## 1 Introduction  
Massive MIMO technology leverages a large number of antennas to improve communication performance. By deploying numerous antennas at both the base station and user sides, this technology enhances signal robustness against interference, increases transmission rates, and improves user connectivity [1, 2, 3]. Due to its massive connectivity, high energy efficiency, and low latency, massive MIMO has become one of the key technologies for building networks such as the Internet of Things (IoT) [4, 5]. However, in a massive MIMO system, each transmission antenna is paired with an individual RF chain, resulting in the number of RF chains being equal to the number of antennas. This setup leads to increased computational complexity and power consumption. A major solution is to perform transmission antenna selection. In this architecture, only a limited number of RF chains are deployed, and a corresponding number of antennasare activated for communication based on the available RF chains [6; 7]. By adopting this approach, both power consumption and latency are significantly reduced. Notably, low power consumption and low latency are critical performance metrics for 5G and beyond 5G (B5G) networks [8]. Reference [9] proposed a greedy algorithm for fast selection of transmit or receive antennas, which significantly reduces computational complexity without compromising capacity performance. In the context of massive MIMO, Reference [10] jointly optimizes precoding and antenna selection. A low-complexity cyclic binary meta-heuristic algorithm is employed to select the optimal antenna combination, thereby enhancing energy efficiency while maintaining spectral efficiency. In addition, user scheduling plays a crucial role in multi-user large-scale MIMO systems, as it enhances multi-user diversity gain and improves communication efficiency [11]. Reference [12] investigates multi-user scheduling in 6G vehicle-to-everything (V2X) communications. Block diagonalization precoding is employed to eliminate multi-user interference, and a group of users with low correlation and high channel quality is selected for scheduling to enhance the overall channel gain and reduce the bit error rate. Reference [13] addresses the scheduling problem in multi-user MIMO broadcast channels, where user scheduling and antenna selection are performed under limited feedback conditions to reduce channel feedback overhead. Furthermore, the combination of antenna selection and user scheduling enables the optimal pairing of antennas and users, resulting in a more favorable channel matrix and improved data transmission rates under the same spectrum resources. User scheduling can reduce inter-user signal interference and optimize the system's signal-to-noise ratio (SNR). In massive MIMO systems, antenna selection significantly reduces the signal processing burden and hardware cost, contributing to the realization of green communication. The advantages of antenna selection and user scheduling have led to their widespread application in practical communication scenarios. In 5G/6G massive MIMO base stations, antenna selection is used to reduce the number of RF chains, while user scheduling enables multi-user parallel transmission, thereby improving system throughput. In the IoT, user scheduling controls active nodes to reduce concurrent interference. In V2X and high-mobility scenarios, user scheduling dynamically adjusts the service order based on vehicle speed, location, and service priority, reducing latency and ensuring reliable communication (e.g., for autonomous driving). In unmanned aerial vehicle (UAV) communications, antenna selection enables directional transmission, while user scheduling is carried out based on the ground user density. In the design of joint antenna selection and user scheduling, the conventional approach is to separate these two processes and implement them in a staged manner. While this strategy reduces computational complexity, it comes at the cost of degraded system sum rate [14].