Congestion control based ant colony optimization algorithm for large MIMO detection

• Concept of negative pheromone is used to avoid the early convergence to a local minima.
• A new probabilistic search approach for detection in large MIMO systems is proposed.
• Improved performance under channel estimation error is achieved.
• Bit error rate performance improves with increase in number of antennas.

Employing multiple antennas in wireless communication systems is a key technology for future generation of wireless systems. Symbol detection in multiple-input multiple-output (MIMO) systems with low complexity is challenging. The minimum bit error rate (BER) performance can be achieved by maximum likelihood (ML) detection. However, with increase in number of antennas in MIMO systems, the ML detection becomes impractical. For example, sphere decoder (SD) is a well known ML detector for MIMO systems, however because of its high complexity it is practical only up to 32 real dimensions. Recently, bio-inspired algorithms are being used for improving the BER performance of MIMO symbol detector, along with low complexity. In this article, we propose a congestion control based ant colony optimization (CC-ACO) algorithm for large MIMO detection. We also discuss the robustness of the proposed algorithm under channel state information (CSI) estimation error. The simulation results shows the effectiveness of the proposed algorithm in terms of achieving better bit error rate (BER) performance with low complexity.