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1. RIS-Assisted Interference Mitigation for Uplink NOMA

   https://doi.org/10.1109/WCNC55385.2023.10119107  

   Tabeshnezhad, Azadeh ; Swindlehurst, A. Lee ; Svensson, Tommy ( March 2023 , 2023 IEEE Wireless Communications and Networking Conference (WCNC))

   Non-orthogonal multiple access (NOMA) has become a promising technology for next-generation wireless communications systems due to its capability to provide access for multiple users on the same resource. In this paper, we consider an uplink power-domain NOMA system aided by a reconfigurable intelligent surface (RIS) in the presence of a jammer that aims to maximize its interference on the base station (BS) uplink receiver. We consider two kinds of RISs, a regular RIS whose elements can only change the phase of the incoming wave, and an RIS whose elements can also attenuate the incoming wave. Our aim is to minimize the total power transmitted by the user terminals under quality-of-service constraints by controlling both the propagation from the users and the jammer to the BS with help of the RIS. The resulting objective function and constraints are both non-linear and non-convex, so we address this problem using numerical optimization. Our numerical results show that the RIS can help to dramatically reduce the per user required transmit power in an interference-limited scenario. 

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2. Optimizing AoI in UAV-RIS Assisted IoT Networks: Off Policy vs. On Policy

   https://doi.org/10.1109/jiot.2023.3246925  

   Sherman, Michelle ; Shao, Sihua ; Sun, Xiang ; Zheng, Jun ( February 2023 , IEEE Internet of Things Journal)

   In urban environments, tall buildings or structures can pose limits on the direct channel link between a base station (BS) and an Internet-of-Thing device (IoTD) for wireless communication. Unmanned aerial vehicles (UAVs) with a mounted reconfigurable intelligent surface (RIS), denoted as UAV-RIS, have been introduced in recent works to enhance the system throughput capacity by acting as a relay node between the BS and the IoTDs in wireless access networks. Uncoordinated UAVs or RIS phase shift elements will make unnecessary adjustments that can significantly impact the signal transmission to IoTDs in the area. The concept of age of information (AoI) is proposed in wireless network research to categorize the freshness of the received update message. To minimize the average sum of AoI (ASoA) in the network, two model-free deep reinforcement learning (DRL) approaches – Off-Policy Deep Q-Network (DQN) and On-Policy Proximal Policy Optimization (PPO) – are developed to solve the problem by jointly optimizing the RIS phase shift, the location of the UAV-RIS, and the IoTD transmission scheduling for large-scale IoT wireless networks. Analysis of loss functions and extensive simulations is performed to compare the stability and convergence performance of the two algorithms. The results reveal the superiority of the On-Policy approach, PPO, over the Off-Policy approach, DQN, in terms of stability, convergence speed, and under diverse environment settings 

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3. Joint Transmit Waveform and Receive Filter Design for Dual-Functional Radar-Communication Systems

   https://doi.org/10.1109/ICC45855.2022.9838990  

   Liu, Rang ; Li, Ming ; Liu, Qian ; Lee Swindlehurst, A. ( May 2022 , Proc. International Conference on Communications)

   Space-time adaptive processing (STAP) is an effective method for multi-input multi-output (MIMO) radar systems to identify moving targets in the presence of multiple interferers. The idea of joint optimization in both spatial and temporal domains for radar detection is consistent with the symbol-level precoding (SLP) technique for MIMO communication systems, that optimizes the transmit waveform according to instantaneous transmitted symbols. Therefore, in this paper we combine STAP and constructive interference (CI)-based SLP techniques to realize dual-functional radar-communication (DFRC). The radar output signal-to-interference-plus-noise ratio (SINR) is maximized by jointly optimizing the transmit waveform and receive filter, while satisfying the communication quality-of-service (QoS) constraints and the constant modulus power constraint. An efficient algorithm based on majorization-minimization (MM) and nonlinear equality constrained alternative direction method of multipliers (neADMM) methods is proposed to solve the non-convex optimization problem. Simulation results verify the effectiveness of the proposed DFRC scheme and the associate algorithm. 

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4. Interleaving Channel Estimation and Limited Feedback for Point-to-Point Systems with a Large Number of Transmit Antennas

   https://doi.org/10.1109/TWC.2018.2864204  

   Koyuncu, Erdem ; Zou, Xun ; Jafarkhani, Hamid ( January 2019 , IEEE Transactions on Wireless Communications)

   We introduce and investigate the opportunities of multi-antenna communication schemes whose training and feedback stages are interleaved and mutually interacting. Specifically, unlike the traditional schemes where the transmitter first trains all of its antennas at once and then receives a single feedback message, we consider a scenario where the transmitter instead trains its antennas one by one and receives feedback information immediately after training each one of its antennas. The feedback message may ask the transmitter to train another antenna; or, it may terminate the feedback/training phase and provide the quantized codeword (e.g., a beamforming vector) to be utilized for data transmission. As a specific application, we consider a multiple-input single-output system with t transmit antennas, a short-term power constraint P, and target data rate ρ. We show that for any t, the same outage probability as a system with perfect transmitter and receiver channel state information can be achieved with a feedback rate of R1 bits per channel state and via training R2 transmit antennas on average, where R1 and R2 are independent of t, and depend only on ρ and P. In addition, we design variable-rate quantizers for channel coefficients to further minimize the feedback rate of our scheme. 

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5. Distributed Power Allocation for 6-GHz Unlicensed Spectrum Sharing via Multi-agent Deep Reinforcement Learning

   https://doi.org/10.1109/ICIT58465.2023.10143125  

   Zhang, Xiang ; Bhuyan, Arupjyoti ; Kasera, Sneha Kumar ; Ji, Mingyue ( April 2023 , 2023 IEEE International Conference on Industrial Technology (ICIT))

   We consider the problem of spectrum sharing by multiple cellular operators. We propose a novel deep Reinforcement Learning (DRL)-based distributed power allocation scheme which utilizes the multi-agent Deep Deterministic Policy Gradient (MA-DDPG) algorithm. In particular, we model the base stations (BSs) that belong to the multiple operators sharing the same band, as DRL agents that simultaneously determine the transmit powers to their scheduled user equipment (UE) in a synchronized manner. The power decision of each BS is based on its own observation of the radio environment (RF) environment, which consists of interference measurements reported from the UEs it serves, and a limited amount of information obtained from other BSs. One advantage of the proposed scheme is that it addresses the single-agent non-stationarity problem of RL in the multi-agent scenario by incorporating the actions and observations of other BSs into each BS's own critic which helps it to gain a more accurate perception of the overall RF environment. A centralized-training-distributed-execution framework is used to train the policies where the critics are trained over the joint actions and observations of all BSs while the actor of each BS only takes the local observation as input in order to produce the transmit power. Simulation with the 6 GHz Unlicensed National Information Infrastructure (U-NII)-5 band shows that the proposed power allocation scheme can achieve better throughput performance than several state-of-the-art approaches. 

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