Linear Precoding based on Minimum Mean Square Error in MISO Downlink Cognitive Radio Network
Shunran Wang, Yanhui Lu, Duanyang Li · Advances in computer science research · 2015
Conventionally, linear precoding methods in a Cognitive Radio (CR) Multiple Input Multiple Output (MIMO) network mainly aim to cancel the interference received by cognitive Users (SUs) completely, and most of time, the effect of noise often be ignored.To achieve superior performance of the CR system, this paper presents two novel precoding methods from the perspective of utilizing interference and noise, namely Cognitive Radio Minimum Mean Square Error Partial Linear Precoding (CR-MMSE-PLP) and Cognitive Radio Minimum Mean Square Error Phase Alignment Linear Precoding (CR-MMSE-PALP).The theoretical analysis and simulation results show that these two methods both enhance the Signal to Interference and Noise Ratio (SINR) through taking advantage of constructive interference and noise.Contrary to CR-MMSE-PLP where the destructive interference is zeroed, CR-MMSE-PALP rotates and converts it into constructive part.Then less Symbol Error Rate (SER) and enhanced information transmission rate are achieved with lower computational complexity.