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Special Issue "Advances in Multiuser Information Theory"

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Guest Editor

State Key Laboratory of ISN, Xidian University, Xi’an 710071, China
Interests: multiuser information theory; Gaussian noise; information theory; broadcast channel; capacity region

Dr. Yinfei Xu

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Guest Editor

School of Information Science and Engineering, Southeast University, Nanjing 210096, China
Interests: capacity region; information inequality; Gaussian noise; multiuser information theory; rate distortion

Dr. Min Zhu

E-Mail Website
Guest Editor

State Key Laboratory of ISN, Xidian University, Xi’an 710071, China
Interests: information theory; coding theory and practice; digital communications; machine learning; constellation shaping

Special Issue Information

Dear Colleagues,

With the development of 5G/6G communications, Internet of Things and smart cities, communication scenarios that involve multiple users are becoming increasingly common. In general, for these scenarios the capacity regions remain open, let alone other performance criteria such as delay and reliability. Multiuser information theory, as the theoretical guidance of communications, deserves further study.

In this Special Issue, we focus on (but are not limited to) characterizing the fundamental limits on practical performance criteria in multiuser communication scenarios. This also includes proposing outer bounds and inner bounds on these limits, how to compute these bounds, and how to practically achieve the inner bounds. New ideas and promising methods are very welcome.

This Special Issue will accept unpublished original papers and comprehensive reviews focused on the advances in multiuser information theory.

Prof. Dr. Yanlin Geng
Dr. Yinfei Xu
Dr. Min Zhu
Guest Editors

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Keywords

multiuser information theory
capacity region
inner bound
outer bound
information inequality
distributed system
network coding
Gaussian noise
multiuser coding theory

Published Papers (3 papers)

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Research

Open AccessArticle

Lossy State Communication over Fading Multiple Access Channels

Viswanathan Ramachandran

Entropy 2023, 25(4), 588; https://doi.org/10.3390/e25040588 - 29 Mar 2023

Viewed by 438

Abstract

Joint communications and sensing functionalities integrated into the same communication network have become increasingly relevant due to the large bandwidth requirements of next-generation wireless communication systems and the impending spectral shortage. While there exist system-level guidelines and waveform design specifications for such systems, an information-theoretic analysis of the absolute performance capabilities of joint sensing and communication systems that take into account practical limitations such as fading has not been addressed in the literature. Motivated by this, we undertake a network information-theoretic analysis of a typical joint communications and sensing system in this paper. Towards this end, we consider a state-dependent fading Gaussian multiple access channel (GMAC) setup with an additive state. The state process is assumed to be independent and identically distributed (i.i.d.) Gaussian, and non-causally available to all the transmitting nodes. The fading gains on the respective links are assumed to be stationary and ergodic and available only at the receiver. In this setting, with no knowledge of fading gains at the transmitters, we are interested in joint message communication and estimation of the state at the receiver to meet a target distortion in the mean-squared error sense. Our main contribution here is a complete characterization of the distortion-rate trade-off region between the communication rates and the state estimation distortion for a two-sender GMAC. Our results show that the optimal strategy is based on static power allocation and involves uncoded transmissions to amplify the state, along with the superposition of the digital message streams using appropriate Gaussian codebooks and dirty paper coding (DPC). This acts as a design directive for realistic systems using joint sensing and transmission in next-generation wireless standards and points to the relative benefits of uncoded communications and joint source-channel coding in such systems. Full article

(This article belongs to the Special Issue Advances in Multiuser Information Theory)

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Open AccessArticle

Joint Design of Polar Coding and Physical Network Coding for Two−User Downlink Non−Orthogonal Multiple Access

Zhaopeng Xie

Pingping Chen

and

Yong Li

Entropy 2023, 25(2), 233; https://doi.org/10.3390/e25020233 - 27 Jan 2023

Viewed by 537

Abstract

In this paper, we propose a joint polar coding and physical network coding (PNC) for two−user downlink non−orthogonal multiple access (PN−DNOMA) channels, since the successive–interference–cancellation–aided polar decoding cannot be optimal for finite blocklength transmissions. In the proposed scheme, we first constructed the XORed message of two user messages. Then, the XORed message was superimposed with the message of the weak User 2 for broadcast. By doing so, we can utilize the PNC mapping rule and polar decoding to directly recover the message of User 1, while at User 2, we equivalently constructed a long−length polar decoder to obtain its user message. The channel polarization and decoding performance can be greatly improved for both users. Moreover, we optimized the power allocation of the two users with their channel conditions by considering the user fairness and the performance. The simulation results showed that the proposed PN−DNOMA can achieve performance gains of about 0.4−0.7 dB over the conventional schemes in two−user downlink NOMA systems. Full article

(This article belongs to the Special Issue Advances in Multiuser Information Theory)

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Open AccessArticle

Rank-Adaptive Tensor Completion Based on Tucker Decomposition

Siqi Liu

Xiaoyu Shi

and

Qifeng Liao

Entropy 2023, 25(2), 225; https://doi.org/10.3390/e25020225 - 24 Jan 2023

Viewed by 654

Abstract

Tensor completion is a fundamental tool to estimate unknown information from observed data, which is widely used in many areas, including image and video recovery, traffic data completion and the multi-input multi-output problems in information theory. Based on Tucker decomposition, this paper proposes a new algorithm to complete tensors with missing data. In decomposition-based tensor completion methods, underestimation or overestimation of tensor ranks can lead to inaccurate results. To tackle this problem, we design an alternative iterating method that breaks the original problem into several matrix completion subproblems and adaptively adjusts the multilinear rank of the model during optimization procedures. Through numerical experiments on synthetic data and authentic images, we show that the proposed method can effectively estimate the tensor ranks and predict the missing entries. Full article

(This article belongs to the Special Issue Advances in Multiuser Information Theory)

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Special Issue "Advances in Multiuser Information Theory"

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