Efficient ZF-WF Strategy for Sum-Rate Maximization of MU-MISO Cognitive Radio Networks

TL;DR

This paper introduces an efficient ZF-WF algorithm for maximizing sum rate in MU-MISO cognitive radio networks, considering power and interference constraints, and demonstrates its effectiveness through numerical analysis.

Contribution

It proposes a quasi-optimal sum rate maximization method using ZF-WF tailored for CRNs, including conditions for optimal user number and performance analysis.

Findings

The ZF-WF technique outperforms MMSE in sum capacity and BER.

The method effectively manages interference constraints in CRNs.

Numerical results validate the proposed approach's efficiency.

Abstract

This article presents an efficient quasi-optimal sum rate (SR) maximization technique based on zero-forcing water-filling (ZFWF) algorithm directly applied to cognitive radio networks (CRNs). We have defined the non-convexity nature of the optimization problem in the context of CRNs while we have offered all necessary conditions to solve the related SR maximization problem, which considers power limit at cognitive transmitter and interference levels at primary users (PUs) and secondary users (SUs). A general expression capable to determine the optimal number of users as a function of the main system parameters, namely the signal-to-interference-plus-noise ratio (SINR) and the number of BS antennas is proposed. Our numerical results for the CRN performance are analyzed in terms of both BER and sum-capacity for the proposed ZF-WF precoding technique, and compared to the classical minimum mean square error (MMSE), corroborating the effectiveness of the proposed technique operating in multi user multiple input single output (MU-MISO) CRNs