相关论文: DRL-Based Dynamic Channel Access and SCLAR Maximiz…
Semantic communication (SemCom) aims to transmit only task-relevant information, thereby improving communication efficiency but also exposing semantic information to potential eavesdropping. In this paper, we propose a deep reinforcement…
We investigate THz communication uplink multiple access using cascaded intelligent reflecting surfaces (IRSs) assuming correlated channels. Two independent objectives to be achieved via adjusting the phases of the cascaded IRSs: 1)…
Deep reinforcement learning (DRL) has been increasingly employed to handle the dynamic and complex resource management in network slicing. The deployment of DRL policies in real networks, however, is complicated by heterogeneous cell…
With increasing density and heterogeneity in unlicensed wireless networks, traditional MAC protocols, such as carrier-sense multiple access with collision avoidance (CSMA/CA) in Wi-Fi networks, are experiencing performance degradation. This…
Attitude control of fixed-wing unmanned aerial vehicles (UAVs) is a difficult control problem in part due to uncertain nonlinear dynamics, actuator constraints, and coupled longitudinal and lateral motions. Current state-of-the-art…
In this chapter, the regulation of Unmanned Aerial Vehicle (UAV) communication network is investigated in the presence of dynamic changes in the UAV lineup and user distribution. We target an optimal UAV control policy which is capable of…
Existing studies on federated learning (FL) are mostly focused on system orchestration for static snapshots of the network and making static control decisions (e.g., spectrum allocation). However, real-world wireless networks are…
This paper investigates a new class of carrier-sense multiple access (CSMA) protocols that employ deep reinforcement learning (DRL) techniques for heterogeneous wireless networking, referred to as carrier-sense deep-reinforcement learning…
Deep Reinforcement Learning (DRL) has recently been proposed as a methodology to discover complex Active Flow Control (AFC) strategies [Rabault, J., Kuchta, M., Jensen, A., Reglade, U., & Cerardi, N. (2019): "Artificial neural networks…
This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware…
In future cell-free (or cell-less) wireless networks, a large number of devices in a geographical area will be served simultaneously in non-orthogonal multiple access scenarios by a large number of distributed access points (APs), which…
We present a novel Deep Reinforcement Learning (DRL) based policy to compute dynamically feasible and spatially aware velocities for a robot navigating among mobile obstacles. Our approach combines the benefits of the Dynamic Window…
Mobile users are prone to experience beam failure due to beam drifting in millimeter wave (mmWave) communications. Sensing can help alleviate beam drifting with timely beam changes and low overhead since it does not need user feedback. This…
Medium Access Control (MAC) protocols, essential for wireless networks, are typically manually configured. While deep reinforcement learning (DRL)-based protocols enhance task-specified network performance, they suffer from poor…
The proper setting of contention window (CW) values has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is not scalable enough to maintain stable throughput for an…
The growing performance demands and higher deployment densities of next-generation wireless systems emphasize the importance of adopting strategies to manage the energy efficiency of mobile networks. In this demo, we showcase a framework…
As the quantity and complexity of information processed by software systems increase, large-scale software systems have an increasing requirement for high-performance distributed computing systems. With the acceleration of the Internet in…
In different situations, like disaster communication and network connectivity for rural locations, unmanned aerial vehicles (UAVs) could indeed be utilized as airborne base stations to improve both the functionality and coverage of…
Network slicing is a key enabler for providing a differentiated service support to heterogeneous use cases and applications in 5G and beyond networks through creating multiple logical slices. Resource allocation for satisfying diverse…
Co-existence of 5G New Radio (5G-NR) with IoT devices is considered as a promising technique to enhance the spectral usage and efficiency of future cellular networks. In this paper, a unified framework has been proposed for allocating…