Blind Massive MIMO for Dense IoT Networks
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
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2025
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| _version_ | 1866929717526921216 |
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| author | Lee, Jeongjae Hong, Songnam |
| author_facet | Lee, Jeongjae Hong, Songnam |
| contents | In this paper, we investigate the downlink communication challenges in heavy-load Internet-of-Things (IoT) networks supported by frequency-division-duplexing (FDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The excessive overhead required for obtaining channel state information at the transmitter (CSIT) is essential to achieve high spectral efficiency through conventional massive MIMO techniques; however, it hinders the deployment of ultra-reliable low-latency communications (URLLC) and leads to significant energy expenditure, particularly in dense IoT networks. To address this challenge, we propose an innovative CSIT-Free MIMO precoding method, referred to as CIRculant information Classification via Linear Estimation (CIRCLE). Our major contribution is the design of a CSIT-independent (or deterministic) precoding, which is constructed by leveraging the circulant permutation of the discrete Fourier transform (DFT) matrix. This design enables interference-free signal combining at the IoT devices. Through theoretical analysis and simulations, we verify the effectiveness of the proposed CIRCLE method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10836 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Blind Massive MIMO for Dense IoT Networks Lee, Jeongjae Hong, Songnam Signal Processing In this paper, we investigate the downlink communication challenges in heavy-load Internet-of-Things (IoT) networks supported by frequency-division-duplexing (FDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The excessive overhead required for obtaining channel state information at the transmitter (CSIT) is essential to achieve high spectral efficiency through conventional massive MIMO techniques; however, it hinders the deployment of ultra-reliable low-latency communications (URLLC) and leads to significant energy expenditure, particularly in dense IoT networks. To address this challenge, we propose an innovative CSIT-Free MIMO precoding method, referred to as CIRculant information Classification via Linear Estimation (CIRCLE). Our major contribution is the design of a CSIT-independent (or deterministic) precoding, which is constructed by leveraging the circulant permutation of the discrete Fourier transform (DFT) matrix. This design enables interference-free signal combining at the IoT devices. Through theoretical analysis and simulations, we verify the effectiveness of the proposed CIRCLE method. |
| title | Blind Massive MIMO for Dense IoT Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2502.10836 |