Abstract:We consider the simultaneous wireless information and power transfer in two-phase decode-and-forward two-way relaying networks, where a relay harvests the energy from the signal to be relayed through either power splitting or time splitting. Here, we formulate the resource allocation problems optimizing the time-phase and signal splitting ratios to maximize the sum rate of the two communicating devices. The joint optimization problems are shown to be convex for both the power splitting and time splitting approaches after some transformation if required to be solvable with an existing solver. To lower the computational complexity, we also present the suboptimal methods optimizing the splitting ratio for the fixed time-phase and derive a closed-form solution for the suboptimal method based on the power splitting. The results demonstrate that the power splitting approaches outperform their time splitting counterparts and the suboptimal power splitting approach provides a performance close to the optimal one while reducing the complexity significantly.
Recently, intelligent reflecting surfaces (IRSs) have drawn huge attention as a promising solution for 6G networks to enhance diverse performance metrics in a cost-effective way. For massive connectivity toward a higher spectral efficiency, we address an intelligent reflecting surface (IRS) to an uplink nonorthogonal multiple access (NOMA) network supported by a multiantenna receiver. We maximize the sum rate of the IRS-aided NOMA network by optimizing the IRS reflection pattern under unit modulus and practical reflection. For a moderate-sized IRS, we obtain an upper bound on the optimal sum rate by solving a determinant maximization (max-det) problem after rank relaxation, which also leads to a feasible solution through Gaussian randomization. For a large number of IRS elements, we apply the iterative algorithms relying on the gradient, such as Broyden–Fletcher–Goldfarb–Shanno (BFGS) and limited-memory BFGS algorithms for which the gradient of the sum rate is derived in a computationally efficient form. The results show that the max-det approach provides a near-optimal performance under unit modulus reflection, while the gradient-based iterative algorithms exhibit merits in performance and complexity for a large-sized IRS with practical reflection.
This letter addresses an intelligent reflecting surface (IRS) to the uplink nonorthogonal multiple access (NOMA) served by a multiantenna receiver for more efficient data collection from massive devices. For rate fairness, we formulate a problem of maximizing the minimum rate of the devices by optimizing receive beamforming (BF), IRS reflection, and transmit power allocation (PA) of the devices jointly. We first design a block coordinate descent (BCD) algorithm optimizing receive BF, IRS reflection, and PA iteratively. We then reformulate the problem as a nonlinear optimization (NLO) problem with a smooth objective function of the IRS phase and PA vectors by incorporating the optimal receive BF into the objective and using an approximate minimum. To handle massive IRS elements and devices efficiently, we solve the NLO problem with the limitedmemory Broyden-Fletcher-Goldfarb-Shanno bounded (L-BFGS-B) algorithm using the gradient derived in a closed form. The results show that the L-BFGS-B optimizing the IRS phase and PA vectors concurrently reduces the computational complexity of the BCD algorithm significantly at a slight performance gain.
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