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Found 30 papers, 7 papers with code
An overview of domain-specific foundation model: key technologies, applications and challenges
The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application scenarios.
Coordinated Spectral Efficiency Prediction for Real-World 5G CoMP Systems
However, characterizing the CSE is intractable due to the inherent complexity of the CoMP channel model and the diversity of the 5G dynamic network environment, which poses a great challenge for CSE prediction in real-world 5G CoMP systems.
Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
The introduction of fifth-generation (5G) radio technology has revolutionized communications, bringing unprecedented automation, capacity, connectivity, and ultra-fast, reliable communications.
A Lightweight Neural Architecture Search Model for Medical Image Classification
Accurate classification of medical images is essential for modern diagnostics.
Joint Beamforming Design and Stream Allocation for Non-Coherent Joint Transmission in Cell-Free MIMO Networks
We then propose a joint beamforming and linear stream allocation algorithm, termed as RWMMSE-LSA, which yields closed-form updates with linear stream allocation complexity and is guaranteed to converge to the stationary points of the original joint optimization problem.
Frame Structure and Protocol Design for Sensing-Assisted NR-V2X Communications
In this study, we propose novel frame structures that incorporate ISAC signals for three crucial stages in the NR-V2X system: initial access, connected mode, and beam failure and recovery.
Towards Structural Sparse Precoding: Dynamic Time, Frequency, Space, and Power Multistage Resource Programming
This requires multistage optimization for full resource domain dynamic programming.
Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples
However, current detection methods tailored to DBP only consider ideal white Gaussian noise scenarios, while in practice, the noise is often colored due to interference from neighboring cells.
Once and for All: Scheduling Multiple Users Using Statistical CSI under Fixed Wireless Access
In this paper, we investigate the scheduling problem of a fixed wireless access (FWA) network using only statistical CSI.
A Physics-based and Data-driven Approach for Localized Statistical Channel Modeling
Localized channel modeling is crucial for offline performance optimization of 5G cellular networks, but the existing channel models are for general scenarios and do not capture local geographical structures.
Why Batch Normalization Damage Federated Learning on Non-IID Data?
In view of this, we develop a new FL algorithm that is tailored to BN, called FedTAN, which is capable of achieving robust FL performance under a variety of data distributions via iterative layer-wise parameter aggregation.
ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks
In order to achieve high data rate and ubiquitous connectivity in future wireless networks, a key task is to efficiently manage the radio resource by judicious beamforming and power allocation.
A Data Quality Assessment Framework for AI-enabled Wireless Communication
Using artificial intelligent (AI) to re-design and enhance the current wireless communication system is a promising pathway for the future sixth-generation (6G) wireless network.
Learning Cooperative Beamforming with Edge-Update Empowered Graph Neural Networks
However, the current GNNs are only equipped with the node-update mechanism, which restricts it from modeling more complicated problems such as the cooperative beamforming design, where the beamformers are on the graph edges of wireless networks.
Rethinking WMMSE: Can Its Complexity Scale Linearly With the Number of BS Antennas?
Precoding design for maximizing weighted sum-rate (WSR) is a fundamental problem for downlink of massive multi-user multiple-input multiple-output (MU-MIMO) systems.
Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems with Limited Feedback
The downlink channel covariance matrix (CCM) acquisition is the key step for the practical performance of massive multiple-input and multiple-output (MIMO) systems, including beamforming, channel tracking, and user scheduling.
Decentralized Linear MMSE Equalizer Under Colored Noise for Massive MIMO Systems
Conventional uplink equalization in massive MIMO systems relies on a centralized baseband processing architecture.
Quantized Federated Learning under Transmission Delay and Outage Constraints
Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy.
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition
Deep neural networks have been shown as a class of useful tools for addressing signal recognition issues in recent years, especially for identifying the nonlinear feature structures of signals.
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization
XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency.
Efficient Algorithms for Rotation Averaging Problems
While a sufficient optimality condition is available in the literature, there is a lack of \yhedit{a} fast convergent algorithm to achieve stationary points.
Towards Overfitting Avoidance: Tuning-free Tensor-aided Multi-user Channel Estimation for 3D Massive MIMO Communications
Channel estimation has long been deemed as one of the most critical problems in three-dimensional (3D) massive multiple-input multiple-output (MIMO), which is recognized as the leading technology that enables 3D spatial signal processing in the fifth-generation (5G) wireless communications and beyond.
Pushing The Limit of Type I Codebook For FDD Massive MIMO Beamforming: A Channel Covariance Reconstruction Approach
There is a fundamental trade-off between the channel representation resolution of codebooks and the overheads of feedback communications in the fifth generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems.
Learning-Based Massive Beamforming
Conventional optimization-based iterative resource allocation algorithms often suffer from slow convergence, especially for massive multiple-input-multiple-output (MIMO) beamforming problems.
Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using The Generalized Hyperbolic Prior
However, the optimal determination of a tensor rank is known to be a non-deterministic polynomial-time hard (NP-hard) task.
FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques and is a key step to improve the performance of machine learning algorithms.
Joint Channel Assignment and Power Allocation for Multi-UAV Communication
Unmanned aerial vehicle (UAV) swarm has emerged as a promising novel paradigm to achieve better coverage and higher capacity for future wireless network by exploiting the more favorable line-of-sight (LoS) propagation.
Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems
Then, we implement the proposed deepunfolding framework to solve the sum-rate maximization problem for precoding design in MU-MIMO systems.
Optimally Combining Classifiers for Semi-Supervised Learning
In this paper, we find these two methods have complementary properties and larger diversity, which motivates us to propose a new semi-supervised learning method that is able to adaptively combine the strengths of Xgboost and transductive support vector machine.
SR2CNN: Zero-Shot Learning for Signal Recognition
This paper proposes a ZSL framework, signal recognition and reconstruction convolutional neural networks (SR2CNN), to address relevant problems in this situation.
