Technology Category
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Energy-Efficient Irregular RIS-aided UAV-Assisted Optimization: A Deep Reinforcement Learning Approach
🤖 AI Summary
In UAV-assisted reconfigurable intelligent surface (RIS) communication systems, low radio-frequency (RF) energy harvesting efficiency and limited UAV endurance severely constrain practical deployment.
Method: This paper proposes a joint dynamic resource allocation and nonlinear energy harvesting optimization framework. We innovatively design an irregular RIS element switching mechanism coupled with a time-switching (TS) protocol, jointly optimizing base station transmit power, RIS continuous phase shifts, time-slot allocation factors, and discrete RIS element activation states. A realistic nonlinear RF energy harvesting model is incorporated, leading to a mixed-integer nonlinear programming (MINLP) formulation. To address hardware impairments and non-convex constraints, we develop the EE-DDPG algorithm, integrating action clipping and Softmax-weighted Q-value estimation for robust convergence.
Results: The proposed scheme achieves energy harvesting efficiencies of 81.5% (single-user) and 73.2% (multi-user), outperforming baseline deep reinforcement learning methods in both convergence speed and asymptotic performance, while maintaining computational complexity suitable for real-time implementation.
Technology Category
Application Category
📝 Abstract
Reconfigurable intelligent surfaces (RISs) enhance unmanned aerial vehicles (UAV)-assisted communication by extending coverage, improving efficiency, and enabling adaptive beamforming. This paper investigates a multiple-input single-output system where a base station (BS) communicates with multiple single-antenna users through a UAV-assisted RIS, dynamically adapting to user mobility to maintain seamless connectivity. To extend UAV-RIS operational time, we propose a hybrid energy-harvesting resource allocation (HERA) strategy that leverages the irregular RIS ON/OFF capability while adapting to BS-RIS and RIS-user channels. The HERA strategy dynamically allocates resources by integrating non-linear radio frequency energy harvesting (EH) based on the time-switching (TS) approach and renewable energy as a complementary source. A non-convex mixed-integer nonlinear programming problem is formulated to maximize EH efficiency while satisfying quality-of-service, power, and energy constraints under channel state information and hardware impairments. The optimization jointly considers BS transmit power, RIS phase shifts, TS factor, and RIS element selection as decision variables. To solve this problem, we introduce the energy-efficient deep deterministic policy gradient (EE-DDPG) algorithm. This deep reinforcement learning (DRL)-based approach integrates action clipping and softmax-weighted Q-value estimation to mitigate estimation errors. Simulation results demonstrate that the proposed HERA method significantly improves EH efficiency, reaching up to 81.5% and 73.2% in single-user and multi-user scenarios, respectively, contributing to extended UAV operational time. Additionally, the proposed EE-DDPG model outperforms existing DRL algorithms while maintaining practical computational complexity.
Problem
Research questions and friction points this paper is trying to address.
Optimizing energy-efficient UAV-RIS communication with dynamic resource allocation
Maximizing energy harvesting efficiency under QoS and hardware constraints
Enhancing UAV operational time via hybrid energy-harvesting RIS strategy
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hybrid energy-harvesting resource allocation strategy
Energy-efficient deep deterministic policy gradient algorithm
Irregular RIS ON/OFF capability adaptation
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