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Expectation Propagation-Based Parallel Iterative Detection and Decoding for Massive MIMO
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DOI：10.1109/TVT.2023.3310343.png)
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摘要
En 中文
Expectation propagation (EP) has recently been considered with iterative detection and decoding (IDD) to enhance the bit error rate (BER) performance for coded massive Multiple-input Multiple-output (M-MIMO) system. However, current state-of-the-art (SOA) EP-based IDD schemes like double-EP (DEP) are still far from realization due to unaffordable latency brought by their serial structure and large computational complexity. To relieve this issue, in this article, an efficient EP-based parallel IDD method is proposed for coded M-MIMO. First, by investigating the factor graph (FG)-based message passing in EP-based IDD, a double non-resetting framework named EP-dNRe is proposed to improve the IDD efficiency. Based on this framework, a novel parallel EP-based IDD scheme named EP-based parallel detection and decoding (PDD-EP) is further proposed. After proper initialization, the parallel loops of PDD-EP can execute the EP detection and decoding modules simultaneously, which brings latency reduction and improved performance-complexity trade-off. Simulation results and complexity analysis are presented to confirm the efficiency of the proposed PDD-EP. Particularly, both DEP with double non-resetting (DEP-dNRe) and PDD-EP can greatly reduce the required number of iterations to reach the same performance as the SOA DEP. Furthermore, the proposed PDD-EP can attain the same BER as DEP-dNRe with about 33.1% less complexity, and outperforms DEP-dNRe up to 0.2 dB by similar complexity in various LDPC-coded M-MIMO systems.
Keyword:
Massive MIMO
expectation propagation
iterative detection and decoding
Bayesian message passing