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This paper aims at solving two classes of energy efficiency (EE) maximization problems in multiple channels wireless communication systems. First, the EE maximization problem with sum power constraint is solved based on the geometric water-filling approach; and second, the approach is extended into the EE maximization problem with additional least throughput requirement constraint. Our proposed algorithms make use of the water-filling structure of the optimal solution and provide exact and computation efficient solution to the energy-efficient power allocation problems. The proposed algorithms also have excellent scalability, which is applicable for large-scale wireless communication systems. Optimality of the proposed algorithms is strictly proved, and the proposed algorithms only require low-degree polynomial computational complexity. Numerical results are presented to demonstrate the efficiency of the proposed algorithms. To the best of our knowledge, no prior algorithms in the existing literature could provide such solutions to the EE maximization problems under the merit of exactness and the efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we study the energy efficiency (EE) optimization for simultaneous wireless information and power transfer in multiple-input-multiple-output two-way amplify-and-forward relay networks, where the energy-constrained relay harvests energy from two sources’ signals by power splitting (PS) scheme to forward the information of two sources. Our objective is to jointly design the sources and relay precoding matrices and the PS ratio to maximize the EE of the network, under the transmit power constraints at sources and relay and the minimum spectral efficiency requirement. We transform the nonconvex precoding and PS design problem into a difference of convex programming and propose an efficient iterative algorithm based on the constrained concave convex procedure to obtain a local optimum. To reduce computational complexity, we propose a generalized singular value decomposition based channel diagonalization scheme to simplify the optimization into a joint source and relay power allocation problem. The alternating optimization method is employed to solve this problem and a suboptimal solution to the EE maximization problem is obtained. Simulation results demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

Multistream carrier aggregation (MSCA) is a promising technique to boost data rates by enabling users to connect with multiple base stations (BSs) simultaneously. In this paper, we investigate joint user association and resource allocation for MSCA systems to achieve energy efficiency (EE) balance among different BSs. We aim at maximizing the weighted summation of EE values for different BSs, which is modeled as a nonconvex combinatorial sum-of-ratios optimization problem. To develop an upper bound algorithm, we first relax the combinatorial variables and then transform the problem into a series of convex optimization problems by the successive convex approximation (SCA) method. We also propose a low-complexity suboptimal algorithm, which divides the original optimization problem into two steps: user association and channel allocation, as well as power allocation. Numerical results show that the proposed algorithms can achieve flexible EE tradeoff among BSs, and the EE can be significantly improved with MSCA. [ABSTRACT FROM AUTHOR]