Social-Spider-Optimized Hybrid Pilot Decontamination: Fairness-Aware Refinement of Soft Pilot Reuse and Weighted Graph Coloring for Multi-Cell Massive MIMO

Author

Puja Kumari, Dr. Pramod Sharma

Abstract

 Pilot contamination, which is caused by reusing non-orthogonal training sequences in neighboring cells, limits multi-cell massive multiple-input multiple-output (MIMO) systems and does not disappear as the number of base-station (BS) antennas grows. Soft pilot reuse (SPRS) protects cell-edge users with orthogonal pilots, and weighted-graph-coloring pilot decontamination (WGC-PD) assigns the reused pilots of cell-center users greedily. Both are one-shot heuristics whose decisions are never revised. This paper proposes SSO-HPD, a hybrid framework in which a discrete social-spider optimization (SSO) algorithm refines the SPRS+WGC-PD assignment. The optimizer searches over within-cell pilot permutations using female-attraction (row-copy) and male-mating (crossover) operators, and its fitness function combines the sum rate with a low-percentile fairness term. Because the search is seeded with the WGC solution and accepts only improving moves, SSO-HPD never performs worse than its seed. In a 7-cell network with K = 8 users per cell, SSO-HPD increases the average uplink rate by 3.2–4.0% compared with SPRS+WGC-PD and by 7.8–15.0% compared with random reuse as the number of BS antennas grows from 32 to 512, and it raises the median user rate by 9.6%. We also analyze the trade-off between the median rate and the worst-user rate.

Keywords

Massive MIMO, pilot contamination, pilot assignment, soft pilot reuse, graph coloring, social spider optimization, swarm intelligence, 5G.

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References

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