Low-Complexity BFGS-Based Soft-Output MMSE Detector for Massive MIMO Uplink
For the massive multiple-input multiple-output (MIMO) uplink, the linear minimum mean square error (MMSE) detector is near-optimal but involves undesirable matrix inversion. In this paper, we propose a low-complexity soft-output detector based on the simplified Broyden–Fletcher–Goldfarb–Shanno metho...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2023-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2023/8887060 |
| Summary: | For the massive multiple-input multiple-output (MIMO) uplink, the linear minimum mean square error (MMSE) detector is near-optimal but involves undesirable matrix inversion. In this paper, we propose a low-complexity soft-output detector based on the simplified Broyden–Fletcher–Goldfarb–Shanno method to realize the matrix-inversion-free MMSE detection iteratively. To accelerate convergence with minimal computational overhead, an appropriate initial solution is presented leveraging the channel-hardening property of massive MIMO. Moreover, we employ a low-complexity approximated approach to calculating the log-likelihood ratios with negligible performance losses. Simulation results finally verify that the proposed detector can achieve the near-MMSE performance with a few iterations and outperforms the recently reported linear detectors in terms of lower complexity and faster convergence for the realistic massive MIMO systems. |
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| ISSN: | 1751-9683 |