Energy-Efficient Optimization for Mobile Edge Computing With Quantum Machine Learning

• Published in: IEEE wireless communications letters Vol. 13; no. 3; pp. 661 - 665
• Main Authors: Adu Ansere, James, Tran, Dung T., Dobre, Octavia A., Shin, Hyundong, Karagiannidis, George K., Duong, Trung Q.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Caching; Computation offloading; Computational modeling; Deep learning; Edge computing; Energy consumption; Energy efficiency; Internet of Things; IoT; Machine learning; MEC; Mobile computing; Optimization; Power consumption; Quantum computing; Quantum reinforcement learning; Resource allocation; Servers; Task analysis
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2023.3338571

We investigate the joint optimization problem of stochastic computation offloading, content caching strategy, and dynamic resource allocation to maximize the energy efficiency of mobile edge computing in Internet-of-Things. Specifically, we propose a quantum deep reinforcement learning algorithm to exponentially increase the caching learning speed and content caching delivery efficiency in multi-dimensional continuous and large action spaces. Furthermore, we utilize the modified Grover's algorithm with faster computation time to improve the processing efficiency and data-content retrieval for transition quantum state probabilities. The numerical results show that our proposed quantum machine learning scheme significantly outperforms other benchmarks in terms of energy-efficiency maximization subject to transmission power, energy consumption, and transmission latency.