1 Introduction
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We have designed a 2D genetic algorithm tailored to the case of pilot assignment problem in massive MIMO systems. Also, we have employed an elitism strategy to prevent the best chromosomes from the likelihood of being destroyed by crossover and mutation operations and hence improve the performance of the 2D genetic algorithm.
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To enhance the convergence speed of the genetic algorithm to the ideal pilot assignment solution, we have integrated a local-search optimization algorithm based on Tabu-Search as an initial population generator for the genetic algorithm.
2 Methods
2.1 System model
2.2 Achievable uplink rate
2.3 Problem formulation
2.4 Proposed pilot allocation scheme
2.4.1 Two-dimensional genetic algorithm
2.4.2 Tabu-search (TS)
2.4.3 Integrated pilot allocation scheme
3 Complexity analysis
Algorithm | Order of complexity |
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TS-PA | \(\mathcal{O}(L{K}^{3})\) |
GATS-PA | \(\mathcal{O}\left(PL{K}^{3}\right)+\mathcal{O}\left(PL{K}^{2}\right), T=K\) |
EX-PA | \(\mathcal{O}({K!}^{L-1})\) |
4 Numerical results
4.1 Simulation setup
Parameters | Value |
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Number of cells, L | 3, 4, 7 |
Number of BS antennas per cell, M | 128 |
Number of users per cell, K | 4, 5, 6 |
Log normal shadow fading, δshad | 8 dB |
Cell radius, R | 500 m |
Path loss exponent, α | 3.8 |
Population size of GA, N | 20 |
Number of iterations of GA, T | K |
Number of iterations of TS, I | K |
Mutation probability, pm | 0.1 |
Crossover probability, pc | 0.9 |
Loss of spectral efficiency factor, μ0 | 0.05 |