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Found 13 papers, 1 papers with code
RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing
Nevertheless, resource-constrained IoT devices and the intricacies of deep neural networks necessitate transmitting CSI to cloud servers for sensing.
Toward Energy Efficient Multiuser IRS-Assisted URLLC Systems: A Novel Rank Relaxation Method
An alternating optimization (AO) approach is then used to solve the problem.
A Tractable Handoff-aware Rate Outage Approximation with Applications to THz-enabled Vehicular Network Optimization
The CAVs' traffic flow is modeled using Log-Normal distribution.
Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching
This paper presents a deep reinforcement learning solution for optimizing multi-UAV cell-association decisions and their moving velocity on a 3D aerial highway.
Power Control with QoS Guarantees: A Differentiable Projection-based Unsupervised Learning Framework
Utilizing a differentiable projection function, two novel deep learning (DL) solutions are pursued.
Dynamic Unicast-Multicast Scheduling for Age-Optimal Information Dissemination in Vehicular Networks
This paper investigates the problem of minimizing the age-of-information (AoI) and transmit power consumption in a vehicular network, where a roadside unit (RSU) provides timely updates about a set of physical processes to vehicles.
Liquid State Machine-Empowered Reflection Tracking in RIS-Aided THz Communications
It is numerically demonstrated that, in the first step, employing the Xavier initialization technique to fine-tune the LSM results in at most 26% lower LSM prediction variance and as much as 46% achievable spectral efficiency (SE) improvement over the existing counterparts, when an RIS of size 11x11 is deployed.
Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies
It is thus critical to simultaneously optimize the AVs' network selection and driving policies in order to minimize road collisions while maximizing the communication data rates.
Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks
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.
Deep Unsupervised Learning for Generalized Assignment Problems: A Case-Study of User-Association in Wireless Networks
More specifically, we propose a new approach that facilitates to train a deep neural network (DNN) using a customized loss function.
Joint Transmission in QoE-Driven Backhaul-Aware MC-NOMA Cognitive Radio Network
In particular, we formulate a joint optimization problem of power control and scheduling (i. e., user association and subcarrier allocation) in secondary tier to maximize total achievable QoE for the secondary users.
Multi-Objective Energy Efficient Resource Allocation and User Association for In-band Full Duplex Small-Cells
In this paper, we develop a framework to maximize the network energy efficiency (EE) by optimizing joint user-base station~(BS) association,~subchannel assignment, and power control considering an in-band full-duplex (IBFD)-enabled small-cell network.
Optimization of Wireless Relaying With Flexible UAV-Borne Reflecting Surfaces
This paper presents a theoretical framework to analyze the performance of integrated unmanned aerial vehicle (UAV)-intelligent reflecting surface (IRS) relaying system in which IRS provides an additional degree of freedom combined with the flexible deployment of full-duplex UAV to enhance communication between ground nodes.
