Mitigating Pilot Contamination and Enabling IoT Scalability in Massive MIMO Systems
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| Format: | Preprint |
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2023
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| _version_ | 1866913497250529280 |
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| author | Saeed, Muhammad Kamran Kamal, Ahmed E. Khokhar, Ashfaq |
| author_facet | Saeed, Muhammad Kamran Kamal, Ahmed E. Khokhar, Ashfaq |
| contents | Massive MIMO is expected to play an important role in the development of 5G networks. This paper addresses the issue of pilot contamination and scalability in massive MIMO systems. The current practice of reusing orthogonal pilot sequences in adjacent cells leads to difficulty in differentiating incoming inter- and intra-cell pilot sequences. One possible solution is to increase the number of orthogonal pilot sequences, which results in dedicating more space of coherence block to pilot transmission than data transmission. This, in turn, also hinders the scalability of massive MIMO systems, particularly in accommodating a large number of IoT devices within a cell. To overcome these challenges, this paper devises an innovative pilot allocation scheme based on the data transfer patterns of IoT devices. The scheme assigns orthogonal pilot sequences to clusters of devices instead of individual devices, allowing multiple devices to utilize the same pilot for periodically transmitting data. Moreover, we formulate the pilot assignment problem as a graph coloring problem and use the max k-cut graph partitioning approach to overcome the pilot contamination in a multicell massive MIMO system. The proposed scheme significantly improves the spectral efficiency and enables the scalability of massive MIMO systems; for instance, by using ten orthogonal pilot sequences, we are able to accommodate 200 devices with only a 12.5% omission rate. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_03278 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Mitigating Pilot Contamination and Enabling IoT Scalability in Massive MIMO Systems Saeed, Muhammad Kamran Kamal, Ahmed E. Khokhar, Ashfaq Information Theory Computer Science and Game Theory Machine Learning Networking and Internet Architecture Signal Processing Massive MIMO is expected to play an important role in the development of 5G networks. This paper addresses the issue of pilot contamination and scalability in massive MIMO systems. The current practice of reusing orthogonal pilot sequences in adjacent cells leads to difficulty in differentiating incoming inter- and intra-cell pilot sequences. One possible solution is to increase the number of orthogonal pilot sequences, which results in dedicating more space of coherence block to pilot transmission than data transmission. This, in turn, also hinders the scalability of massive MIMO systems, particularly in accommodating a large number of IoT devices within a cell. To overcome these challenges, this paper devises an innovative pilot allocation scheme based on the data transfer patterns of IoT devices. The scheme assigns orthogonal pilot sequences to clusters of devices instead of individual devices, allowing multiple devices to utilize the same pilot for periodically transmitting data. Moreover, we formulate the pilot assignment problem as a graph coloring problem and use the max k-cut graph partitioning approach to overcome the pilot contamination in a multicell massive MIMO system. The proposed scheme significantly improves the spectral efficiency and enables the scalability of massive MIMO systems; for instance, by using ten orthogonal pilot sequences, we are able to accommodate 200 devices with only a 12.5% omission rate. |
| title | Mitigating Pilot Contamination and Enabling IoT Scalability in Massive MIMO Systems |
| topic | Information Theory Computer Science and Game Theory Machine Learning Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2310.03278 |