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).

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.

Reconfigurable intelligent surface (RIS) is considered one of the most promising technologies for beyond 5G and 6G communications with its potential to redefine the propagation environment and the simplicity of implementation. This paper proposes a closed-form low-complexity scheme for RIS configuration in a distributed multi-user MIMO (MU-MIMO) network and introduces a joint initial access and grouping scheme for RIS assignment. By leveraging the eigenvalue distribution of the access point-side channel, the proposed method aligns signals at orthogonal subspaces and effectively increases the degree of freedom for interference suppression. To verify the performance, we derive two achievable spectral efficiency (SE) equations for centralised and distributed operations. Numerical results show that the proposed scheme outperforms the baseline obviously under three different uplink combining methods.

Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising network architecture for beyond-5G communications with its potential of supporting the exponentially increasing mobile connections and resolving the inherent disadvantage of canonical cellular networks. This paper proposes a scalable pilot assignment algorithm for user-centric cell-free massive MIMO systems. The proposed scheme considers the eigenspace of channel vectors and minimizes the sum pilot contamination caused to all the serving APs of the currently considered UE. Interference-suppressing combining schemes are utilized in both centralized and distributed operations to evaluate the performance of the proposed algorithm. Furthermore, max-min SE fairness is considered by applying fractional power control. Numerical results show that the proposed scheme outperforms previous methods while providing the scalability to the network.