Abstract-The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension. This approach provides performance on the benchmark functions superior to any other published results known by the authors. '

This paper investigates the application of simultaneous wireless information and power transfer (SWIPT) to cooperative non-orthogonal multiple access (NOMA). A new cooperative multiple-input single-output (MISO) SWIPT NOMA protocol is proposed, where a user with a strong channel condition acts as an energy-harvesting (EH) relay to help a user with a poor channel condition. The power splitting (PS) scheme is adopted at the EH relay. By jointly optimizing the PS ratio and the beamforming vectors, the design objective is to maximize the data rate of the "strong user" while satisfying the QoS requirement of the "weak user". It boils down to a challenging nonconvex problem. To resolve this issue, the semidefinite relaxation (SDR) technique is applied to relax the quadratic terms related with the beamformers, and then it is solved to its global optimality by two-dimensional exhaustive search. We prove the rank-one optimality, i.e., the SDR tightness, which establishes the equivalence between the relaxed problem and the original one. To further reduce the high complexity due to the exhaustive search, an iterative algorithm based on successive convex approximation (SCA) is proposed, which can at least attain its stationary point efficiently. In view of the potential application scenarios, e.g., Internet of Things (IoT), the single-input single-output (SISO) case of the cooperative SWIPT NOMA system is also studied. The formulated problem is proved to be strictly unimodal with respect to the PS ratio. Hence, a golden section search (GSS) based algorithm with closed-form solution at each step is proposed to find the unique global optimal solution. It is worth pointing out that the SCA method can also converge to the optimal solution in SISO cases. In the numerical simulation, the proposed algorithm is numerically shown to converge within a few iterations, and the SWIPT-aided Manuscript

In next-generation wireless networks, energy efficiency optimization needs to take individual link fairness into account. In this paper, we investigate a max-min energy efficiency-optimal problem (MEP) to ensure fairness among links in terms of energy efficiency in OFDMA systems. In particular, we maximize the energy efficiency of the worst-case link subject to the rate requirements, transmit power, and subcarrier assignment constraints. Due to the nonsmooth and mixed combinatorial features of the formulation, we focus on low-complexity suboptimal algorithms design. Using a generalized fractional programming theory and the Lagrangian dual decomposition, we first propose an iterative algorithm to solve the problem. We then devise algorithms to separate the subcarrier assignment and power allocation to further reduce the computational cost. Our simulation results verify the convergence performance and the fairness achieved among links by comparing the MEP with the existing algorithms. We also study a tradeoff between the network energy efficiency and fairness, similarly to the tradeoff between the sum rates and fairness.Index Terms-Energy efficiency, subcarrier assignment, power allocation, max-min fairness, OFDMA.

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