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This paper proposes a multiple-input multiple-output full-duplex relay Internet of Things system under imperfect channel state information, where the relay uses time switching relaying protocol to harvest energy. Particularly, due to the uncertainty channel circumstance, the self-interference in full-duplex relay cannot be eliminated completely. For such a system, a joint source-relay beamforming optimization problem which maximizes the system achievable rate subject to the transmit power and harvested energy constraints is studied. In light of the intractability of the problem, a singular value decomposition based geometric programming algorithm is investigated. The considered problem is reformulated to a diagonalized form by using singular value decomposition method. Then, the geometric programming algorithm is adopted to obtain the optimal solution for the reformulated problem. Numerical results validate the effectiveness and superiority of the proposed optimization scheme.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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In Internet of Things scenarios, the problem of restricted energy and limited computation capability of user node usually exists. To address the problem, a multiple-input-multipleoutput full-duplex multi-hop relay simultaneous wireless information and power transfer (SWIPT) mobile-edge computing (MEC) system is proposed in this paper, where energy harvesting model is non-linear. With the aid of MEC and SWIPT, user node equipped with power splitting receiver can locally execute computation tasks or offload partial or all of it to access point associated with an MEC server by using its battery energy, and harvest energy to replenish its battery while downloading the computation results. In addition, the system introduces full-duplex multi-hop relay and non-linear energy model to improve network coverage and model practical energy harvesting circuit. Aiming at minimising the system energy consumption, an energy efficient optimisation problem is formulated while satisfying the latency and energy constraints. Since the original problem is non-linear and non-convex, it is converted into two subproblems and successive convex approximation-based algorithm and geometric programming-based algorithm based alternating optimisation technique are adopted to solve them. Numerical results verify the superiority of the proposed optimisation scheme compared with other benchmark schemes.