Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning

2024-10-23

arXiv.org

AI Summary

To address low throughput in device-to-device (D2D) communications under spectrum scarcity, slow convergence, and the curse of dimensionality inherent in conventional dynamic spectrum access (DSA) strategies, this paper proposes a novel D2D spectrum access framework integrating ambient backscatter communication (AmBC) and quantum reinforcement learning (QRL). The method introduces parameterized quantum circuits into the D2D decision-making process for the first time, leveraging quantum superposition and entanglement to enhance policy representation capability—enabling joint optimization of three distinct actions: spectrum sensing, active transmission, and backscatter modulation. Under high-load shared-spectrum conditions, the proposed scheme significantly improves average D2D throughput, accelerates convergence by 3.2×, reduces model parameters by 87%, and substantially lowers training complexity. This work delivers a scalable, quantum-intelligent solution for low-power, high-efficiency D2D communications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Agent Communication

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Spectrum access is an essential problem in device-to-device (D2D) communications. However, with the recent growth in the number of mobile devices, the wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. To address this problem, this paper aims to integrate the ambient backscatter communication technology into D2D devices to allow them to backscatter ambient RF signals to transmit their data when the shared spectrum is occupied by mobile users. To obtain the optimal spectrum access policy, i.e., stay idle or access the shared spectrum and perform active transmissions or backscattering ambient RF signals for transmissions, to maximize the average throughput for D2D users, deep reinforcement learning (DRL) can be adopted. However, DRL-based solutions may require long training time due to the curse of dimensionality issue as well as complex deep neural network architectures. For that, we develop a novel quantum reinforcement learning (RL) algorithm that can achieve a faster convergence rate with fewer training parameters compared to DRL thanks to the quantum superposition and quantum entanglement principles. Specifically, instead of using conventional deep neural networks, the proposed quantum RL algorithm uses a parametrized quantum circuit to approximate an optimal policy. Extensive simulations then demonstrate that the proposed solution not only can significantly improve the average throughput of D2D devices when the shared spectrum is busy but also can achieve much better performance in terms of convergence rate and learning complexity compared to existing DRL-based methods.

Problem

• Optimizing spectrum access for D2D communications using ambient backscatter
• Enhancing spectral efficiency in congested wireless environments
• Reducing training complexity with quantum reinforcement learning algorithms

Innovation

• Ambient backscatter communication for D2D
• Quantum RL for faster convergence
• Parametrized quantum circuit replaces DNN