A Data-Aided Channel Estimation Scheme for Decoupled Systems in Heterogeneous Networks
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Uplink/downlink (UL/DL) decoupling promises more flexible cell association and higher throughput in heterogeneous networks (HetNets), however, it hampers the acquisition of DL channel state information (CSI) in time-division-duplex (TDD) systems due to different base stations (BSs) connected in UL/DL. In this paper, we propose a novel data-aided (DA) channel estimation scheme to address this problem by utilizing decoded UL data to exploit CSI from received UL data signal in decoupled HetNets where a massive multiple-input multiple-output BS and dense small cell BSs are deployed. We analytically estimate BER performance of UL decoded data, which are used to derive an approximated normalized mean square error (NMSE) expression of the DA minimum mean square error (MMSE) estimator. Compared with the conventional least square (LS) and MMSE, it is shown that NMSE performances of all estimators are determined by their signal-to-noise ratio (SNR)-like terms and there is an increment consisting of UL data power, UL data length and BER values in the SNR-like term of DA method, which suggests DA method outperforms the conventional ones in any scenarios. Higher UL data power, longer UL data length and better BER performance lead to more accurate estimated channels with DA method. Numerical results verify that the analytical BER and NMSE results are close to the simulated ones and a remarkable gain in both NMSE and DL rate can be achieved by DA method in multiple scenarios with different modulations.
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