Reconfigurable intelligent surfaces (RIS) can be crucial in next-generation communication systems. However, designing the RIS phases according to the instantaneous channel state information (CSI) can be challenging in practice due to the short coherent time of the channel. In this regard, we propose a novel algorithm based on the channel statistics of massive multiple input multiple output systems rather than the instantaneous CSI. The beamforming at the base station (BS), power allocation of the users, and phase shifts at the RIS elements are optimized to maximize the minimum signal-to-interference and noise ratio (SINR), guaranteeing fair operation among various users. In particular, we design the RIS phases by leveraging the asymptotic deterministic equivalent of the minimum SINR that depends only on the channel statistics. This significantly reduces the computational complexity and the amount of controlling data between the BS and RIS for updating the phases. This setup is also useful for electromagnetic fields (EMF)-aware systems with constraints on the maximum user’s exposure to EMF. The numerical results show that the proposed algorithms achieve more than 100 % gain in terms of minimum SINR, compared to a system with random RIS phase shifts, when 40 RIS elements, 20 antennas at the BS and 10 users, are considered.

In this work, we study the outage probability (OP) at the destination of an intelligent reflecting surface (IRS) assisted communication system in a$\kappa -\mu $fading environment. A practical system model that takes into account the presence of phase error due to quantization at the IRS when a) source-destination (SD) link is present and b) SD link is absent is considered. First, an exact expression is derived, and then we derive three simple approximations for the OP using the following approaches: (i) uni-variate dimension reduction, (ii) moment matching and, (iii) Kullback–Leibler (KL) divergence minimization. The resulting expressions for OP are simple to evaluate and quite tight even in the tail region. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We also study the impact of the number of bits available for quantization, the position of IRS with respect to the source and destination and the number of IRS elements on the OP for systems with and without an SD link. We also demonstrate how the method of moment matching and KL divergence minimization can be used to analyze systems experiencing spatial correlation between the IRS elements.

In this paper, we propose a scheme for the joint optimization of the user transmit power and the antenna selection at the access points (AP)s of a user-centric cell-free massive multiple-input-multiple-output (UC CF-mMIMO) system. We derive an approximate expression for the achievable uplink rate of the users in a UC CF-mMIMO system in the presence of a mixed analog-to-digital converter (ADC) resolution profile at the APs. Using the derived approximation, we propose to maximize the uplink sum-rate of UC CF-mMIMO systems subject to energy constraints at the APs. An alternating-optimization solution is proposed using binary particle swarm optimization (BPSO) and successive convex approximation (SCA). We also propose a complete meta-heuristic-based solution that can be used as an alternative solution for applications where latency is the critical metric. Along with this, we used a genetic algorithm (GA)-based approach to compare the performance of the proposed algorithm. We study the impact of various system parameters on the performance of the system.

This paper derives the asymptotic distribution of the normalized$k$-th maximum order statistics of a sequence of non-central chi-square random variables with non-identical non-centrality parameters. We demonstrate the utility of these results in characterizing the signal-to-noise ratio in three different applications in wireless communication systems where the statistics of the$k$-th maximum channel power over Rician fading links are of interest. Furthermore, we derive simple expressions for the asymptotic outage probability, average throughput, achievable throughput, and average bit error probability. The proposed results are validated via extensive Monte Carlo simulations.

This paper derives the distribution of the maximum end-to-end (e2e) signal to noise ratio (SNR) in an opportunistic relay selection based cooperative relaying network having a large number of non-identical relay links. The source is simultaneous wireless information and power transfer enabled, and the relays are capable of both time splitting (TS) and power splitting (PS) based energy harvesting (EH). Contrary to the majority of literature in communication, which uses extreme value theory (EVT) to derive the statistics of extremes of sequences of independent and identically distributed random variables (RVs), we demonstrate how EVT can be used to derive the maximum statistics of sequences of independent and non-identically distributed normalised SNR and hence the distribution of the maximum SNR. Using these results, we derive simple expressions for evaluating the outage capacity, and achievable throughput at the destination. Finally, we present the utility of the results for deciding the optimum TS and PS factors of the hybrid EH relays that maximise outage capacity, and achievable throughput at the destination. Furthermore, we establish the stochastic ordering of the e2e SNR, which in turn allows the characterisation of the variations in the e2e performance with respect to the variations in different system parameters.

This paper determines the optimum secondary user (SU) power allocation and ergodic multicast rate of point-to-multipoint communication in a cognitive radio network (CRN) in the presence of various quality of service (QoS) constraints for the primary users (PUs). Using tools from extreme value theory (EVT), it is first proved that the limiting distribution of the minimum of independent and identically distributed (i.i.d.) signal-to-interference ratio (SIR) random variables (RVs) is a Weibull distribution, when the user signal and the interferer signals undergo independent and non-identically distributed (i.n.i.d.) κ-μ shadowed fading. Also, the rate of convergence of the actual minimum distribution to the Weibull distribution is derived. This limiting distribution is then used for determining the optimum transmit power of a secondary network in an underlay CRN subject to three different QoS constraints at the primary network in a generalized fading scenario. Furthermore, the optimum transmit power and the asymptotic ergodic multicast rate of SUs is analyzed for varying channel fading parameters.

Using tools from extreme value theory (EVT), it is proved that when the user signal and the interferer signals undergo independent and non-identically distributed (i.n.i.d.) κ-μ shadowed fading, the limiting distribution of the maximum of L independent and identically distributed (i.i.d.) signal-to-interference ratio (SIR) random variables (RVs) is a Frechet distribution. It is observed that this limiting distribution is close to the true distribution of maximum for maximum SIR evaluated over moderate L. Furthermore, moments of the maximum RV is shown to converge to the moments of the Frechet RV. In addition, the rate of convergence of the actual distribution of the maximum to the Frechet distribution is derived and analyzed for different κ and μ parameters. Finally, results from the stochastic ordering are used to analyze the variation in the limiting distribution with respect to the variation in source fading parameters. These results are then used to derive upper bound for the rate in full array selection (FAS) schemes for the antenna selection and the asymptotic outage probability and the ergodic rate in maximum-sum-capacity (MSC) scheduling systems.