Dynamic AP-UE Association and Power Allocation in Sparse LSFD for Energy-Constrained Networks

The rise of 5G and beyond has intensified the need for energy-efficient, high-capacity wireless networks, especially in IoT, smart cities, and dense urban settings. Sparse Large-Scale Fading Decoding (S-LSFD) addresses this challenge by dynamically associating User Equipment (UE) with a selective subset of Access Points (APs) offering the strongest channel gains. This selective association reduces interference, improves spectral efficiency, and minimizes energy consumption while maintaining Quality of Service (QoS).S-LSFD incorporates optimized power allocation to focus energy on high-quality AP connections, maximizing transmission efficiency. Its adaptability is governed by the control parameter λ, which balances reliability and energy savings by adjusting the number of APs each UE connects to. This flexibility makes S-LSFD ideal for energy-constrained scenarios like IoT ecosystems and densely populated areas.This study explores dynamic AP-UE association and power allocation mechanisms in S-LSFD using mathematical modeling and real-world scenarios. The proposed framework ensures scalability, energy efficiency, and high data rates, providing a sustainable solution for next-generation wireless networks.