All Outputs (15)

Combinatorial Optimization of Reconfigurable Intelligence Surfaces at Wireless Endpoints using the Ising Hamiltonian Model (2023)
Presentation / Conference Contribution
Ross, C., Lim, Q., You, M., Gradoni, G., & Peng, Z. (2023, July). Combinatorial Optimization of Reconfigurable Intelligence Surfaces at Wireless Endpoints using the Ising Hamiltonian Model. Presented at IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (2023), Portland, Oregon, USA

The reconfigurable intelligent surface (RIS) based on discrete meta-surfaces with tunable elements has been widely studied in wireless communication and electromagnetics communities. Researchers have devoted substantial efforts to investigating large... Read More about Combinatorial Optimization of Reconfigurable Intelligence Surfaces at Wireless Endpoints using the Ising Hamiltonian Model.

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. Paper presented at 2022 EuCNC & 6G Summit, Grenoble, France & online

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
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 Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming
Presentation / Conference Contribution
You, M., Zheng, G., & Sun, H. (2021, June). A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming. Presented at ICC 2021 - IEEE International Conference on Communications, Montreal, Canada

This paper studies the long-standing problem of outage-constrained robust downlink beamforming in multi-user multi-antenna wireless communications systems. State of the art solutions have very high computational complexity which poses a major challen... Read More about A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming.

Real-Time Price Elasticity Reinforcement Learning for Low Carbon Energy Hub Scheduling Based on Conditional Random Field
Presentation / Conference Contribution
Hua, W., You, M., & Sun, H. (2019, August). Real-Time Price Elasticity Reinforcement Learning for Low Carbon Energy Hub Scheduling Based on Conditional Random Field. Presented at 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops), Changchun, China

Energy hub scheduling plays a vital role in optimally integrating multiple energy vectors, e.g., electricity and gas, to meet both heat and electricity demand. A scalable scheduling model is needed to adapt to various energy sources and operating con... Read More about Real-Time Price Elasticity Reinforcement Learning for Low Carbon Energy Hub Scheduling Based on Conditional Random Field.

A Cognitive Radio Enabled Smart Grid Testbed Based on Software Defined Radio and Real Time Digital Simulator
Presentation / Conference Contribution
You, M., Liu, Q., & Sun, H. (2018, May). A Cognitive Radio Enabled Smart Grid Testbed Based on Software Defined Radio and Real Time Digital Simulator. Presented at 2018 IEEE International Conference on Communications Workshops (ICC Workshops), Kansas City, MO, USA

With the development of Smart Grid, there is an increasing need for the inter discipline research, analysis and evaluation, especially in the joint research area of communication system and power system. In this paper, we propose a Cognitive Radio en... Read More about A Cognitive Radio Enabled Smart Grid Testbed Based on Software Defined Radio and Real Time Digital Simulator.

Energy Hub Scheduling Method with Voltage Stability Considerations
Presentation / Conference Contribution
You, M., Hua, W., Shahbazi, M., & Sun, H. (2018, August). Energy Hub Scheduling Method with Voltage Stability Considerations. Presented at IEEE/CIC International Conference on Communications in China (ICCC Workshops 2018), Beijing, China

Energy Hub is expected to be one of the most effective methods to address the integrated system with multiple energy carriers. In this work, an Energy Hub scheduling method is proposed, which could not only meet various energy load demands but also a... Read More about Energy Hub Scheduling Method with Voltage Stability Considerations.

L-Index Sensitivity Based Voltage Stability Enhancement
Presentation / Conference Contribution
Liu, Q., You, M., Sun, H., & Matthews, P. (2017, June). L-Index Sensitivity Based Voltage Stability Enhancement. Presented at 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW

Voltage stability is a long standing issue in power systems. Due to the requirements of on-line monitoring and high computation efficiency, L-index is used as voltage stability metric in this paper. We propose a novel L-index sensitivity based contro... Read More about L-Index Sensitivity Based Voltage Stability Enhancement.

Power grid observability redundancy analysis under communication constraints
Presentation / Conference Contribution
You, M., Liu, Q., Jiang, J., & Sun, H. (2017, October). Power grid observability redundancy analysis under communication constraints. Presented at 2017 IEEE/CIC International Conference on Communications in China (ICCC), Qingdao, China

The Phasor Measurement Units (PMUs) have largely empowered the current and future smart grid applications, which play important roles in both power systems and Information and Communication Technology (ICT) systems within smart grid. They can provide... Read More about Power grid observability redundancy analysis under communication constraints.

Realising energy-aware communication over fading channels under QoS constraints
Presentation / Conference Contribution
You, M., & Sun, H. (2016, October). Realising energy-aware communication over fading channels under QoS constraints. Presented at 2016 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), Nanjing, China

There exists a trade-off between energy consumption and spectral efficiency in wireless communication systems under quality of service (QoS) constraints. This paper studies the use of effective capacity theory to characterise the maximum supported ch... Read More about Realising energy-aware communication over fading channels under QoS constraints.

Effective capacity analysis of smart grid communication networks
Presentation / Conference Contribution
You, M., Mou, X., & Sun, H. (2015, September). Effective capacity analysis of smart grid communication networks. Presented at 2015 IEEE 20th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), Guildford, United Kingdom

Smart grid represents a significant new technology of improving the efficiency, reliability and economics of the production, transmission and distribution of electricity that helps reduce carbon emissions. Communication networks become a key to achie... Read More about Effective capacity analysis of smart grid communication networks.

Protecting privacy in microgrids using federated learning and deep reinforcement learning
Presentation / Conference Contribution
Chen, W., Sun, H., Jiang, J., You, M., & Piper, W. J. (2022, November). Protecting privacy in microgrids using federated learning and deep reinforcement learning. Presented at 12th IET International Conference on Advances in Power System Control, Operation and Management, Hyatt Regency Tsim Sha Tsui, Hong Kong and Online

This paper aims to improve the energy management efficiency of home microgrids while preserving privacy. The proposed microgrid model includes energy storage systems, PV panels, loads, and the connection to the main grid. A federated multi-objective... Read More about Protecting privacy in microgrids using federated learning and deep reinforcement learning.

An Unsupervised Domain Adaptation-Based Device-Free Sensing Approach for Cross-Weather Target Recognition in Foliage Environments
Presentation / Conference Contribution
Zhong, Y., Bi, T., Wang, J., You, M., & Jiang, T. (2023, May). An Unsupervised Domain Adaptation-Based Device-Free Sensing Approach for Cross-Weather Target Recognition in Foliage Environments. Presented at 12th International Conference on Communications, Signal Processing, and Systems, Singapore (and online)

In recent years, the emerging technique of device-free sensing (DFS) has gained popularity for foliage penetration (FOPEN) target recognition. This popularity is primarily attributed to its inherent advantage of not requiring specialized sensing equi... Read More about An Unsupervised Domain Adaptation-Based Device-Free Sensing Approach for Cross-Weather Target Recognition in Foliage Environments.

Privacy Leakage in Federated Home Applications Using Gradient Inversion Algorithms
Presentation / Conference Contribution
Chen, W., Sun, H., You, M., & Jiang, J. (2024, March). Privacy Leakage in Federated Home Applications Using Gradient Inversion Algorithms. Presented at 2024 International Conference on Industrial Technology (ICIT), Bristol, UK

With advances in smart metering infrastructure, household electricity metering data are remotely collected, leading to concerns about household privacy leakage. Federated learning is a promising solution because it avoids direct data uploading. Howev... Read More about Privacy Leakage in Federated Home Applications Using Gradient Inversion Algorithms.

Accurate Action Recommendations and Demand Response for Smart Homes via Knowledge Graphs
Presentation / Conference Contribution
Chen, W., Sun, H., You, M., & Jiang, J. (2024, March). Accurate Action Recommendations and Demand Response for Smart Homes via Knowledge Graphs. Presented at The 2024 International Conference on Industrial Technology (ICIT), Bristol, UK

Accurate action recommendations can enhance the convenience of daily life, such as automatically turning on the dining area lights during meals or playing music based on residential habits. Generating precise recommendations for the next household de... Read More about Accurate Action Recommendations and Demand Response for Smart Homes via Knowledge Graphs.