two users’ MRRs simultaneously for high signal to noise ratio (SNR) scenario and alternately guaranteeing the two users’ MRRs for low SNR scenario.

Short-packet communication for MIMO NOMA relay networks: BLER and AoI analysis

This work considers a multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) dual-hop relay system in which the size of signal packets is finite and the propagation environment follows the Nakagami-m distribution. Based on an approximation of the Gaussian Q-function, Riemann integral, and incomplete Gamma function, we derive the closed-form expressions of the block error rate (BLER), age of information (AoI), and latency of the proposed system in both exact and asymptotic cases. On the other hand, the automatic repeat request (ARQ) protocol and acknowledgment (ACK) are applied to confirm the accuracy of the receiving data packet process. Moreover, the maximal-ratio transmission (MRT), and maximal-ratio combining (MRC) are used to improve the BLER, AoI, and latency of the proposed system. The accuracy of mathematical analysis frameworks is confirmed by simulation results. The calculation results show that the considered system satisfies the requirement of ultra-reliable and low-latency communications (URLLC). The results also represent that the MIMO-NOMA system outperforms the MIMO-OMA system in terms of BLER performance. The results also provide insight into the optimizations of the power allocation coefficient and the number of transmission bits that minimize the BLER and AoI of the considered system.

Multi-IRS-Aided Millimeter-Wave Multi-User MISO Systems for Power Minimization Using Generalized Benders Decomposition

Difficulties in controlling IRSs to form the optimized passive beamforming have rarely been considered in intelligent reflecting surface (IRS)-aided systems, which are summarized as follows: 1) sending the optimized passive precoding vectors to the IRS controller incurs significant control overheads; 2) implementing the optimized passive precoding needs to set massive modes in the IRS control circuit. To address these issues, we investigate codebook-based passive beamforming for multi-IRS-aided millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) systems, where the control overheads are reduced to several scalars and the number of modes set in the IRS control circuit is reduced to that of codewords. Moreover, we formulate a joint passive and active precoding problem in the multi-IRS-aided mmWave MU-MISO system as a mixed-integer nonlinear programming (MINLP) problem, and then develop a generalized Benders decomposition (GBD)-based joint passive and active precoding algorithm. The proposed algorithm offers near-optimal performance (≥ 99.9%) with significantly-reduced computational complexity. Simulation results show that the proposed algorithm achieves energy savings of up to 50% and 95%, compared to the benchmark by the maximum ratio transmission and that without IRSs, respectively. In addition, the energy savings increase with the number of reflecting elements packed on each IRS as well as that of codewords.

Performance Analysis and Deep Learning Design of Short-Packet Communication in Multi-RIS-Aided Multiantenna Wireless Systems

This paper studies short-packet communication (SPC) in multi-reconfigurable intelligent surface (RIS)-assisted multi-antenna wireless systems. In this system, a sensor node communicates with another sensor node through the help of an access point (AP) and two sets of distributed RISs. Aiming to enhance system performance, we combine the best RIS selection strategies with maximum-ratio transmission (MRT) beamforming designs to improve the transmitted signal and selection combining (SC) or maximum-ratio combining (MRC) to increase the received signals. Closed-form expressions for the block error rate (BLER) BLER, throughput, latency, and reliability of the receivers over Rayleigh fading channels are derived to evaluate the system performance. Numerical results show that, in the first transmission phase, employing MRC with optimal phase shift (OPS) occurring at RIS can help AP achieve the best BLER performance, while SC with OPS provides better BLER performance than MRC and uncertain phase shift (UPS). In the second transmission phase, the reflective-path beamforming design shows better performance than the direct-path beamforming design, where OPS also attains outstanding performance when compared to UPS. Aiming for real-time system configurations with high reliability along with minimizing costs and resources, we propose a deep learning (DL) approach to optimize the number of reflective elements at each RIS or the number of RIS in a set of distributed RISs. Our work shows that the prediction results of the DL framework match the analytical derivations and the deep neural network (DNN) can help the systems save overhead and resources while satisfying the requirement of real-time communication.

On performance of short-packet ultra-reliable and low-latency communications NOMA MIMO relay networks

This paper proposes and analyzes the performance of a multi-antenna NOMA relay system over the Nakagami-m fading channel, in which the size of signal packets is finite. Based on the application of the Gaussian–Chebyshev quadrature, Riemann integral and incomplete Gamma function, the exact and asymptotic closed-form expressions of the block error rate (BLER), throughput, goodput of the proposed finite block-length multi-antenna NOMA relay system are derived. Moreover, in order to improve the BLER, throughput, and goodput of the proposed system, maximal-ratio combining and maximal-ratio transmission are applied to receivers and transmitters, respectively. The provided analysis and simulation results indicate that the proposed system meets the requirements of ultra-reliable and low-latency communications (URLLC). Especially, the reliability is higher than 99.99%, i.e., the BLER is less than 10−4. The accuracy of analysis results is verified by simulation results. Furthermore, the number of transmission bits and power allocation coefficients are optimized to maximize the throughput, and goodput and minimize the BLER.

Secure Beamforming in Multi-User Multi-IRS Millimeter Wave Systems

We study the secrecy rate maximization problem in a millimeter wave (mmWave) network, consisting of a base station (BS), multiple intelligent reflecting surfaces (IRSs) (or reconfigurable intelligent surfaces (RISs)), multiple users, and a single eavesdropper. To ensure a fair secrecy rate among all the users, we adopt a max-min fairness criterion which results in a mixed integer problem. We first relax discrete IRSs phase shifts to the continuous ones. To cope with the non-convexity of the relaxed optimization problem, we leverage the penalty method and block coordinate descent approach to divide it into two sub-problems, which are solved by successive convex approximation (SCA). Then, we propose a low-complexity mapping algorithm where feasible IRSs phase shifts are obtained. Mathematical evaluation shows the convergence of sub-problems to a Karush-Kuhn-Tucker (KKT) point of the original ones. Furthermore, the convergence guarantee of the overall proposed algorithm and computational complexity are investigated. Finally, simulation results show our proposed algorithm outweighs the conventional solutions based on the semi-definite programming (SDP) in terms of convergence and secrecy rate, especially in a larger number of IRSs and phase shifts where SDP suffers from rank-one approximation. Maximum ratio transmission (MRT) and IRS-free systems are also considered as other benchmarks.

Transmitter Space Shift Keying With Maximum- Ratio Combining and Receiver Spatial Modulation With Maximum-Ratio Transmission in Downlink Cellular Systems

The author investigates the performance of transmitter space shift keying (TSSK) with maximum-ratio combining (MRC) and receiver spatial modulation (RSM) with maximum-ratio transmission (MRT) in an unbalanced number of antennas at the transmitter and receiver. The author shows that RSM-MRT exhibits a superior performance under the same data rate, transmit power, and bandwidth in downlink cellular network systems where the number of transmit antennas at the base station is much larger than the number of receiver antennas at the mobile station. The author identifies a figure of metric as fading parameters and finds their probability density function (PDF) and cumulative distribution function (CDF). The performance gap between TSSK-MRC and RSM-MRT expands as the number of antennas increases while maintaining the same ratio of the number of antennas at the transmitter and receiver for a chosen modulation.

Impact of Channel Aging on Dual-Function Radar-Communication Systems: Performance Analysis and Resource Allocation

In conventional dual-function radar-communication (DFRC) systems, the radar and communication channels are routinely estimated at fixed time intervals based on their worst-case operation scenarios. Such situation-agnostic repeated estimations cause significant training overhead and dramatically degrade the system performance, especially for applications with dynamic sensing/communication demands and limited radio resources. In this paper, we leverage the channel aging characteristics to reduce training overhead and to design a situation-dependent channel re-estimation interval optimization-based resource allocation in a multi-target tracking DFRC system. Specifically, we exploit the channel temporal correlation to predict radar and communication channels for reducing the need for training preamble retransmission. Then, we characterize the channel aging effects on the Cramer-Rao lower bounds (CRLBs) for radar tracking performance analysis and achievable rates with maximum ratio transmission (MRT) and zero-forcing (ZF) transmit beamforming for communication performance analysis. In particular, the aged CRLBs and achievable rates are derived as closed-form expressions with respect to the channel aging time, bandwidth, and power. Based on the analyzed results, we optimize these factors to maximize the average total aged achievable rate subject to individual target tracking precision demand, communication rate requirement, and other practical constraints. Since the formulated problem belongs to a non-convex problem, we develop an efficient one-dimensional search based optimization algorithm to obtain its suboptimal solutions. Finally, simulation results are presented to validate the correctness of the derived theoretical results and the effectiveness of the proposed allocation scheme.

Beamforming Design for In-Band Full-Duplex Multi-Cell Multi-User MIMO Networks With Global and Local CSI

This paper investigates beamforming techniques for in-band full-duplex multi-cell multi-user (IBFD-MCMU) wireless networks considering hardware impairments (HWIs), channel uncertainty, and limited channel state information (CSI). For the global CSI scenario, we first enhance zero-forcing (ZF), maximum ratio transmission and combining (MRTC), and minimum mean-squared error (MMSE) beamforming to be compatible with multi-antenna users and IBFD base stations. Then, we investigate beamforming schemes for the local CSI assumption where only intra-cell channel knowledge is fully available at the base stations, and inter-cell channels are known statistically due to the limited training resources. With these limitations, two MMSE-based methods are proposed, which regard unknown CSI as random instances (i.e., eMMSE-RI) and noise (i.e., eMMSE-N). We explore the self-interference cancellation (SIC) capability of beamformers and evaluate the performance of these methods in 3GPP scenarios. Numerical results reveal that the enhanced MMSE beamforming can achieve the desired IBFD with low analog SIC depth, inspiring a low-cost IBFD transceiver design. The practical imperfections (e.g., transceiver HWIs, channel uncertainty, limited CSI) decrease the achievable sum rate but do not reduce the IBFD gain. With CSI limitations, eMMSE-RI outperforms eMMSE-N for microcells, where the probability of the presence of LOS is high, whereas eMMSE-N performs better when cell size is enlarged.

Secrecy Performance Analysis for MIMO-DF Relay Systems With MRT/MRC and TZF/MRC Schemes

Transmit beamforming is a diversity technique in multiple-input multiple-output (MIMO) systems. It improves the diversity order and enhances the information security of the MIMO systems. In this paper, we mathematically analyze the secure performance of a MIMO decode-and-forward (DF) relay system under the presence of an eavesdropper and with the imperfect channel state information (CSI) of the eavesdropping channel. To enhance the secrecy performance, we employ multiple antennas at all legitimate nodes and utilize two linear beamforming schemes, i.e., maximal ratio transmission (MRT) and transmission zero-forcing (TZF), at the transmitter and maximal ratio combining (MRC) scheme at the receiver. The probability density function (PDF) of the output SNR for different numbers of antennas at the transmitter and receiver is plotted and discussed. After that, we apply this PDF to derive the closed-form expressions of the secrecy outage probability (SOP) and ergodic secrecy capacity (ESC) of the considered MIMO-DF relay system over Rayleigh fading channels for MRT/MRC and TZF/MRC schemes. In addition, based on the obtained analysis results, we provide the location of the relay that minimizes the system SOPs. All the analysis results are validated by Monte-Carlo simulations. The results show that the TZF/MRC scheme provides better SOP and ESC than the MRT/MRC scheme. Moreover, the number of antennas at the legitimate nodes and the location of the eavesdropper significantly impact the secrecy performance.

Numerical Assessment of Human EMF Exposure to Collocated and Distributed Massive MIMO Deployments in an Industrial Indoor Environment

This article presents a comparative numerical study of collocated and distributed massive multiple-input–multiple-output (MIMO) deployments at 3.5 GHz in an industrial indoor environment from the point of view of downlink human electromagnetic field (EMF) exposure. A collection of environmental models incorporating elements with stochastic geometry is generated, in which the EMF propagation is calculated using the ray-tracing (RT) method. To evaluate the human exposure, the finite-difference time-domain (FDTD) method is used, including a realistic human phantom and a user equipment model, into which the excitation is introduced based on the RT results. Single-user maximum ratio transmission and multiuser zero-forcing scenarios are studied. Small-scale EMF distributions in proximity to the phantom's head are assessed in FDTD and analysed for different user locations in the environment and user equipment placement with respect to the head. The massive MIMO hot-spot is characterized in terms of its size, instantaneous and time-averaged EMF enhancement, and position with respect to the head and the user equipment. The human exposure is assessed using the peak-spatial specific absorption rate averaged over 10 g, referenced to the hot-spot EMF amplitude, and compared to international guidelines. It is shown that the distributed deployment results in a more accurate and consistent EMF hot-spot around the user equipment with a higher average E-field gain, compared to the collocated deployment. In addition, the distributed configuration produced more compact hot-spots relative to the collocated one, leading to a more than tenfold average exposure reduction in a multiuser scenario.

Downlink Massive MIMO Systems: Reduction of Pilot Contamination for Channel Estimation with Perfect Knowledge of Large-Scale Fading

Massive multiple-input multiple-output (MIMO) technology is considered crucial for the development of future fifth-generation (5G) systems. However, a limitation of massive MIMO systems arises from the lack of orthogonality in the pilot sequences transmitted by users from a single cell to neighboring cells. To address this constraint, a proposed solution involves utilizing orthogonal pilot reuse sequences (PRS) and zero forced (ZF) pre-coding techniques. The primary objective of these techniques is to eradicate channel interference and improve the experience of end users who are afflicted by low-quality channels. The assessment of the channel involves evaluating its quality through channel assessment, conducting comprehensive evaluations of large-scale shutdowns, and analyzing the maximum transmission efficiency. By assigning PRS to a group of users, the proposed approach establishes lower bounds for the achievable downlink data rate (DR) and signal-to-interference noise ratio (SINR). These bounds are derived by considering the number of antennas approaches infinity which helps mitigate interference. Simulation results demonstrate that the utilization of improved channel evaluation and reduced loss leads to higher DR. When comparing different precoding techniques, the ZF method outperforms maximum ratio transmission (MRT) precoders in achieving a higher DR, particularly when the number of cells reaches 𝛶𝛶𝑝𝑝=7.

Two Low-Complexity Efficient Beamformers for an IRS- and UAV-Aided Directional Modulation Network

As excellent tools for aiding communication, an intelligent reflecting surface (IRS) and an unmanned aerial vehicle (UAV) can extend the coverage area, remove the blind area, and achieve a dramatic rate improvement. In this paper, we improve the secrecy rate (SR) performance of directional modulation (DM) networks using an IRS and UAV in combination. To fully explore the benefits of the IRS and UAV, two efficient methods are proposed to enhance the SR performance. The first approach computes the confidential message (CM) beamforming vector by maximizing the SR, and the signal-to-leakage-noise ratio (SLNR) method is used to optimize the IRS phase shift matrix (PSM), which is called Max-SR-SLNR. To reduce the computational complexity, the CM, artificial noise (AN) beamforming, and IRS phase shift design are independently designed in the following method. The CM beamforming vector is constructed based on the maximum ratio transmission (MRT) criteria along the channel from Alice-to-IRS, the AN beamforming vector is designed by null-space projection (NSP) on the remaining two channels, and the PSM of the IRS is directly given by the phase alignment (PA) method. This method is called the MRT-NSP-PA. The simulation results show that the SR performance of the Max-SR-SLNR method outperforms the MRT-NSP-PA method in the cases of small-scale and medium-scale IRSs, and the latter approaches the former in performance as the IRS tends to a larger scale.

Analysis and Optimization of a Double-IRS Cooperatively Assisted System With a Quasi-Static Phase Shift Design

The analysis and optimization of single intelligent reflecting surface (IRS)-assisted systems have been extensively studied, whereas little is known regarding multiple-IRS-assisted systems. This paper investigates the analysis and optimization of a double-IRS cooperatively assisted downlink system (D-IRS-C), where a multi-antenna base station (BS) serves a single-antenna user with the help of two multi-element IRSs, connected by an inter-IRS channel. The channel between any two nodes is modeled with Rician fading. The BS adopts the instantaneous CSI-adaptive maximum-ratio transmission (MRT) beamformer, and the two IRSs adopt a cooperative quasi-static phase shift design. The goal is to maximize the average achievable rate, which can be reflected by the average channel power of the equivalent channel between the BS and user at low channel estimation and phase adjustment costs and computational complexity. First, we obtain tractable expressions of the average channel power of the equivalent channel in the general (Rician factor), pure line of sight (LoS), and pure non-line of sight (NLoS) regimes, respectively. Then, we jointly optimize the phase shifts of the two IRSs to maximize the average channel power of the equivalent channel in these regimes. The optimization problems are challenging non-convex problems. We obtain globally optimal closed-form solutions for some cases and propose computationally efficient iterative algorithms to obtain stationary points for the other cases. Next, we compare the computational complexity for optimizing the phase shifts and the optimal average channel power of D-IRS-C with those of a counterpart double-IRS non-cooperatively assisted system (D-IRS-NC) and a counterpart single-IRS-assisted system (S-IRS) at a large number of reflecting elements in the three regimes. Finally, we numerically demonstrate notable gains of the proposed solutions over the existing solutions at different system parameters. To our knowledge, this is the first work that optimizes the quasi-static phase shift design of D-IRS-C and characterizes its advantages over the optimal quasi-static phase shift design of the counterpart D-IRS-NC and S-IRS.

DRL-based max-min fair RIS discrete phase shift optimization for MISO-OFDM systems

In this paper, we investigate a reconfigurable intelligent surface (RIS) assisted downlink orthogonal frequency division multiplexing (OFDM) transmission system. Taking into account hardware constraint, the RIS is considered to be organized into several blocks, and each block of RIS share the same phase shift, which has only 1-bit resolution. With multiple antennas at the base station (BS) serving multiple single-antenna users, we try to design the BS precoder and the RIS reflection phase shifts to maximize the minimum user spectral efficiency, so as to ensure fairness. A deep reinforcement learning (DRL) based algorithm is proposed, in which maximum ratio transmission (MRT) precoding is utilized at the BS and the dueling deep Q-network (DQN) framework is utilized for RIS phase shift optimization. Simulation results demonstrate that the proposed DRL-based algorithm can achieve almost optimal performance, while has much less computation consumption.