Energy-Efficient Beamforming for Heterogeneous Industrial IoT Networks With Phase and Distortion Noises - Xu, Yongjun; Xie, Hao; Li, Dong; Hu, Rose Qingyang (IEEE transactions on industrial informatics, 2022)
The industrial Internet of Things (IIoT) is one of the key applications in 5G heterogeneous networks. To support high energy efficiency (EE) and reliability of IIoT equipment, it is important to design an efficient resource allocation algorithm in dynamic and complex environments. However, most of the studies on 5G heterogeneous IIoT networks did not address the transceiver hardware impairment (HWI) issues (e.g., phase noises, amplifier nonlinearities, and quantization errors) and the corresponding algorithms may not be applicable in practice. To this end, in this article, we investigate a realistic beamforming algorithm in a multicell downlink multiple-input single-output heterogeneous IIoT network by incorporating HWIs in our design. In particular, a beamforming design problem is formulated as a nonconvex optimization problem for maximizing the total EE of all equipment subject to the quality of service constraints of the IIoT equipment in both the macrocell and femtocells and the maximum transmit power constraints of base stations. In light of the intractability of the considered problem, we develop an EE-based iterative beamforming algorithm to tackle the formulated problem by employing the semidefinite relaxation method, Dinkelbach's method, and the successive convex approximation method. Simulation results show that the proposed algorithm can achieve higher EE and bring less interference power to the macrocell IIoT equipment by comparing it with baseline algorithms.