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Found 78 papers, 8 papers with code
A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model
Notably, we find that inference costs and exogenous factors can have a significant impact on total budget.
Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP Standards
Recent studies show that large language models (LLMs) struggle with technical standards in telecommunications.
Beyond Diagonal RIS: Passive Maximum Ratio Transmission and Interference Nulling Enabler
In addition, at moderate to high signal to noise ratios (SNRs), passive interference nulling reduces the complexity at the BS by relaxing the need for precoding or water-filling power allocation design.
Goal-Oriented State Information Compression for Linear Dynamical System Control
In this paper, we consider controlled linear dynamical systems in which the controller has only access to a compressed version of the system state.
TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models
Large Language Models (LLMs) have the potential to revolutionize the Sixth Generation (6G) communication networks.
Generative AI for RF Sensing in IoT systems
The development of wireless sensing technologies, using signals such as Wi-Fi, infrared, and RF to gather environmental data, has significantly advanced within Internet of Things (IoT) systems.
Flexible Antenna Arrays for Wireless Communications: Modeling and Performance Evaluation
Flexible antenna arrays (FAAs), distinguished by their rotatable, bendable, and foldable properties, are extensively employed in flexible radio systems to achieve customized radiation patterns.
Large Language Models for Power Scheduling: A User-Centric Approach
While traditional optimization and scheduling schemes are designed to meet fixed, predefined system requirements, future systems are moving toward user-driven approaches and personalized services, aiming to achieve high quality-of-experience (QoE) and flexibility.
SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead
To optimize the pruning process itself, only thresholds are communicated between a server and clients instead of parameters, thereby learning how to prune.
Robust Image Semantic Coding with Learnable CSI Fusion Masking over MIMO Fading Channels
In this paper, we incorporate MIMO CSI into system designs from a new perspective and propose the learnable CSI fusion semantic communication (LCFSC) framework, where CSI is treated as side information by the semantic extractor to enhance the semantic coding.
From Single to Multi-Functional RIS: Architecture, Key Technologies, Challenges, and Applications
Although reconfigurable intelligent surfaces (RISs) have demonstrated the potential to boost network capacity and expand coverage by adjusting their electromagnetic properties, existing RIS architectures have certain limitations, such as double-fading attenuation and restricted half-space coverage.
Meta Reinforcement Learning for Resource Allocation in Unmanned Aerial Vehicles with MIMO Visible Light Communication
This paper centers around a multiple-input-multiple-output (MIMO) visible light communication (VLC) system, where an unmanned aerial vehicle (UAV) benefits from a light emitting diode (LED) array to serve photo-diode (PD)-equipped users for illumination and communication simultaneously.
Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence
Over the past year, there has been a robust debate about the benefits and risks of open sourcing foundation models.
Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G
These systems acquire common sense by exploiting different cognitive abilities such as perception, analogy, and reasoning, that enable them to generalize and deal with unforeseen scenarios.
Agent-driven Generative Semantic Communication with Cross-Modality and Prediction
In contrast to the existing research on semantic communication (SemCom), which mainly focuses on either semantic extraction or semantic sampling, we seamlessly integrate both by jointly considering the intrinsic attributes of source information and the contextual information regarding the task.
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse
With the aim of rigorously understanding model collapse in language models, we consider in this paper a statistical model that allows us to characterize the impact of various recursive training scenarios.
Channel Deduction: A New Learning Framework to Acquire Channel from Outdated Samples and Coarse Estimate
How to reduce the pilot overhead required for channel estimation?
GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning
Connecting GenAI agents over a wireless network can potentially unleash the power of collective intelligence and pave the way for artificial general intelligence (AGI).
CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledge
The results can serve as a reference for comparing the general cybersecurity knowledge of humans and LLMs.
Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach
The energy consumption of mobile networks poses a critical challenge.
Internet of Federated Digital Twins (IoFDT): Connecting Twins Beyond Borders for Society 5.0
The concept of digital twin (DT), which enables the creation of a programmable, digital representation of physical systems, is expected to revolutionize future industries and will lie at the heart of the vision of a future smart society, namely, Society 5. 0, in which high integration between cyber (digital) and physical spaces is exploited to bring economic and societal advancements.
Do VSR Models Generalize Beyond LRS3?
The Lip Reading Sentences-3 (LRS3) benchmark has primarily been the focus of intense research in visual speech recognition (VSR) during the last few years.
UAV Immersive Video Streaming: A Comprehensive Survey, Benchmarking, and Open Challenges
However, achieving this immersive experience necessitates encoding omnidirectional videos in high resolution, leading to increased bitrates.
TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge
Afterwards, using the provided dataset, an evaluation is conducted to assess the capabilities of LLMs, including GPT-3. 5 and GPT-4.
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks
We showcase how incorporating causal discovery can assist in achieving dynamic adaptability, resilience, and cognition in addressing these challenges.
Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks
In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption.
Joint Semantic-Native Communication and Inference via Minimal Simplicial Structures
Specifically, the teacher first maps its data into a k-order simplicial complex and learns its high-order correlations.
Lip2Vec: Efficient and Robust Visual Speech Recognition via Latent-to-Latent Visual to Audio Representation Mapping
Visual Speech Recognition (VSR) differs from the common perception tasks as it requires deeper reasoning over the video sequence, even by human experts.
Large Language Models for Telecom: Forthcoming Impact on the Industry
Large Language Models (LLMs), AI-driven models that can achieve general-purpose language understanding and generation, have emerged as a transformative force, revolutionizing fields well beyond Natural Language Processing (NLP) and garnering unprecedented attention.
Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive Review
Different from earlier surveys, this paper presents a comprehensive treatment and technology survey on THz communications and sensing in terms of advantages, applications, propagation characterization, channel modeling, measurement campaigns, antennas, transceiver devices, beamforming, networking, the integration of communications and sensing, and experimental testbeds.
SecureFalcon: Are We There Yet in Automated Software Vulnerability Detection with LLMs?
SecureFalcon achieves 94% accuracy in binary classification and up to 92% in multiclassification, with instant CPU inference times.
Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices
The field of Natural Language Processing (NLP) is currently undergoing a revolutionary transformation driven by the power of pre-trained Large Language Models (LLMs) based on groundbreaking Transformer architectures.
Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework
Most existing works focus on transmitting and delivering the explicit semantic meaning that can be directly identified from the source signal.
Large Generative AI Models for Telecom: The Next Big Thing?
To be specific, large GenAI models are envisioned to open up a new era of autonomous wireless networks, in which multi-modal GenAI models trained over various Telecom data, can be fine-tuned to perform several downstream tasks, eliminating the need for building and training dedicated AI models for each specific task and paving the way for the realization of artificial general intelligence (AGI)-empowered wireless networks.
Understanding Telecom Language Through Large Language Models
In particular, we fine-tune several LLMs including BERT, distilled BERT, RoBERTa and GPT-2, to the Telecom domain languages, and demonstrate a use case for identifying the 3rd Generation Partnership Project (3GPP) standard working groups.
A Nested Matrix-Tensor Model for Noisy Multi-view Clustering
In order to study the theoretical performance of this approach, we characterize the behavior of this best rank-one approximation in terms of the alignments of the obtained component vectors with the hidden model parameter vectors, in the large-dimensional regime.
Unleashing 3D Connectivity in Beyond 5G Networks with Reconfigurable Intelligent Surfaces
Reconfigurable intelligent surfaces (RISs) bring various benefits to the current and upcoming wireless networks, including enhanced spectrum and energy efficiency, soft handover, transmission reliability, and even localization accuracy.
Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences
Second, a non-autoregressive multi-resolution generative artificial intelligence (AI) framework integrated with an adversarial transformer is proposed to predict missing and future sensing information.
The Seven Worlds and Experiences of the Wireless Metaverse: Challenges and Opportunities
We then articulate how these experiences bring forth interactions between diverse metaverse constituents, namely, a) humans and avatars and b) connected intelligence systems and their digital twins (DTs).
Low Complexity Optimization for Line-of-Sight RIS-Aided Holographic Communications
The synergy of metasurface-based holographic surfaces (HoloS) and reconfigurable intelligent surfaces (RIS) is considered a key aspect for future communication networks.
Regularization of the policy updates for stabilizing Mean Field Games
This work studies non-cooperative Multi-Agent Reinforcement Learning (MARL) where multiple agents interact in the same environment and whose goal is to maximize the individual returns.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study
However, we need to also consider the potential of attacks targeting the underlying AI systems (e. g., adversaries seek to corrupt data on the IoT devices during local updates or corrupt the model updates); hence, in this article, we propose an anticipatory study for poisoning attacks in federated edge learning for digital twin 6G-enabled IoT environments.
Robust Millimeter Beamforming via Self-Supervised Hybrid Deep Learning
Beamforming with large-scale antenna arrays has been widely used in recent years, which is acknowledged as an important part in 5G and incoming 6G.
Harris Hawks Feature Selection in Distributed Machine Learning for Secure IoT Environments
Although IoT applications are helpful in smart building applications, they present a real risk as the large number of interconnected devices in those buildings, using heterogeneous networks, increases the number of potential IoT attacks.
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques
Nevertheless, edge-powered ML solutions are more complex to realize due to the joint constraints from both edge computing and AI domains, and the corresponding solutions are expected to be efficient and adapted in technologies such as data processing, model compression, distributed inference, and advanced learning paradigms for Edge ML requirements.
Optimizing Orthogonalized Tensor Deflation via Random Tensor Theory
Relying on recently developed random tensor tools, this paper deals precisely with the non-orthogonal case by deriving an asymptotic analysis of a parameterized deflation procedure performed on an order-three and rank-two spiked tensor.
Cooperative Beamforming and RISs Association for Multi-RISs Aided Multi-Users MmWave MIMO Systems through Graph Neural Networks
Reconfigurable intelligent surface (RIS) is considered as a promising solution for next-generation wireless communication networks due to a variety of merits, e. g., customizing the communication environment.
RSMA for Dual-Polarized Massive MIMO Networks: A SIC-Free Approach
Aiming at overcoming practical issues of successive interference cancellation (SIC), this paper proposes a dual-polarized rate-splitting multiple access (RSMA) technique for a downlink massive multiple-input multiple-output (MIMO) network.
Less Data, More Knowledge: Building Next Generation Semantic Communication Networks
In this tutorial, we present the first rigorous vision of a scalable end-to-end semantic communication network that is founded on novel concepts from artificial intelligence (AI), causal reasoning, and communication theory.
Dual-Polarized Massive MIMO-RSMA Networks: Tackling Imperfect SIC
This paper takes advantage of this additional DoF to alleviate practical issues of successive interference cancellation (SIC) in rate-splitting multiple access (RSMA) schemes.
Semantic-Native Communication: A Simplicial Complex Perspective
In this paper, we study semantic communication from a topological space perspective, in which higher-order data semantics live in a simplicial complex.
The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approaches
The evolution of network virtualization and native artificial intelligence (AI) paradigms have conceptualized the vision of future wireless networks as a comprehensive entity operating in whole over a digital platform, with smart interaction with the physical domain, paving the way for the blooming of the Digital Twin (DT) concept.
Machine Learning and Analytical Power Consumption Models for 5G Base Stations
The energy consumption of the fifth generation(5G) of mobile networks is one of the major concerns of the telecom industry.
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design
In this paper, a green-quantized FL framework, which represents data with a finite precision level in both local training and uplink transmission, is proposed.
Environment Sensing Considering the Occlusion Effect: A Multi-View Approach
In this paper, we consider the problem of sensing the environment within a wireless cellular framework.
Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language
In contrast, in this paper, a holistic goal-oriented semantic communication framework is proposed to enable a speaker and a listener to cooperatively execute a set of sequential tasks in a dynamic environment.
Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks
In this paper, a novel framework is proposed to optimize the reliability of MEC networks by considering the distribution of E2E service delay, encompassing over-the-air transmission and edge computing latency.
On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks
In this paper, a quantized FL framework, that represents data with a finite level of precision in both local training and uplink transmission, is proposed.
Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework
In this paper, a comprehensive semantic communications framework is proposed for enabling goal-oriented task execution.
Towards Federated Learning-Enabled Visible Light Communication in 6G Systems
Nevertheless, concerns pertaining to privacy and communication overhead when sharing raw data of the involved clients with a server constitute major bottlenecks in the implementation of centralized ML techniques.
Joint Multi-User Communication and Sensing Exploiting Both Signal and Environment Sparsity
As a potential technology feature for 6G wireless networks, the idea of sensing-communication integration requires the system not only to complete reliable multi-user communication but also to achieve accurate environment sensing.
Spectrum Learning-Aided Reconfigurable Intelligent Surfaces for 'Green' 6G Networks
In the sixth-generation (6G) era, emerging large-scale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy consumption for the end users, whose devices are usually energy-constrained.
Wavenumber-Division Multiplexing in Line-of-Sight Holographic MIMO Communications
The simplest implementation provides the same spectral efficiency of a singular-value decomposition architecture with water-filling when the receiver size is comparable to the transmission range.
Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing
Based on these fundamentals, we characterize seven unique defining features of THz wireless systems: 1) Quasi-opticality of the band, 2) THz-tailored wireless architectures, 3) Synergy with lower frequency bands, 4) Joint sensing and communication systems, 5) PHY-layer procedures, 6) Spectrum access techniques, and 7) Real-time network optimization.
Information Theory Information Theory
Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design
We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss.
Predictive Ultra-Reliable Communication: A Survival Analysis Perspective
Results show that the accuracy of detecting channel blocking events is higher using the model-based method for low to moderate reliability targets requiring low sample complexity.
Survival Analysis
Networking and Internet Architecture
Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces
Reconfigurable Intelligent Surfaces (RISs) are recently gaining remarkable attention as a low-cost, hardware-efficient, and highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation.
Reconfigurable Intelligent Surfaces and Metamaterials: The Potential of Wave Propagation Control for 6G Wireless Communications
Motivated by the late research excitement on the RIS potential for intelligent EM wave propagation modulation, we describe the status on RIS hardware architectures and present key open challenges and future research directions for RIS design and RIS-empowered 6G wireless communications.
6G White Paper on Edge Intelligence
We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers.
On the Optimality of Reconfigurable Intelligent Surfaces (RISs): Passive Beamforming, Modulation, and Resource Allocation
In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations.
Information Theory Signal Processing Information Theory
Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach
In the proposed framework, we model the online power control problem as a discrete-time mean-field game (MFG), and analytically show that the MFG has a unique stationary solution.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Deep Learning Based Online Power Control for Large Energy Harvesting Networks
In this paper, we propose a deep learning based approach to design online power control policies for large EH networks, which are often intractable stochastic control problems.
A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks
Specifically, thanks to its reduced complexity, the proposed method can be used to train an artificial neural network to predict the optimal resource allocation.
User Association and Load Balancing for Massive MIMO through Deep Learning
This work investigates the use of deep learning to perform user cell association for sum-rate maximization in Massive MIMO networks.
Deep Learning Power Allocation in Massive MIMO
The use of deep learning significantly improves the complexity-performance trade-off of power allocation, compared to traditional optimization-oriented methods.
Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications
In this paper, the problem of joint power and resource allocation (JPRA) for ultra-reliable low-latency communication (URLLC) in vehicular networks is studied.
Information Theory Information Theory
Problem-Adapted Artificial Intelligence for Online Network Optimization
To this end, we propose the framework of Online Network Optimization (ONO), which seeks to maintain both agile and efficient control over time, using an arsenal of data-driven, online learning, and AI-based techniques.
Federated Learning for Ultra-Reliable Low-Latency V2V Communications
It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79\% reductions in the amount of data that need to be exchanged.
