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Found 198 papers, 13 papers with code
A Kernel-Based Nonparametric Test for Anomaly Detection over Line Networks
If anomalous interval does not exist, then all nodes receive samples generated by p. It is assumed that the distributions p and q are arbitrary, and are unknown.
Nonparametric Detection of Anomalous Data Streams
samples drawn from a distribution p, whereas each anomalous sequence contains m i. i. d.
Fusion of Image Segmentation Algorithms using Consensus Clustering
A new segmentation fusion method is proposed that ensembles the output of several segmentation algorithms applied on a remotely sensed image.
Machine Learning Methods for Attack Detection in the Smart Grid
The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods.
Nonparametric Detection of Geometric Structures over Networks
Sufficient conditions on minimum and maximum sizes of candidate anomalous intervals are characterized in order to guarantee the proposed test to be consistent.
Stochastic Games for Smart Grid Energy Management with Prospect Prosumers
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.
A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification
Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem.
Two-dimensional Anti-jamming Mobile Communication Based on Reinforcement Learning
It is shown that, by applying reinforcement learning techniques, a mobile device can achieve an optimal communication policy without the need to know the jamming and interference model and the radio channel model in a dynamic game framework.
MVG Mechanism: Differential Privacy under Matrix-Valued Query
To address this challenge, we propose a novel differential privacy mechanism called the Matrix-Variate Gaussian (MVG) mechanism, which adds a matrix-valued noise drawn from a matrix-variate Gaussian distribution, and we rigorously prove that the MVG mechanism preserves $(\epsilon,\delta)$-differential privacy.
Reinforcement Learning-based Energy Trading for Microgrids
The Nash quilibrium (NE) of the game is provided, revealing the conditions under which the local energy generation satisfies the energy demand of the MG and providing the performance bound of the energy trading scheme.
Systems and Control
A Differential Privacy Mechanism Design Under Matrix-Valued Query
noise to each element of the matrix, this method is often sub-optimal as it forfeits an opportunity to exploit the structural characteristics typically associated with matrix analysis.
Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches
Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid.
On the Capacity of the Peak Power Constrained Vector Gaussian Channel: An Estimation Theoretic Perspective
This paper characterizes the necessary and sufficient conditions on the constraint $R$ such that the input distribution supported on a single sphere is optimal.
Information Theory Information Theory
Partial Recovery of Erdős-Rényi Graph Alignment via $k$-Core Alignment
This estimator searches for an alignment in which the intersection of the correlated graphs using this alignment has a minimum degree of $k$.
The Capacity Achieving Distribution for the Amplitude Constrained Additive Gaussian Channel: An Upper Bound on the Number of Mass Points
Finally, the third part provides bounds on the number of points for the case of $n=1$ with an additional power constraint.
Information Theory Information Theory
Annealing for Distributed Global Optimization
The paper considers algorithms for optimizing the sum function.
Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach
The key idea is to exploit input-output samples obtained from data detection, to compensate the mismatch in the likelihood function.
Scheduling Policies for Federated Learning in Wireless Networks
Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration.
Information Theory Signal Processing Information Theory
On Safeguarding Privacy and Security in the Framework of Federated Learning
Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL).
Networking and Internet Architecture
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
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.
Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches
To address the power allocation problem, two novel methods are proposed.
Subspace Estimation from Unbalanced and Incomplete Data Matrices: $\ell_{2,\infty}$ Statistical Guarantees
This paper is concerned with estimating the column space of an unknown low-rank matrix $\boldsymbol{A}^{\star}\in\mathbb{R}^{d_{1}\times d_{2}}$, given noisy and partial observations of its entries.
Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks
Federated learning (FL) is a machine learning model that preserves data privacy in the training process.
Information Theory Signal Processing Information Theory
Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication
Formally, the URLLC resource allocation problem is posed as a power minimization problem under reliability, latency, and rate constraints.
Federated Learning with Differential Privacy: Algorithms and Performance Analysis
Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i. e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level.
Nonconvex Low-Rank Tensor Completion from Noisy Data
We study a noisy tensor completion problem of broad practical interest, namely, the reconstruction of a low-rank tensor from highly incomplete and randomly corrupted observations of its entries.
Machine Intelligence at the Edge with Learning Centric Power Allocation
However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient.
Toward Optimal Adversarial Policies in the Multiplicative Learning System with a Malicious Expert
We consider a learning system based on the conventional multiplicative weight (MW) rule that combines experts' advice to predict a sequence of true outcomes.
Convergence Time Optimization for Federated Learning over Wireless Networks
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.
RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design
A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) with the aid of non-orthogonal multiple access (NOMA) technology.
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge
At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources, while each participating device must compress its model update to accommodate to its link capacity.
Reconfigurable Intelligent Surface Assisted MIMO Symbiotic Radio Networks
In this paper, a novel reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) symbiotic radio (SR) system is proposed, in which an RIS, operating as a secondary transmitter (STx), sends messages to a multi-antenna secondary receiver (SRx) by using cognitive backscattering communication, and simultaneously, it enhances the primary transmission from a multi-antenna primary transmitter (PTx) to a multi-antenna primary receiver (PRx) by intelligently reconfiguring the wireless environment.
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization
According to our analysis, the UDP framework can realize $(\epsilon_{i}, \delta_{i})$-LDP for the $i$-th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes.
Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima
In centralized settings, it is well known that stochastic gradient descent (SGD) avoids saddle points and converges to local minima in nonconvex problems.
Optimization and Control
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems
One major challenge in system design is to reconstruct local gradient vectors accurately at the central server, which are computed-and-sent from the wireless devices.
Malicious Experts versus the multiplicative weights algorithm in online prediction
We assume that one of the experts is honest and makes correct prediction with probability $\mu$ at each round.
Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks
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.
The Vector Poisson Channel: On the Linearity of the Conditional Mean Estimator
The first result shows that the only distribution that induces the linearity of the conditional mean estimator is a product gamma distribution.
Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees
We discuss local minima convergence guarantees and explore the simple but critical role of the stable-manifold theorem in analyzing saddle-point avoidance.
Optimization and Control
Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces
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.
Downlink and Uplink Intelligent Reflecting Surface Aided Networks: NOMA and OMA
Intelligent reflecting surfaces (IRSs) are envisioned to provide reconfigurable wireless environments for future communication networks.
Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning
Second is the distributed setting in which each device trains its own model and send its model parameters to a central server where these model parameters are aggregated to create one final model.
Nonparametric Estimation of the Fisher Information and Its Applications
First, an estimator proposed by Bhattacharya is revisited and improved convergence rates are derived.
Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks
Meanwhile, the probability that the DBS serves over 50% of user requests increases about 27%, compared to the baseline policy gradient algorithm.
Wireless Communications for Collaborative Federated Learning
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.
UVeQFed: Universal Vector Quantization for Federated Learning
We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion.
Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality
Furthermore, our findings unveil the statistical optimality of nonconvex tensor completion: it attains un-improvable $\ell_{2}$ accuracy -- including both the rates and the pre-constants -- when estimating both the unknown tensor and the underlying tensor factors.
Gradient Free Minimax Optimization: Variance Reduction and Faster Convergence
In this paper, we focus on such a gradient-free setting, and consider the nonconvex-strongly-concave minimax stochastic optimization problem.
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters
Although model-agnostic meta-learning (MAML) is a very successful algorithm in meta-learning practice, it can have high computational cost because it updates all model parameters over both the inner loop of task-specific adaptation and the outer-loop of meta initialization training.
Federated Learning With Quantized Global Model Updates
We analyze the convergence behavior of the proposed LFL algorithm assuming the availability of accurate local model updates at the server.
Decentralized Beamforming Design for Intelligent Reflecting Surface-enhanced Cell-free Networks
Cell-free networks are considered as a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems.
RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network
Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection.
Delay Minimization for Federated Learning Over Wireless Communication Networks
In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated.
Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm
Hence, in this review, a pair of dominant methodologies of using DL for wireless communications are investigated.
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
In federated optimization, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client in each communication round.
A Novel Spectrally-Efficient Uplink Hybrid-Domain NOMA System
This paper proposes a novel hybrid-domain (HD) non-orthogonal multiple access (NOMA) approach to support a larger number of uplink users than the recently proposed code-domain NOMA approach, i. e., sparse code multiple access (SCMA).
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks
Since the data size of each computational task is different, as the requested computational task varies, the BSs must adjust their resource (subcarrier and transmit power) and task allocation schemes to effectively serve the users.
Learning Centric Power Allocation for Edge Intelligence
While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power.
Fast-Convergent Federated Learning
In this paper, we propose a fast-convergent federated learning algorithm, called FOLB, which performs intelligent sampling of devices in each round of model training to optimize the expected convergence speed.
Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learning
In this paper, we consider a cellular Internet of UAVs, where the UAVs execute sensing tasks through cooperative sensing and transmission to minimize the age of information (AoI).
Convergence of Federated Learning over a Noisy Downlink
The PS has access to the global model and shares it with the devices for local training, and the devices return the result of their local updates to the PS to update the global model.
Wireless for Machine Learning
As data generation increasingly takes place on devices without a wired connection, machine learning (ML) related traffic will be ubiquitous in wireless networks.
A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning
As one of the key communication scenarios in the 5th and also the 6th generation (6G) of mobile communication networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.
When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm
Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged.
Networking and Internet Architecture
Learning Mixtures of Low-Rank Models
We study the problem of learning mixtures of low-rank models, i. e. reconstructing multiple low-rank matrices from unlabelled linear measurements of each.
Enhanced First and Zeroth Order Variance Reduced Algorithms for Min-Max Optimization
Specifically, a novel variance reduction algorithm SREDA was proposed recently by (Luo et al. 2020) to solve such a problem, and was shown to achieve the optimal complexity dependence on the required accuracy level $\epsilon$.
Towards Self-learning Edge Intelligence in 6G
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.
Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing
A class of mini-batch stochastic alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model.
Blind Federated Edge Learning
At each iteration, wireless devices perform local updates using their local data and the most recent global model received from the PS, and send their local updates to the PS over a wireless fading multiple access channel (MAC).
Intelligent Omni-Surface: Ubiquitous Wireless Transmission by Reflective-Transmissive Metasurface
Intelligent reflecting surface (IRS), which is capable to adjust propagation conditions by controlling phase shifts of the reflected waves that impinge on the surface, has been widely analyzed for enhancing the performance of wireless systems.
Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks
The data uploading performance of IoT network and the computational capacity of edge servers are entangled with each other in influencing the FL model training process.
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients
The proposed BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning.
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks
Analytical results show that, the proposed VD-RL algorithm is guaranteed to converge to a local optimal solution of the non-convex optimization problem.
Robotic Communications for 5G and Beyond: Challenges and Research Opportunities
Furthermore, dynamic trajectory design and resource allocation for both indoor and outdoor robots are provided to verify the performance of robotic communications in the context of typical robotic application scenarios.
Robotics Systems and Control Signal Processing Systems and Control
6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities
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
A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations
To overcome this drawback, we propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach.
Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA
To enable a capacity-optimal network, a novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
Context-Aware Security for 6G Wireless The Role of Physical Layer Security
Sixth generation systems are expected to face new security challenges, while opening up new frontiers towards context awareness in the wireless edge.
Cryptography and Security Signal Processing
Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation
Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
Artificial Intelligence Driven UAV-NOMA-MEC in Next Generation Wireless Networks
To this end, the fundamentals of this framework are first introduced.
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization
An attacker in FL may control a number of participant clients, and purposely craft the uploaded model parameters to manipulate system outputs, namely, model poisoning (MP).
Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks
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.
On the Application of BAC-NOMA to 6G umMTC
This letter studies the application of backscatter communications (BackCom) assisted non-orthogonal multiple access (BAC-NOMA) to the envisioned sixth-generation (6G) ultra-massive machine type communications (umMTC).
Information Theory Information Theory
MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning
We consider a setting that lies between pure offline reinforcement learning (RL) and pure online RL called deployment constrained RL in which the number of policy deployments for data sampling is limited.
Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems
Then, the problem of user selection and bandwidth allocation is studied for FL implemented over a hybrid VLC/RF system aiming to optimize the FL performance.
Spatial Equalization Before Reception: Reconfigurable Intelligent Surfaces for Multi-path Mitigation
Reconfigurable intelligent surfaces (RISs), which enable tunable anomalous reflection, have appeared as a promising method to enhance wireless systems.
Information Theory Information Theory
Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks
The effective deployment of connected vehicular networks is contingent upon maintaining a desired performance across spatial and temporal domains.
Federated Learning: A Signal Processing Perspective
Learning in a federated manner differs from conventional centralized machine learning, and poses several core unique challenges and requirements, which are closely related to classical problems studied in the areas of signal processing and communications.
Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems
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.
A General Derivative Identity for the Conditional Mean Estimator in Gaussian Noise and Some Applications
In the second part of the paper, via various choices of ${\bf U}$, the new identity is used to generalize many of the known identities and derive some new ones.
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
Then, we present a detailed literature review on the use of communication techniques for its efficient deployment.
Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps
Eigenvector perturbation analysis plays a vital role in various data science applications.
Federated Learning for Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI).
Present and Future of Reconfigurable Intelligent Surface-Empowered Communications
Signal processing and communication communities have witnessed the rise of many exciting communication technologies in recent years.
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design
Owing to the low communication costs and privacy-promoting capabilities, Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients.
Intelligent Reflecting Surface-assisted Free-space Optical Communications
Free-space optical (FSO) systems are able to offer the high data-rate, secure, and cost-efficient communication links required for applications such as wireless front- and backhauling for 5G and 6G communication networks.
PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement Learning Policies
We propose a novel reinforcement learning based framework PoBRL for solving multi-document summarization.
Federated Learning for Industrial Internet of Things in Future Industries
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries.
Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling
For this purpose, we formulate the framework of decision-making oriented clustering and propose an algorithm providing a decision-based partition of the data space and good representative decisions.
Low-Latency Federated Learning over Wireless Channels with Differential Privacy
Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach.
Federated Learning with Downlink Device Selection
At each iteration, the PS broadcasts different quantized global model updates to different participating devices based on the last global model estimates available at the devices.
Quality of Service Guarantees for Physical Unclonable Functions
A public ring oscillator (RO) output dataset is used to illustrate that a truncated Gaussian distribution can be fitted to transformed RO outputs that are inputs to uniform scalar quantizers such that reliability guarantees can be provided for each bit extracted from any PUF device under additive Gaussian noise components by eliminating a small subset of PUF outputs.
RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning
We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimization problem of maximizing the total network sum-rate.
6G Internet of Things: A Comprehensive Survey
The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems.
Simultaneously Transmitting and Reflecting (STAR) Intelligent Omni-Surfaces, Their Modeling and Implementation
With the rapid development of advanced electromagnetic manipulation technologies, researchers and engineers are starting to study smart surfaces that can achieve enhanced coverages, high reconfigurability, and are easy to deploy.
MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces
Semantic segmentation is a process of partitioning an image into multiple segments for recognizing humans and objects, which can be widely applied in scenarios such as healthcare and safety monitoring.
Rate-Splitting Multiple Access for Downlink MIMO: A Generalized Power Iteration Approach
Rate-splitting multiple access (RSMA) is a general multiple access scheme for downlink multi-antenna systems embracing both classical spatial division multiple access and more recent non-orthogonal multiple access.
Federated Distributionally Robust Optimization for Phase Configuration of RISs
In this article, we study the problem of robust reconfigurable intelligent surface (RIS)-aided downlink communication over heterogeneous RIS types in the supervised learning setting.
Joint LED Selection and Precoding Optimization for Multiple-User Multiple-Cell VLC Systems
First, a MU-MC-VLC system model is established, and then a sum-rate maximization problem under dimming level and illumination uniformity constraints is formulated.
Physical Layer Anonymous Precoding: The Path to Privacy-Preserving Communications
In contrast to traditional security and privacy designs that aim to prevent confidential information from being eavesdropped upon by adversaries, or learned by unauthorized parties, in this paper we consider designs that mask the users' identities during communication, hence resulting in anonymous communications.
MURO: Deployment Constrained Reinforcement Learning with Model-based Uncertainty Regularized Batch Optimization
In the high support region (low uncertainty), we encourage our policy by taking an aggressive update.
Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning
The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining.
Federated Learning over Wireless IoT Networks with Optimized Communication and Resources
Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of things (IoT) networks.
Communication-Efficient Federated Learning via Quantized Compressed Sensing
We also present a low-complexity approach for the gradient reconstruction.
Collaborative Learning over Wireless Networks: An Introductory Overview
This is partly due to the communication bottleneck limiting the overall computation speed.
BScNets: Block Simplicial Complex Neural Networks
Simplicial neural networks (SNN) have recently emerged as the newest direction in graph learning which expands the idea of convolutional architectures from node space to simplicial complexes on graphs.
Reconfigurable Holographic Surfaces for Future Wireless Communications
Future wireless communications look forward to constructing a ubiquitous intelligent information network with high data rates through cost-efficient devices.
Adversarial Neural Networks for Error Correcting Codes
To demonstrate the performance of our framework, we combine it with the very recent neural decoders and show improved performance compared to the original models and traditional decoding algorithms on various codes.
Learning Mixtures of Linear Dynamical Systems
We study the problem of learning a mixture of multiple linear dynamical systems (LDSs) from unlabeled short sample trajectories, each generated by one of the LDS models.
An Indirect Rate-Distortion Characterization for Semantic Sources: General Model and the Case of Gaussian Observation
A new source model, which consists of an intrinsic state part and an extrinsic observation part, is proposed and its information-theoretic characterization, namely its rate-distortion function, is defined and analyzed.
Federated Stochastic Gradient Descent Begets Self-Induced Momentum
Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistical model utilizing the data and computation resources of the clients without directly exposing their privacy-sensitive data.
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence
A common way of characterizing minimax estimators in point estimation is by moving the problem into the Bayesian estimation domain and finding a least favorable prior distribution.
Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission
In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate.
Proximal Policy Optimization-based Transmit Beamforming and Phase-shift Design in an IRS-aided ISAC System for the THz Band
Then the joint optimization of transmit beamforming and phase-shift design is achieved by gradient-based, primal-dual proximal policy optimization (PPO) in the multi-user multiple-input single-output (MISO) scenario.
Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing
However, existing algorithms face issues with slow convergence and/or robustness of performance due to the considerable heterogeneity of data distribution, computation and communication capability at the edge.
Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning
Data-aided channel estimation is a promising solution to improve channel estimation accuracy by exploiting data symbols as pilot signals for updating an initial channel estimate.
RIS-Assisted Visible Light Communication Systems: A Tutorial
Therefore, the skip-zone dilemma must be resolved to ensure the efficient operation of 5G and beyond networks.
Sensing RISs: Enabling Dimension-Independent CSI Acquisition for Beamforming
In this paper, we propose a dimension-independent channel state information (CSI) acquisition approach in which the required pilot overhead is independent of the number of RIS elements.
Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks.
Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication
It is anticipated that 6G wireless networks will accelerate the convergence of the physical and cyber worlds and enable a paradigm-shift in the way we deploy and exploit communication networks.
Decentralized Stochastic Optimization with Inherent Privacy Protection
Decentralized stochastic optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing.
Secure and Private Source Coding with Private Key and Decoder Side Information
The problem of secure source coding with multiple terminals is extended by considering a remote source whose noisy measurements are the correlated random variables used for secure source reconstruction.
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification
However, it is challenging to determine the network resources and road sensor placements for multi-stage training with multi-modal datasets in multi-variant scenarios.
Matching Pursuit Based Scheduling for Over-the-Air Federated Learning
Compared to the state-of-the-art, the proposed scheme poses a drastically lower computational load on the system: For $K$ devices and $N$ antennas at the parameter server, the benchmark complexity scales with $\left(N^2+K\right)^3 + N^6$ while the complexity of the proposed scheme scales with $K^p N^q$ for some $0 < p, q \leq 2$.
Meta-material Sensor Based Internet of Things: Design, Optimization, and Implementation
However, to maximize the sensing accuracy, the structures of meta-IoT sensors need to be optimized considering their joint influence on sensing and transmission, which is challenging due to the high computational complexity in evaluating the influence, especially given a large number of sensors.
Sensor Deployment and Link Analysis in Satellite IoT Systems for Wildfire Detection
Climate change has been identified as one of the most critical threats to human civilization and sustainability.
Reconfigurable Intelligent Computational Surfaces: When Wave Propagation Control Meets Computing
The envisioned sixth-generation (6G) of wireless networks will involve an intelligent integration of communications and computing, thereby meeting the urgent demands of diverse applications.
Beamforming Design for the Performance Optimization of Intelligent Reflecting Surface Assisted Multicast MIMO Networks
Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS.
Interference Cancellation GAN Framework for Dynamic Channels
Symbol detection is a fundamental and challenging problem in modern communication systems, e. g., multiuser multiple-input multiple-output (MIMO) setting.
Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach
Hence, the BS must select an appropriate resource block for each user as well as determine and transmit part of the semantic information to the users.
On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations
Furthermore, we define an optimization problem to reduce this upper bound and the total privacy leakage.
Alternating Differentiation for Optimization Layers
In this paper, we developed a new framework, named Alternating Differentiation (Alt-Diff), that differentiates optimization problems (here, specifically in the form of convex optimization problems with polyhedral constraints) in a fast and recursive way.
Toward Secure and Private Over-the-Air Federated Learning
In this paper, a novel secure and private over-the-air federated learning (SP-OTA-FL) framework is studied where noise is employed to protect data privacy and system security.
Impact of Channel Models on Performance Characterization of RIS-Assisted Wireless Systems
The performance characterization of communication systems assisted by large reconfigurable intelligent surfaces (RISs) significantly depends on the adopted models for the underlying channels.
Random Orthogonalization for Federated Learning in Massive MIMO Systems
We propose a novel communication design, termed random orthogonalization, for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system.
Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments
We propose a deep actor-critic algorithm that accounts for channel correlations and destination motion by constructing the state representation to include the current position of the receiver and the phase shift values and receiver positions that correspond to a window of previous time steps.
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.
Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions
In this survey, we present a comprehensive overview of the latest advances in the HMIMO communications paradigm, with a special focus on their physical aspects, their theoretical foundations, as well as the enabling technologies for HMIMO systems.
Adversarial Learning for Implicit Semantic-Aware Communications
In this paper, we consider the implicit semantic communication problem in which hidden relations and closely related semantic terms that cannot be recognized from the source signals need to also be delivered to the destination user.
Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods
Efficient computation of the optimal transport distance between two distributions serves as an algorithm subroutine that empowers various applications.
Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy
This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server.
On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds
A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc.
Challenges and Opportunities for Beyond-5G Wireless Security
The demand for broadband wireless access is driving research and standardization of 5G and beyond-5G wireless systems.
An Extended Model for Ecological Robustness to Capture Power System Resilience
The RECO of resilient ecosystems favors a balance of food webs' network efficiency and redundancy.
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning
The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on deep neural network-based classification models.
Secure Federated Learning for Cognitive Radio Sensing
This paper considers reliable and secure Spectrum Sensing (SS) based on Federated Learning (FL) in the Cognitive Radio (CR) environment.
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks
Model-based, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques.
25 Years of Signal Processing Advances for Multiantenna Communications
In 1998, mobile phones were still in the process of becoming compact and affordable devices that could be widely utilized in both developed and developing countries.
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.
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.
Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges
In this paper, we propose leveraging deep reinforcement learning for interference management to tackle this shortcoming.
YOLO: An Efficient Terahertz Band Integrated Sensing and Communications Scheme with Beam Squint
Moreover, we propose a supporting method based on extended array signal estimation, which utilizes the phase changes of different frequency subcarriers within different OFDM symbols to estimate the distance and velocity of dynamic targets.
Reconstructing Graph Diffusion History from a Single Snapshot
They are exclusively based on the maximum likelihood estimation (MLE) formulation and require to know true diffusion parameters.
Efficient Reinforcement Learning with Impaired Observability: Learning to Act with Delayed and Missing State Observations
We present algorithms and establish near-optimal regret upper and lower bounds, of the form $\tilde{\mathcal{O}}(\sqrt{{\rm poly}(H) SAK})$, for RL in the delayed and missing observation settings.
MIMO Detection under Hardware Impairments: Learning with Noisy Labels
By using the outputs of coarse data detection as noisy training data, the model-driven method avoids the need for additional training overhead beyond traditional pilot overhead for channel estimation.
Analysis of the Relative Entropy Asymmetry in the Regularization of Empirical Risk Minimization
The analysis of the solution unveils the following properties of relative entropy when it acts as a regularizer in the ERM-RER problem: i) relative entropy forces the support of the Type-II solution to collapse into the support of the reference measure, which introduces a strong inductive bias that dominates the evidence provided by the training data; ii) Type-II regularization is equivalent to classical relative entropy regularization with an appropriate transformation of the empirical risk function.
Differentially Private Wireless Federated Learning Using Orthogonal Sequences
A new FL convergence bound is derived which, combined with the privacy guarantees, allows for a smooth tradeoff between the achieved convergence rate and differential privacy levels.
Overcoming Beam Squint in Dual-Wideband mmWave MIMO Channel Estimation: A Bayesian Multi-Band Sparsity Approach
A probabilistic model is built to induce the common sparsity in the spatial domain, and the first-order Taylor expansion is adopted to get rid of the grid mismatch in the dictionaries.
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.
On the Validation of Gibbs Algorithms: Training Datasets, Test Datasets and their Aggregation
The dependence on training data of the Gibbs algorithm (GA) is analytically characterized.
Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions
The development of applications based on artificial intelligence and implemented over wireless networks is increasingly rapidly and is expected to grow dramatically in the future.
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks
Moreover, an error feedback strategy is introduced to compensate for both compression and reconstruction errors.
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity
With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training.
Estimation of Complex Valued Laplacian Matrices for Topology Identification in Power Systems
In this paper, we investigate the problem of estimating a complex-valued Laplacian matrix with a focus on its application in the estimation of admittance matrices in power systems.
ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting
The criticality of prompt and precise traffic forecasting in optimizing traffic flow management in Intelligent Transportation Systems (ITS) has drawn substantial scholarly focus.
$L^1$ Estimation: On the Optimality of Linear Estimators
Consider the problem of estimating a random variable $X$ from noisy observations $Y = X+ Z$, where $Z$ is standard normal, under the $L^1$ fidelity criterion.
Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications
The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration.
Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning
However, since the model is split at a specific layer, known as a cut layer, into both client-side and server-side models for the SFL, the choice of the cut layer in SFL can have a substantial impact on the energy consumption of clients and their privacy, as it influences the training burden and the output of the client-side models.
Modelling the Formation of Peer-to-Peer Trading Coalitions and Prosumer Participation Incentives in Transactive Energy Communities
Our experimental study shows that, for both market models, only a small number of P2P contracts, and only a fraction of total prosumers in the community are required to achieve the majority of the maximal potential Gains from Trade.
OFDMA-F$^2$L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface
Federated learning (FL) can suffer from a communication bottleneck when deployed in mobile networks, limiting participating clients and deterring FL convergence.
From OTFS to DD-ISAC: Integrating Sensing and Communications in the Delay Doppler Domain
The recently proposed Orthogonal Time Frequency Space (OTFS) modulation, which exploits various advantages of Delay Doppler (DD) channels, has been shown to support reliable communication in high-mobility scenarios.
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach
The attacker then adversarially regenerates the graph structural correlations while maximizing the FL training loss, and subsequently generates malicious local models using the adversarial graph structure and the training data features of the benign ones.
Acceleration Estimation of Signal Propagation Path Length Changes for Wireless Sensing
Furthermore, in multi-target scenarios, the fall detection achieves an average true positive rate of 89. 56% and a false positive rate of 11. 78%, demonstrating its importance in enhancing indoor wireless sensing capabilities.
Hazard resistance-based spatiotemporal risk analysis for distribution network outages during hurricanes
This method converts the time-varying failure probability of a component into a hazard resistance as a time-invariant value during the simulation of evolving hazards.
Equivalence of the Empirical Risk Minimization to Regularization on the Family of f-Divergences
The solution to empirical risk minimization with $f$-divergence regularization (ERM-$f$DR) is presented under mild conditions on $f$.
Block-Sparse Tensor Recovery
This work explores the fundamental problem of the recoverability of a sparse tensor being reconstructed from its compressed embodiment.
Private Knowledge Sharing in Distributed Learning: A Survey
In this light, it is crucial to utilize information in learning processes that are either distributed or owned by different entities.
Digital versus Analog Transmissions for Federated Learning over Wireless Networks
In this paper, we quantitatively compare these two effective communication schemes, i. e., digital and analog ones, for wireless federated learning (FL) over resource-constrained networks, highlighting their essential differences as well as their respective application scenarios.
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling.
Adaptive Federated Learning Over the Air
In contrast, an Adam-like algorithm converges at the $\mathcal{O}( 1/T )$ rate, demonstrating its advantage in expediting the model training process.
A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids
The distinctive contribution of this paper lies in its holistic approach to both static and dynamic uncertainties in smart grids.
DASA: Delay-Adaptive Multi-Agent Stochastic Approximation
We consider a setting in which $N$ agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server.
SignSGD with Federated Voting
SignSGD with majority voting (signSGD-MV) is an effective distributed learning algorithm that can significantly reduce communication costs by one-bit quantization.
Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm
Based on our analytical results, we then propose a joint communication and computation resource management design to minimize an average squared gradient norm bound, subject to constraints on the transmit power, overall system energy consumption, and training delay.
Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning
This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs.
Decision Transformer for Wireless Communications: A New Paradigm of Resource Management
By leveraging the power of DT models learned over extensive datasets, the proposed architecture is expected to achieve rapid convergence with many fewer training epochs and higher performance in a new context, e. g., similar tasks with different state and action spaces, compared with DRL.
Integrated Sensing and Communication for Edge Inference with End-to-End Multi-View Fusion
Integrated sensing and communication (ISAC) is a promising solution to accelerate edge inference via the dual use of wireless signals.
