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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40 results for “Downlink”

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 ↗

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 ↗

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 ↗

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 ↗

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

Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains. However, in a frequency division duplex (FDD) massive MIMO system, CSI feedback overhead degrades the overall spectral…

2019-10-23abs ↗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 ↗

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 ↗

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 ↗

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 ↗

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

The automatic classification of applications and services is an invaluable feature for new generation mobile networks. Here, we propose and validate algorithms to perform this task, at runtime, from the raw physical channel of an operative mobile network, without having to decode and/or decrypt the transmitted flows. T…

2019-10-25abs ↗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 ↗

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 ↗

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 ↗

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 ↗

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 ↗

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 ↗

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 ↗

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 ↗

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 ↗

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 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 ↗

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 ↗

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 ↗

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 ↗

Deployment of Reference Nodes to Guarantee a LoS Condition for Accurate Indoor Positioningmath.OC

This paper improves indoor positioning accuracy by deploying reference nodes to ensure Line-of-Sight.

problem Systematic bias errors in indoor positioning due to non-LoS propagation.

method Model indoor service area as a graph, partition into cliques for reference nodes, set minimum distance and angle parameters.

result Guaranteed LoS to reference nodes improves indoor positioning accuracy and precision.

indoor positioning Line-of-Sight reference nodes triangulation trilateration

2023-12-07abs ↗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 ↗

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 ↗

Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systemscs.IT

Paper proposes neural network for efficient MIMO channel estimation and pilot reduction.

problem High overhead from pilot transmission in wideband MIMO systems.

method Neural network architecture for frequency-aware pilot design and channel estimation, with pruning technique.

result Neural network outperforms linear minimum mean square error (LMMSE) estimation.

MIMO OFDM Channel Estimation Pilot Design Neural Networks

2020-06-21abs ↗pdf ↗

Transfer Learning with Reconstruction Losscs.LG

This paper proposes a method to train multiple neural networks with shared parameters using a reconstruction loss.

problem Training multiple neural networks for correlated tasks separately is inefficient.

method Introduces a novel approach with a reconstruction loss to encourage shared features across multiple tasks.

result The proposed method achieves efficient transfer learning with competitive performance.

transfer learning reconstruction loss neural networks correlated tasks feature transfer

2024-03-31abs ↗pdf ↗

Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Banditscs.LG

New algorithm optimizes beam and rate allocation in mmWave systems for multiple users.

problem Optimizing beam and rate allocation in mmWave systems for multiple users with limited feedback.

method Introducing SAT-CTS, a combinatorial semi-bandit policy with satisficing objective.

result SAT-CTS achieves finite-time regret bounds and reduces satisficing regret in mmWave systems.

mmWave beamforming rate adaptation semi-bandit satisficing

2026-04-16abs ↗pdf ↗

Wavelet-Guided Water-Level Estimation for ISACeess.SP

Low-cost water-level tracking using LTE power metrics and wavelet analysis.

problem Real-time water-level monitoring across many locations with fixed instruments.

method Extracts per-antenna RSRP, RSSI, and RSRQ, applies CWT to RSRP, and uses a neural network to track water-level changes.

result Achieves root-mean-square and mean-absolute errors of 0.8 cm and 0.5 cm, respectively, under line-of-sight conditions.

water-level monitoring LTE power metrics wavelet transform neural networks flood response

2025-11-26abs ↗pdf ↗

Channel Vector Subspace Estimation from Low-Dimensional Projectionscs.IT

Massive MIMO is a variant of multiuser MIMO where the number of base-station antennas $M$ is very large (typically 100), and generally much larger than the number of spatially multiplexed data streams (typically 10). Unfortunately, the front-end A/D conversion necessary to drive hundreds of antennas, with a signal band…

2015-09-24abs ↗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 ↗

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approacheess.SY

Graph neural network optimizes energy-efficient precoding for massive MIMO systems.

problem Energy bottleneck in massive MIMO systems due to high DAC complexity and power consumption.

method Proposes a graph neural network to directly output precoded quantized vectors from channel matrix and transmit symbols.

result Significant increase in achievable sum rate with reduced DAC power consumption.

Massive MIMO Graph Neural Network Precoding Energy Efficiency DACs

2025-07-14abs ↗pdf ↗