52 citations · 184 across the 30 of their papers we have counts for
30 papers
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…
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…
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…
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…
Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes
Lili Chen, Jingge Zhu, Jamie Evans
Due to mutual interference between users, power allocation problems in wireless networks are often non-convex and computationally challenging. Graph neural networks (GNNs) have rec…
Fast Rate Information-theoretic Bounds on Generalization Errors
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
The generalization error of a learning algorithm refers to the discrepancy between the loss of a learning algorithm on training data and that on unseen testing data. Various inform…
Committed Private Information Retrieval
Quang Cao, Hong Yen Tran, Son Hoang Dau +7
A private information retrieval (PIR) scheme allows a client to retrieve a data item among items from servers, without revealing what is even…
Design and Analysis of Hardware-limited Non-uniform Task-based Quantizers
Neil Irwin Bernardo, Jingge Zhu, Yonina C. Eldar +1
Hardware-limited task-based quantization is a new design paradigm for data acquisition systems equipped with serial scalar analog-to-digital converters using a small number of bits…
On the Generalization for Transfer Learning: An Information-Theoretic Analysis
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different probability distributions. In t…
A Learning-Based Approach to Approximate Coded Computation
Navneet Agrawal, Yuqin Qiu, Matthias Frey +4
Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are rep…
Fast Rate Generalization Error Bounds: Variations on a Theme
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
A recent line of works, initiated by Russo and Xu, has shown that the generalization error of a learning algorithm can be upper bounded by information measures. In most of the rele…
On the Capacity-Achieving Input of the Gaussian Channel with Polar Quantization
Neil Irwin Bernardo, Jingge Zhu, Jamie Evans
The polar receiver architecture is a receiver design that captures the envelope and phase information of the signal rather than its in-phase and quadrature components. Several stud…
On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models
Xuetong Wu, Mingming Gong, Jonathan H. Manton +2
Recent advancements in unsupervised domain adaptation (UDA) and semi-supervised learning (SSL), particularly incorporating causality, have led to significant methodological improve…
Capacity Bounds for One-Bit MIMO Gaussian Channels with Analog Combining
Neil Irwin Bernardo, Jingge Zhu, Yonina C. Eldar +1
The use of 1-bit analog-to-digital converters (ADCs) is seen as a promising approach to significantly reduce the power consumption and hardware cost of multiple-input multiple-outp…
A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem. While conceivable that knowledge from one task cou…
On Minimizing Symbol Error Rate Over Fading Channels with Low-Resolution Quantization
Neil Irwin Bernardo, Jingge Zhu, Jamie Evans
We analyze the symbol error probability (SEP) of -ary pulse amplitude modulation (-PAM) receivers equipped with optimal low-resolution quantizers. We first show that the opti…
On the Capacity-Achieving Input of Channels with Phase Quantization
Neil Irwin Bernardo, Jingge Zhu, Jamie Evans
Several information-theoretic studies on channels with output quantization have identified the capacity-achieving input distributions for different fading channels with 1-bit in-ph…
Online Transfer Learning: Negative Transfer and Effect of Prior Knowledge
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
Transfer learning is a machine learning paradigm where the knowledge from one task is utilized to resolve the problem in a related task. On the one hand, it is conceivable that kno…
Is Phase Shift Keying Optimal for Channels with Phase-Quantized Output?
Neil Irwin Bernardo, Jingge Zhu, Jamie Evans
This paper establishes the capacity of additive white Gaussian noise (AWGN) channels with phase-quantized output. We show that a rotated -phase shift keying scheme is the capa…
Information-theoretic analysis for transfer learning
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different distributions (denoted as a…
Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful
Jingge Zhu
Semi-supervised learning algorithms attempt to take advantage of relatively inexpensive unlabeled data to improve learning performance. In this work, we consider statistical models…
Analysis of Nakamoto Consensus, Revisited
Jianyu Niu, Chen Feng, Hoang Dau +2
In the Bitcoin white paper, Nakamoto proposed a very simple Byzantine fault tolerant consensus algorithm that is also known as Nakamoto consensus. Despite its simplicity, some exis…
Customized Local Differential Privacy for Multi-Agent Distributed Optimization
Roel Dobbe, Ye Pu, Jingge Zhu +2
Real-time data-driven optimization and control problems over networks may require sensitive information of participating users to calculate solutions and decision variables, such a…
Compute--Forward Multiple Access (CFMA): Practical Code Design
Erixhen Sula, Jingge Zhu, Adriano Pastore +2
We present a practical strategy that aims to attain rate points on the dominant face of the multiple access channel capacity using a standard low complexity decoder. This technique…