Energy-Efficient Resource Allocation in Mobile Edge Computing Using NOMA and Massive MIMO

This work addresses the critical challenge of energy consumption in Mobile Edge Computing (MEC), a burgeoning field that extends cloud computing capabilities to the edge of cellular networks. Given the exponential growth of mobile devices and the resultant surge in energy demands, there is an urgent need for efficient energy management strategies to ensure sustainable development and operation of MEC infrastructures. This paper introduces a comprehensive framework for reducing energy consumption in MEC environments by leveraging advanced optimization techniques and energy-efficient resource allocation algorithms. We propose a novel approach that dynamically adjusts the computational resources based on the current network load and the type of services requested, thus minimizing unnecessary energy consumption. We derive and propose an optimized energy consumption for local processing. Then, we study the two network scenarios: Non-Orthogonal Multiple Access (NOMA) and Massive Multiple-Input Multiple-Output (mMIMO). We propose an optimized energy consumption algorithm in NOMA based on the derived processing resource requirements. Then, in mMIMO, we derive optimized power allocation algorithms. Simulations validate the effectiveness of our proposed framework, demonstrating significant energy savings.