Fairness-Based Resource Allocation for Multiple Weights Opportunistic Beamforming in Internet of Things Networks

Abstract

Multiple-input–multiple-output (MIMO) is a promising technique in Internet of Things (IoT) Networks, which can effectively multiplex more IoT users and improve the spectrum efficiencies (SEs) of the users. However, when the number of IoT users is large, the complexity becomes enormous. Moreover, perfect channel state information (CSI) is a basic assumption in MIMO systems, which is impractical with a large number of IoT users. In order to reduce the complexity and break the CSI limitation, an opportunistic-beamforming (OBF)-based IoT network is proposed, which can achieve high SE with low complexity and feedback under a large number of IoT users’ conditions. Then, to solve the unfairness problem and further improve SE, a downlink multiple-weight and multiple receive antenna OBF (MW-OBF-MRA) system with the proportional fairness (PF) strategy is proposed, where the transmitter and receiver are jointly designed. Moreover, a joint resource allocation scheme is provided to achieve the maximum SE with PF constraints. Numerical results show that our proposed system can achieve fairness with the minimum SE loss and provide better SE than the other beamforming schemes with the limited feedback information.