Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks

TLDR

A cognitive radio network allows secondary users to operate in a band licensed to primary users, but must protect primary users’ QoS while maximizing secondary throughput or SINR. The paper investigates joint beamforming and power allocation for single‑input multiple‑output multiple‑access channels in cognitive radio networks. The authors formulate two optimization problems—sum‑rate maximization and SINR balancing—subject to primary‑user interference and secondary‑user peak‑power constraints, using zero‑forcing decision‑feedback equalizers with a capped multi‑level water‑filling algorithm for the single‑PU case, a recursive decoupled power‑allocation scheme for multiple PUs, and linear MMSE receivers that decouple constraints into single‑constraint subproblems for SINR balancing. Theoretical analysis and numerical simulations demonstrate the effectiveness of the proposed algorithms and compare the performance of various power‑allocation schemes.

Abstract

A cognitive radio (CR) network refers to a secondary network operating in a frequency band originally licensed/allocated to a primary network consisting of one or multiple primary users (PUs). A fundamental challenge for realizing such a system is to ensure the quality of service (QoS) of the PUs as well as to maximize the throughput or ensure the QoS, such as signal-to-interference-plus-noise ratios (SINRs), of the secondary users (SUs). In this paper, we study single-input multiple output multiple access channels (SIMO-MAC) for the CR network. Subject to interference constraints for the PUs as well as peak power constraints for the SUs, two optimization problems involving a joint beamforming and power allocation for the CR network are considered: the sum-rate maximization problem and the SINR balancing problem. For the sum-rate maximization problem, zero-forcing based decision feedback equalizers are used to decouple the SIMO-MAC, and a capped multi-level (CML) water-filling algorithm is proposed to maximize the achievable sum-rate of the SUs for the single PU case. When multiple PUs exist, a recursive decoupled power allocation algorithm is proposed to derive the optimal power allocation solution. For the SINR balancing problem, it is shown that, using linear minimum mean-square-error receivers, each of the interference constraints and peak power constraints can be completely decoupled, and thus the multi-constraint optimization problem can be solved through multiple single-constraint sub-problems. Theoretical analysis for the proposed algorithms is presented, together with numerical simulations which compare the performances of different power allocation schemes.