Optimizing Energy Efficiency in 5G Networks: Integrating Multi-Packet Reception with Reinforcement Learning and Genetic Algorithms

The rapidly expanding data demand and the explosion of connected devices require energy-efficient answers in 5G networks. This study proposes a three-pronged solution that integrates Multi-Packet Reception (MPR), Reinforcement Learning (RL), and Genetic Algorithms (GA) to improve energy efficiency in 5G networks. MPR allows base stations to process incoming data more efficiently by receiving multiple packets at once. This not only helps the base station “talk” more efficiently with the devices that are supposed to be connected to it at any moment (which obviously is more inefficient under 5G, the way that MPR can help be more efficient, makes it the prime candidate for meeting the challenge at hand) but also allows the incoming data to be processed at the same rate it could under the old conditions of processing one packet at a time. We use Genetic Algorithms to directly tackle what we consider the core problems of the 5G system: how to efficiently (jointly) serve users and how to efficiently allocate power among the serving base stations to minimize power consumption without sacrificing service quality (QoS). We developed an integrated method that demonstrates how to simultaneously achieve three goals: (1) significant energy savings; (2) improved network performance; and (3) enhanced sustainability under 5G conditions…. We assume a base station [BS] model close to real-world. Based on this model, we simulate our method in several scenarios. In these scenarios, we validate the network performance gains, energy savings, and enhanced sustainability that we design our method to achieve.