All Outputs (7)

Design and Analysis of SWIPT With Safety Constraints (2021)
Journal Article
Psomas, C., You, M., Liang, K., Zheng, G., & Krikidis, I. (2022). Design and Analysis of SWIPT With Safety Constraints. Proceedings of the IEEE, 110(1), 107-126. https://doi.org/10.1109/jproc.2021.3130084

Simultaneous wireless information and power transfer (SWIPT) has long been proposed as a key solution for charging and communicating with low-cost and low-power devices. However, the employment of radio frequency (RF) signals for information/power tr... Read More about Design and Analysis of SWIPT With Safety Constraints.

Graph Neural Network Based Beamforming in D2D Wireless Networks (2021)
Conference Proceeding
Chen, T., You, M., Zheng, G., & Lambotharan, S. (2021). Graph Neural Network Based Beamforming in D2D Wireless Networks. In WSA 2021: 25th International ITG Workshop on Smart Antennas : 10-21 November, 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.

Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties (2021)
Journal Article
You, M., Wang, Q., Sun, H., Castro, I., & Jiang, J. (2022). Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties. Applied Energy, 305, Article 117899. https://doi.org/10.1016/j.apenergy.2021.117899

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT’s predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission r... Read More about Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties.

Model-driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information (2021)
Journal Article
Zhang, J., You, M., Zheng, G., Krikidis, I., & Zhao, L. (2022). Model-driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information. IEEE Transactions on Wireless Communications, 21(4), 2368-2382. https://doi.org/10.1109/twc.2021.3111843

Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, wh... Read More about Model-driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information.

A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming (2021)
Conference Proceeding
You, M., Zheng, G., & Sun, H. (2021). A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming. . https://doi.org/10.1109/ICC42927.2021.9500736

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.

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. (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.