Energy-Conscious Operation: Network Efficiency for Wireless Sustainability

Background

As radio communication infrastructures are moving beyond their fifth generation, they are becoming increasingly convoluted and heterogeneous in pursuit of accommodating a wider variety of demanding use cases. The many deployment-ready options and mechanisms, if not prioritized and optimized with respect to the underlying applications, threaten to degrade system efficiency by complicating the operations unnecessarily. Application of Artificial Intelligence (AI) and Machine Learning (ML) does, on the one hand, promise to improve system performance and satisfy dissimilar service requirements, but it also risks compromising the fundamental demand for system sustainability.

In this research project, we deeply explore the connection between information and energy in emerging intelligent wireless networks to reveal their fundamental limits, scaling laws, and tradeoffs. Our goal is to offer a novel perspective on the sustainable evolution of wireless communications technology to (i) reduce energy consumption and dissipation in large-scale semantics-aware systems, (ii) efficiently manage energy-performance tradeoffs in different time scales and applications, and (iii) optimize communication protocols subject to the communication environment uncertainty.

In sharp contrast to prior research on energy efficiency in wireless networks, we focus on a system-wide perspective considering system goals and various network deployment and management options, including network scale, dynamics, and computing capabilities.  The central research outcomes are comprehensive methodology, algorithms, system bounds, optimal policies, and predictions that will enable intelligent, reliable, in-time control in 5G and beyond wireless networks to achieve sustainable operation of dedicated goal-oriented intelligent systems. These outcomes are all novel, indicating a huge potential for scientific breakthroughs and for promoting scientific renewal, as well as technological innovation. The results of ECO-NEWS  are expected to support the development of truly sustainable ICT in the near future. Therefore, ECO-NEWS is well aligned with the UN Sustainable Development Goals.

Principal investigators

Prof. Pedro Nardelli (LUT University)

Prof. Sergey Andreev (Tampere University)

Publications

Journal

1. Dester, P. S., Costa, M., Nardelli, P. H., Silva, P. E. G., & Moualeu, J. M. (2024). Novel Energy-Centric Analysis for Random Access Networks. IEEE Wireless Communications Letters.

2. Silva, P. E. G., Dester, P. S., Siljak, H., Marchetti, N., Nardelli, P. H., & de Souza, R. A. (2024). A novel semantic-functional approach for multiuser event-trigger communication. Ad Hoc Networks, 159, 103496.

3. Sadovaya, Y., Vikhrova, O., Andreev, S., & Yanikomeroglu, H. (2024). Enhancing Service Continuity in Non-Terrestrial Networks via Multi-Connectivity Offloading. IEEE Communications Letters.

4. Tafintsev, N., Moltchanov, D., Mao, W., Nikopour, H., Yeh, S. P., Talwar, S., ... & Andreev, S. (2024). Analysis of Duplexing Patterns in Multi-Hop mmWave Integrated Access and Backhaul Systems. IEEE Open Journal of the Communications Society.

5. Ali, A., Galinina, O., Hosek, J., & Andreev, S. (2024). Effects of Small-Scale User Mobility on Highly Directional XR Communications. IEEE Communications Magazine, 62(8), 16-22.

6. Sadovaya, Y., Vikhrova, O., Mao, W., Yeh, S. P., Semiari, O., Nikopour, H., ... & Andreev, S. (2024). Delay-Aware Link Scheduling in IAB Networks with Dynamic User Demands. IEEE Transactions on Vehicular Technology.

7. Gutierrez-Rojas, D., Kalalas, C., Christou, I., Almeida, G., Eldeeb, E., Bakri, S., ... & Nardelli, P. H. (2024). Detection and Classification of Anomalies in WSN-enabled Cyber-physical Systems. IEEE Sensors Journal.

8. Kolackova, A., Phan, V. A., Jerabek, J., Andreev, S., & Hosek, J. (2025). ML-Driven Energy Savings for Cellular Baseband Units via Traffic Prediction. IEEE Open Journal of the Communications Society.

9. Ponomarenko-Timofeev, A., Galinina, O., Balakrishnan, R., Himayat, N., Andreev, S., & Koucheryavy, Y. (2025). Multi–Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks. IEEE Transactions on Mobile Computing.

10. Chukhno, N., Saafi, S., & Andreev, S. (2025). ML-Aided Dynamic BSR Periodicity Adjustment for Enhanced UL Scheduling in Cellular Systems. IEEE Open Journal of the Communications Society.

11. Kirubakaran, B., Vikhrova, O., Andreev, S., & Hosek, J. (2025). UAV-BS Integration with Urban Infrastructure: An Energy Efficiency Perspective. IEEE Communications Magazine.

12. Melgarejo, D. C., Kumar, S., Fraidenraich, G., & Nardelli, P. H. (2025). Analytical derivation of the SINR for GFDM signals on Rician fading channels with MMSE receiver. IEEE Transactions on Vehicular Technology.

13. Yadav, K., Upadhyay, P. K., Moualeu, J. M., Osman, A. A., & Nardelli, P. H. (2025). Deep Learning-Based Secrecy Performance of UAV-IRS NOMA Systems with Friendly Jamming. IEEE Open Journal of the Communications Society.

14. Cheepurupalli, S., Egu, D. K., Upadhyay, P. K., Salhab, A. M., Moualeu, J. M., & Nardelli, P. H. (2025). Deep Learning-Enabled Secrecy Performance Analysis of UAV-Aided Reconfigurable Intelligent Surfaces With Non-Orthogonal Multiple Access. IEEE Transactions on Cognitive Communications and Networking.

15. Souza, D. D., Freitas, M. M., Fernandes, A. L., Nardelli, P. H., da Costa, D. B., Cavalcante, A. M., & Costa, J. C. W. A. (2025). Trajectory Optimization in User-Centric Distributed Massive MIMO Systems Enabled by UAV Swarms. IEEE Transactions on Vehicular Technology.

Conference

1. Gória Silva, P. E., Moualeu, J. M., Nardelli, P. H., Li, Y., & de Souza, R. A. (2024, June). An Efficient Machine Learning-Based Channel Prediction Technique for OFDM Sub-Bands. In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring) (pp. 1-5). IEEE.

2. Brancati, G., Chukhno, O., Molinaro, A., Andreev, S., & Araniti, G. (2024, June). Performance Assessment of 6G XR Applications in Realistic Urban RIS-Aided Environment. In 2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) (pp. 1-6). IEEE.

3. Sadovaya, Y., Moltchanov, D., Mao, W., Yeh, S. P., Semiari, O., Nikopour, H., ... & Andreev, S. (2024, June). Impact of System-Specific Factors on Scheduling and Resource Allocation in mmWave IAB Networks. In ICC 2024-IEEE International Conference on Communications (pp. 43-48). IEEE.

4. Yekaterina Sadovaya, Olga Vikhrova, Wei Mao, Omid Semiari, Shu-ping Yeh, Hosein Nikopour, Shilpa Talwar, Sergey Andreev: Distributed Delay-Aware Link Scheduling and Route Selection in mmWave IAB Networks. IEEE Globecom 2024

5. Iuliia Tropkina, Olga Galinina, Sergey Andreev, Robert W. Heath Jr.: Multi-Armed Bandits for Low-Complexity Beam Management in High-Speed mmWave IoT. IEEE Globecom Workshops 2024

6. Balaji Kirubakaran, Sergey Andreev, Jiri Hosek: Hierarchical Resource Management, Node Placement, and User Association in ATINs. IEEE ICC Workshops 2025

7. Silva, P. E. G., Moualeu, J. M., Marchetti, N., Gutierrez-Rojas, D., de Souza, R. A., & Nardelli, P. H. (2025, May). Cosine Phase Based Representation of Signals for Remote Monitoring in Multiuser Wireless Networks. In 2025 23rd International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) (pp. 111-117). IEEE.

8. Souza, D. D., Fernandes, A. L., Freitas, M. M., da Costa, D. B., Cavalcante, A. M., Nardelli, P. H., & Costa, J. C. (2025, March). Effective Channel Hybrid Estimation in User-Centric Distributed Massive MIMO Networks. In 2025 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 01-06). IEEE.

Doctoral thesis

1. Gória Silva, P. E. (2024). Communications in cyber-physical systems: semantic-functional approach, vulnerability, and physical layer performance. LUT.