Outputs (7)

Smart Wireless Environment Enhanced Telecommunications: An Industrial Review on Network Stabilisation (2024)
Journal Article
Zhang, Y., Ziwenjere, K. M., Walker, A., Chen, T., You, M., Burton, F., Gradoni, G., & Zheng, G. (2025). Smart Wireless Environment Enhanced Telecommunications: An Industrial Review on Network Stabilisation. IEEE Network, 39(1), 21-29. https://doi.org/10.1109/MNET.2024.3484573

The 5G wireless network is now part of the critical national infrastructure of developed nations. Wireless network operators must strive to improve performance in order to be competitive, whilst reducing costs in order to be profitable. State-of-the-... Read More about Smart Wireless Environment Enhanced Telecommunications: An Industrial Review on Network Stabilisation.

Model-Free Optimization and Experimental Validation of RIS-Assisted Wireless Communications Under Rich Multipath Fading (2023)
Journal Article
Chen, T., You, M., Zhang, Y., Zheng, G., Gros, J. B., Lerosey, G., Nasser, Y., Burton, F., & Gradoni, G. (2024). Model-Free Optimization and Experimental Validation of RIS-Assisted Wireless Communications Under Rich Multipath Fading. IEEE Wireless Communications Letters, 13(3), 627-631. https://doi.org/10.1109/lwc.2023.3337709

Reconfigurable intelligent surface (RIS) devices have emerged as an effective way to control the propagation channels for enhancing the end-users' performance. However, RIS optimization involves configuring the radio frequency response of a large num... Read More about Model-Free Optimization and Experimental Validation of RIS-Assisted Wireless Communications Under Rich Multipath Fading.

Federated Learning Enabled Link Scheduling in D2D Wireless Networks (2023)
Journal Article
Chen, T., Zhang, X., You, M., Zheng, G., & Lambotharan, S. (2024). Federated Learning Enabled Link Scheduling in D2D Wireless Networks. IEEE Wireless Communications Letters, 13(1), 89-92. https://doi.org/10.1109/LWC.2023.3321500

Centralized machine learning methods for device-to-device (D2D) link scheduling may lead to a computing burden for a central server, transmission latency for decisions, and privacy issues for D2D communications. To mitigate these challenges, a federa... Read More about Federated Learning Enabled Link Scheduling in D2D Wireless Networks.

Towards Distributed Cell-less MIMO Testbed: An RFNoC Implementation (2022)
Presentation / Conference Contribution
You, M., Chen, T., & Zheng, G. (2022, June). Towards Distributed Cell-less MIMO Testbed: An RFNoC Implementation. Presented at 2022 EuCNC & 6G Summit, Grenoble, France

In this extended abstract, we will present a novel cell-less multiple-input multiple-output (MIMO) testbed design and an initial implementation based on the design. The key objective is to design a flexible and scalable architecture to facilitate the... Read More about Towards Distributed Cell-less MIMO Testbed: An RFNoC Implementation.

Graph Neural Network Based Beamforming in D2D Wireless Networks (2021)
Presentation / Conference Contribution
Chen, T., You, M., Zheng, G., & Lambotharan, S. (2021, November). Graph Neural Network Based Beamforming in D2D Wireless Networks. Presented at 25th International ITG Workshop on Smart Antennas (WSA 2021), Eurecom, French Riviera

An unsupervised graph neural network (GNN) approach is proposed to solve the beamforming design problem in device-to-device (D2D) wireless networks. Instead of directly learning the beamforming, the GNN is utilized to learn primal power and dual vari... Read More about Graph Neural Network Based Beamforming in D2D Wireless Networks.

A GNN-Based Supervised Learning Framework for Resource Allocation in Wireless IoT Networks (2021)
Journal Article
Chen, T., Zhang, X., You, M., Zheng, G., & Lambotharan, S. (2022). A GNN-Based Supervised Learning Framework for Resource Allocation in Wireless IoT Networks. IEEE Internet of Things Journal, 9(3), 1712-1724. https://doi.org/10.1109/JIOT.2021.3091551

The Internet of Things (IoT) allows physical devices to be connected over the wireless networks. Although device-to-device (D2D) communication has emerged as a promising technology for IoT, the conventional solutions for D2D resource allocation are u... Read More about A GNN-Based Supervised Learning Framework for Resource Allocation in Wireless IoT Networks.

Delay Guaranteed Joint User Association and Channel Allocation for Fog Radio Access Networks (2021)
Journal Article
You, M., Zheng, G., Chen, T., Sun, H., & Chen, K.-C. (2021). Delay Guaranteed Joint User Association and Channel Allocation for Fog Radio Access Networks. IEEE Transactions on Wireless Communications, 20(6), 3723-3733. https://doi.org/10.1109/twc.2021.3053155

In the Fog Radio Access Networks (F-RANs), the local storage and computing capability of Fog Access Points (FAPs) provide new communication resources to address the latency and computing constraints for delay-sensitive applications. To achieve the ul... Read More about Delay Guaranteed Joint User Association and Channel Allocation for Fog Radio Access Networks.