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 “Downlink Communication”

Federated Distributionally Robust Optimization for Phase Configuration of RISscs.LG

Study improves communication efficiency in RIS-assisted downlink communication.

problem Improving performance of RIS-aided downlink communication over heterogeneous designs.

method Distributed learning with distributionally robust optimization.

result Our algorithm achieves 50% fewer communication rounds for similar worst-case performance.

RIS Robust Optimization Downlink Communication Heterogeneous Designs Phase Configuration

2021-08-20abs ↗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 ↗

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 ↗

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 ↗

DoCoFL: Downlink Compression for Cross-Device Federated Learningcs.LG

DoCoFL compresses model updates for cross-device federated learning.

problem Downlink compression for cross-device federated learning where clients may appear only once.

method Proposes DoCoFL framework for downlink compression in cross-device federated learning.

result Significant bi-directional bandwidth reduction with competitive accuracy.

Federated Learning Downlink Compression Cross-Device Bi-Directional Heterogeneous Clients

2023-02-01abs ↗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 ↗

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 ↗

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 ↗

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 ↗

Machine Learning in Downlink Coordinated Multipoint in Heterogeneous Networksstat.ML

We propose a method for downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks. The primary contribution of our paper is an algorithm to enhance the trigger of DL CoMP using online machine learning. We use support vector machine (SVM) classifiers to enhance the user downlink…

2016-08-30abs ↗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 ↗

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 ↗

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 ↗

CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systemscs.IT

Proposes CNN-based analog CSI feedback for FDD MIMO-OFDM systems.

problem High CSI feedback overhead in FDD MIMO systems.

method AnalogDeepCMC: maps downlink CSI to uplink channel input, reconstructs channel estimate.

result Significantly improves downlink spectral efficiency and simplifies operation.

CNN CSI feedback FDD MIMO Analog Deep learning

2019-10-23abs ↗pdf ↗

Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cellscs.NI

We propose a reinforcement learning (RL) based closed loop power control algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an indoor environment served by small cells. The main contributions of our paper are to 1) use RL to solve performance tuning problems in an indoor cellular network for voic…

2017-07-10abs ↗pdf ↗

Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Banditscs.IT

Cost-efficient distributed learning via combinatorial bandits.

problem Trade-off between error and runtime in distributed SGD.

method Combinatorial multi-armed bandit model to learn fastest workers.

result Significantly lower errors with same computational efforts and less communication.

distributed learning SGD combinatorial bandits response times regret minimization

2022-02-16abs ↗pdf ↗

Uncertainty Injection: A Deep Learning Method for Robust Optimizationcs.LG

Paper introduces uncertainty injection for deep learning robust optimization.

problem Uncertainty in input data affects deep learning model performance in optimization problems.

method Uncertainty injection scheme for training deep learning models to produce robust solutions.

result Proposed scheme improves robustness of solutions in wireless communications applications.

deep learning robust optimization uncertainty injection wireless communications power loading

2023-02-23abs ↗pdf ↗

A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networkscs.NI

Optimal resource allocation is a fundamental challenge for dense and heterogeneous wireless networks with massive wireless connections. Because of the non-convex nature of the optimization problem, it is computationally demanding to obtain the optimal resource allocation. Recently, deep reinforcement learning (DRL) has…

2019-04-30abs ↗pdf ↗

A Framework for Automated Cellular Network Tuning with Reinforcement Learningcs.NI

Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environme…

2018-08-13abs ↗pdf ↗

Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RANcs.NI

Meta-DRL improves resource allocation in O-RAN networks.

problem Dynamic resource allocation in O-RAN networks.

method Meta Deep Reinforcement Learning (Meta-DRL) inspired by MAML.

result 19.8% improvement in network management performance.

O-RAN Meta-DRL Reinforcement Learning Resource Allocation Wireless Networks

2024-09-30abs ↗pdf ↗

Mobile Traffic Classification through Physical Channel Fingerprinting: a Deep Learning Approacheess.SP

Deep learning classifies mobile traffic from LTE PDCCH.

problem Automatic classification of mobile applications and services.

method Convolutional Neural Network (CNN) trained on DCI messages.

result 99% accuracy in classifying traffic from LTE PDCCH.

mobile networks deep learning LTE PDCCH traffic classification radio cell fingerprinting

2019-10-25abs ↗pdf ↗

Enabling FDD Massive MIMO through Deep Learning-based Channel Predictioncs.IT

A major obstacle for widespread deployment of frequency division duplex (FDD)-based Massive multiple-input multiple-output (MIMO) communications is the large signaling overhead for reporting full downlink (DL) channel state information (CSI) back to the basestation (BS), in order to enable closed-loop precoding. We com…

2019-01-08abs ↗pdf ↗

Real-Time Video Inference on Edge Devices via Adaptive Model Streamingcs.LG

AMS improves video inference on edge devices by adapting a small model with online knowledge distillation.

problem High computation cost of Deep Neural Networks for real-time video inference on edge devices.

method AMS uses a remote server to continually train and adapt a small model on edge devices, using online knowledge distillation from a large model.

result 0.4--17.8 percent mean Intersection-over-Union improvement in video semantic segmentation.

video inference edge devices model streaming knowledge distillation mobile phones

2020-06-11abs ↗pdf ↗

Multi-Band Covariance Interpolation with Applications in Massive MIMOcs.IT

In this paper, we study the problem of multi-band (frequency-variant) covariance interpolation with a particular emphasis towards massive MIMO applications. In a massive MIMO system, the communication between each BS with $M \gg 1$ antennas and each single-antenna user occurs through a collection of scatterers in the e…

2018-01-11abs ↗pdf ↗

Deep Reinforcement Learning Based Power control for Wireless Multicast Systemscs.NI

We consider a multicast scheme recently proposed for a wireless downlink in [1]. It was shown earlier that power control can significantly improve its performance. However for this system, obtaining optimal power control is intractable because of a very large state space. Therefore in this paper we use deep reinforceme…

2019-09-27abs ↗pdf ↗

Distributed Deep Convolutional Compression for Massive MIMO CSI Feedbackeess.SP

DeepCMC compresses CSI for massive MIMO systems, reducing overhead and improving performance.

problem High CSI overhead in massive MIMO systems limits spectral efficiency.

method Deep learning-based fully convolutional neural network with residual layers and entropy coding.

result DeepCMC outperforms state-of-the-art schemes in CSI reconstruction quality for the same compression rate.

CSI compression massive MIMO deep learning distributed systems channel state information

2020-03-07abs ↗pdf ↗

Flood Detection On Low Cost Orbital Hardwareeess.IV

Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding. Despite the capabilities of modern satellites, there is still much to be desired from the perspective of first response organisations like UNICEF. Two main challenges are rapid access to data, and the ability…

2019-10-04abs ↗pdf ↗

Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvementcs.NI

We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments. This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio through ex…

2017-07-10abs ↗pdf ↗

Nonlinear Channel Estimation for OFDM System by Complex LS-SVM under High Mobility Conditionscs.LG

A nonlinear channel estimator using complex Least Square Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink under high mobility conditions. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the …

2011-08-31abs ↗pdf ↗

Cell-Free Data Power Control Via Scalable Multi-Objective Bayesian Optimisationeess.SY

Optimizes data power control in cell-free networks for better spectral efficiency.

problem Maximizing overall spectral efficiency in cell-free networks with multi-objective optimisation.

method Applied scalable multi-objective Bayesian optimisation to solve convergence-time limitations.

result Improved radio resource management in cell-free networks.

cell-free networks Bayesian optimisation multi-objective optimisation spectral efficiency

2022-12-20abs ↗pdf ↗

Learning in the Sky: An Efficient 3D Placement of UAVseess.SP

Paper proposes efficient UAV placement for aerial base stations.

problem Efficient placement of UAVs as aerial base stations to serve varying traffic demand.

method Modeling UAV deployment as a non-cooperative game and using a learning-based algorithm to update UAV locations.

result Significant performance gains up to 52% and 74% in terms of throughput and dropped users compared to an optimized baseline algorithm.

UAVs Aerial Base Stations Learning-based Placement Non-Cooperative Game Throughput

2020-03-02abs ↗pdf ↗

A Community-Aware Framework for Influence Maximization with Explicit Accounting for Inter-Community Influencecs.SI

A new framework maximizes influence spread in social networks by accounting for inter-community diffusion.

problem Real-world social networks have inter-community influence that is often overlooked in community-based IM approaches.

method Community-IM++ uses a heuristic based on community-based diffusion degree and progressive budgeting to model and prioritize cross-community diffusion.

result Community-IM++ achieves near-greedy influence spread at up to 100 times lower runtime than existing methods.

Influence Maximization Community Networks Inter-Community Influence Social Networks Viral Marketing

2025-12-30abs ↗pdf ↗

A Note on Community Trees in Networksstat.ML

We introduce the concept of community trees that summarizes topological structures within a network. A community tree is a tree structure representing clique communities from the clique percolation method (CPM). The community tree also generates a persistent diagram. Community trees and persistent diagrams reveal topol…

2017-10-11abs ↗pdf ↗

Community detection and portfolio optimizationq-fin.PM

Community detection improves stock market portfolio optimization.

problem Improving portfolio optimization in financial markets.

method Community detection in correlation-based networks of worldwide stock markets.

result Portfolios constructed using community detection outperform traditional methods.

community detection portfolio optimization stock markets financial risk management

2021-12-26abs ↗pdf ↗

Hidden Community Detection in Social Networkscs.SI

We introduce a new paradigm that is important for community detection in the realm of network analysis. Networks contain a set of strong, dominant communities, which interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, t…

2017-02-24abs ↗pdf ↗

Exact Recovery of Community Detection in k-Community Gaussian Mixture Modelcs.SI

Study exact community detection in k-community Gaussian mixtures with different intensities.

problem Community detection in k-community Gaussian mixtures with varying intensities.

method Explicitly find the threshold for exact recovery of maximum likelihood estimation.

result Threshold for exact recovery of maximum likelihood estimation is identified.

community detection Gaussian mixture model maximum likelihood estimation hypergraphs

2020-08-29abs ↗pdf ↗

Principal Graph Encoder Embedding and Principal Community Detectioncs.SI

The paper introduces a method for detecting principal communities and embedding vertices.

problem Detecting and embedding vertices in graphs with community structure.

method Principal graph encoder embedding method that detects principal communities and produces vertex embeddings.

result The method successfully detects principal communities and produces accurate vertex embeddings.

community detection graph embedding principal graph encoder vertex embedding

2025-01-24abs ↗pdf ↗

Distributed Learning: Sequential Decision Making in Resource-Constrained Environmentscs.LG

Improved distributed learning with reduced communication costs.

problem Efficient communication in resource-constrained environments for distributed learning.

method Proposed a cost-effective partial communication protocol.

result Communication cost is reduced to

O(\log T)

, improving significantly on full communication.

distributed learning sequential decision making resource-constrained partial communication cost-effectiveness

2020-04-13abs ↗pdf ↗

FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscs.LG

FedSpace optimizes ML training on satellites and ground stations.

problem Training machine learning models on satellites with limited bandwidth.

method Federated Learning framework that dynamically schedules model aggregation based on satellite orbits.

result Reduces training time by 1.7 days over state-of-the-art algorithms.

Federated Learning Satellites Machine Learning Low Earth Orbit Optimization

2022-02-02abs ↗pdf ↗

Ensemble approaches for improving community detection methodsphysics.soc-ph

Statistical estimates can often be improved by fusion of data from several different sources. One example is so-called ensemble methods which have been successfully applied in areas such as machine learning for classification and clustering. In this paper, we present an ensemble method to improve community detection by…

2013-09-01abs ↗pdf ↗

Learning to Communicate Using Counterfactual Reasoningcs.LG

MACC learns communication protocols by adapting counterfactual reasoning.

problem Credit assignment and non-stationarity in communication environments.

method Adapts counterfactual reasoning to overcome credit assignment and uses action policy and Q-function of other agents to handle non-stationarity.

result MACC outperforms state-of-the-art baselines in four scenarios.

multi-agent reinforcement learning counterfactual reasoning communication protocols credit assignment non-stationarity

2020-06-12abs ↗pdf ↗

Learning Structured Communication for Multi-agent Reinforcement Learningcs.LG

A novel framework for adaptive multi-agent communication in reinforcement learning.

problem Manual specification of communication structures in multi-agent reinforcement learning.

method Learning Structured Communication (LSC) framework using hierarchical graph neural networks.

result Adaptive hierarchical formations and efficient message propagation among agents.

multi-agent reinforcement learning communication structures hierarchical graph neural networks adaptive agent grouping message propagation

2020-02-11abs ↗pdf ↗

Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channelseess.SP

A framework for multi-agent communication over noisy channels in reinforcement learning.

problem Effective communication between multiple agents in a noisy environment.

method A novel multi-agent partially observable Markov decision process (MA-POMDP) framework considering noisy communication channels.

result Jointly learned policies outperform separate learning of communication and decision making.

multi-agent reinforcement learning noisy channels communication deep reinforcement learning multi-user communication systems

2021-01-02abs ↗pdf ↗

Community structure: A comparative evaluation of community detection methodscs.SI

Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practioners to determine which method would be suitable to get insights into the structural information of…

2018-12-14abs ↗pdf ↗

Spectral Multi-scale Community Detection in Temporal Networks with an Applicationphysics.soc-ph

The analysis of temporal networks has a wide area of applications in a world of technological advances. An important aspect of temporal network analysis is the discovery of community structures. Real data networks are often very large and the communities are observed to have a hierarchical structure referred to as mult…

2019-01-29abs ↗pdf ↗

A Survey on Theoretical Advances of Community Detection in Networkscs.SI

Real-world networks usually have community structure, that is, nodes are grouped into densely connected communities. Community detection is one of the most popular and best-studied research topics in network science and has attracted attention in many different fields, including computer science, statistics, social sci…

2018-08-26abs ↗pdf ↗

GRADE: Graph Dynamic Embeddingcs.LG

GRADE models evolving graph dynamics by learning node and community representations.

problem Lack of tools to study temporal community dynamics in evolving graphs.

method GRADE is a probabilistic model that learns evolving node and community representations via a random walk prior and variational inference.

result GRADE outperforms baselines in dynamic link prediction and dynamic community detection.

graph dynamics representation learning community detection dynamic link prediction random walk

2020-07-16abs ↗pdf ↗