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.
Study improves communication efficiency in RIS-assisted downlink communication.
BICompFL tackles bi-directional compression challenges in stochastic FL, reducing communication costs by an order of magnitude.
Paper improves communication in distributed optimization, reducing worker-to-server data exchanges.
Mix2FLD improves FL accuracy with FD, reducing convergence time.
DoCoFL compresses model updates for cross-device federated learning.
CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.
MARINA-P improves non-smooth federated optimization with adaptive stepsizes.
Random orthogonalization improves FL in massive MIMO systems without CSI.
This study analyzes satellite communication latency using a stochastic geometry model.
Study investigates FL performance over a noisy downlink, showing analog approach outperforms digital.
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…
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…
FedRec learns universal receivers for fading channels without channel statistics.
Optimizes wireless power control using graph neural networks and counterfactual optimization.
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…
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…
Cost-efficient distributed learning via combinatorial bandits.
Paper introduces uncertainty injection for deep learning robust optimization.
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…
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…
Meta-DRL improves resource allocation in O-RAN networks.
Deep learning classifies mobile traffic from LTE PDCCH.
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…
AMS improves video inference on edge devices by adapting a small model with online knowledge distillation.
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 antennas and each single-antenna user occurs through a collection of scatterers in the e…
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…
DeepCMC compresses CSI for massive MIMO systems, reducing overhead and improving performance.
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…
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…
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 …
Optimizes data power control in cell-free networks for better spectral efficiency.
Paper proposes efficient UAV placement for aerial base stations.
A new framework maximizes influence spread in social networks by accounting for inter-community diffusion.
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…
Community detection improves stock market portfolio optimization.
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…
Study exact community detection in k-community Gaussian mixtures with different intensities.
The paper introduces a method for detecting principal communities and embedding vertices.
Improved distributed learning with reduced communication costs.
FedSpace optimizes ML training on satellites and ground stations.
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…
MACC learns communication protocols by adapting counterfactual reasoning.
A novel framework for adaptive multi-agent communication in reinforcement learning.
A framework for multi-agent communication over noisy channels in reinforcement learning.
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…
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…
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…
GRADE models evolving graph dynamics by learning node and community representations.