Reconfigurable intelligent surfaces (RISs) are an emerging technology for improving spectral efficiency and reducing power consumption in future wireless systems. This paper investigates the joint design of the transmit precoding matrices and the RIS phase shift vector in a multi-user RIS-aided multiple-input multiple-output (MIMO) communication system. We formulate a max-min optimization problem to maximize the minimum achievable rate while considering transmit power and reradiation mask constraints. The achievable rate is simplified using the Arimoto-Blahut algorithm, and the problem is broken into quadratic programs with quadratic constraints (QPQC) sub-problems using an alternating optimization approach. To improve efficiency, we develop a model-based neural network optimization that utilizes the one-hot encoding for the angles of incidence and reflection. We address practical RIS limitations by using a greedy search algorithm to solve the optimization problem for discrete phase shifts. Simulation results demonstrate that the proposed methods effectively shape the multi-beam radiation pattern towards desired directions while satisfying reradiation mask constraints. The neural network design reduces the execution time, and the discrete phase shift scheme performs well with a small reduction of the beamforming gain by using only four phase shift levels.

In this paper, the energy efficiency of a two-user downlink NOMA system aided by several ambient backscatter devices is investigated. We analyze both the tradeoff and the ratio between achievable rates versus power consumption, assuming that the backscatter devices are in fully cooperative mode. In the case of two backscatter devices, we derive a closed-form solution in terms of the optimal reflection coefficients and power allocation policy by exploiting the properties of the energy-efficiency objective and the Pareto boundary of the feasible set. For more than two backscatter devices, the problem becomes difficult and our methodology cannot be extended easily. Nevertheless, we evaluate the performance of NOMA aided by several (up to four) backscatter devices via numerical simulations. Our numerical results show that the energy efficiency of the two-user NOMA system increases with the number of cooperative backscatter devices. Moreover, in the high noise regime, the relative efficiency gain increases with the number of backscatter devices reaching up to 370 % compared to conventional NOMA.

In this paper, the energy efficiency of multi-user non orthogonal multiple access (NOMA) systems in the presence of a backscatter device is investigated. The energy efficiency maximization problem is formulated as a tradeoff between the sum rate and the total power consumption and shown to be non-convex. We then derive a closed-form expression of the optimal reflection coefficient. Remarkably, the obtained expression allows the reformulation of the optimization in terms of the power allocation policy into a convex optimization problem that has recently been solved in closed form. This overall solution can then be exploited to reduce the computational complexity of Dinkelbach's algorithm for maximizing the ratio sum rate vs. total power. Simulation results show that the presence of backscatter devices significantly improve the energy efficiency of NOMA systems and reach up to 450% relative gains compared to OMA.

This paper investigates the energy efficiency of a multi-user downlink non-orthogonal multiple access (NOMA) system. The energy-efficiency maximization representing the tradeoff between the sum rate versus the overall power consumption is formulated as a bi-criterion optimization problem; being convex, scalarization is suitable for its solution. Assuming that each individual user has a minimum quality of service constraint, we prove that the solution of the scalarized objective can be obtained in closed form. Hence, our solution characterizes the entire Pareto-optimal boundary of the rate vs. power tradeoffs. In the special case of maximizing the ratio sum rate vs. overall power, our solution reduces the complexity of the Dinkelbach procedure to a univariate bisection method. At last, our simulation results highlight the benefit of NOMA over OMA in terms of the rate vs. power optimal tradeoff.