Low-Complexity, Space Splitting-based User Selection in MU-MIMO for Massive Connectivity and AI-Native Traffic
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866913035413618688 |
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| author | Lima, João Paulo S. H. Filo, Marcin L. Jayawardena, Chathura Nikitopoulos, Konstantinos |
| author_facet | Lima, João Paulo S. H. Filo, Marcin L. Jayawardena, Chathura Nikitopoulos, Konstantinos |
| contents | The rise of Artificial Intelligence (AI)-driven services, machine-type communications, and massive Internet of Things (IoT) deployments is reshaping wireless traffic toward dense, uplink-oriented, bursty, and latency-critical patterns. In these regimes, Multi-User Multiple-Input Multiple-Output (MU-MIMO) is essential to support massive concurrent connectivity through spatial multiplexing. However, the need for frequent, low-latency scheduling decisions exposes fundamental scalability barriers in existing user selection approaches. The inherently combinatorial nature of MU-MIMO user selection leads computational complexity to grow rapidly with both the number of candidate users and spatial layers, rendering existing near-optimal heuristic methods impractical in dense and highly dynamic scenarios. This paper introduces the Space Splitting-based User Selection (SS-US) algorithm, a complexity barrier-breaking, massively parallelizable method that departs from subset-based selection by constructing orthonormal spatial bases and independently matching users to spatial directions. Simulation results across diverse MIMO configurations, channel conditions, and user densities show that SS-US reduces computational complexity by over three orders of magnitude while achieving spectral efficiency comparable to state-of-the-art practical baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13952 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Low-Complexity, Space Splitting-based User Selection in MU-MIMO for Massive Connectivity and AI-Native Traffic Lima, João Paulo S. H. Filo, Marcin L. Jayawardena, Chathura Nikitopoulos, Konstantinos Signal Processing The rise of Artificial Intelligence (AI)-driven services, machine-type communications, and massive Internet of Things (IoT) deployments is reshaping wireless traffic toward dense, uplink-oriented, bursty, and latency-critical patterns. In these regimes, Multi-User Multiple-Input Multiple-Output (MU-MIMO) is essential to support massive concurrent connectivity through spatial multiplexing. However, the need for frequent, low-latency scheduling decisions exposes fundamental scalability barriers in existing user selection approaches. The inherently combinatorial nature of MU-MIMO user selection leads computational complexity to grow rapidly with both the number of candidate users and spatial layers, rendering existing near-optimal heuristic methods impractical in dense and highly dynamic scenarios. This paper introduces the Space Splitting-based User Selection (SS-US) algorithm, a complexity barrier-breaking, massively parallelizable method that departs from subset-based selection by constructing orthonormal spatial bases and independently matching users to spatial directions. Simulation results across diverse MIMO configurations, channel conditions, and user densities show that SS-US reduces computational complexity by over three orders of magnitude while achieving spectral efficiency comparable to state-of-the-art practical baselines. |
| title | Low-Complexity, Space Splitting-based User Selection in MU-MIMO for Massive Connectivity and AI-Native Traffic |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.13952 |