On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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48 results for “uplink wireless communication”

Channel Assignment in Uplink Wireless Communication using Machine Learning Approacheess.SP

This letter tackles channel assignment in uplink wireless communication systems.

problem Maximizing the sum rate of all users in uplink wireless communication systems with integer channel assignment constraints.

method A convex optimization based algorithm is used to find the optimal channel assignment. Machine learning approaches, including CNNs, FNNs, random forest, and GRUs, are employed to reduce computation time.

result Machine learning methods largely reduce computation time with slightly compromised prediction accuracy.

channel assignment uplink wireless communication machine learning convex optimization sum rate

2020-01-12abs ↗pdf ↗

A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscs.NI

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station …

2019-09-17abs ↗pdf ↗

Differentially Private Wireless Federated Learning Using Orthogonal Sequencescs.IT

FLORAS uses orthogonal sequences for SISO FL, offering both DP and convergence guarantees.

problem Privacy-preserving wireless federated learning in SISO systems.

method Leverages orthogonal sequences to eliminate CSIT requirement and provide DP guarantees.

result FLORAS achieves a smooth tradeoff between convergence rate and DP levels.

differential privacy federated learning orthogonal sequences wireless communication

2023-06-14abs ↗pdf ↗

Random Orthogonalization for Federated Learning in Massive MIMO Systemscs.IT

Random orthogonalization improves FL in massive MIMO systems without CSI.

problem Efficient model aggregation in FL with minimal channel estimation overhead.

method Combining FL with massive MIMO's channel hardening and favorable propagation, random orthogonalization reduces channel estimation overhead.

result Achieves model aggregation without CSI, significantly reducing channel estimation overhead.

Federated Learning Massive MIMO Random Orthogonalization Channel Hardening Model Aggregation

2022-10-18abs ↗pdf ↗

Convergence of Federated Learning over a Noisy Downlinkcs.IT

Study investigates FL performance over a noisy downlink, showing analog approach outperforms digital.

problem Impact of bandwidth-limited downlink on federated learning performance.

method Modelled downlink and uplink channels; proposed digital and analog downlink approaches; analyzed convergence behavior.

result Analog downlink approach provides significant improvement over digital approach, especially with biased data distribution.

Federated Learning Downlink Analog Digital Broadcast Channel

2020-08-25abs ↗pdf ↗

Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networkscs.LG

Meta-learning improves drone trajectory design for dynamic wireless networks.

problem Designing optimal trajectories for energy-constrained drones in dynamic network environments.

method Proposes a meta-learning algorithm to adaptively tune a reinforcement learning solution for trajectory design.

result Meta-tuned RL yields faster convergence and improved communication performance compared to baseline algorithms.

meta-reinforcement learning wireless drone networks trajectory design dynamic environments reinforcement learning

2020-05-25abs ↗pdf ↗

Deep UL2DL: Channel Knowledge Transfer from Uplink to Downlinkeess.SP

Knowledge of the channel state information (CSI) at the transmitter side is one of the primary sources of information that can be used for the efficient allocation of wireless resources. Obtaining downlink (DL) CSI in Frequency Division Duplexing (FDD) systems from uplink (UL) CSI is not as straightforward as in TDD sy…

2018-12-16abs ↗pdf ↗

BICompFL: Stochastic Federated Learning with Bi-Directional Compressioncs.LG

BICompFL tackles bi-directional compression challenges in stochastic FL, reducing communication costs by an order of magnitude.

problem Communication bottleneck in federated learning, especially with stochastic updates.

method Introduces BICompFL, a bi-directional compression approach for stochastic federated learning.

result Significantly reduces communication costs (by an order of magnitude) while maintaining accuracy.

federated learning stochastic compression bi-directional communication cost

2025-01-31abs ↗pdf ↗

ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communicationscs.LG

The growing role that artificial intelligence and specifically machine learning is playing in shaping the future of wireless communications has opened up many new and intriguing research directions. This paper motivates the research in the novel direction of \textit{vision-aided wireless communications}, which aims at …

2019-11-14abs ↗pdf ↗

Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixupcs.LG

Mix2FLD improves FL accuracy with FD, reducing convergence time.

problem Uplink-downlink capacity asymmetry in federated learning.

method Two-way mixup of local samples and model parameters, preserving privacy.

result Achieves up to 16.7% higher test accuracy with reduced convergence time.

federated learning federated distillation two-way mixup privacy-preserving asymmetric channels

2020-06-17abs ↗pdf ↗

Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similaritymath.OC

Paper improves communication in distributed optimization, reducing worker-to-server data exchanges.

problem Efficiency in server-to-worker communication in distributed optimization.

method MARINA-P, a novel downlink compression method using correlated compressors; M3, combining MARINA-P with uplink compression.

result MARINA-P achieves provably superior server-to-worker communication complexity with increasing number of workers.

distributed optimization communication complexity downlink compression nonconvex optimization permutation compressors

2024-02-09abs ↗pdf ↗

Federated Learning for Wireless Communications: Motivation, Opportunities and Challengeseess.SP

There is a growing interest in the wireless communications community to complement the traditional model-based design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always…

2019-07-30abs ↗pdf ↗

Scalable Learning Paradigms for Data-Driven Wireless Communicationcs.LG

The paper discusses scalable learning for wireless data-driven systems.

problem Expanding data volume and model complexity limit centralized learning solutions.

method Discusses scalable architecture and local learning strategies.

result Promising research directions in scalable data-driven wireless communications.

scalable learning wireless communication data-driven machine learning model complexity

2020-03-01abs ↗pdf ↗

Stochastic Controlled Averaging for Federated Learning with Communication Compressionmath.OC

Paper proposes SCALLION and SCAFCOM for compressed FL with reduced communication.

problem Reducing communication overhead in Federated Learning with data heterogeneity and partial participation.

method Revisit and simplify stochastic controlled averaging, proposing SCALLION and SCAFCOM for unbiased and biased compression.

result SCALLION and SCAFCOM outperform existing methods in communication and computation complexities.

Federated Learning Communication Compression Stochastic Controlled Averaging Data Heterogeneity Partial Participation

2023-08-16abs ↗pdf ↗

Indoor Planning of Optical Wireless Networks for LoS Condition in Access and Backhaulingmath.OC

Paper proposes a graph model for optimal AP deployment in indoor optical wireless networks.

problem Challenges in deploying optical wireless networks due to LoS requirement and limited range.

method Graph modeling approach to identify minimum number of APs and their optimal locations.

result Optimal deployment of APs ensures connectivity and minimizes interference in indoor environments.

optical wireless LoS condition indoor planning graph modeling VLC networks

2023-12-19abs ↗pdf ↗

Decentralized learning for wireless communications and networkingmath.OC

This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using the alternating-direction method of multipliers (ADMM) so as to gain the desired degree of paralleliza…

2015-03-30abs ↗pdf ↗

Semantic Information Recovery in Wireless Networkscs.IT

New system preserves message meaning in wireless networks, improving data rate.

problem Efficiently transmitting message meaning in wireless networks.

method Modeling semantics as hidden random variables, using Information Bottleneck for compression.

result 20 dB SNR improvement for semantic communication.

Wireless Communications Semantic Communication Machine Learning Information Bottleneck Hidden Markov Models

2022-04-28abs ↗pdf ↗

Convergence Time Optimization for Federated Learning over Wireless Networkscs.LG

Optimizes convergence time of federated learning over wireless networks.

problem Limited resource blocks in wireless networks affect federated learning convergence time and performance.

method Formulates an optimization problem to minimize convergence time while optimizing performance, proposes a probabilistic user selection scheme and uses ANNs for estimation.

result Improves convergence time and performance of federated learning over wireless networks.

Federated Learning Wireless Networks Convergence Time User Selection Resource Allocation

2020-01-22abs ↗pdf ↗

Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applicationscs.LG

This article improves communication efficiency in distributed ML over wireless networks.

problem Achieving high ML inference accuracy at scale with zero communication latency.

method Optimizing communication payload types, techniques, scheduling, and ML architectures.

result Communication-efficient and distributed learning frameworks are presented.

distributed learning wireless networks communication efficiency machine learning 5G

2020-08-06abs ↗pdf ↗

Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learningcs.LG

CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.

problem Byzantine attacks and communication inefficiency in federated learning.

method Transformed robust aggregation for zero-order optimization under client heterogeneity.

result CyBeR-0 achieves stable performance with minimal communication costs and reduced memory usage.

federated learning zero-order optimization Byzantine resilience communication efficiency heterogeneous clients

2025-01-31abs ↗pdf ↗

MARINA-P: Superior Performance in Non-smooth Federated Optimization with Adaptive Stepsizescs.LG

MARINA-P improves non-smooth federated optimization with adaptive stepsizes.

problem Non-smooth federated optimization in machine learning applications.

method Extends EF21-P and MARINA-P to non-smooth convex setting, proving optimal convergence rate and communication complexity bounds.

result MARINA-P achieves

O(1/\sqrt{T})

convergence rate and communication complexity matching classical subgradient methods.

federated learning non-smooth optimization adaptive stepsizes communication efficiency convergence rate

2024-12-22abs ↗pdf ↗

On the Communication Latency of Wireless Decentralized Learningcs.IT

Study on communication delays in decentralized learning networks.

problem Optimizing communication latency in decentralized learning networks.

method Utilized network information theory and random geometric graph theory.

result Communication delay scales as O(n^(2-3β)/βlog n).

Decentralized Learning Wireless Networks Communication Latency Random Geometric Graphs Network Information Theory

2020-02-10abs ↗pdf ↗

Learning the Wireless V2I Channels Using Deep Neural Networkseess.SP

For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenge…

2019-07-10abs ↗pdf ↗

Latency Analysis of LEO Satellite Relay Communication: An Application of Conditional Contact Angle Distributionmath.GT

This study analyzes satellite communication latency using a stochastic geometry model.

problem Latency analysis of LEO satellite relay communication systems.

method Stochastic geometry framework with spherical BPP models, suboptimal satellite relay selection strategy.

result Derives distance distributions and analytical expressions for transmission delays.

satellite communication LEO satellites latency analysis stochastic geometry conditional contact angle distribution

2023-09-11abs ↗pdf ↗

Chain-of-Thought Enhanced Shallow Transformers for Wireless Symbol Detectioncs.LG

CHOOSE enhances shallow Transformers for wireless symbol detection.

problem Improving wireless symbol detection with shallow Transformers.

method Introducing autoregressive latent reasoning steps within hidden space.

result Lightweight Transformers achieve comparable performance to deep models.

Transformers wireless communication symbol detection shallow models resource-constrained

2025-06-26abs ↗pdf ↗

Communication Efficient Federated Learning with Energy Awareness over Wireless Networkscs.LG

This work improves communication efficiency in federated learning over wireless networks by optimizing energy consumption.

problem Optimizing energy consumption in federated learning over wireless networks.

method Adopting SignSGD for gradient sign exchange, considering channel capacity with outage, and proposing a stochastic sign-based algorithm for uneven data distribution.

result Proposed methods achieve a balance between learning performance and energy consumption.

Federated Learning Wireless Networks Energy Efficiency SignSGD Channel Capacity

2020-04-15abs ↗pdf ↗

Decentralized SGD with Over-the-Air Computationeess.SP

DSGD with OAC-MAC scheme achieves better convergence in noisy wireless networks.

problem Performance of decentralized SGD in noisy wireless environments.

method Proposed OAC-MAC scheme for over-the-air computation in DSGD.

result OAC-MAC scheme converges faster with fewer communication rounds.

Decentralized SGD Over-the-Air Computation Wireless Networks Graph Coloring Convergence Performance

2020-03-06abs ↗pdf ↗

How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacyeess.SP

Adversarial machine learning hides 5G communications from eavesdroppers.

problem Hiding 5G communications from eavesdroppers using DL classifiers.

method Craft adversarial perturbations to fool DL classifiers.

result Adversarial perturbations can hide 5G communications with high probability.

5G wireless privacy adversarial machine learning covert communications deep learning

2020-05-15abs ↗pdf ↗

Model-free Reinforcement Learning of Semantic Communication by Stochastic Policy Gradienteess.SP

New approach uses SPG for semantic communication without a known channel model.

problem Designing efficient semantic communication systems without a known channel model.

method Applying Stochastic Policy Gradient (SPG) for reinforcement learning.

result Achieves comparable performance to model-aware approaches with a decreased convergence rate.

semantic communication reinforcement learning policy gradient wireless communications information theory

2023-05-05abs ↗pdf ↗

Reverse Ordering Techniques for Attention-Based Channel Predictionstat.ML

New techniques improve channel prediction in noisy wireless systems.

problem Predicting channels in wireless communication systems from noisy observations.

method Adapted sequence-to-sequence models and transformers with reverse positional encoding and reversed encoder outputs.

result Improved robustness and relationship capture in channel prediction models.

channel prediction wireless communication sequence-to-sequence transformer attention

2023-02-01abs ↗pdf ↗

Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learningcs.LG

A-FADMM improves FL scalability and privacy via wireless channel perturbations and interference.

problem Challenges in model training due to wireless channel randomness and interference.

method Formulated a novel constrained optimization problem and proposed A-FADMM framework.

result Proves convergence and privacy guarantees for A-FADMM under time-varying channels.

Federated Learning Wireless Channels Privacy Bandwidth Efficiency Scalability

2020-07-03abs ↗pdf ↗

Wireless Power Control via Counterfactual Optimization of Graph Neural Networkseess.SP

Optimizes wireless power control using graph neural networks and counterfactual optimization.

problem Mitigating interference in wireless networks with multiple transmitter-receiver pairs.

method Graph neural network architecture combined with unsupervised primal-dual counterfactual optimization.

result Guarantees a minimum rate constraint that adapts to network size, balancing user rates.

wireless power control graph neural networks counterfactual optimization interference mitigation network topology

2020-02-17abs ↗pdf ↗

Physics-Informed Generative Modeling of Wireless Channelseess.SP

Generative model learns wireless channel distributions efficiently.

problem Learning precise wireless channel distributions for optimal communication.

method Physics-informed sparse Bayesian generative modeling (SBGM) with compressed data.

result Model learns channel parameters from compressed AP observations, is physically interpretable, and generalizes across different systems.

wireless channels generative modeling sparse Bayesian physics-informed compressed data

2025-02-14abs ↗pdf ↗

Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusioncs.LG

This paper addresses privacy in federated learning with wireless clients and base stations.

problem Privacy of clients' data in federated learning with hierarchical wireless architecture.

method Derives communication cost limits and introduces private aggregation schemes tailored for hierarchical wireless systems.

result Private aggregation schemes reduce communication costs by multiplicative factors compared to information-theoretic limits.

federated learning privacy hierarchical wireless aggregation schemes communication cost

2023-06-25abs ↗pdf ↗

Differentiable and Learnable Wireless Simulation with Geometric Transformerscs.LG

Wi-GATr learns to simulate wireless signals with high accuracy and speed.

problem Inaccurate wireless signal propagation models limit modern communication system design.

method Wi-GATr uses a Geometric Algebra Transformer to learn from scene primitives.

result Wi-GATr achieves more accurate predictions than existing methods.

wireless simulation neural network Geometric Algebra signal strength delay spread

2024-06-21abs ↗pdf ↗

Almost Cost-Free Communication in Federated Best Arm Identificationcs.LG

A novel algorithm reduces communication costs in federated best arm identification.

problem Identifying the best arm in a federated multi-armed bandit setup with minimal communication cost.

method Proposes a novel algorithm called FedElim that communicates only in exponential time steps.

result Demonstrates that communication is almost cost-free in FedElim, with a total cost at most 3 times the maximum under its variant.

federated learning multi-armed bandit best arm identification successive elimination communication cost

2022-08-19abs ↗pdf ↗

FedLion: Faster Adaptive Federated Optimization with Fewer Communicationcs.LG

FedLion improves Federated Learning by speeding up convergence and reducing communication costs.

problem Slow convergence and high communication costs in Federated Learning.

method Integrates Lion's adaptive approach into Federated Learning framework, using signed gradients.

result FedLion outperforms existing adaptive algorithms in convergence rate and communication efficiency.

Federated Learning Adaptive Optimization Convergence Rate Communication Costs Signed Gradients

2024-02-15abs ↗pdf ↗

A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layercs.LG

Survey on ML for wireless network optimization across PHY, MAC, and network layers.

problem Improving wireless network performance using machine learning.

method Comprehensive review of ML-based techniques for wireless network optimization.

result Machine learning can significantly enhance wireless network QoS and QoE across all layers.

wireless networks machine learning performance improvement PHY MAC

2020-01-13abs ↗pdf ↗

Energy Efficient Federated Learning Over Wireless Communication Networkscs.IT

In this paper, the problem of energy efficient transmission and computation resource allocation for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then,…

2019-11-06abs ↗pdf ↗

Generative Diffusion Models for Radio Wireless Channel Modelling and Samplingcs.AI

Generative diffusion models improve channel sampling from limited data.

problem Challenges in channel modelling and data collection for wireless systems.

method Diffusion model with U-Net architecture for frequency domain synthesis.

result Stable training and diverse high-fidelity samples generated from true channel distribution.

channel modelling wireless communication diffusion models GAN 2-Wasserstein distance

2023-08-10abs ↗pdf ↗

Few-Shot Domain Adaptation For End-to-End Communicationcs.LG

Few-shot domain adaptation improves autoencoder performance in changing wireless channels.

problem Frequent retraining of autoencoder for low decoding error rate in changing channel conditions is impractical.

method Uses Gaussian mixture density network and class and component-conditional affine transformations for few-shot adaptation.

result Effective adaptation using very small number of target domain samples, improving performance in real mmWave setups.

domain adaptation few-shot learning autoencoder channel adaptation wireless communication

2021-08-02abs ↗pdf ↗

Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysiseess.SP

Optimized CNNs for AMC on edge devices reduce complexity without sacrificing accuracy.

problem Developing efficient DL models for AMC on resource-constrained edge devices.

method Pruning, quantization, and knowledge distillation techniques applied to CNNs.

result Optimized models maintain or improve AMC accuracy with reduced complexity.

modulation classification deep learning edge devices model optimization wireless communications

2024-04-11abs ↗pdf ↗

FedRec: Federated Learning of Universal Receivers over Fading Channelscs.IT

FedRec learns universal receivers for fading channels without channel statistics.

problem Training neural network-based receivers for diverse fading channels without accurate statistics.

method Federated learning of a MAP detector for downlink fading channels.

result Performance approaches MAP without channel statistics, reduced communication overhead.

federated learning fading channels MAP detector neural networks wireless communications

2020-11-14abs ↗pdf ↗

Blind Federated Edge Learningcs.IT

Edge devices learn a global model collaboratively over wireless channels.

problem Learning a global model from edge devices with imperfect channel state information.

method Proposed analog aggregation scheme, receive beamforming at PS, and convergence analysis.

result Performance improvement with more PS antennas, even with imperfect CSI.

federated learning edge computing wireless communication beamforming channel state information

2020-10-19abs ↗pdf ↗

Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MECcs.LG

Scheduling and power allocation improve federated learning efficiency in NOMA networks.

problem Efficiently scheduling and allocating power for federated learning in bandwidth-limited wireless networks.

method Proposed a scheduling policy and power allocation scheme using NOMA to maximize data rate and convergence speed.

result Simulation results show improved federated learning accuracy in NOMA networks.

Federated Learning NOMA Wireless Networks Scheduling Policy Power Allocation

2020-06-21abs ↗pdf ↗

Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analyticseess.SP

In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and establish the first open dataset of real modulated signals for wireless communication systems. Specifically, we propose a flexible communication prototype platform for measuring real modulation dataset. Then, based on the measured …

2019-03-08abs ↗pdf ↗

Balancing Lifetime and Classification Accuracy of Wireless Sensor Networkscs.NI

Wireless sensor networks are composed of distributed sensors that can be used for signal detection or classification. The likelihood functions of the hypotheses are often not known in advance, and decision rules have to be learned via supervised learning. A specific such algorithm is Fisher discriminant analysis (FDA),…

2012-08-10abs ↗pdf ↗

Reinforcement Learning for Optimized Beam Training in Multi-Hop Terahertz Communicationscs.IT

A novel beam training scheme optimizes multi-hop THz communications with up to 75% performance gain.

problem Optimizing beam training for multi-hop THz communications with high data rates and low time overhead.

method Developed a reinforcement learning-based hierarchical beam training scheme with dynamic training levels.

result The proposed scheme achieves up to 75% performance gain in spectral efficiency compared to conventional methods.

Reinforcement Learning Beam Training Multi-Hop THz Communications Optimization

2021-02-10abs ↗pdf ↗