This paper deals with the energy efficiency (EE) maximization problem in multiple-input multiple-output (MIMO) two-way relay networks with simultaneous wireless information and power transfer (SWIPT). The network consists of a multiple-antenna amplify-and-forward relay node which provides bidirectional communications between two multiple-antenna transceiver nodes.In addition, one of the transceivers is considered battery limited and has the capability of energy harvesting from the received signals. Assuming the network EE as the objective function, we design power splitting factor and optimum precoding matrices at the relay node and two transceivers. The constraints are transmit power of the nodes, harvested energy and quality of service of two transceivers. The resulting non-convex optimization problem is divided into three sub-problems which are then solved via an alternation approach. In addition, sufficient conditions for optimality are derived and the computational complexity of the proposed algorithm is analyzed. Simulation results are provided to evaluate the performance and confirm the efficiency of the proposed scheme as well as its convergence.

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In this paper, the problem of opportunistic spectrum sharing for the next generation of wireless systems empowered by the cloud radio access network (C-RAN) is studied. More precisely, low-priority users employ cooperative spectrum sensing to detect a vacant portion of the spectrum that is not currently used by high-priority users. The authors' aim is to maximize the overall throughput of the low-priority users while guaranteeing the quality of service of the high-priority users. This objective is attained by optimally adjusting spectrum sensing time, with respect to target probabilities of detection and false alarm, as well as dynamically allocating C-RAN resources, that is, powers, sub-carriers, remote radio heads, and base-band units. To solve this problem, which is non-convex and NP-hard, a low-complex iterative solution is proposed. Numerical results demonstrate the necessity of sensing time adjustment as well as effectiveness of the proposed solution.

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In this paper we consider a heterogeneous network which consists of a macro base station and some pico base stations utilizing massive MIMO and MIMO techniques, respectively. A central software-defined mobile network (SDMN) controller is adopted in order to provide user association and energy scheduling. The users are considered battery limited and are capable of simultaneous wireless information and power transfer (SWIPT) in order to harvest energy and address the energy shortage issue. These users harvest energy from the received signals in the downlink and consume it via their uplink communications. This paper deals with the downlink user association by jointly optimizing the overall sum-rate of the network and the harvested energy by introducing an appropriate utility function. In this regard, the optimum user association and power splitting factor for each user are calculated via the downlink optimization stage. Then, the process of uplink scheduling is defined as choosing the best users in each time epoch to transfer data as well as optimizing their transmit power by solving Lyapunov drift-plus-penalty function. Simulation results are provided in order to confirm the optimality of the proposed algorithm in comparison with the previous user association and uplink scheduling approaches in terms of providing fairness and battery management among users.