Blind Null-space Tracking for MIMO Underlay Cognitive Radio Networks

TL;DR

This paper introduces the Blind Null Space Tracking (BNST) algorithm, which enhances blind null space learning by enabling simultaneous channel tracking and data transmission in time-varying MIMO channels for cognitive radio.

Contribution

It proposes a novel channel tracking algorithm that maintains low interference to primary users while transmitting data, improving upon existing blind null space learning methods.

Findings

BNST outperforms BNSL in time-varying Rayleigh fading channels.

The algorithm maintains interference below a threshold with high probability.

Simulation results confirm improved performance in dynamic channel conditions.

Abstract

Blind Null Space Learning (BNSL) has recently been proposed for fast and accurate learning of the null-space associated with the channel matrix between a secondary transmitter and a primary receiver. In this paper we propose a channel tracking enhancement of the algorithm, namely the Blind Null Space Tracking (BNST) algorithm that allows transmission of information to the Secondary Receiver (SR) while simultaneously learning the null-space of the time-varying target channel. Specifically, the enhanced algorithm initially performs a BNSL sweep in order to acquire the null space. Then, it performs modified Jacobi rotations such that the induced interference to the primary receiver is kept lower than a given threshold $P_{Th}$ with probability $p$ while information is transmitted to the SR simultaneously. We present simulation results indicating that the proposed approach has strictly better performance over the BNSL algorithm for channels with independent Rayleigh fading with a small Doppler frequency.