This paper investigates the secrecy performance of a spatially correlated multi-reconfigurable intelligent surface (RIS)-assisted cell-free massive multiple-input multiple-output system in the presence of imperfect channel state information, electromagnetic interference (EMI), and transceiver hardware impairments (T-HWI). Under these imperfections, we derive the linear minimum mean-square error channel estimate and a closed-form expression for the downlink secrecy sum rate (SSR). We employ artificial noise at each access point (AP) in the downlink data transmission phase to improve the achievable SSR. We deduce several analytical insights from the derived closed-form expressions to validate the system performance. We further formulate a joint optimization problem that maximizes the SSR by optimally allocating the power sharing factor and the RIS phase shift matrix. A closed-form solution for the optimal power sharing factor is obtained using a fixed-point equation method, while the RIS phase shifts are designed through a generative diffusion model. Numerical results validate the derived analytical expressions and reveal a substantial degradation in secrecy performance due to the presence of T-HWI and EMI. Notably, the legitimate user-side hardware impairments have a more detrimental effect on the system performance compared to the AP side. Furthermore, a trade-off is observed between the number of RISs and EMI power in achieving optimal SSR. Moreover, simulation results demonstrate that the proposed joint optimization scheme yields substantial gains in achievable SSR over the individual optimization schemes and the random baseline.

This paper investigates the secrecy performance of a spatially correlated multi-reconfigurable intelligent surface (RIS)-assisted cell-free massive multiple-input multiple-output system in the presence of imperfect channel state information, electromagnetic interference (EMI) at each RIS, and transceiver hardware impairments (T-HWI). Under these imperfections, we calculate the linear minimum mean-square error estimation to acquire the channel estimate and derive a closed-form expression for the downlink secrecy sum rate (SSR). We employ artificial noise (AN) at each access point (AP) in the downlink data transmission phase to improve the achievable SSR. Our analysis reveals that, under specific scenarios, hardware distortion at the AP can act as effective AN. Numerical results validate the theoretical analysis and demonstrate that the secrecy performance significantly deteriorates due to the impact of T-HWI and EMI. Furthermore, maintaining the secrecy performance under higher EMI power necessitates increased AN power. Additionally, a performance trade-off exists between the number of RISs and the EMI power for achieving an optimal SSR.

This paper investigates the impact of hardware impairments in terms of low-cost radio frequency (RF) chains and dynamic-resolution analog-to-digital/digital-to-analog converters (ADCsIDACs) over a reconfigurable intelligent surface (RIS) enhanced cell-free massive multiple-input multiple-output system. We aim to model a practical scenario by considering phase noise and spatial correlation at the RIS. We utilize the well-known linear minimum mean square error estimation to obtain the channel estimates and present a closed-form expression for the downlink spectral efficiency (SE). To reduce the fronthaul load, we implement an access point (AP) selection strategy that assigns each user to a specific set of APs rather than all the APs. Our analysis reveals that i) the SE with RIS-aided links is significant in scenarios with low transmit power, especially when the direct link is weak, ii) the effect of ADCslDACs imperfection is more acute than RF impairments at higher transmit power levels, iii) the energy efficiency shows minimal impact at lower impairment levels but becomes significantly affected as the impairment level increases, and iv) dynamic-resolution ADCslDACs playa vital role on the system performance.

We consider a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted massive multiple-input multiple-output (mMIMO) integrated sensing and communication (ISAC) system, accounting for both direct and indirect paths to serve the communication users. We practically model the system by employing hybrid partial zero forcing (PZF) precoding to achieve a balance between system performance and computational complexity. We derive the closedform expressions for the achievable sum-rate and mean-to-average sidelobe ratio (MASR) as the communication and sensing metric for the proposed system, respectively. We numerically analyze the impact of STAR-RIS on the trade-off between sensing and communication performance. Additionally, we investigate the performance of PZF precoding for the proposed system.

We consider a reconfigurable intelligent surface (RIS) assisted cell-free massive multiple-input multiple-output non-orthogonal multiple access (NOMA) system, where each access point (AP) serves all the users with the aid of the RIS. We practically model the system by considering imperfect instantaneous channel state information (CSI) and employing imperfect successive interference cancellation at the users’ end. We first obtain the channel estimates using linear minimum mean square error approach considering the spatial correlation at the RIS and then derive a closed-form downlink spectral efficiency (SE) expression using the statistical CSI. We next formulate a joint optimization problem to maximize the sum SE of the system. We first introduce a novel successive Quadratic Transform (successive-QT) algorithm to optimize the transmit power coefficients using the concept of block optimization along with quadratic transform and then use the particle swarm optimization technique to design the RIS phase shifts. Note that most of the existing works on RIS-aided cell-free systems are specific instances of the general scenario studied in this work. We numerically show that i) the RIS-assisted link is more advantageous at lower transmit power regions where the direct link between AP and user is weak, ii) NOMA outperforms orthogonal multiple access schemes in terms of SE, and iii) the proposed joint optimization framework significantly improves the sum SE of the system.

Most of the prior research in multipair two-way full-duplex (FD) massive multiple input multiple output (mMIMO) relaying systems considers a Rician channel with a deterministic line of sight component assuming fixed locations of the transceivers. We consider random phase shifts due to phase noise and user mobility in the LoS component to capture a more realistic scenario. We estimate the phase aware and phase unaware Rician channel via the minimum mean square error and linear minimum mean square error-based estimators, respectively, and use a maximal ratio combiner/maximal ratio transmitter to derive the closed-form spectral efficiency (SE) for an arbitrary number of relay antennas. The numerical outcomes validate the correctness of the derived closed-form SE expression when compared with its ergodic equivalent. We numerically investigate the impact of phase shift and loop interference on the SE.