Joint Channel Estimation and Pilot Allocation in Underlay Cognitive MISO Networks
Maha Alodeh, Symeon Chatzinotas, Bjorn Ottersten

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…
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