Search Results for author:
Found 103 papers, 10 papers with code
Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments
Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i. e., the subflow size.
Integrated Sensing and Communication Under DISCO Physical-Layer Jamming Attacks
Moreover, a theoretical analysis is conducted to quantify the impact of DISCO jamming attacks.
Harnessing the Power of AI-Generated Content for Semantic Communication
Recognizing the transformative capabilities of AI-generated content (AIGC) technologies in content generation, this paper explores a pioneering approach by integrating them into SemCom to address the aforementioned challenges.
From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection
To address the challenges posed by system and data heterogeneities in the FL process, we study a goal-directed client selection problem based on the model analytics framework by selecting a subset of clients for the model training.
Wavenumber Domain Sparse Channel Estimation in Holographic MIMO
In this paper, we investigate the sparse channel estimation in holographic multiple-input multiple-output (HMIMO) systems.
Unleashing the True Power of Age-of-Information: Service Aggregation in Connected and Autonomous Vehicles
With an increase in AoI, incremental service aggregation issues are observed with out-of-sequence information updates, which hampers the performance of low-latency applications in CAVs.
A Zero Trust Framework for Realization and Defense Against Generative AI Attacks in Power Grid
Understanding the potential of generative AI (GenAI)-based attacks on the power grid is a fundamental challenge that must be addressed in order to protect the power grid by realizing and validating risk in new attack vectors.
Towards Intelligent Communications: Large Model Empowered Semantic Communications
First, we propose a new semantic communication architecture that seamlessly integrates large models into semantic communication through the introduction of a memory module.
Passive Beamforming For Practical RIS-Assisted Communication Systems With Non-Ideal Hardware
Reconfigurable intelligent surface (RIS) technology is a promising solution to improve the performance of existing wireless communications.
When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment
AI agents based on multimodal large language models (LLMs) are expected to revolutionize human-computer interaction and offer more personalized assistant services across various domains like healthcare, education, manufacturing, and entertainment.
ClST: A Convolutional Transformer Framework for Automatic Modulation Recognition by Knowledge Distillation
With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy.
Optical Integrated Sensing and Communication: Architectures, Potentials and Challenges
Integrated sensing and communication (ISAC) is viewed as a crucial component of future mobile networks and has gained much interest in both academia and industry.
Scalable AI Generative Content for Vehicular Network Semantic Communication
Experimental results suggest that the proposed method surpasses the baseline in perceiving vehicles in blind spots and effectively compresses communication data.
Cross-Domain Dual-Functional OFDM Waveform Design for Accurate Sensing/Positioning
For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communications according to prior knowledge of the communication channel.
Free Space Optical Communication for Inter-Satellite Link: Architecture, Potentials and Trends
The sixth-generation (6G) network is expected to achieve global coverage based on the space-air-ground integrated network, and the latest satellite network will play an important role in it.
An Efficient Federated Learning Framework for Training Semantic Communication System
Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy.
SemantIC: Semantic Interference Cancellation Towards 6G Wireless Communications
This letter proposes a novel anti-interference technique, semantic interference cancellation (SemantIC), for enhancing information quality towards the sixth-generation (6G) wireless networks.
Semantic-Forward Relaying: A Novel Framework Towards 6G Cooperative Communications
This letter proposes a novel relaying framework, semantic-forward (SF), for cooperative communications towards the sixth-generation (6G) wireless networks.
Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework
To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL.
DISCO Might Not Be Funky: Random Intelligent Reflective Surface Configurations That Attack
Moreover, we outline future research directions and challenges for the DIRS-based FPJ and its anti-jamming precoding to stimulate this line of research and pave the way for practical applications.
Channel Reciprocity Attacks Using Intelligent Surfaces with Non-Diagonal Phase Shifts
While reconfigurable intelligent surface (RIS) technology has been shown to provide numerous benefits to wireless systems, in the hands of an adversary such technology can also be used to disrupt communication links.
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT).
A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward?
By creating multipath backscatter links and amplify signal strength, reconfigurable intelligent surfaces (RIS) and decode-and-forward (DF) relaying are shown to degrade the latency of the ultrareliable low-latency communications (URLLCs) and enhanced mobile broadband (eMBB) multiplexing system.
Mean Field Game-based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems
In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game~(MFG).
Anti-Jamming Precoding Against Disco Intelligent Reflecting Surfaces Based Fully-Passive Jamming Attacks
Numerical results are also presented to evaluate the effectiveness of the proposed anti-jamming precoder against the DIRS-based FPJs and the feasibility of the designed data frame used by the legitimate AP to estimate the statistical characteristics.
RIS-assisted High-Speed Railway Integrated Sensing and Communication System
One technology that has the potential to improve wireless communications in years to come is integrated sensing and communication (ISAC).
A New Heterogeneous Hybrid Massive MIMO Receiver with An Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning
To address this problem, a new heterogeneous sub-connected hybrid analog and digital (HAD) MIMO structure is proposed with an intrinsic ability of removing phase ambiguity and a corresponding new framework is developed to implement a rapid high-precision DOA estimation using only single time-slot.
An Anti-Jamming Strategy for Disco Intelligent Reflecting Surfaces Based Fully-Passive Jamming Attacks
Emerging intelligent reflecting surfaces (IRSs) significantly improve system performance, while also pose a huge risk for physical layer security.
Dynamic UAV Swarm Collaboration for Multi-Targets Tracking under Malicious Jamming: Joint Power, Path and Target Association Optimization
First, a cluster-evolutionary target association (CETA) algorithm is proposed, which involves dividing the UAV swarm into the multiple sub-swarms and individually matching these sub-swarms to targets.
Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications
We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare.
Location Tracking for Reconfigurable Intelligent Surfaces Aided Vehicle Platoons: Diverse Sparsities Inspired Approaches
In this paper, we investigate the employment of reconfigurable intelligent surfaces (RISs) into vehicle platoons, functioning in tandem with a base station (BS) in support of the high-precision location tracking.
Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things
In this paper, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering.
Robust Secrecy via Aerial Reflection and Jamming: Joint Optimization of Deployment and Transmission
The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform.
Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses
In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems.
Computation and Privacy Protection for Satellite-Ground Digital Twin Networks
Satellite-ground integrated digital twin networks (SGIDTNs) are regarded as innovative network architectures for reducing network congestion, enabling nearly-instant data mapping from the physical world to digital systems, and offering ubiquitous intelligence services to terrestrial users.
Disco Intelligent Reflecting Surfaces: Active Channel Aging for Fully-Passive Jamming Attacks
A theoretical analysis of the proposed DIRS-based FPJ that provides an evaluation of the DIRS-based jamming attacks is derived.
Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse
Furthermore, we propose a multi-task enhanced auction-based mechanism to match and price AVs and MARs for RSUs to provision real-time and effective services.
Multiple-Antenna Aided Aeronautical Communications in Air-Ground Integrated Networks: Channel Estimation, Reliable Transmission, and Multiple Access
To provide seamless coverage during all flight phases, aeronautical communications systems (ACS) have to integrate space-based, air-based, as well as ground-based platforms to formulate aviation-oriented space-air-ground integrated networks (SAGINs).
Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control
In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.
Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems
To further improve the efficiency of wireless data aggregation and model learning, over-the-air computation (AirComp) is emerging as a promising solution by using the superposition characteristics of wireless channels.
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.
3-D Positioning and Resource Allocation for Multi-UAV Base Stations Under Blockage-Aware Channel Model
Based on the proposed channel model, we formulate the joint optimization problem of UAV three-dimensional (3-D) positioning and resource allocation, by power allocation, user association, and subcarrier allocation, to maximize the minimum achievable rate among users.
Identifying Operation Equilibrium in Integrated Electricity, Natural Gas, and Carbon-Emission Markets
Carbon emission markets can play a significant role in this transition by putting a price on carbon and giving electricity producers an incentive to reduce their emissions.
Deep Decarbonization of Multi-Energy Systems: A Carbon-Oriented Framework with Cross Disciplinary Technologies
The retirement of unabated coal power plants, the plummeting cost of renewable energy technologies, along with more aggressive public policies and regulatory reforms, are occurring at an unprecedented speed to decarbonize the power and energy systems towards the 2030 and 2050 climate goals.
Terahertz-Band Near-Space Communications: From a Physical-Layer Perspective
Facilitated by rapid technological development of the near-space platform stations (NSPS), near-space communication (NS-COM) is envisioned to play a pivotal role in the space-air-ground integrated network for sixth-generation (6G) communications and beyond.
LEO Satellite Access Network (LEO-SAN) Towards 6G: Challenges and Approaches
With the rapid development of satellite communication technologies, the space-based access network has been envisioned as a promising complementary part of the future 6G network.
Near Space Communications (NS-COM): A New Regime in Space-Air-Ground Integrated Network (SAGIN)
Precipitated by the technological innovations of the near-space platform stations (NSPS), the near space communication (NS-COM) network has emerged as an indispensable part of the next-generation space-air-ground integrated network (SAGIN) that facilitates ubiquitous coverage and broadband data transfer.
Data-and-Knowledge Dual-Driven Automatic Modulation Recognition for Wireless Communication Networks
In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of different modulations.
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.
Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks
In this paper, we propose a novel generative adversarial network (GAN)-based countermeasure approach to safeguard the DNN-based AMC systems against adversarial attack examples.
LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality
In this paper, we design an edge-based energy-aware MAR system that enables MAR devices to dynamically change their configurations, such as CPU frequency, computation model size, and image offloading frequency based on user preferences, camera sampling rates, and available radio resources.
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review
Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of the Earth's surface at a distance of data acquisition devices, such as aircraft, spacecraft, and satellite.
LEOPARD: Parallel Optimal Deep Echo State Network Prediction Improves Service Coverage for UAV-Assisted Outdoor Hotspots
Unmanned aerial vehicle (UAV) base stations (BSs) can help meet the dynamic traffic demand of flash mobile crowds, but user movements also pose a significant challenge on fast-tracking for avoiding service interruption.
MetaRadar: Multi-target Detection for Reconfigurable Intelligent Surface Aided Radar Systems
To address this issue, we propose to use the reconfigurable intelligent surface (RIS) to improve the detection accuracy of radar systems due to its capability to customize channel conditions by adjusting its phase shifts, which is referred to as MetaRadar.
Artificial Intelligence for the Metaverse: A Survey
Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform.
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT
Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment.
An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic
Recently, the applications of deep neural network (DNN) have been very prominent in many fields such as computer vision (CV) and natural language processing (NLP) due to its superior feature extraction performance.
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.
Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information
To improve communication capacity, we first model the blockage effect caused by buildings according to the three-dimensional (3-D) geographic information.
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.
SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers
Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies.
On the Impact of Oscillator Phase Noise in an IRS-assisted MISO TDD System
In this work, we study the impact of the multiplicative phase noise in an IRS-assisted system.
Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT
Alternating direction method of multipliers (ADMM) has a structure that allows for decentralized implementation, and has shown faster convergence than gradient descent based methods.
Minimizing Delay in Network Function Visualization with Quantum Computing
There are vast service chains in NFV to meet users' requests, which are composed of a sequence of network functions.
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.
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
Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions
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
Coalition Game Based Full-duplex Popular Content Distribution in mmWave Vehicular Networks
We evaluate the proposed scheme by extensive simulations in mmWave vehicular networks.
Fairness
Information Theory
Networking and Internet Architecture
Information Theory
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.
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices
Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications.
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.
Edge-assisted Democratized Learning Towards Federated Analytics
Therefore, we need to design a robust learning mechanism than the FL that (i) unleashes a viable infrastructure for FA and (ii) trains learning models with better generalization capability.
Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications
In order to accurately measure the energy consumption on the smartphone and obtain the breakdown of energy consumed by each phase of the object detection processing pipeline, we propose a new measurement strategy.
Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks
Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices.
MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning
To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals.
MetaLocalization: Reconfigurable Intelligent Surface Aided Multi-user Wireless Indoor Localization
As the RIS is able to customize the radio channels by adjusting the phase shifts of the signals reflected at the surface, the localization accuracy in the RIS aided scheme can be improved by choosing the proper phase shifts with significant differences of RSS values among adjacent locations.
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.
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
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
Architectural Design Alternatives based on Cloud/Edge/Fog Computing for Connected Vehicles
These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications.
Networking and Internet Architecture
An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach
In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users.
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
On Spatial Multiplexing Using Reconfigurable Intelligent Surfaces
We consider an uplink multi-user scenario and investigate the use of reconfigurable intelligent surfaces (RIS) to optimize spatial multiplexing performance when a linear receiver is used.
Sense-Store-Send: Trajectory Optimization for a Buffer-aided Internet of UAVs
To minimize the overall completion time for all the sensing tasks, we formulate a joint trajectory, sensing location, and sensing time optimization problem.
Beyond Intelligent Reflecting Surfaces: Reflective-Transmissive Metasurface Aided Communications for Full-dimensional Coverage Extension
In this paper, we study an intelligent omni-surface (IOS)-assisted downlink communication system, where the link quality of a mobile user (MU) can be improved with a proper IOS phase shift design.
Signal Processing
Dispersed Federated Learning: Vision, Taxonomy, and Future Directions
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
MetaRadar: Indoor Localization by Reconfigurable Metamaterials
However, in MetaRadar, it is challenging to build radio maps for all the radio environments generated by metamaterial units and select suitable maps from all the possible maps to realize a high accuracy localization.
Adaptive Compressive Sampling for Mid-infrared Spectroscopic Imaging
Minfrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology.
Deep Learning Techniques for Future Intelligent Cross-Media Retrieval
With the advancement in technology and the expansion of broadcasting, cross-media retrieval has gained much attention.
Towards Ubiquitous Positioning by Leveraging Reconfigurable Intelligent Surface
The received signal strength (RSS) based technique is widely utilized for ubiquitous positioning due to its advantage of simple implementability.
Codebook-Based Beam Tracking for Conformal ArrayEnabled UAV MmWave Networks
Aiming to address the beam tracking difficulties, we propose to integrate the conformal array (CA) with the surface of each UAV, which enables the full spatial coverage and the agile beam tracking in highly dynamic UAV mmWave networks.
Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities
Recently there has been a flurry of research on the use of reconfigurable intelligent surfaces (RIS) in wireless networks to create smart radio environments.
Decentralized Learning for Channel Allocation in IoT Networks over Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game
We study a decentralized channel allocation problem in an ad-hoc Internet of Things network underlaying on the spectrum licensed to a primary cellular network.
Hyperspectral-Multispectral Image Fusion with Weighted LASSO
Hyperspectral imaging provides superior material specificity, while multispectral images are faster to collect at greater fidelity.
Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach
First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy.
Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach
First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the expected residual of scheduled energy for the MEC networks and we show this problem is an NP-hard problem.
Optimal Pricing of Internet of Things: A Machine Learning Approach
For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers.
Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
IoT devices with intelligence require the use of effective machine learning paradigms.
Distributed, Parallel, and Cluster Computing
A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network
In this paper, we propose a system framework consisting of UEs clustering, UAV-BS placement, UEs trajectories prediction, and UAV-BS reposition matching scheme, to serve the UEs seamlessly as well as minimize the energy cost of UAV-BSs' reposition trajectories.
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather.
Regional Robust Secure Precise Wireless Transmission Design for Multi-user UAV Broadcasting System
Proposed regional robust schemes are designed for optimizing the secrecy performance in the whole error region around the estimated location.
Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks
In terms of how to dynamically adjust traffic signals' duration, existing works either split the traffic signal into equal duration or extract limited traffic information from the real data.
Mobile Big Data Analytics Using Deep Learning and Apache Spark
The proliferation of mobile devices, such as smartphones and Internet of Things (IoT) gadgets, results in the recent mobile big data (MBD) era.
Non-parametric Bayesian Learning with Deep Learning Structure and Its Applications in Wireless Networks
In this paper, we present an infinite hierarchical non-parametric Bayesian model to extract the hidden factors over observed data, where the number of hidden factors for each layer is unknown and can be potentially infinite.
