Joint Beamforming Design and Bit Allocation in Massive MIMO with Resolution-Adaptive ADCs
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arXiv
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| Format: | Preprint |
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2024
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| author | Ma, Mengyuan Nguyen, Nhan Thanh Atzeni, Italo Juntti, Markku |
| author_facet | Ma, Mengyuan Nguyen, Nhan Thanh Atzeni, Italo Juntti, Markku |
| contents | Low-resolution analog-to-digital converters (ADCs) have emerged as a promising technology for reducing power consumption and complexity in massive multiple-input multiple-output (MIMO) systems while maintaining satisfactory spectral and energy efficiencies (SE/EE). In this work, we first identify the essential properties of optimal quantization and leverage them to derive a closed-form approximation of the covariance matrix of the quantization distortion. The theoretical finding facilitates the system SE analysis in the presence of low-resolution ADCs. We then focus on the joint optimization of the transmit-receive beamforming and bit allocation to maximize the SE under constraints on the transmit power and the total number of active ADC bits. To solve the resulting mixed-integer problem, we first develop an efficient beamforming design for fixed ADC resolutions. Then, we propose a low-complexity heuristic algorithm to iteratively optimize the ADC resolutions and beamforming matrices. Numerical results for a $64 \times 64$ MIMO system demonstrate that the proposed design offers $6\%$ improvement in both SE and EE with $40\%$ fewer active ADC bits compared with the uniform bit allocation. Furthermore, we numerically show that receiving more data streams with low-resolution ADCs can achieve higher SE and EE compared to receiving fewer data streams with high-resolution ADCs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03796 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Joint Beamforming Design and Bit Allocation in Massive MIMO with Resolution-Adaptive ADCs Ma, Mengyuan Nguyen, Nhan Thanh Atzeni, Italo Juntti, Markku Signal Processing Low-resolution analog-to-digital converters (ADCs) have emerged as a promising technology for reducing power consumption and complexity in massive multiple-input multiple-output (MIMO) systems while maintaining satisfactory spectral and energy efficiencies (SE/EE). In this work, we first identify the essential properties of optimal quantization and leverage them to derive a closed-form approximation of the covariance matrix of the quantization distortion. The theoretical finding facilitates the system SE analysis in the presence of low-resolution ADCs. We then focus on the joint optimization of the transmit-receive beamforming and bit allocation to maximize the SE under constraints on the transmit power and the total number of active ADC bits. To solve the resulting mixed-integer problem, we first develop an efficient beamforming design for fixed ADC resolutions. Then, we propose a low-complexity heuristic algorithm to iteratively optimize the ADC resolutions and beamforming matrices. Numerical results for a $64 \times 64$ MIMO system demonstrate that the proposed design offers $6\%$ improvement in both SE and EE with $40\%$ fewer active ADC bits compared with the uniform bit allocation. Furthermore, we numerically show that receiving more data streams with low-resolution ADCs can achieve higher SE and EE compared to receiving fewer data streams with high-resolution ADCs. |
| title | Joint Beamforming Design and Bit Allocation in Massive MIMO with Resolution-Adaptive ADCs |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2407.03796 |