Temporal-correlation-based compressive channel estimation for universal filtered multicarrier system over fast-fading channels
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The universal filtered multicarrier technique is a competitive candidate multicarrier modulation scheme for 5G communication systems. Conventional channel-estimation algorithms suffer from significant performance losses due to the large spread in the delay of multipath channels in high-speed scenarios. To address this problem, we here propose a low-complexity, partial priori information-sparsity adaptive matching pursuit (PPI-SAMP) algorithm. Unlike the conventional SAMP algorithm, the PPI-SAMP algorithm improves performance over fast-fading channels by adequately exploiting the sparse characteristics and temporal correlation of wireless channels. First, the PPI-SAMP algorithm averages the channel impulse responses (CIRs) of consecutive symbols over the coherence time for achieving the accuracy required for coarse channel estimation. Second, the improved SAMP algorithm acquires the accurate CIRs with low complexity based on the coarse CIR. Moreover, the MSE performance and recovery probability with varying sizes of IBI-free region indicate that the proposed PPI-SAMP algorithm offers a longer CIR for multipath interference and is more robust against larger multipath-channel delay than the conventional SAMP and CoSaMP algorithms. The proposed algorithm also estimates channels more accurately than conventional SAMP and CoSaMP algorithms despite having a complexity reduced by approximately 52% compared to conventional SAMP algorithm.
KeywordsChannel estimation Compression algorithms Time-varying channels 5G mobile communication
This research was supported in part by the National Science and Technology Specific Program of China (2016ZX03002019-007).
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