Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing
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| Název: | Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing |
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| Autoři: | Shuaifei Chen, Jiayi Zhang, Emil Björnson, Özlem Tuğfe Demir, Bo Ai |
| Zdroj: | IEEE Transactions on Wireless Communications. 22:9374-9389 |
| Publication Status: | Preprint |
| Informace o vydavateli: | Institute of Electrical and Electronics Engineers (IEEE), 2023. |
| Rok vydání: | 2023 |
| Témata: | Signal Processing (eess.SP), Electric power utilization, Energy efficiency, Mean square error, Optimization, Signal processing, Spectrum efficiency, Cell-free, Cell-free massive MIMO, Distributed database, Distributed processing, Downlink, Large-scale fading, Large-scales, Power demands, Signal-processing, Sparse optimizations, Task analysis, Uplink, Wireless communications, MIMO systems, 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Electrical Engineering and Systems Science - Signal Processing, 7. Clean energy, distributed processing, energy efficiency, large-scale fading, sparse optimization |
| Popis: | Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling. 37 pages, 9 figures, accepted for publication in the IEEE Transactions on Wireless Communications |
| Druh dokumentu: | Article Other literature type |
| ISSN: | 1558-2248 1536-1276 |
| DOI: | 10.1109/twc.2023.3270299 |
| DOI: | 10.48550/arxiv.2208.13552 |
| DOI: | 10.1109/twc.2023.327029910.1109/twc.2023.3270299 |
| Přístupová URL adresa: | http://arxiv.org/abs/2208.13552 https://hdl.handle.net/20.500.11851/10998 https://doi.org/10.1109/TWC.2023.3270299 10.1109/TWC.2023.3270299 https://hdl.handle.net/20.500.11851/10998 https://doi.org/10.1109/TWC.2023.3270299 |
| Rights: | IEEE Copyright arXiv Non-Exclusive Distribution |
| Přístupové číslo: | edsair.doi.dedup.....893b7615cb66ebe1203e02bacc0e8b6a |
| Databáze: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling.<br />37 pages, 9 figures, accepted for publication in the IEEE Transactions on Wireless Communications |
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| ISSN: | 15582248 15361276 |
| DOI: | 10.1109/twc.2023.3270299 |