Related papers: A Deep Reinforcement Learning-Based Resource Sched…

Millions of battery-powered sensors deployed for monitoring purposes in a multitude of scenarios, e.g., agriculture, smart cities, industry, etc., require energy-efficient solutions to prolong their lifetime. When these sensors observe a…

Management and orchestration (MANO) of resources by virtual network functions (VNFs) represents one of the key challenges towards a fully virtualized network architecture as envisaged by 5G standards. Current threshold-based policies…

We investigate the problem of supporting Industrial Internet of Things user equipment (IIoT UEs) with intent (i.e., requested quality of service (QoS)) and random traffic arrival. A deep reinforcement learning (DRL) based centralized…

In this paper, we consider the problem of joint user scheduling and dynamic pilot allocation in a Time-Division Duplex (TDD) based Massive MIMO network under varying traffic condition. One of the main problems with Massive MIMO systems is…

By enabling spectrum sharing between radar and communication operations, the cell-free dual-functional radar-communication (CF-DFRC) system is a promising candidate to significantly improve spectrum efficiency in future sixth-generation…

In this paper, a new user-scheduling-and-beamforming method is proposed for multi-user massive multiple-input multiple-output (massive MIMO) broadcast channels in the context of two-stage beamforming. The key ideas of the proposed…

We study the problem of real-time scheduling in a multi-hop millimeter-wave (mmWave) mesh. We develop a model-free deep reinforcement learning algorithm called Adaptive Activator RL (AARL), which determines the subset of mmWave links that…

Deep reinforcement learning (DRL) has demonstrated its potential in solving complex manufacturing decision-making problems, especially in a context where the system learns over time with actual operation in the absence of training data. One…

In this paper, we investigate a novel digital network twin (DNT) assisted deep learning (DL) model training framework. In particular, we consider a physical network where a base station (BS) uses several antennas to serve multiple mobile…

Developing resource allocation algorithms with strong real-time and high efficiency has been an imperative topic in wireless networks. Conventional optimization-based iterative resource allocation algorithms often suffer from slow…

We formulate throughput maximizing, max-min fair, weighted max-min fair, and proportionally fair scheduling problems for cognitive radio networks managed by a centralized cognitive base station. We propose a very general scheduling model…

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this…

In this paper, we study joint power control and scheduling in uplink massive multiple-input multiple-output (MIMO) systems with randomly arriving data traffic. We consider both co-located and Cell-Free (CF) Massive MIMO, where the…

This paper addresses key challenges in task scheduling for multi-tenant distributed systems, including dynamic resource variation, heterogeneous tenant demands, and fairness assurance. An adaptive scheduling method based on reinforcement…

Neural schedulers based on deep reinforcement learning (DRL) have shown considerable potential for solving real-world resource allocation problems, as they have demonstrated significant performance gain in the domain of cluster computing.…

Dependency-aware job scheduling in the cluster is NP-hard. Recent work shows that Deep Reinforcement Learning (DRL) is capable of solving it. It is difficult for the administrator to understand the DRL-based policy even though it achieves…

In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding client access and diversifying data enhance models by…

Integrated Access and Backhaul (IAB) is critical for dense 5G and beyond deployments, especially in mmWave bands where fiber backhaul is infeasible. We propose a novel Deep Reinforcement Learning (DRL) framework for joint link scheduling…

In millimeter wave (mmWave) systems, we investigate uplink user scheduling when a basestation employs low-resolution analog-to-digital converters (ADCs) with a large number of antennas. To reduce power consumption in the receiver,…

Clustered cell-free networking paves a new way for enabling scalable joint transmission among access points (APs) by partitioning the whole network into non-overlapping subnetworks. Previous works adopted clustering algorithms, graph…