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Deep reinforcement learning (DRL) has become a powerful tool for complex decision-making in machine learning and AI. However, traditional methods often assume perfect action execution, overlooking the uncertainties and deviations between an…

In the user-centric cell-free massive MIMO (UC-mMIMO) network scheme, user mobility necessitates updating the set of serving access points to maintain the user-centric clustering. Such updates are typically performed through handoff (HO)…

The integration of distributed energy resources (DER) has escalated the challenge of voltage magnitude regulation in distribution networks. Traditional model-based approaches, which rely on complex sequential mathematical formulations,…

The rapid uptake of intelligent applications is pushing deep learning (DL) capabilities to Internet-of-Things (IoT). Despite the emergence of new tools for embedding deep neural networks (DNNs) into IoT devices, providing satisfactory…

This paper presents a novel framework for traffic prediction of IoT devices activated by binary Markovian events. First, we consider a massive set of IoT devices whose activation events are modeled by an On-Off Markov process with known…

Advances in mobile computing capabilities and an increasing number of Internet of Things (IoT) devices have enriched the possibilities of the IoT but have also increased the cognitive load required of IoT users. Existing context-aware…

A resource-constrained unmanned aerial vehicle (UAV) can be used as a flying LoRa gateway (GW) to move inside the target area for efficient data collection and LoRa resource management. In this work, we propose deep reinforcement learning…

Unmanned Aerial Vehicles (UAVs) are increasingly being utilized in various private and commercial applications, e.g., traffic control, parcel delivery, and Search and Rescue (SAR) missions. Machine Learning (ML) methods used in UAV-Assisted…

Fog nodes in the vicinity of IoT devices are promising to provision low latency services by offloading tasks from IoT devices to them. Mobile IoT is composed by mobile IoT devices such as vehicles, wearable devices and smartphones. Owing to…

We apply deep reinforcement learning (DRL) to design of a networked controller with network delays to complete a temporal control task that is described by a signal temporal logic (STL) formula. STL is useful to deal with a specification…

In this paper, we propose a federated deep reinforcement learning framework to solve a multi-objective optimization problem, where we consider minimizing the expected long-term task completion delay and energy consumption of IoT devices.…

Developing deep learning models for resource-constrained Internet-of-Things (IoT) devices is challenging, as it is difficult to achieve both good quality of results (QoR), such as DNN model inference accuracy, and quality of service (QoS),…

Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining growing popularity recently. To solve the underlying task scheduling problem, the deep reinforcement learning (DRL) based methods demonstrate notable advantage over the…

This report investigates the application of deep reinforcement learning (DRL) algorithms for dynamic resource allocation in wireless communication systems. An environment that includes a base station, multiple antennas, and user equipment…

This paper addresses the critical issue of spectrum scarcity and the need to support diverse services, including communication and learning tasks, by presenting a reconfigurable intelligent surface (RIS)-aided wireless network framework…

Deep reinforcement learning (DRL) has long been a promising solution for sequential resource management in wireless networks. However, conventional DRL methods are fundamentally limited by their reliance on unimodal policy distributions,…

IoT networks often face conflicting routing goals such as maximizing packet delivery, minimizing delay, and conserving limited battery energy. These priorities can also change dynamically: for example, an emergency alert requires high…

Deep Reinforcement Learning (DRL) is regarded as a promising tool for optical network optimization. However, the flexibility and efficiency of current DRL-based solutions for optical network optimization require further improvement.…

Wireless support of virtual reality (VR) has challenges when a network has multiple users, particularly for 3D VR gaming, digital AI avatars, and remote team collaboration. This work addresses these challenges through investigation of the…

In this paper, a non-orthogonal multiple access (NOMA) based downlink vehicle-to-infrastructure network is considered. Particularly, we focus on the specific case of two users, one of which requires reliable road-safety-critical data…