Angela Doufexi

This paper proposes a closed-form low-complexity method for RIS configuration in a multi-user cell-free massive MIMO (maMIMO) network. Leveraging the large-scale fading (LSF) distribution and spatial diversity of the distributed network architecture, the proposed method jointly enhances the end-to-end channel gain and the degrees of freedom for interference suppression. An LSF-based algorithm is introduced to maintain fairness among user equipment (UEs). We have analysed the proposed method under short-term and long-term RIS configuration schemes and different CSI acquisition strategies. A closed-form channel estimation error variance for $b$ -bit digital RIS is derived, and the non-negligible impact of digital RIS on pilot contamination is quantified. To explore different trade-offs between spectral efficiency (SE) maximisation and fairness, we also propose an element-selection based RIS configuration method. To verify the performance, we derive three achievable SE equations for perfect CSI and imperfect CSI scenarios with a fully centralised cell-free maMIMO operation. Numerical results show that the proposed schemes outperform the baseline under three linear uplink combining methods and non-linear successive interference cancellation (SIC).

Considering a multi-user Downlink scenario, this paper studies transmit antenna selection (AS) for uniform rectangular arrays (URA). A key contribution of this work is the investigation of the trade-off between AS and the array directivity (or gain), and its impact on spectral efficiency (SE) and energy efficiency (EE). In this context, to improve EE of the system, a directivity-aware transmit AS and power allocation problem is formulated and solved using a sequential approach. For AS, two schemes are proposed: Block selection (BS) and L´evy flight based binary particle swarm optimization (LFBPSO), while for power allocation, an iterative quadratic transform method is employed in combination with Dinkelbach algorithm for EE maximization. The performance of the AS schemes is evaluated with respect to SE, EE and computational complexity. The results show that the array directivity varies with the subset of selected antennas and has a direct impact on the performance of the AS schemes in terms of achievable SE and EE. Furthermore, AS without considering the directivity can incur a loss in the array gain of up to 2 dB in a λ/2 spaced array. The results also show that LFBPSO outperforms other AS schemes with respect to SE and EE. Finally, another important contribution of this work is the experimental validation of the impact of array directivity on the AS process. This is demonstrated by replacing the isotropic antenna array with a measured 4×8 uniform rectangular array of patch antennas in the simulation framework.

This paper investigates the block error rate (BLER) of three encoding schemes: joint encoding (JE), separate encoding (SE), and rate splitting (RS) in low-throughput massive multiple input and multiple output (mMIMO) networks with finite blocklength transmission. To reduce pilot overhead, we first partition users via k-means clustering based on their spatial coordinates, by which the pilot sequences can be reused among different groups. Then, we apply a minimum mean square error (MMSE) estimator to examine their instantaneous signal-to-interference-plus-noise ratios (SINRs). In particular, a local successful interference cancellation is proposed to mitigate co-channel interference for the RS scheme. Then, we formulate min-max problems for optimizing the error performance, with power control coefficients, combining vectors, and blocklength being their variables. Additionally, we employ the iterative MMSE combiner as the combining vector and then transform the problem for minimizing the BLER to a max-min problem maximizing the SINR at each iteration based on the monotonicity of the BLER expression with respect to the SINR. Power control coefficients are then updated by solving the reformulated geographic programming (GP) problem. Furthermore, the golden-section and bisection methods are applied to optimize the blocklength allocation strategy for the SE scheme and to search for the achievable BLER for the RS scheme. Simulation results, using network availability as the performance metric, demonstrate the comparable performance of the JE and RS schemes when combined with the proposed solving methods, whereas the SE scheme exhibits moderate performance.

In this paper, we focus on the energy efficiency (EE) optimization and analysis of reconfigurable intelligent surface (RIS)-assisted multiuser downlink near-field communications. Specifically, we conduct a comprehensive study on several key factors affecting EE performance, including the number of RIS elements, the types of reconfigurable elements, reconfiguration resolutions, and the maximum transmit power. To accurately capture the power characteristics of RISs, we adopt more practical power consumption models for three commonly used reconfigurable elements in RISs: PIN diodes, varactor diodes, and radio frequency (RF) switches. These different elements may result in RIS systems exhibiting significantly different energy efficiencies (EEs), even when their spectral efficiencies (SEs) are similar. Considering discrete phases implemented at most RISs in practice, which makes their optimization NP-hard, we develop a nested alternating optimization framework to maximize EE, consisting of an outer integer-based optimization for discrete RIS phase reconfigurations and a nested non-convex optimization for continuous transmit power allocation within each iteration. Extensive comparisons with multiple benchmark schemes validate the effectiveness and efficiency of the proposed framework. Furthermore, based on the proposed optimization method, we analyze the EE performance of RISs across different key factors and identify the optimal RIS architecture yielding the highest EE.

In this paper, we focus on the energy efficiency (EE) optimization and analysis of reconfigurable intelligent surface (RIS)-assisted multiuser downlink near-field communications. Specifically, we conduct a comprehensive study on several key factors affecting EE performance, including the number of RIS elements, the types of reconfigurable elements, reconfiguration resolutions, and the maximum transmit power. To accurately capture the power characteristics of RISs, we adopt more practical power consumption models for three commonly used reconfigurable elements in RISs: PIN diodes, varactor diodes, and radio frequency (RF) switches. These different elements may result in RIS systems exhibiting significantly different energy efficiencies (EEs), even when their spectral efficiencies (SEs) are similar. Considering discrete phases implemented at most RISs in practice, which makes their optimization NP-hard, we develop a nested alternating optimization framework to maximize EE, consisting of an outer integer-based optimization for discrete RIS phase reconfigurations and a nested non-convex optimization for continuous transmit power allocation within each iteration. Extensive comparisons with multiple benchmark schemes validate the effectiveness and efficiency of the proposed framework. Furthermore, based on the proposed optimization method, we analyze the EE performance of RISs across different key factors and identify the optimal RIS architecture yielding the highest EE.

This paper investigates the uplink power control scheme for massive multiple-input and multiple-output non-orthogonal multiple access (mMIMO-NOMA) in massive ultra-reliable and low-latency communications (mURLLC) for Industrial Internet of Things (IIoT) applications. By the proposed NOMA scheme, the connected sensors are divided into several groups, and only group-level successive interference cancellation (GL-SIC) is considered at the receiver to reduce the decoding complexity and processing delay. Two schemes, i.e., mMIMO-NOMA with and without pilot sharing, are developed to fully explore the superiority of mMIMO-NOMA in mURLLC with a finite blocklength. For both schemes, the closed-form expressions of the achievable rate are obtained for the minimum mean square error (MMSE) estimator and zero-forcing (ZF) detector. Next, to address the formulated sum rate maximization problem, we develop a successive condensation approach (SCA)-based algorithm to jointly optimize pilot and data power. Besides, the max-min fairness (MMF) rate is also analyzed by verifying the feasibility of the problem. Finally, the simulation results demonstrate the effectiveness of the proposed SCA power control scheme. In addition, the advantages of the proposed two NOMA schemes in both sum rate and sum MMF rate are verified compared to traditional multi-user MIMO systems in mURLLC scenarios.

Optimizing discrete phase shifts in large-scale reconfigurable intelligent surfaces (RISs) is challenging due to their non-convex and non-linear nature. In this letter, we propose a heuristic-integrated deep reinforcement learning (DRL) framework that (1) leverages accumulated actions over multiple steps in the double deep Q-network (DDQN) for RIS column-wise control and (2) integrates a greedy algorithm (GA) into each DRL step to refine the state via fine-grained, element-wise optimization of RIS configurations. By learning from GA-included states, the proposed approach effectively addresses RIS optimization within a small DRL action space, demonstrating its capability to optimize phase-shift configurations of large-scale RISs.

Optimizing discrete phase shifts in large-scale reconfigurable intelligent surfaces (RISs) is challenging due to their non-convex and non-linear nature. In this letter, we propose a heuristic-integrated deep reinforcement learning (DRL) framework that (1) leverages accumulated actions over multiple steps in the double deep Q-network (DDQN) for RIS column-wise control and (2) integrates a greedy algorithm (GA) into each DRL step to refine the state via fine-grained, element-wise optimization of RIS configurations. By learning from GA-included states, the proposed approach effectively addresses RIS optimization within a small DRL action space, demonstrating its capability to optimize phase-shift configurations of large-scale RISs.