Sub-optimal user scheduling for MU-MIMO system using channel quantization with two-stage feedback

Choudhary, Deepika; Mishra, Swadhin Kumar

doi:10.1109/cscita.2017.8066547

Cited by 1 publication

(1 citation statement)

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“…In Pattanayak et al, 19 a different suboptimal method based on user grouping is discussed. In Choudhary and Mishra 20 and Min et al 21 , it is suggested to use two‐stage feedback in the MU‐MIMO system to boost the effectiveness of the scheduling technique. A new technique based on the distance‐based scheduling (DBS) algorithm is used for user selection.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%