# Improved Branch-and-Bound Antenna Selection Algorithm for Massive MIMO
###### Abstract  
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 \(10^{3}\) to \(10^{2}\) 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. m 2025 19 March 202511 April  
RF chains without sacrificing performance. [PERSON] et al. [PERSON] et al. (2018) highlighted that an effective antenna selection strategy should incorporate low-power components and leverage channel reciprocity to simplify CSI estimation. Recent advancements have led to a variety of algorithmic approaches, each optimized for different applications and offering unique advantages. Convex optimization has been widely employed in antenna selection algorithms. [PERSON] et al. [PERSON] et al. (2018) reformulated antenna selection as a convex optimization problem by relaxing binary constraints to maximize downlink capacity. While this approach improves performance in high-interference scenarios, it suffers from high computational complexity and a strong reliance on precise CSI. In contrast, [PERSON] et al. [PERSON] et al. (2018) proposed a convex optimization algorithm that prioritizes maximizing total received power, integrating signal-to-leakage-and-noise ratio (SLNR) precoding to optimize the bit error rate (BER). However, this method overlooks channel correlation, limiting its effectiveness in environments with severe user interference. Subsequently, [PERSON] et al. [PERSON] et al. (2018) introduced greedy algorithms based on a matching pursuit (MP) and its quantized variant (MPGBP), which iteratively select antenna subsets to maximize channel correlation while minimizing the mean square error (MSE) and transmission power. Their symbol-level algorithm jointly optimizes antenna selection and precoding, eliminating matrix inversion operations to reduce computational overheads. The MPGBP further alleviates amplifier linearity requirements through quantization. However, greedy algorithms inherently risk converging to local optima, leading to suboptimal solutions. Additionally, symbol-level strategies degrade in performance under rapidly varying channels, while quantization may introduce residual errors. As a result, these algorithms perform well in static channel conditions but require adaptive enhancements for dynamic environments, making them particularly suitable for latency-tolerant applications. [PERSON] et al. [PERSON] et al. (2018) developed an intelligent antenna selection algorithm that integrates a Monte Carlo tree search (MCTS) with self-supervised learning.