52 citations · 184 across the 30 of their papers we have counts for
18 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
A Sequential Approximation Framework for Coded Distributed Optimization
Jingge Zhu, Ye Pu, Vipul Gupta +2
Building on the previous work of Lee et al. and Ferdinand et al. on coded computation, we propose a sequential approximation framework for solving optimization problems in a distri…
Communication versus Computation: Duality for multiple access channels and source coding
Jingge Zhu, Sung Hoon Lim, Michael Gastpar
Computation codes in network information theory are designed for the scenarios where the decoder is not interested in recovering the information sources themselves, but only a func…
Typical sumsets of linear codes
Jingge Zhu, Michael Gastpar
Given two identical linear codes over of length , we independently pick one codeword from each codebook uniformly at random. A is fo…
Gaussian Multiple Access via Compute-and-Forward
Jingge Zhu, Michael Gastpar
Lattice codes used under the Compute-and-Forward paradigm suggest an alternative strategy for the standard Gaussian multiple-access channel (MAC): The receiver successively decodes…
Lattice Codes for Many-to-One Interference Channels With and Without Cognitive Messages
Jingge Zhu, Michael Gastpar
A new achievable rate region is given for the Gaussian cognitive many-to-one interference channel. The proposed novel coding scheme is based on the compute-and-forward approach wit…
Asymmetric Compute-and-Forward with CSIT
Jingge Zhu, Michael Gastpar
We present a modified compute-and-forward scheme which utilizes Channel State Information at the Transmitters (CSIT) in a natural way. The modified scheme allows different users to…