Improved Tabu-Search Preamble Assignment in Cell-Free Massive MIMO Systems

In this paper, an improved Tabu-search preamble assignment algorithm is proposed in cell-free massive multiple input multiple output (MIMO) to find the optimal preamble assignment solution with a low complexity. Different from the existing Tabu-search algorithm, the proposed one exploits the large-scale fading knowledge to refine the solution space so that only a small number of solutions that are more likely to mitigate preamble collision are explored. As a result, the complexity of Tabu search for preamble assignment can be significantly reduced. Simulation results demonstrate that the proposed algorithm is able to provide a good tradeoff between the performance and complexity compared to the state-of-the-art algorithms. In particular, it can achieve an over 50% complexity reduction with only a negligible performance loss in terms of the achievable sum rate compared to the existing Tabu-search algorithm.