Interference Limited Wireless Network

Interference-limited wireless networks pose significant challenges to efficient communication, motivating research focused on optimizing resource allocation to maximize network utility and quality of service. Current research heavily employs machine learning techniques, particularly graph neural networks and reinforcement learning, to address complex problems like dynamic channel allocation, beamforming, and power control in these networks. These approaches aim to improve performance metrics such as signal-to-interference-plus-noise ratio (SINR) and overall network throughput, leading to more efficient and robust wireless systems. The resulting advancements have implications for various applications, including federated learning and ultra-reliable low-latency communications.