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Computationally efficient scheduling methods for MIMO uplink networks
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DOI：10.1007/s00500-021-05946-4.png)
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In this paper, ant colony optimization (ACO) and grey wolf optimization (GWO) algorithms are used for solving the joint user and receive antenna scheduling problem of multi-user multiple input multiple-output systems in the uplink channel. A subset of users among all the available users should be allowed to send their data to the appropriate receive antenna of the base station (BS). The search space for assigning appropriate user with the appropriate receive antenna of the BS is very large. Searching through this large search space is quite time-consuming process which cannot be accomplished within the real-time frame. To overcome this, ACO and GWO soft computing techniques have been implemented for this problem. In this paper, it is shown that both ACO and GWO accomplish this huge task with very low computation complexity. Simulation results presented in this paper are verifying the effectiveness of ACO and GWO for solving such complex problems. Moreover, it has also been showcased that GWO performs better than ACO and binary particle swarm optimization (BPSO) techniques. Both GWO and ACO attains the system sum-rate very close to that of exhaustive search algorithm. Furthermore, different statistical parameters for these soft computing techniques like BPSO, ACO, and GWO have been presented and compared to assess the efficacy of these meta-heuristic methods.
Keyword:
MIMO uplink networks
User and antenna scheduling
Soft computing techniques
Computationally efficient
Ant colony optimization (ACO)
Grey wolf optimization (GWO)
Binary particle swarm optimization (BPSO)
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2.5
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