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Found 28 papers, 4 papers with code
Decentralizing Coherent Joint Transmission Precoding via Fast ADMM with Deterministic Equivalents
Simulation results demonstrate the effectiveness of our proposed decentralized precoding scheme, which achieves performance similar to the optimal centralized precoding scheme.
Decentralizing Coherent Joint Transmission Precoding via Deterministic Equivalents
In order to control the inter-cell interference for a multi-cell multi-user multiple-input multiple-output network, we consider the precoder design for coordinated multi-point with downlink coherent joint transmission.
Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access: A Free Probability Theory Approach
Grant-free random access (RA) has been recognized as a promising solution to support massive connectivity due to the removal of the uplink grant request procedures.
Approximate Message Passing-Enhanced Graph Neural Network for OTFS Data Detection
Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to support high-mobility wireless communications, for which, cost-effective data detectors are critical.
Green Edge AI: A Contemporary Survey
Guided by these principles, we then explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference.
How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels
It is also shown that the uplink communication in FL can tolerate a higher bit error rate (BER) than downlink communication, and this difference is quantified by a proposed formula.
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features
However, devices often have non-independent and identically distributed (non-IID) data, meaning their local data distributions can vary significantly.
Feature Matching Data Synthesis for Non-IID Federated Learning
For better privacy preservation, we propose a hard feature augmentation method to transfer real features towards the decision boundary, with which the synthetic data not only improve the model generalization but also erase the information of real features.
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Without data centralization, FL allows clients to share local information in a privacy-preserving manner.
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
Federated learning (FL) is a promising framework for privacy-preserving collaborative learning, where model training tasks are distributed to clients and only the model updates need to be collected at a server.
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named PO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation.
Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access
Most existing studies on joint activity detection and channel estimation for grant-free massive random access (RA) systems assume perfect synchronization among all active users, which is hard to achieve in practice.
Grant-free Massive Random Access with Retransmission: Receiver Optimization and Performance Analysis
Specifically, by jointly leveraging the user activity correlation between adjacent transmission blocks and the historical channel estimation results, we first develop an activity-correlation-aware receiver for grant-free massive RA systems with retransmission based on the correlated approximate message passing (AMP) algorithm.
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design
During training, the server computes gradients on the global coded dataset to compensate for the missing model updates of the straggling devices.
Resource-Constrained Edge AI with Early Exit Prediction
In this paper, we propose an early exit prediction mechanism to reduce the on-device computation overhead in a device-edge co-inference system supported by early-exit networks.
Federated Learning with GAN-based Data Synthesis for Non-IID Clients
A combination of the local private dataset and synthetic dataset with confident pseudo labels leads to nearly identical data distributions among clients, which improves the consistency among local models and benefits the global aggregation.
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees
Federated learning (FL) has attracted much attention as a privacy-preserving distributed machine learning framework, where many clients collaboratively train a machine learning model by exchanging model updates with a parameter server instead of sharing their raw data.
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity
By exploiting the low-latency communication among edge servers for efficient model sharing, SD-FEEL can incorporate more training data, while enjoying much lower latency compared with conventional federated learning.
Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients
Federated edge learning (FEEL) has drawn much attention as a privacy-preserving distributed learning framework for mobile edge networks.
Task-Oriented Communication for Multi-Device Cooperative Edge Inference
To enable low-latency cooperative inference, we propose a learning-based communication scheme that optimizes local feature extraction and distributed feature encoding in a task-oriented manner, i. e., to remove data redundancy and transmit information that is essential for the downstream inference task rather than reconstructing the data samples at the edge server.
Communication-Computation Efficient Device-Edge Co-Inference via AutoML
Device-edge co-inference, which partitions a deep neural network between a resource-constrained mobile device and an edge server, recently emerges as a promising paradigm to support intelligent mobile applications.
Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-free Massive Random Access
In particular, the common sparsity pattern in the received pilot and data signal has been ignored in most existing studies, and auxiliary information of channel decoding has not been utilized for user activity detection.
Joint Activity Detection and Data Decoding in Massive Random Access via a Turbo Receiver
In this paper, we propose a turbo receiver for joint activity detection and data decoding in grant-free massive random access, which iterates between a detector and a belief propagation (BP)-based channel decoder.
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large communication latency in Cloud-based machine learning solutions, while preserving data privacy.
Supporting More Active Users for Massive Access via Data-assisted Activity Detection
Massive machine-type communication (mMTC) has been regarded as one of the most important use scenarios in the fifth generation (5G) and beyond wireless networks, which demands scalable access for a large number of devices.
Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach
Extensive experiments evidence that the proposed task-oriented communication system achieves a better rate-distortion tradeoff than baseline methods and significantly reduces the feature transmission latency in dynamic channel conditions.
Branchy-GNN: a Device-Edge Co-Inference Framework for Efficient Point Cloud Processing
The recent advancements of three-dimensional (3D) data acquisition devices have spurred a new breed of applications that rely on point cloud data processing.
Distributed, Parallel, and Cluster Computing
Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices
Sample simulation results shall be presented to verify the theoretical analysis as well as validate the effectiveness of the proposed algorithm.
Information Theory Information Theory
