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Reliable and secure communication is essential for mission-critical aerospace and defence operations involving autonomous platforms such as Unmanned Aerial Vehicles (UAVs), satellites, and ground control systems. In contested or dynamic…
Both data ferrying with disruption-tolerant networking (DTN) and mobile cellular base stations constitute important techniques for UAV-aided communication in situations of crises where standard communication infrastructure is unavailable.…
With the development of 5G, innovative applications requiring bounded transmission delays and zero packet loss emerge, e.g., AR, industrial automation, and smart grid. In this circumstance, time-sensitive networking (TSN) is proposed, which…
With the growing popularity of mobile smart devices, the existing networks are unable to meet the requirement of many complex scenarios; current network architectures and protocols do not work well with the network with high latency and…
We present preliminary results from our sixth placed entry to the Flatland international competition for train rescheduling, including two improvements for optimized reinforcement learning (RL) training efficiency, and two hypotheses with…
For unforeseen emergencies, such as natural disasters and pandemic events, it is highly demanded to cope with the explosive growth of mobile data traffic in extremely critical environments. An Unmanned aerial vehicle (UAV) fleet is an…
The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. However, the massive increases in Machine-type Communication…
The fifth-generation (5G) communication systems will enable enhanced mobile broadband, ultra-reliable low latency, and massive connectivity services. The broadband and low-latency services are indispensable to public safety (PS)…
Networking of microgrids can provide the operational flexibility needed for the increasing number of DERs deployed at the distribution level and supporting end-use demand when there is loss of the bulk power system. But, networked…
Deep reinforcement learning (DRL) has been shown to be successful in many application domains. Combining recurrent neural networks (RNNs) and DRL further enables DRL to be applicable in non-Markovian environments by capturing temporal…
Consider planning a trip in a train network. In contrast to, say, a road network, the edges are temporal, i.e., they are only available at certain times. Another important difficulty is that trains, unfortunately, sometimes get delayed.…
Accurate real-time traffic forecast is critical for intelligent transportation systems (ITS) and it serves as the cornerstone of various smart mobility applications. Though this research area is dominated by deep learning, recent studies…
Deep neural network (DNN)-based receivers offer a powerful alternative to classical model-based designs for wireless communication, especially in complex and nonlinear propagation environments. However, their adoption is challenged by the…
Remaining useful life (RUL) estimation is a crucial component in the implementation of intelligent predictive maintenance and health management. Deep neural network (DNN) approaches have been proven effective in RUL estimation due to their…
Unmanned Aerial Vehicles (UAVs) are increasingly adopted in modern communication networks. However, challenges in decision-making and digital modeling continue to impede their rapid advancement. Reinforcement Learning (RL) algorithms face…
The integration of accurate and reproducible wireless network simulations is a key enabler for research on open, virtualized, and intelligent communication systems. Network Digital Twins (NDTs) provide a scalable alternative to costly and…
Water distribution networks (WDNs) are one of the most important man-made infrastructures. Resilience, the ability to respond to disturbances and recover to a desirable state, is of vital importance to our society. There is increasing…
Recently, Unmanned Aerial Vehicle (UAV) based communications systems have attracted increasing research and commercial interest due to their cost effective deployment and ease of mobility.During natural disasters and emergencies, such…
Nodes movements play a significant role in disseminating messages in the sparse mobile ad-hoc network. In the network scenarios, where traditional end-to-end paths do not exist, mobility creates opportunities for nodes to connect and…
We propose a novel deep neural network (DNN) based approximation architecture to learn estimates of measurements. We detail an algorithm that enables training of the DNN. The DNN estimator only uses measurements, if and when they are…