Low-Complexity Distance-Based Scheduling for Multi-User XL-MIMO Systems

González-Coma, José P.; López‐Martínez, F. Javier; Castedo, Luís

doi:10.1109/lwc.2021.3101940

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…, and the fact that sin 2 θ ≤ 1 in (76). Therefore, the closed-form expression of the parameter a and p can be derived by substituting (76) in (33) and (36).…”

Section: Appendix a Proof Of Propositionmentioning

confidence: 99%

“…On the other hand, MIMO communications have been tremendously advanced from small MIMO in 4G to massive MIMO in 5G [31]. Looking forward towards 6G, there have been growing interests in the study of extremely large-scale MIMO (XL-MIMO) [32]- [36], for which the antenna size is so large that conventional far-field assumption with uniform plane wave (UPW) models become invalid. Instead, the more generic spherical wavefront characteristics need to be taken into account [1].…”

Section: Introductionmentioning

confidence: 99%

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SNR Scaling Laws for Radio Sensing with Extremely Large-Scale MIMO

Wang

Zeng

2022

2022 IEEE International Conference on Communications Workshops (ICC Workshops)

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Mobile communication networks were designed to mainly support ubiquitous wireless communications, yet they are also expected to achieve radio sensing capabilities in the near future. However, most prior studies on radio sensing usually rely on far-field assumption with uniform plane wave (UPW) models. With the ever-increasing antenna size, together with the growing demands to sense nearby targets, the conventional far-field UPW assumption may become invalid. Therefore, this paper studies near-field radio sensing with extremely large-scale (XL) antenna arrays, where the more general uniform spheric wave (USW) sensing model is considered. Closed-form expressions of the Cramér-Rao Bounds (CRBs) for both angle and range estimations are derived for near-field XL-MIMO radar mode and XL-phased array radar mode, respectively. Our results reveal that different from the conventional UPW model where the CRB for angle decreases unboundedly as the number of antennas increases, for XL-MIMO radar-based near-field sensing, the CRB decreases with diminishing return and approaches to a certain limit as the number of antennas increases. Besides, different from the farfield model where the CRB for range is infinity since it has no range estimation capability, that for the near-field case is finite. Furthermore, it is revealed that the commonly used spherical wave model based on second-order Taylor approximation is insufficient for near-field CRB analysis. Extensive simulation results are provided to validate our derived CRBs.

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“…, and the fact that sin 2 θ ≤ 1 in (76). Therefore, the closed-form expression of the parameter a and p can be derived by substituting (76) in (33) and (36).…”

Section: Appendix a Proof Of Propositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SNR Scaling Laws for Radio Sensing with Extremely Large-Scale MIMO

Wang

Zeng

2022

2022 IEEE International Conference on Communications Workshops (ICC Workshops)

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“…A new technique based on the distance‐based scheduling (DBS) algorithm is used for user selection. A new technique based on the DBS algorithm is used for user selection 22 . The DBS algorithm classifies users based on their corresponding distance from the array of antenna.…”

Section: Introductionmentioning

confidence: 99%

“…A new technique based on the DBS algorithm is used for user selection. 22 The DBS algorithm classifies users based on their corresponding distance from the array of antenna. The DBS algorithm is able to achieve the same performance as ZF with lower complexity.…”

mentioning

confidence: 99%

Meta‐heuristic optimization techniques for scheduling in multiple‐input multiple‐output multiple‐access channel system

Kumar Mishra,

Mahapatro,

Pattanayak

2024

Int J Communication

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SummaryIn a highly dense multiple‐input multiple‐output (MIMO) communication system, proper resource allocation is exceedingly challenging. The simultaneous selection of antennas at the base‐station (BS) and scheduling of users in an uplink multiple access channel (MAC) system add up to the signal processing load in the MIMO wireless system. The method chosen for combining the user scheduling and antenna selection must have a very low computational complexity. However, the exhaustive search algorithm (ESA), which achieves the best sum‐rate capacity in a MIMO‐MAC system, has a very high level of complexity. In this paper, we use a metaheuristic‐based optimization algorithms to optimize the sum‐rate capacity in a MAC system with significantly less computational complexity. First, we use the beluga whale optimization (BWO) metaheuristic method to solve the optimization problem. The BWO is a swarm‐based algorithm that draws inspiration from beluga whale behavior. An improved BWO (iBWO) is also presented and is utilized for the same optimization challenge. The proposed iBWO scheme improves the search capability of the algorithm and thereby ensures better exploration and exploitation capabilities of the beluga whales. We compare the performance of these algorithms with a standard differential evolution (DE) algorithm. The simulation results demonstrate that the proposed iBWO is a very competitive meta‐heuristic algorithm for solving optimization problems. The proposed iBWO algorithm achieves near optimal throughput in a MIMO‐MAC system. Also, the computational complexity related to the suggested optimization technique in a MIMO‐MAC system is found to be very less compared with the complexity related to ESA.

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“…In order to reap the full benefits of multiple antennas, MIMO communications have been tremendously advanced from small MIMO in 4G to massive MIMO in 5G [13]. Looking forward towards 6G, there have been growing interests in the study of extremely large-scale MIMO (XL-MIMO) communications [14]- [18], for which the antenna size is so large that conventional assumptions, such as far-field propagation with uniform plane wave (UPW) models, are no longer valid. Instead, the more generic spherical wavefront and variations of signal amplitudes across array elements need to be taken into account.…”

Section: Introductionmentioning

confidence: 99%

SNR Scaling Laws for Radio Sensing with Extremely Large-Scale MIMO

Wang¹,

Zeng²

2021

Preprint

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Mobile communication networks were designed to mainly support ubiquitous wireless communications, yet they are expected to also achieve radio sensing capabilities in the near future. Most prior studies on radar sensing focus on distant targets, which usually rely on far-field assumption with uniform plane wave (UPW) models. However, with ever-increasing antenna size, together with the growing need to also sense nearby targets, the far-field assumption may become invalid. This paper studies radar sensing with extremely large-scale (XL) antenna arrays, where a generic model that takes into account both spherical wavefront and amplitude variations across array elements is developed. Furthermore, new closed-form expressions of the sensing signal-to-noise ratios (SNRs) are derived for both XL-MIMO radar and XL-phased-array radar modes. Our results reveal that different from the conventional UPW model where the SNR scales linearly and unboundedly with N for MIMO radar and with M N for phased-array radar, with M and N being the transmit and receive antenna numbers, respectively, more practical SNR scaling laws are obtained. For XL-phased-array radar with optimal power allocation, the SNR increases with M and N with diminishing returns, governed by new parameters called the transmit and receive angular spans. On the other hand, for XL-MIMO radar, while the same SNR scaling as XL-phasedarray radar is obeyed for N , the SNR first increases and then decreases with M .

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Low-Complexity Distance-Based Scheduling for Multi-User XL-MIMO Systems

Cited by 12 publications

References 22 publications

SNR Scaling Laws for Radio Sensing with Extremely Large-Scale MIMO

SNR Scaling Laws for Radio Sensing with Extremely Large-Scale MIMO

Meta‐heuristic optimization techniques for scheduling in multiple‐input multiple‐output multiple‐access channel system

SNR Scaling Laws for Radio Sensing with Extremely Large-Scale MIMO

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