Multiobjective Optimization for Resource Allocation in Full-duplex Large Distributed MIMO Systems

The most conflicting key variables in wireless networks are energy efficiency (EE) and spectral efficiency (SE). In this paper, we propose an energy-efficient allocation algorithm of network resources for multi-input multi-output networks distributed with large-scale antenna systems. We formulate a multiobjective optimization problem (MOOP) to maximize the EE of each distinct user and to show the EE–SE trade-off as a MOOP. To find the Pareto optimal solution, we transform this MOOP into single-objective optimization problem (SOOP) through Tchebycheff scalarization and by exploiting it with Dinkelbach's method. To solve the SOOP, we apply a joint antenna selection and user scheduling (JASUS) algorithm for the joint allocation of antenna scheduled users solved through an iterative approach. The power allocations are applied distinctly for individual cell users by a subgradient iterative method to simplify the SOOP further and improve the EE. The simulation results reveal that our proposed MOOP has a fast convergence, achieving maximum EE after a few iterations. Additionally, our proposed methods unveil an interesting trade-off between EE and SE at a faster speed and demonstrate that an important performance gain is achieved by using the proposed algorithm.