Robust Beamforming for NOMA-Based Cellular Massive IoT With SWIPT

Abstract

In this paper, we study the resource allocation design for non-orthogonal multiple access (NOMA)-based cellular massive Internet-of-Things (IoT) enabled with simultaneous wireless information and power transfer (SWIPT). The design is formulated as a non-convex optimization problem, which takes into account practical and adverse factors, e.g., the channel uncertainty during channel state information (CSI) acquisition, the non-linear receiver during energy harvesting (EH) and the imperfect successive information cancellation (SIC) during the information decoding (ID). The originally harmful co-channel interference in massive access is coordinated to strike a balance between efficient information transmission and efficient energy harvesting via spatial beamforming. Subsequently, two robust beamforming algorithms are designed from the aspects of the weighted sum rate maximization and the total power consumption minimization, respectively. It is found that overall performance can be improved by adding BS antennas due to more array gains. Moreover, it is proved that the proposed algorithms can effectively alleviate the influence of adverse practical conditions and achieve the best performance compared to the baseline ones, which demonstrates the effectiveness and robustness of proposed algorithms for cellular massive IoT.