Search Results for author:
Found 36 papers, 6 papers with code
Offline and Distributional Reinforcement Learning for Wireless Communications
We focus on offline and distributional RL, two advanced RL techniques that can overcome these challenges by training on static datasets and accounting for network uncertainties.
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning
In addition, it is the only algorithm that can achieve optimal joint AoI and transmission power using an offline dataset with few shots of data points and is resilient to network failures due to unprecedented environmental changes.
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks
Age-of-information (AoI) and transmission power are crucial performance metrics in low energy wireless networks, where information freshness is of paramount importance.
An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management
Offline multi-agent reinforcement learning (MARL) addresses key limitations of online MARL, such as safety concerns, expensive data collection, extended training intervals, and high signaling overhead caused by online interactions with the environment.
MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion
Accurate indoor localization remains challenging due to variations in wireless signal environments and limited data availability.
An Analysis of Minimum Error Entropy Loss Functions in Wireless Communications
This paper introduces the minimum error entropy (MEE) criterion as an advanced information-theoretic loss function tailored for deep learning applications in wireless communications.
Offline and Distributional Reinforcement Learning for Radio Resource Management
Online RL has been adopted for radio resource management (RRM), taking over traditional schemes.
On the Spectral Efficiency of Movable and Rotary Antenna Arrays under Rician Fading
We also analyze the performance of a system in which the array is able to both move and rotate.
Neural Network-Based Bandit: A Medium Access Control for the IIoT Alarm Scenario
Efficient Random Access (RA) is critical for enabling reliable communication in Industrial Internet of Things (IIoT) networks.
Distributed MIMO Networks with Rotary ULAs for Indoor Scenarios under Rician Fading
Nevertheless, current 5G networks cannot fully meet the very stringent requirements regarding latency, reliability, and number of connected devices of most MTC use cases.
Activity Detection for Massive Random Access using Covariance-based Matching Pursuit
In general, fewer MTDs are simultaneously activated across the network, resembling targeted sampling in compressed sensing.
A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehicles
We propose a convolutional autoencoder (CAE) that performs the semantic encoding of the road traffic signs.
Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning
When only offline data is available, a direct application of online MARL schemes would generally fail due to the epistemic uncertainty entailed by the lack of exploration during training.
Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models
In this paper, we present a novel learning-based framework that estimates the traffic arrival of IoT devices based on Markovian events.
Energy-Efficient Analog Beamforming for RF-WET with Charging Time Constraint
Internet of Things (IoT) sustainability may hinge on radio frequency wireless energy transfer (RF-WET).
Energy Efficiency Optimization for Subterranean LoRaWAN Using A Reinforcement Learning Approach: A Direct-to-Satellite Scenario
The integration of subterranean LoRaWAN and non-terrestrial networks (NTN) delivers substantial economic and societal benefits in remote agriculture and disaster rescue operations.
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach
In this paper, we apply multi-agent deep reinforcement learning to address the high-dimensional problem that results from deploying a swarm of UAVs to collect fresh information from IoT devices.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
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Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications
Compressed sensing multi-user detection (CS-MUD) algorithms play a key role in optimizing grant-free (GF) non-orthogonal multiple access (NOMA) for massive machine-type communications (mMTC).
LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case
This paper presents a detailed description of the Smart Campus dataset based on LoRaWAN.
Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Recharge
We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption.
Joint Coherent and Non-Coherent Detection and Decoding Techniques for Heterogeneous Networks
Cellular networks that are traditionally designed for human-type communication (HTC) have the potential to provide cost effective connectivity to machine-type communication (MTC).
A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks
Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion.
Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long-Short Term Memory Prediction
Reducing energy consumption is a pressing issue in low-power machine-type communication (MTC) networks.
Statistical Analysis of Received Signal Strength in Industrial IoT Distributed Massive MIMO Systems
The Fifth Generation (5G) of wireless networks introduced native support for Machine-Type Communication (MTC), which is a key enabler for the Internet of Things (IoT) revolution.
User Activity Detection and Channel Estimation of Spatially Correlated Channels via AMP in Massive MTC
This paper addresses the problem of joint user identification and channel estimation (JUICE) for grant-free access in massive machine-type communications (mMTC).
A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory
A Coupled Markov Modulated Poisson Process (CMMPP) traffic model with mixed alarm and regular traffic is applied to compare the proposed FUG allocation to other existing allocation techniques.
Massive Wireless Energy Transfer with Statistical CSI Beamforming
A paramount benefit of the constrained precoder is the encompassment of fairness in the power allocation to the different clusters.
Joint Channel Estimation and Device Activity Detection in Heterogeneous Networks
Internet of Things (IoT) has triggered a rapid increase in the number of connected devices and new use cases of wireless communications.
Effective Energy Efficiency of Ultra-reliable Low Latency Communication
Effective Capacity defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between effective capacity and power consumption.
Information Theory Information Theory
Network Slicing for eMBB and mMTC with NOMA and Space Diversity Reception
In this work we study the coexistence in the same Radio Access Network (RAN) of two generic services present in the Fifth Generation (5G) of wireless communication systems: enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC).
On the SIR Meta Distribution in Massive MTCNetworks with Scheduling and Data Aggregation
Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs).
Non-Orthogonal Multiple Access and Network Slicing: Scalable Coexistence of eMBB and URLLC
The diverse requirements of these services in terms of data-rates, number of connected devices, latency and reliability can lead to a sub-optimal use of the 5G network, thus network slicing is proposed as a solution that creates customized slices of the network specifically designed to meet the requirements of each service.
On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer
In this paper, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint.
On CSI-free Multi-Antenna Schemes for Massive RF Wireless Energy Transfer
We characterize the distribution of the provided energy under correlated Rician fading for each scheme and find out that while AA-IS and SA cannot take advantage of the multiple antennas to improve the average provided energy, its dispersion can be significantly reduced.
Hybrid Coded Replication in LoRa Networks
Low Power Wide Area Networks (LPWAN) are wireless connectivity solutions for Internet-of-Things (IoT) applications, including industrial automation.
Networking and Internet Architecture Signal Processing
Storage Management in Modern Electricity Power Grids
This letter introduces a method to manage energy storage in electricity grids.
Systems and Control
