Improved Branch-and-Bound Antenna Selection Algorithm for Massive MIMO

The rapid proliferation of wireless devices and the escalating demand for ultra-reliable, high-capacity communication networks have propelled massive multiple-input multiple-output systems as a cornerstone technology for next-generation wireless standards. Massive multiple-input multiple-output systems deploy hundreds of antennas at both the transmitter and the receiver, leading to high computational complexity in many antenna selection algorithms. Existing approaches often achieve reduced complexity at the expense of partial performance compromise. To address this challenge, this paper proposes an Improved Branch-and-Bound Antenna Selection algorithm that reduces complexity while maintaining the required performance. The algorithm iteratively eliminates the antenna contributing least to channel capacity from the candidate set. Through the mechanism of reverse-stacking nodes, the conventional stack-based search process is modified. Most critically, by employing dynamic stack management and effective pruning conditions, substantial pruning operations can be implemented during subsequent search procedures, significantly accelerating the identification of the optimal antenna subset. Simulation results demonstrate that the improved algorithm reduces computational complexity from an order of 103 to 102 while maintaining equivalent channel capacity. Furthermore, through a single execution, the algorithm can obtain optimal antenna subsets with varying sizes within specified ranges, effectively overcoming the limitation of the traditional Branch-and-Bound algorithm that requires repeated executions for different subset dimensions.