Joint Optimal Resource Allocation in Energy-Efficient Multicell Large-Scale Distributed Full-Duplex Antenna System with Imperfect CSI

In this paper, we proposes a joint energy-efficient optimal resource allocation algorithm in multicell large-scale distributed antenna system with full-duplex (FD) radio remote heads (RRHs), which enable simultaneous uplink and downlink communications. To maximize the energy efficiency (EE) of the network, a non-convex optimization problem is formulated subject to circuit power consumption, minimum data rate requirements, and a maximum allowed transmit power per RRH. By exploiting the Dinkelbach algorithm, the non-convex optimization problem is transformed into an equivalent optimization problem in subtractive form. An iterative optimization algorithm is proposed to solve the problem by separately implementing antenna selection with user scheduling and power allocation. To address this significant computational complexity, a norm-based joint antenna and user selection algorithm that uses Tabu Search is proposed to select optimum antennas and users for transmission. A Lagrangian dual decomposition method is utilized to derive a power allocation strategy that can run independently in each cell. Simulation results demonstrate that the proposed algorithm can achieve its maximum EE within several iterations. They also show that incrementing the number of antennas or users does not necessarily yield a higher EE. In addition, regardless of the number of selected antennas or users, the proposed iterative algorithm improves the performance of the traditional norm-based algorithm and simplified cloud radio access network based energy efficient power allocation scheme (S-CEEPA). As a result, the proposed algorithm achieves efficient EE in FD system with imperfect CSI and has superior performance over convectional FD and half-duplex baseline algorithms.