Search Results for author: Walid Saad

Found 109 papers, 8 papers with code

Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept

no code implementations20 Feb 2024 Kui Wang, Zongdian Li, Kazuma Nonomura, Tao Yu, Kei Sakaguchi, Omar Hashash, Walid Saad

The performance of SMDT is evaluated from two standpoints: (i) the rewards of the proposed navigation system on traffic efficiency and safety and, (ii) the latency and reliability of the SMDT platform.

Autonomous Driving Blocking

Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems

no code implementations30 Jan 2024 Shengzhe Xu, Christo Kurisummoottil Thomas, Omar Hashash, Nikhil Muralidhar, Walid Saad, Naren Ramakrishnan

Diverging from NLP-based foundation models, the proposed framework promotes the design of large multi-modal models (LMMs) fostered by three key capabilities: 1) processing of multi-modal sensing data, 2) grounding of physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and 3) enabling instructibility from the wireless environment feedback to facilitate dynamic network adaptation thanks to logical and mathematical reasoning facilitated by neuro-symbolic AI.

Mathematical Reasoning

Federated Quantum Long Short-term Memory (FedQLSTM)

no code implementations21 Dec 2023 Mahdi Chehimi, Samuel Yen-Chi Chen, Walid Saad, Shinjae Yoo

The proposed federated QLSTM (FedQLSTM) framework is exploited for performing the task of function approximation.

Federated Learning Quantum Machine Learning

Internet of Federated Digital Twins (IoFDT): Connecting Twins Beyond Borders for Society 5.0

no code implementations11 Dec 2023 Tao Yu, Zongdian Li, Kei Sakaguchi, Omar Hashash, Walid Saad, Merouane Debbah

In contrast, this paper envisions a novel concept of an Internet of Federated Digital Twins (IoFDT) that holistically integrates heterogeneous and physically separated DTs representing different Society 5. 0 services within a single framework and system.

Rate-Distortion-Perception Theory for Semantic Communication

no code implementations9 Dec 2023 Jingxuan Chai, Yong Xiao, Guangming Shi, Walid Saad

Motivated by the fact that the semantic information generally involves rich intrinsic knowledge that cannot always be directly observed by the encoder, we consider a semantic information source that can only be indirectly sensed by the encoder.

Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing

no code implementations8 Dec 2023 Huixiang Zhu, Yong Xiao, Yingyu Li, Guangming Shi, Walid Saad

Motivated by the observation that signals recorded by wireless receivers are closely related to a set of physical-layer semantic features, in this paper we propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.

Gesture Recognition Zero-Shot Learning

Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights

no code implementations7 Dec 2023 Maryam Ben Driss, Essaid Sabir, Halima Elbiaze, Walid Saad

However, massive data, energy consumption, training complexity, and sensitive data protection in wireless systems are all crucial challenges that must be addressed for training AI models and gathering intelligence and knowledge from distributed devices.

Federated Learning

Reasoning with the Theory of Mind for Pragmatic Semantic Communication

no code implementations30 Nov 2023 Christo Kurisummoottil Thomas, Emilio Calvanese Strinati, Walid Saad

In this paper, a pragmatic semantic communication framework that enables effective goal-oriented information sharing between two-intelligent agents is proposed.

Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks

no code implementations23 Sep 2023 Christo Kurisummoottil Thomas, Christina Chaccour, Walid Saad, Merouane Debbah, Choong Seon Hong

We showcase how incorporating causal discovery can assist in achieving dynamic adaptability, resilience, and cognition in addressing these challenges.

Causal Discovery Causal Inference +2

Decentralized Online Learning in Task Assignment Games for Mobile Crowdsensing

no code implementations19 Sep 2023 Bernd Simon, Andrea Ortiz, Walid Saad, Anja Klein

The task assignment problem is modeled as a matching game considering the MCSP's and MUs' individual goals while the MUs learn their efforts online.

Collision Avoidance

Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation

no code implementations5 Jul 2023 Tengchan Zeng, Aidin Ferdowsi, Omid Semiari, Walid Saad, Choong Seon Hong

For both cases, solutions using the convergence of communication theory, control theory, and machine learning are proposed to enable effective and secure CAV navigation.

Autonomous Navigation Decision Making +2

Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework

1 code implementation20 Jun 2023 Yong Xiao, Yiwei Liao, Yingyu Li, Guangming Shi, H. Vincent Poor, Walid Saad, Merouane Debbah, Mehdi Bennis

Most existing works focus on transmitting and delivering the explicit semantic meaning that can be directly identified from the source signal.

Imitation Learning

Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences

no code implementations29 Apr 2023 Christina Chaccour, Walid Saad, Merouane Debbah, H. Vincent Poor

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.

Tensor Decomposition

Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach

no code implementations25 Apr 2023 Christo Kurisummoottil Thomas, Walid Saad, Yong Xiao

The causal structure in the source data is extracted using novel approaches from the framework of deep end-to-end causal inference, thereby enabling the creation of a semantic representation that is causally invariant, which in turn helps generalize the learned knowledge of the system to unseen scenarios.

Causal Inference Edge-computing +2

The Seven Worlds and Experiences of the Wireless Metaverse: Challenges and Opportunities

no code implementations20 Apr 2023 Omar Hashash, Christina Chaccour, Walid Saad, Tao Yu, Kei Sakaguchi, Merouane Debbah

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).

Reliable Beamforming at Terahertz Bands: Are Causal Representations the Way Forward?

no code implementations14 Mar 2023 Christo Kurisummoottil Thomas, Walid Saad

Future wireless services, such as the metaverse require high information rate, reliability, and low latency.

Causal Inference

Collaborative Learning with a Drone Orchestrator

1 code implementation3 Mar 2023 Mahdi Boloursaz Mashhadi, Mahnoosh Mahdavimoghadam, Rahim Tafazolli, Walid Saad

For this system, the convergence rate of collaborative learning is derived while considering data heterogeneity, sensor noise levels, and communication errors, then, the drone trajectory that maximizes the final accuracy of the trained NN is obtained.

Semantic Segmentation

Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference

no code implementations15 Feb 2023 Tilahun M. Getu, Walid Saad, Georges Kaddoum, Mehdi Bennis

Although deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler by minimizing irrelevant information transmission -- minimizing power usage, bandwidth consumption, and transmission delay, its benefits can be limited by radio frequency interference (RFI) that causes substantial semantic noise.

Disentangling Learnable and Memorizable Data via Contrastive Learning for Semantic Communications

no code implementations18 Dec 2022 Christina Chaccour, Walid Saad

Deep semantic clusters of highest confidence are considered learnable, semantic-rich data, i. e., data that can be used to build a language in a semantic communications system.

Contrastive Learning

Less Data, More Knowledge: Building Next Generation Semantic Communication Networks

no code implementations25 Nov 2022 Christina Chaccour, Walid Saad, Merouane Debbah, Zhu Han, H. Vincent Poor

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.

Novel Concepts Representation Learning

Goal-Oriented Communications for the IoT and Application to Data Compression

no code implementations10 Nov 2022 Chao Zhang, Hang Zou, Samson Lasaulce, Walid Saad, Marios Kountouris, Mehdi Bennis

Internet of Things (IoT) devices will play an important role in emerging applications, since their sensing, actuation, processing, and wireless communication capabilities stimulate data collection, transmission and decision processes of smart applications.

Data Compression

Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communications

no code implementations21 Oct 2022 Christo Kurisummoottil Thomas, Walid Saad

In this paper, a novel emergent SC (ESC) system framework is proposed and is composed of a signaling game for emergent language design and a neuro-symbolic (NeSy) artificial intelligence (AI) approach for causal reasoning.

Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network

no code implementations13 Oct 2022 Md. Shirajum Munir, Ki Tae Kim, Apurba Adhikary, Walid Saad, Sachin Shetty, Seong-Bae Park, Choong Seon Hong

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +2

Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks

no code implementations21 Sep 2022 Sihua Wang, Mingzhe Chen, Christopher G. Brinton, Changchuan Yin, Walid Saad, Shuguang Cui

Compared to model-free RL, this model-based RL approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead.

Federated Learning Model-based Reinforcement Learning +2

Recurrent Neural Network-based Anti-jamming Framework for Defense Against Multiple Jamming Policies

no code implementations19 Aug 2022 Ali Pourranjbar, Georges Kaddoum, Walid Saad

Moreover, when 70 % of the spectrum is under jamming attacks from multiple jammers, the proposed method achieves an STR and ER greater than 75 % and 80 %, respectively.

Q-Learning

Downlink Interference Analysis of UAV-based mmWave Fronthaul for Small Cell Networks

no code implementations11 Aug 2022 Mohammad Taghi Dabiri, Mazen Hasna, Walid Saad

In this paper, an unmanned aerial vehicles (UAV)-based heterogeneous network is studied to solve the problem of transferring massive traffic of distributed small cells to the core network.

Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design

no code implementations19 Jul 2022 Minsu Kim, Walid Saad, Mohammad Mozaffari, Merouane Debbah

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.

Federated Learning Quantization

Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks

no code implementations2 Jun 2022 Bassel Al Homssi, Kosta Dakic, Ke Wang, Tansu Alpcan, Ben Allen, Russell Boyce, Sithamparanathan Kandeepan, Akram Al-Hourani, Walid Saad

This article introduces the application of AI techniques for integrated terrestrial satellite networks, particularly massive satellite network communications.

Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication

no code implementations22 May 2022 Christo Kurisummoottil Thomas, Walid Saad

Novel analytical formulations are developed to define key metrics for semantic message transmission, including semantic distortion, semantic similarity, and semantic reliability.

Semantic Similarity Semantic Textual Similarity

Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language

no code implementations21 Apr 2022 Mohammad Karimzadeh Farshbafan, Walid Saad, Merouane Debbah

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.

Positioning Using Visible Light Communications: A Perspective Arcs Approach

no code implementations18 Apr 2022 Zhiyu Zhu, Caili Guo, Rongzhen Bao, Mingzhe Chen, Walid Saad, Yang Yang

In this paper, the arc feature of the circular luminaire and the coordinate information obtained via visible light communication (VLC) are jointly used for VLC-enabled indoor positioning, and a novel perspective arcs approach is proposed.

Edge Continual Learning for Dynamic Digital Twins over Wireless Networks

no code implementations10 Apr 2022 Omar Hashash, Christina Chaccour, Walid Saad

To guarantee a robust connection between these two worlds, DTs should maintain accurate representations of the physical applications, while preserving synchronization between real and digital entities.

Continual Learning

Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission

no code implementations12 Mar 2022 Lunan Sun, Yang Yang, Mingzhe Chen, Caili Guo, Walid Saad, H. Vincent Poor

In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate.

Image Reconstruction

Jamming Pattern Recognition over Multi-Channel Networks: A Deep Learning Approach

no code implementations19 Dec 2021 Ali Pourranjbar, Georges Kaddoum, Walid Saad

To evaluate the performance of the proposed recognition method, the accuracy of the detection is derived as a function of the jammer policy switching time.

Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks

no code implementations23 Nov 2021 Fatemeh Lotfi, Omid Semiari, Walid Saad

To address these challenges, in this paper, a novel semantic-aware CDRL method is proposed to enable a group of heterogeneous untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network.

Decision Making reinforcement-learning +1

On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks

no code implementations15 Nov 2021 Minsu Kim, Walid Saad, Mohammad Mozaffari, Merouane Debbah

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.

Federated Learning Quantization

Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework

no code implementations15 Nov 2021 Mohammad Karimzadeh Farshbafan, Walid Saad, Merouane Debbah

In this paper, a comprehensive semantic communications framework is proposed for enabling goal-oriented task execution.

Reinforcement Learning (RL)

Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learning

no code implementations6 Oct 2021 Mohammad Aghapour, Aidin Ferdowsi, Walid Saad

In federated learning (FL), a machine learning model is trained on multiple nodes in a decentralized manner, while keeping the data local and not shared with other nodes.

Federated Learning Vocal Bursts Valence Prediction

A Data-Driven Democratized Control Architecture for Regional Transmission Operators

no code implementations20 Sep 2021 Xiaoyuan Fan, Daniel Moscovitz, Liang Du, Walid Saad

As probably the most complicated and critical infrastructure system, U. S. power grids become increasingly vulnerable to extreme events such as cyber-attacks and severe weather, as well as higher DER penetrations and growing information mismatch among system operators, utilities (transmission or generation owners), and end-users.

Ensuring Reliable Connectivity to Cellular-Connected UAVs with Up-tilted Antennas and Interference Coordination

no code implementations11 Aug 2021 Md Moin Uddin Chowdhury, Ismail Guvenc, Walid Saad, Arupjyoti Bhuyan

Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas.

Semi-Supervised Learning for Channel Charting-Aided IoT Localization in Millimeter Wave Networks

no code implementations3 Aug 2021 Qianqian Zhang, Walid Saad

In this paper, a novel framework is proposed for channel charting (CC)-aided localization in millimeter wave networks.

Quantum Federated Learning with Quantum Data

1 code implementation30 May 2021 Mahdi Chehimi, Walid Saad

First, given the lack of existing quantum federated datasets in the literature, the proposed framework begins by generating the first quantum federated dataset, with a hierarchical data format, for distributed quantum networks.

BIG-bench Machine Learning Federated Learning +1

Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems

no code implementations4 Apr 2021 Sihua Wang, Mingzhe Chen, Zhaohui Yang, Changchuan Yin, Walid Saad, Shuguang Cui, H. Vincent Poor

In this paper, the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices is studied.

reinforcement-learning Reinforcement Learning (RL) +1

Ultra-Reliable Indoor Millimeter Wave Communications using Multiple Artificial Intelligence-Powered Intelligent Surfaces

no code implementations31 Mar 2021 Mehdi Naderi Soorki, Walid Saad, Mehdi Bennis, Choong Seon Hong

Simulation results show that the error between policies of the optimal and the RNN-based controllers is less than 1. 5%.

Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks

no code implementations25 Mar 2021 Ali Pourranjbar, Georges Kaddoum, Aidin Ferdowsi, Walid Saad

Different from existing works, in this paper, a novel anti-jamming strategy is proposed based on the idea of deceiving the jammer into attacking a victim channel while maintaining the communications of legitimate users in safe channels.

reinforcement-learning Reinforcement Learning (RL)

Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks

no code implementations13 Mar 2021 Juntong Liu, Yong Xiao, Yingyu Li, Guangming Shiyz, Walid Saad, H. Vincent Poor

The effective deployment of connected vehicular networks is contingent upon maintaining a desired performance across spatial and temporal domains.

Graph Reconstruction

Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions

no code implementations24 Feb 2021 Latif U. Khan, Walid Saad, Dusit Niyato, Zhu Han, Choong Seon Hong

Therefore, enabling IoE applications over 6G requires a new framework that can be used to manage, operate, and optimize the 6G wireless system and its underlying IoE services.

Edge-computing Networking and Internet Architecture

Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing

no code implementations15 Feb 2021 Christina Chaccour, Mehdi Naderi Soorki, Walid Saad, Mehdi Bennis, Petar Popovski, Merouane Debbah

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

Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles

no code implementations5 Feb 2021 Tengchan Zeng, Omid Semiari, Mingzhe Chen, Walid Saad, Mehdi Bennis

The results also validate the feasibility of the contract-theoretic incentive mechanism and show that the proposed mechanism can improve the convergence speed of the DFP algorithm by 40% compared to the baselines.

Autonomous Vehicles Federated Learning

Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks

no code implementations2 Feb 2021 Qianqian Zhang, Aidin Ferdowsi, Walid Saad, Mehdi Bennis

To guarantee an efficient learning process, necessary and sufficient conditions for the optimal UAV network topology that maximizes the learning rate for cooperative channel modeling are derived, and the optimal CGAN learning solution per UAV is subsequently characterized, based on the distributed network structure.

Generative Adversarial Network

Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks

1 code implementation29 Jan 2021 Yining Wang, Mingzhe Chen, Zhaohui Yang, Walid Saad, Tao Luo, Shuguang Cui, H. Vincent Poor

The problem is formulated as an optimization problem whose goal is to maximize the reliability of the VR network by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs.

Meta-Learning Meta Reinforcement Learning +2

Predictive Ultra-Reliable Communication: A Survival Analysis Perspective

no code implementations22 Dec 2020 Sumudu Samarakoon, Mehdi Bennis, Walid Saad, Merouane Debbah

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

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

no code implementations14 Dec 2020 Md. Noor-A-Rahim, Zilong Liu, Haeyoung Lee, M. Omar Khyam, Jianhua He, Dirk Pesch, Klaus Moessner, Walid Saad, H. Vincent Poor

Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking.

Autonomous Vehicles Information Theory Networking and Internet Architecture Information Theory

Performance Analysis of Age of Information in Ultra-Dense Internet of Things (IoT) Systems with Noisy Channels

no code implementations9 Dec 2020 Bo Zhou, Walid Saad

Then, a mean-field approximation approach with guaranteed accuracy is developed to analyze the asymptotic performance for the considered system with an infinite number of devices and the effects of the system parameters on the average AoI are characterized.

Information Theory Networking and Internet Architecture Information Theory

Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning

no code implementations7 Dec 2020 Mohamed K. Abdel-Aziz, Cristina Perfecto, Sumudu Samarakoon, Mehdi Bennis, Walid Saad

Simulation results show the ability of the RL agents to efficiently learn the vehicles' association, RB allocation, and message content selection while maximizing vehicles' satisfaction in terms of the received sensory information.

reinforcement-learning Reinforcement Learning (RL)

Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks

no code implementations6 Dec 2020 Ye Hu, Mingzhe Chen, Walid Saad, H. Vincent Poor, Shuguang Cui

Analytical results show that, the proposed VD-RL algorithm is guaranteed to converge to a local optimal solution of the non-convex optimization problem.

Meta-Learning Navigate

Ensuring Data Freshness for Blockchain-enabled Monitoring Networks

no code implementations12 Nov 2020 Minsu Kim, Sungho Lee, Chanwon Park, Jemin Lee, Walid Saad

The age of information (AoI) is a recently proposed metric for quantifying data freshness in real-time status monitoring systems where timeliness is of importance.

Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV

no code implementations3 Nov 2020 Qianqian Zhang, Walid Saad, Mehdi Bennis

In this paper, a novel communication framework that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed to enhance multi-user downlink transmissions over millimeter wave (mmWave) frequencies.

Distributional Reinforcement Learning reinforcement-learning +1

Towards Self-learning Edge Intelligence in 6G

no code implementations1 Oct 2020 Yong Xiao, Guangming Shi, Yingyu Li, Walid Saad, H. Vincent Poor

Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing.

Edge-computing Self-Learning

Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges

no code implementations28 Sep 2020 Latif U. Khan, Walid Saad, Zhu Han, Ekram Hossain, Choong Seon Hong

However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT.

Networking and Internet Architecture

What Role Do Intelligent Reflecting Surfaces Play in Multi-Antenna Non-Orthogonal Multiple Access?

no code implementations20 Sep 2020 Arthur S. de Sena, Dick Carrillo, Fang Fang, Pedro H. J. Nardelli, Daniel B. da Costa, Ugo S. Dias, Zhiguo Ding, Constantinos B. Papadias, Walid Saad

Massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) are two key techniques for enabling massive connectivity in future wireless networks.

Fairness

Dispersed Federated Learning: Vision, Taxonomy, and Future Directions

no code implementations12 Aug 2020 Latif U. Khan, Walid Saad, Zhu Han, Choong Seon Hong

However, federated learning still has privacy concerns due to sensitive information inferring capability of the aggregation server using end-devices local learning models.

Distributed, Parallel, and Cluster Computing

Delay Minimization for Federated Learning Over Wireless Communication Networks

no code implementations5 Jul 2020 Zhaohui Yang, Mingzhe Chen, Walid Saad, Choong Seon Hong, Mohammad Shikh-Bahaei, H. Vincent Poor, Shuguang Cui

In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated.

Federated Learning

Wireless Communications for Collaborative Federated Learning

no code implementations3 Jun 2020 Mingzhe Chen, H. Vincent Poor, Walid Saad, Shuguang Cui

However, due to resource constraints and privacy challenges, edge IoT devices may not be able to transmit their collected data to a central controller for training machine learning models.

BIG-bench Machine Learning Federated Learning +2

Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks

no code implementations25 May 2020 Ye Hu, Mingzhe Chen, Walid Saad, H. Vincent Poor, Shuguang Cui

Meanwhile, the probability that the DBS serves over 50% of user requests increases about 27%, compared to the baseline policy gradient algorithm.

Meta-Learning Meta Reinforcement Learning +2

Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces

no code implementations1 May 2020 Zhaohui Yang, Mingzhe Chen, Walid Saad, Wei Xu, Mohammad Shikh-Bahaei, H. Vincent Poor, Shuguang Cui

In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs.

Neurosciences and 6G: Lessons from and Needs of Communicative Brains

no code implementations4 Apr 2020 Renan C. Moioli, Pedro H. J. Nardelli, Michael Taynnan Barros, Walid Saad, Amin Hekmatmanesh, Pedro Gória, Arthur S. de Sena, Merim Dzaferagic, Harun Siljak, Werner van Leekwijck, Dick Carrillo, Steven Latré

In particular, we propose a novel systematization that divides the contributions into two groups, one focused on what neurosciences will offer to 6G in terms of new applications and systems architecture (Neurosciences for Wireless), and the other focused on how wireless communication theory and 6G systems can provide new ways to study the brain (Wireless for Neurosciences).

Signal Processing Emerging Technologies Information Theory Information Theory Neurons and Cognition

Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks

no code implementations19 Mar 2020 Sihua Wang, Mingzhe Chen, Changchuan Yin, Walid Saad, Choong Seon Hong, Shuguang Cui, H. Vincent Poor

This problem is posed as an optimization problem whose goal is to minimize the energy and time consumption for task computing and transmission by adjusting the user association, service sequence, and task allocation scheme.

Edge-computing Federated Learning

Distributed and Democratized Learning: Philosophy and Research Challenges

1 code implementation18 Mar 2020 Minh N. H. Nguyen, Shashi Raj Pandey, Kyi Thar, Nguyen H. Tran, Mingzhe Chen, Walid Saad, Choong Seon Hong

Consequently, many emerging cross-device AI applications will require a transition from traditional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform multiple complex learning tasks.

Philosophy

Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning

no code implementations24 Feb 2020 Qianqian Zhang, Walid Saad, Mehdi Bennis

Furthermore, under limited knowledge of CSI, simulation results show that the proposed QR-DRL method, which learns a full distribution of the downlink rate, yields a better prediction accuracy and improves the downlink rate by 10% for online deployments, compared with a Q-learning baseline.

Distributional Reinforcement Learning Q-Learning +2

Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems

no code implementations20 Feb 2020 Md. Shirajum Munir, Nguyen H. Tran, Walid Saad, Choong Seon Hong

In particular, each BS plays the role of a local agent that explores a Markovian behavior for both energy consumption and generation while each BS transfers time-varying features to a meta-agent.

Edge-computing Meta Reinforcement Learning +3

Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms

no code implementations19 Feb 2020 Tengchan Zeng, Omid Semiari, Mohammad Mozaffari, Mingzhe Chen, Walid Saad, Mehdi Bennis

Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition.

Federated Learning Scheduling +1

Brainstorming Generative Adversarial Networks (BGANs): Towards Multi-Agent Generative Models with Distributed Private Datasets

no code implementations2 Feb 2020 Aidin Ferdowsi, Walid Saad

In this paper, to address this multi-agent GAN problem, a novel brainstorming GAN (BGAN) architecture is proposed using which multiple agents can generate real-like data samples while operating in a fully distributed manner.

Convergence Time Optimization for Federated Learning over Wireless Networks

no code implementations22 Jan 2020 Mingzhe Chen, H. Vincent Poor, Walid Saad, Shuguang Cui

Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step.

Federated Learning

Cellular-Connected Wireless Virtual Reality: Requirements, Challenges, and Solutions

no code implementations13 Jan 2020 Fenghe Hu, Yansha Deng, Walid Saad, Mehdi Bennis, A. Hamid Aghvami

Cellular-connected wireless connectivity provides new opportunities for virtual reality(VR) to offer seamless user experience from anywhere at anytime.

Deep Learning for Optimal Deployment of UAVs with Visible Light Communications

no code implementations28 Nov 2019 Yining Wang, Mingzhe Chen, Zhaohui Yang, Tao Luo, Walid Saad

Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution.

Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach

no code implementations27 Nov 2019 Mohamed K. Abdel-Aziz, Sumudu Samarakoon, Mehdi Bennis, Walid Saad

Therefore, to effectively allocate power and RBs, the proposed approach allows the network to actively learn its dynamics by balancing a tradeoff between minimizing the probability that the vehicles' AoI exceeds a predefined threshold and maximizing the knowledge about the network dynamics.

Active Learning GPR

Deep Learning with Persistent Homology for Orbital Angular Momentum (OAM) Decoding

no code implementations15 Nov 2019 Soheil Rostami, Walid Saad, Choong Seon Hong

To maintain lower error rate in presence of severe atmospheric turbulence, a new approach that combines effective machine learning tools from persistent homology and convolutional neural networks (CNNs) is proposed to decode the OAM modes.

Classification General Classification

Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism

no code implementations6 Nov 2019 Latif U. Khan, Nguyen H. Tran, Shashi Raj Pandey, Walid Saad, Zhu Han, Minh N. H. Nguyen, Choong Seon Hong

IoT devices with intelligence require the use of effective machine learning paradigms.

Distributed, Parallel, and Cluster Computing

Energy Efficient Federated Learning Over Wireless Communication Networks

no code implementations6 Nov 2019 Zhaohui Yang, Mingzhe Chen, Walid Saad, Choong Seon Hong, Mohammad Shikh-Bahaei

To solve this problem, an iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived.

Federated Learning Total Energy

Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication

no code implementations1 Nov 2019 Ali Taleb Zadeh Kasgari, Walid Saad, Mohammad Mozaffari, H. Vincent Poor

Formally, the URLLC resource allocation problem is posed as a power minimization problem under reliability, latency, and rate constraints.

On the Optimality of Reconfigurable Intelligent Surfaces (RISs): Passive Beamforming, Modulation, and Resource Allocation

no code implementations2 Oct 2019 Minchae Jung, Walid Saad, Merouane Debbah, Choong Seon Hong

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

Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVs

no code implementations17 Sep 2019 Yining Wang, Mingzhe Chen, Zhaohui Yang, Xue Hao, Tao Luo, Walid Saad

This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users.

A Joint Learning and Communications Framework for Federated Learning over Wireless Networks

1 code implementation17 Sep 2019 Mingzhe Chen, Zhaohui Yang, Walid Saad, Changchuan Yin, H. Vincent Poor, Shuguang Cui

This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm.

Federated Learning

Analysis of Memory Capacity for Deep Echo State Networks

no code implementations11 Jun 2019 Xuanlin Liu, Mingzhe Chen, Changchuan Yin, Walid Saad

Then, a series architecture ESN is proposed in which ESN reservoirs are placed in cascade that the output of each ESN is the input of the next ESN in the series.

Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things

no code implementations3 Jun 2019 Aidin Ferdowsi, Walid Saad

To this end, in this paper, a distributed generative adversarial network (GAN) is proposed to provide a fully distributed IDS for the IoT so as to detect anomalous behavior without reliance on any centralized controller.

Generative Adversarial Network Intrusion Detection

Cyber-Physical Security and Safety of Autonomous Connected Vehicles: Optimal Control Meets Multi-Armed Bandit Learning

no code implementations13 Dec 2018 Aidin Ferdowsi, Samad Ali, Walid Saad, Narayan B. Mandayam

For sensors having a prior information, a DIA detection approach is proposed and an optimal threshold level is derived for the difference between the actual and estimated values of sensors data which enables ACV to stay robust against cyber attacks.

Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks

no code implementations4 Dec 2018 Mingzhe Chen, Omid Semiari, Walid Saad, Xuanlin Liu, Changchuan Yin

The proposed algorithm uses concept from federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users' locations and orientations.

Information Theory Information Theory

Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications

no code implementations21 Jul 2018 Sumudu Samarakoon, Mehdi Bennis, Walid Saad, Merouane Debbah

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

Federated Learning for Ultra-Reliable Low-Latency V2V Communications

no code implementations11 May 2018 Sumudu Samarakoon, Mehdi Bennis, Walid Saad, Merouane Debbah

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.

Federated Learning

Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems

no code implementations2 May 2018 Aidin Ferdowsi, Ursula Challita, Walid Saad, Narayan B. Mandayam

To this end, in this paper, the state estimation process for monitoring AV dynamics, in presence of CP attacks, is analyzed and a novel adversarial deep reinforcement learning (RL) algorithm is proposed to maximize the robustness of AV dynamics control to CP attacks.

Autonomous Vehicles reinforcement-learning +1

Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs

no code implementations15 Apr 2018 Ursula Challita, Aidin Ferdowsi, Mingzhe Chen, Walid Saad

Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users.

BIG-bench Machine Learning Management

Deep Learning for Signal Authentication and Security in Massive Internet of Things Systems

no code implementations1 Mar 2018 Aidin Ferdowsi, Walid Saad

In the massive IoT system, due to a large set of available actions for the cloud, it is shown that analytically deriving the MSNE is challenging and, thus, a learning algorithm proposed that converges to the MSNE.

Decision Making

Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management

no code implementations16 Jan 2018 Ursula Challita, Walid Saad, Christian Bettstetter

The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.

Management reinforcement-learning +1

Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems

no code implementations12 Dec 2017 Aidin Ferdowsi, Ursula Challita, Walid Saad

However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment.

Edge-computing

Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things

no code implementations3 Nov 2017 Aidin Ferdowsi, Walid Saad

In this paper, a novel deep learning method is proposed for dynamic watermarking of IoT signals to detect cyber attacks.

Information Theory Cryptography and Security Multimedia Information Theory

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

no code implementations9 Oct 2017 Mingzhe Chen, Ursula Challita, Walid Saad, Changchuan Yin, Mérouane Debbah

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment.

BIG-bench Machine Learning

Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective

no code implementations22 Feb 2017 Ursula Challita, Li Dong, Walid Saad

LTE in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a promising approach to overcome the wireless spectrum scarcity.

Fairness Management

Stochastic Games for Smart Grid Energy Management with Prospect Prosumers

no code implementations6 Oct 2016 Seyed Rasoul Etesami, Walid Saad, Narayan Mandayam, H. Vincent Poor

For this case, it is shown that such an optimization problem admits a no-regret algorithm meaning that regardless of the actual outcome of the game among the prosumers, the utility company can follow a strategy that mitigates its allocation costs as if it knew the entire demand market a priori.

energy management Management

Backhaul-Aware Interference Management in the Uplink of Wireless Small Cell Networks

no code implementations27 Aug 2013 Sumudu Samarakoon, Mehdi Bennis, Walid Saad, Matti Latva-aho

In this paper, a novel, backhaul-aware approach to interference management in wireless small cell networks is proposed.

Management

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