Energy-Efficient Resource Allocation in LTE-Based MIMO-OFDMA Systems With User Rate Constraints

Energy-Efficient Resource Allocation in LTE-Based MIMO-OFDMA Systems With User Rate Constraints In recent years, the enormous energy consumption of wireless communication systems has aroused universal concerns throughout the world. Hence, energy efficiency has become one of the central topics in today’s wireless communication industry. In this paper, we study energy-efficient resource allocation for Long Term Evolution (LTE) cellular mobile systems. To be applicable in LTE systems, both multiple-input multiple-output (MIMO) orthogonal frequency-division multiple access (OFDMA) radio access network and resource blocks (RBs) for subchannel assignment are considered in this work, functioning as two distinguishing characteristics for LTE resource allocation. In particular, an optimization problem concerning joint RB assignment, and power allocation is formulated to maximize the energy efficiency measured by “bits-per-Joule” metric, under per-user quality-of-service (QoS) requirements in the form of user rate constraints. We first show that this energy-efficient resource allocation problem can be converted into a more tractable equivalent problem by which the fractional objective of energy efficiency is well settled. With the Lagrange dual method then, we decouple the RB assignment and power allocation on different subchannels using dual decomposition, which greatly simplifies the combinatorial RB assignment, and address the equivalent problem effectively via solving a series of dual optimization problems. Moreover, a computationally efficient but near-optimal algorithm is proposed to perform QoS-aware energy-efficient resource allocation in practice. Simulation results show that our proposed algorithm may not only improve energy efficiency significantly but fully satisfy users’ rate requirements as well. Moreover, with MIMO-OFDMA and RB assignment being considered, the proposed algorithm may be more applicable in LTE systems than most existing schemes on energy-efficient resource allocation.