Channel Estimation for Spectrum Sharing in Massive MIMO Communications

IEEE Transactions on Cognitive Communications and Networking(2023)

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摘要
In this paper, we investigate the problem of channel estimation in a multi-user massive multiple-input multiple-output (MIMO) secondary network (SN) aiming to access the licensed spectrum of a multi-user massive MIMO primary network (PN) using the underlay spectrum sharing approach. We estimate the channels of the single-antenna primary users (PUs) and those of the secondary users (SUs) at the secondary base station by exploiting a learning phase. To do so, we design the SN’s training phase with the priority of mitigating pilot contamination at the PN. This aim is pursued under the desired restriction that the PN is not meant to change its training phase length in the presence of the SN. The proposed estimator of PUs’ channels is based on the PUs’ data in addition to their pilots. To estimate the SUs’ channels, we present two seemingly different pilot-based approaches and prove rigorously that they result in the same estimator. Our numerical results illustrate that employing the proposed technique enables the SN to control the interference that it causes at the PN at the cost of slight performance degradation in terms of the quality of SUs’ channel estimates at the SN base station.
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关键词
Channel estimation, Training, Massive MIMO, Base stations, Interference, Antennas, Degradation, underlay spectrum sharing, interference cancelation, data-based least squares estimator, cognitive radio, multi-user massive MIMO
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