Energy-Efficient Resource Management in Wireless Communication Networks
Summary
As the volume of wireless data traffic surges worldwide, the energy footprint of communication networks has become a critical challenge. Energy-efficient resource management seeks to reduce power consumption across radio access, core networks and user devices while preserving quality of service and capacity. Techniques include dynamic power control, sleep modes for under-utilised base stations, adaptive subcarrier and user scheduling, and traffic offloading to alternative access technologies. Recent advances leverage machine learning and distributed optimisation to adapt allocation policies in real time, accounting for channel conditions, user mobility and network congestion. Simultaneously, energy harvesting and wireless power transfer offer sustainable alternatives for low-power devices in Internet of Things deployments. A holistic approach integrates cross-layer strategies—combining efficient hardware design, green radio architectures and intelligent software—to achieve substantial reductions in operational expenditure and carbon emissions. This research underpins the development of 5G and beyond networks capable of supporting massive connectivity with a minimal energy footprint, thereby enabling sustainable digital economies and reducing the environmental impact of telecommunications.
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Energy-Efficient Resource Management in Wireless Communication Networks publication trend
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Technical terms
Energy efficiency (EE): The ratio of useful data throughput to the total energy consumed by a network or device, typically expressed in bits per joule.
OFDMA: Orthogonal Frequency-Division Multiple Access, a multi-carrier modulation scheme that enables flexible subcarrier allocation among users to improve spectral and energy efficiency.
Reinforcement learning (RL): A class of machine-learning algorithms that learn to make sequential decisions by receiving rewards or penalties from interactions with the environment.
Hypergraph: A generalisation of a graph in which edges, called hyperedges, can connect more than two vertices, used to model complex resource conflicts in networks.
References
- Hypergraph-Based Resource-Efficient Collaborative Reinforcement Learning for B5G Massive IoT. IEEE Open Journal of the Communications Society (2023).
- Maximizing Energy Efficiency in Multiuser Multicarrier Broadband Wireless Systems: Convex Relaxation and Global Optimization Techniques. IEEE Transactions on Vehicular Technology (2016).
- First 20 Years of Green Radios. IEEE Transactions on Green Communications and Networking (2019).
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