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.

Spectrum handoff has a negative impact on the performance of cognitive users (CUs) in terms ofhandoff delay in cognitive radio (CR) networks. In this study, a pre‐emptiveresume priority (PRP) M/G/1/K queuing network model is proposed with a finitenumber of allowable interruptions for proactive decision spectrum handoff schemein order to minimise the cumulative handoff delay (CHD) and total service time(TST) for CUs. The CHD and TST for different proactive decision handoff schemes:non‐switching spectrum handoff, switching spectrum handoff, and random spectrumhandoff are modeled under the proposed PRP M/G/1/K queuing network model. Comprehensive results of CHD and TST are obtained to compare the performances ofthe proactive decision handoff schemes under the proposed PRP M/G/1/K queuingnetwork model. This study also presents an analytical framework to examine theeffect of primary users activity and buffer size on spectrum handoff delayperformance with a finite number of allowable interruptions in a CR network. Thereafter, the optimal buffer size ( K ) is estimated for theproposed PRP M/G/1/K queuing network model, which gives performance similar tothe infinite buffer size PRP M/G/1 queuing model with negligible (<1%)error.