Efficient ZF-WF strategy for sum-rate maximization of MU-MISO cognitive radio networks

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.

Linear MMSE Channel Estimation for Underlay Cognitive Radio Networks

“…During UL, the CR-BS simultaneously receives PUs and SUs pilot sequences and estimates both channel matrices. After receiving and processing the data, the CR-BS makes the best use of such a information to design efficient precoders according to the strategy developed in [3].…”

“…A possible time-slot structure is depicted in Figure 2, where the channel estimation is composed of two stages that must be no longer than N symbols. Firstly, the CR-BS receives all transmitted pilot sequences and then processes it to estimate the channel matrix and design efficient transmission precoders according to theory of [3]. Subsequently, the CR-BS transmits the estimated channel to allow the CR node to properly decode the following message; finally, the CR-BS transmits all desired data.…”

“…In this case, research of LMMSE technique adapted to MISO-CRN came up with a novelty to prove that knowledge of primary and secondary pilot sequence and noise variances are strictly necessary. Moreover, the downlink data transmission subsection explains how the channel estimative will be used to efficiently design optimal precoders according to the analytical findings in [3].…”

“…All researches until now focus on estimating H and G separately or only one of them. However, the precoder studied in [3] relies on knowledge of both Ĥ and Ĝ , and the fastest way of doing such estimation is to only one received signal of length N to estimate both channel matrix.…”

“…The high throughput mesh network is a promising scenario for cognitive radios [2]. Also, our previous analysis [3] has demonstrated that accurate (virtually perfect) CSI estimations bring significant improvements in capacity, once correct interference cancellation and power allocation can be applied together. Indeed, the optimization techniques are deployed in [3] to design CR precoding strategies aiming at maximizing the sum capacity of multiple input-single-output cognitive radio networks (MISO-CRNs).…”