Intelligent Reflecting Surfaces and Next Generation Wireless Systems
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910028291637248 |
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| author | Cao, Yashuai Wang, Hetong Lv, Tiejun Ni, Wei |
| author_facet | Cao, Yashuai Wang, Hetong Lv, Tiejun Ni, Wei |
| contents | Intelligent reflecting surface (IRS) is a potential candidate for massive multiple-input multiple-output (MIMO) 2.0 technology due to its low cost, ease of deployment, energy efficiency and extended coverage. This chapter investigates the slot-by-slot IRS reflection pattern design and two-timescale reflection pattern design schemes, respectively. For the slot-by-slot reflection optimization, we propose exploiting an IRS to improve the propagation channel rank in mmWave massive MIMO systems without need to increase the transmit power budget. Then, we analyze the impact of the distributed IRS on the channel rank. To further reduce the heavy overhead of channel training, channel state information (CSI) estimation, and feedback in time-varying MIMO channels, we present a two-timescale reflection optimization scheme, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the active beamformers and power allocation are updated based on quickly outdated instantaneous CSI (I-CSI) per slot. The achievable average sum-rate (AASR) of the system is maximized without excessive overhead of cascaded channel estimation. A recursive sampling particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS reflection pattern efficiently with reduced samplings of channel samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16453 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intelligent Reflecting Surfaces and Next Generation Wireless Systems Cao, Yashuai Wang, Hetong Lv, Tiejun Ni, Wei Signal Processing Intelligent reflecting surface (IRS) is a potential candidate for massive multiple-input multiple-output (MIMO) 2.0 technology due to its low cost, ease of deployment, energy efficiency and extended coverage. This chapter investigates the slot-by-slot IRS reflection pattern design and two-timescale reflection pattern design schemes, respectively. For the slot-by-slot reflection optimization, we propose exploiting an IRS to improve the propagation channel rank in mmWave massive MIMO systems without need to increase the transmit power budget. Then, we analyze the impact of the distributed IRS on the channel rank. To further reduce the heavy overhead of channel training, channel state information (CSI) estimation, and feedback in time-varying MIMO channels, we present a two-timescale reflection optimization scheme, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the active beamformers and power allocation are updated based on quickly outdated instantaneous CSI (I-CSI) per slot. The achievable average sum-rate (AASR) of the system is maximized without excessive overhead of cascaded channel estimation. A recursive sampling particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS reflection pattern efficiently with reduced samplings of channel samples. |
| title | Intelligent Reflecting Surfaces and Next Generation Wireless Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2402.16453 |