activity
20082023
most citedDesign and Analysis of Hardware-limited Non-uniform Task-based Quantizers

17 citations · 98 across the 45 of their papers we have counts for

collaborators

50 papers

cs.IT2023

Performance Analysis of Relay Selection Schemes in Multi-Hop Decode-and-Forward Networks

Shalanika Dayarathna, Rajitha Senanayake, Jamie Evans

This paper analyses the data rate achieved by various relay selection schemes in a single-user multi-hop relay network with decode-and-forward (DF) relaying. While the single-user…

eess.SP2023

A Sample-Deficient Analysis of the Leading Generalized Eigenvalue for the Detection of Signals in Colored Gaussian Noise

Prathapasinghe Dharmawansa, Saman Atapattu, Jamie Evans +1

This paper investigates the signal detection problem in colored Gaussian noise with an unknown covariance matrix. To be specific, we consider a sample deficient scenario in which t…

cs.IT2023

CFMA for Gaussian MIMO Multiple Access Channels

Lanwei Zhang, Jamie Evans, Jingge Zhu

Compute-forward multiple access (CFMA) is a multiple access transmission scheme based on Compute-and-Forward (CF) which allows the receiver to first decode linear combinations of t…

cs.IT2023★ 2 cited

Accelerating Graph Neural Networks via Edge Pruning for Power Allocation in Wireless Networks

Lili Chen, Jingge Zhu, Jamie Evans

Graph Neural Networks (GNNs) have recently emerged as a promising approach to tackling power allocation problems in wireless networks. Since unpaired transmitters and receivers are…

cs.IT2023

Learning Channel Codes from Data: Performance Guarantees in the Finite Blocklength Regime

Neil Irwin Bernardo, Jingge Zhu, Jamie Evans

This paper examines the maximum code rate achievable by a data-driven communication system over some unknown discrete memoryless channel in the finite blocklength regime. A class o…

cs.LG2023

On the Value of Stochastic Side Information in Online Learning

Junzhang Jia, Xuetong Wu, Jingge Zhu +1

We study the effectiveness of stochastic side information in deterministic online learning scenarios. We propose a forecaster to predict a deterministic sequence where its performa…