Joint Channel Estimation and Pilot Allocation in Underlay Cognitive MISO Networks

TL;DR

This paper introduces novel joint channel estimation and pilot allocation methods for underlay cognitive MISO networks, enhancing interference mitigation and channel accuracy through covariance-based MMSE techniques.

Contribution

It proposes new algorithms leveraging covariance information for improved channel estimation and pilot allocation in cognitive radio systems, addressing contamination issues.

Findings

Enhanced channel estimation accuracy demonstrated in simulations

Improved interference mitigation compared to existing methods

Effective separation of overlapping channels using covariance-based MMSE

Abstract

Cognitive radios have been proposed as agile technologies to boost the spectrum utilization. This paper tackles the problem of channel estimation and its impact on downlink transmissions in an underlay cognitive radio scenario. We consider primary and cognitive base stations, each equipped with multiple antennas and serving multiple users. Primary networks often suffer from the cognitive interference, which can be mitigated by deploying beamforming at the cognitive systems to spatially direct the transmissions away from the primary receivers. The accuracy of the estimated channel state information (CSI) plays an important role in designing accurate beamformers that can regulate the amount of interference. However, channel estimate is affected by interference. Therefore, we propose different channel estimation and pilot allocation techniques to deal with the channel estimation at the cognitive systems, and to reduce the impact of contamination at the primary and cognitive systems. In an effort to tackle the contamination problem in primary and cognitive systems, we exploit the information embedded in the covariance matrices to successfully separate the channel estimate from other users' channels in correlated cognitive single input multiple input (SIMO) channels. A minimum mean square error (MMSE) framework is proposed by utilizing the second order statistics to separate the overlapping spatial paths that create the interference. We validate our algorithms by simulation and compare them to the state of the art techniques.