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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‐multiple‐output 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. [ABSTRACT FROM AUTHOR]

A multi-user multiple-input single-output simultaneous wireless information and power transfer full-duplex system with imperfect channel state information is considered. Under the assumption that the unknown channel estimation error is within a certain range, the self-interference cannot be suppressed completely at the base station and at the multi-user nodes. The proposed design problem is formulated as maximizing the average harvested energy of multi-user nodes and maintaining the signal-to-interference-plus-noise ratio requirement, which is proved to be non-convex. Utilizing semidefinite relaxation and S-procedure methods, a convex optimization problem of optimal beamforming vector for the multi-antenna transmitter is obtained. Moreover, semidefinite relaxation is proved to be tight by showing the existence of rank-one optimal solutions. Simulation results are presented to verify the robustness and effectiveness of the proposed algorithm compared with the non-robust design. [ABSTRACT FROM AUTHOR]

A simultaneous wireless information and power transfer scheme for amplify-and-forward relay systems with a direct source–receiver link is investigated. According to the minimum-mean-square-error criterion, we formulate a joint beamforming problem under an energy harvesting constraint at the receiver plus transmitting power constraints at source and relay. Using an iterative algorithm, the non-convex problem is decoupled into two subproblems. A successive convex approximation method with iterative algorithm is designed for obtaining the local optimum. Assuming that \mathbf Ms\leq {\mathbf {min}(\mathbf {N}, \mathbf Md)} , we propose a simplified measure using singular value decomposition and generalized singular value decomposition techniques. Numerical results for the number of iterations and bit error rate are carried out to evaluate the performance of the three different schemes. [ABSTRACT FROM PUBLISHER]

A multiple-input multiple-output two-way amplify-and-forward relay network with energy harvesting is considered in this paper. Under the transmit power constraints at sources and relay, a beamforming design problem is formulated, which minimizes the total mean square error while still guaranteeing sufficient energy harvested at the sources. Since this problem is nonconvex, an alternating optimization method based on generalized singular value decomposition (GSVD) is proposed and the algorithm is proved to be convergent. Moreover, based on the successive convex approximation, an iterative refinement is further designed to improve the solution quality of the GSVD scheme. Numerical results show good performance of the proposed schemes. [ABSTRACT FROM PUBLISHER]

A MIMO two-hop relay network with power-splitting-based relaying is considered. Using a minimum mean-square-error criterion, the joint source-relay optimization problem with transmit power constraint and relay energy harvesting constraint is formulated. In order to address this non-convex problem, an iterative algorithm is proposed which converges to a local optimum. The Lagrangian dual function and successive convex approximation are used for solution. To reduce the computational complexity, a diagonalizing beamforming scheme is employed using singular value decomposition, water-filling and successive convex approximation. Results for MSE versus iterations and bit-error-rate versus signal-to-noise ratio demonstrate the good performance of the proposed schemes. [ABSTRACT FROM PUBLISHER]

In this paper, a multiple-input single-output (MISO) full-duplex two-way cooperative non-orthogonal multiple access (NOMA) system is considered. The relay is energy-restricted and may not accomplish the information exchange between two end users. To address this problem, simultaneous wireless information and power transfer (SWIPT) is proposed, which employs power splitting (PS) at the relay to harvest energy. To maximize the harvested energy of the relay under QoS and power constraints, a joint optimization problem of the beamforming vectors and the PS ratio is formulated. The problem is nonconvex, and the original problem is transformed into a tractable form by applying the semidefinite relaxation (SDR) technique. Then a bilevel programming (BLP) based method is proposed to obtain an approximately optimal solution. Moreover, to reduce the computational complexity, a successive convex approximation (SCA)-based iterative algorithm is developed to derive a suboptimal solution. Numerical results show that our proposed strategy significantly enhances the energy harvesting performance compared with the existing transmission strategies. [ABSTRACT FROM AUTHOR]

We consider a multiple-input multiple-output amplify-and-forward wireless multiple-hop sensor network (WMSN). The simultaneous wireless information and power transfer technology is deployed to potentially achieve an autonomous system. We investigate two practical receiver schemes, which are the power splitting (PS) and the time switching (TS). The power splitting receiver splits received signals into two streams, one for information decoding (ID) and the other for energy harvesting (EH). On the other hand, the time switching receiver only serves in ID mode or energy harvesting mode during a certain time slot. Subject to transmit power constraints and destination harvested energy constraint, we aim to obtain a joint beam-forming solution of source and relay precoders to maximize the maximum achievable rate of the WSN. In order to make the non-convex problem tractable, diagonalization-based alternating optimization algorithms are proposed. Numerical results show the convergence and good performance of the proposed algorithms under both PS and TS protocols. [ABSTRACT FROM AUTHOR]

The simultaneous wireless information and power transfer (SWIPT) technique has been regarded as an appealing approach to prolong the lifetime of wireless sensor networks. However, co-channel interferences with SWIPT in wireless networks have not been investigated from a green communication perspective. In this paper, joint transmit and receive beamforming design for a full-duplex multiple-input multiple-output amplify-and-forward relay system with simultaneous wireless information and power transfer in WSNs is investigated. Multiple co-channel interferers are considered at the relay and destination sensor nodes. To minimize the mean-squared-error of the system, joint source and relay beamforming optimization is proposed while guaranteeing the transmit power constraints and destination's energy harvesting constraint. An iterative algorithm based on alternating optimization with successive convex approximation which converges to a local optimum is proposed to solve the non-convex problem. Moreover, a low-complexity scheme is derived to reduce the computational complexity. Simulations for MSE versus iterations and MSE versus signal-to-noise ratio (SNR) demonstrate the convergence and good performance of the proposed schemes. [ABSTRACT FROM AUTHOR]