SWIPT-Assisted Energy Efficiency Optimization in 5G/B5G Cooperative IoT Network

Resource use in point-to-point and point-to-multipoint communication emerges with the tremendous growth in wireless communication technologies. One of the technologies is wireless power transfer which may be used to provide sufficient resources for energy-constrained networks. With the implication of cooperative communication in 5G/B5G and the Internet of Things (IoT), simultaneous wireless information and power transfer (SWIPT)-assisted energy efficiency and appropriate resource use become challenging tasks. In this paper, multiple IoT-enabled devices are deployed to cooperate with the source node through intermediate/relay nodes powered by radio-frequency (RF) energy. The relay forwards the desired information generated by the source node to the IoT devices with the fusion of decode/amplify processes and charges itself at the same time through energy harvesting technology. In this regard, a problem with throughput, energy efficiency, and joint throughput with user admission maximization is formulated while assuring the useful, practical network constraints, which contemplate the upper/lower bounds of power transmitted by the source node, channel condition, and energy harvesting. The formulated problem is a mixed-integer non-linear problem (MINLP). To solve the formulated problem, the rate of individual IoT-enabled devices (b/s), number of selected IoT devices, and the sum-rate maximization are prosecuted for no-cooperation, cooperation with diversity, and cooperation without diversity. Moreover, a comparison of the outer approximation algorithm (OAA) and mesh adaptive direct search algorithm (MADS) for non-linear optimization with the exhaustive search algorithm is provided. The results with reference to the complexity of the algorithms have also been evaluated which show that 4.68×10−10 OAA and 7.81×10−11 MADS as a percent of ESA, respectively. Numerous simulations are carried out to exhibit the usefulness of the analysis to achieve the convergence to e-optimal solution.