This paper focuses on energy-efficient resource allocation in reconfigurable intelligent surface (RIS)-assisted multiple-input-single-output (MISO) communication systems. Specifically, it revisits the solution to the energy efficiency (EE) problem using the alternating optimization (AO) approach. In each AO iteration, the RIS phase optimization is achieved using the gradient descent method, which unfortunately does not guarantee convergence. To overcome this limitation, we propose two alternatives: the Wolfe-based gradient-descent (GAW) EE maximization Algorithm and the trust region (TR)-based EE maximization algorithm. Additionally, we use Dinkelbach’s algorithm to obtain the optimal transmit power allocation. Our results demonstrate that the proposed methods outperform the existing approach that uses sequential fractional programming (SFP) for phase optimization and the traditional relay-based method.

Future wireless systems must be designed to meet high data rates and a variety of services such as voice, video, etc. which consume more energy. Therefore, designing energy-efficient systems in recent years has become an inevitable trend. In this paper energy efficient resource allocation in orthogonal frequency division multiple access (OFDMA) systems is discussed. After formulating the resource allocation problem, maximizing-energy efficiency (EE)-lower-bound-based downlink subcarrier assignment (MDSA) algorithm is used to solve the problem that allocates subcarrier and power recursively. In this algorithm, first primary subcarriers are assigned to users and then power is allocated and this process continues recursively until all subcarriers are assigned to the users. Power allocation is done by the use of proposed sequential Quadratic programming (SQP) method. Then, simulation results are compared with bisection based power adaptation (BPA) algorithm method which has different power allocation. Results show that power allocation by SQP method has higher energy efficiency. Moreover, SQP shows higher convergence rate in solving the problem (finding the answer) in comparison to BPA method with less simulation time.

The pursuit of energy-efficient solutions in the context of reconfigurable intelligent surface (RIS)-assisted wireless networks has become imperative and transformative. This paper investigates the integration of RIS into an orthogonal frequency-division multiple access (OFDMA) framework for multi-user downlink communication systems. We address the challenge of jointly optimizing RIS reflection coefficients alongside OFDMA frequency and power allocations, with the aim of maximizing energy efficiency. This optimization is subject to specific quality-of-service (QoS) requirements for each user equipment (UE) and a constraint on transmission power and the RIS phase shift matrix. To address this complex optimization problem, we propose a novel practical and low-complexity approach that is based on optimizing a computationally efficient and numerically tractable lower bound on energy efficiency. The numerical results highlight the effectiveness of our approach, demonstrating a substantial increase in energy efficiency compared to scenarios without RIS, with random RIS integration, and with the scheme using the Genetic Algorithm (GA).

This paper considers energy-efficient resource allocation in reconfigurable intelligent surface (RIS)-assisted multiple-input-single-output (MISO) communication systems. In particular, it revisits the energy efficiency (EE) problem that uses the alternating optimization (AO) approach. In each AO's iteration, RIS phase optimization is achieved using gradient decent method whose convergence is unfortunately not guaranteed. We therefore, discuss the convergence issue herein while proposing two alternatives, namely, the Wolfe based gradient-descent algorithm (GAW) and the trust region (TR) method. However, the optimal transmit power allocation is obtained using Dinkelbach’s algorithm. Our results show that the proposed methods outperform the existing method that uses sequential fractional programming (SFP) for phase optimization and the traditional relay-based method.

Reconfigurable intelligent surfaces (RIS) have emerged as pivotal elements in enhancing integrated sensing and communication (ISAC) systems. Among RIS architectures, the beyond-diagonal (BD-RIS) design stands out for its advanced beamforming capabilities compared to traditional diagonal RIS. This paper investigates the deployment of BD-RIS to optimize energy efficiency through passive and active beamforming strategies while ensuring robust communication and sensing quality. The task is particularly challenging due to constraints inherent in BD-RIS configurations, including orthogonality, quartic inequalities, and the fractional nature of the objective function. To address these complexities, we employ twin delayed deep deterministic policy gradient (TD3) models, a state-of-the-art deep reinforcement learning (DRL) approach. Numerical validations confirm the effectiveness of our proposed algorithm and highlight the benefits of integrating BD-RIS into ISAC systems. Simulations demonstrate that incorporating BD-RIS leads to significant energy efficiency gains compared to benchmarks using diagonal RIS and RIS with random phase shifts.