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Advances in Multiuser Information Theory

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (10 March 2024) | Viewed by 7397

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Special Issue Editors

Prof. Dr. Yanlin Geng

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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

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This Special Issue will accept unpublished original papers and comprehensive reviews focused on the advances in multiuser information theory.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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (7 papers)

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Research

13 pages, 612 KiB

Open AccessArticle

Hierarchical Cache-Aided Networks for Linear Function Retrieval

by Lingyu Zhang, Yun Kong, Youlong Wu and Minquan Cheng

Entropy 2024, 26(3), 195; https://doi.org/10.3390/e26030195 - 25 Feb 2024

Viewed by 681

Abstract

In a hierarchical caching system, a server is connected to multiple mirrors, each of which is connected to a different set of users, and both the mirrors and the users are equipped with caching memories. All the existing schemes focus on single file retrieval, i.e., each user requests one file. In this paper, we consider the linear function retrieval problem, i.e., each user requests a linear combination of files, which includes single file retrieval as a special case. We propose a new scheme that reduces the transmission load of the first hop by jointly utilizing the two layers’ cache memories, and we show that our scheme achieves the optimal load for the second hop in some cases. Full article

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

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27 pages, 748 KiB

Open AccessArticle

Blahut–Arimoto Algorithms for Inner and Outer Bounds on Capacity Regions of Broadcast Channels

by Yanan Dou, Yanqing Liu, Xueyan Niu, Bo Bai, Wei Han and Yanlin Geng

Entropy 2024, 26(3), 178; https://doi.org/10.3390/e26030178 - 20 Feb 2024

Viewed by 689

Abstract

The celebrated Blahut–Arimoto algorithm computes the capacity of a discrete memoryless point-to-point channel by alternately maximizing the objective function of a maximization problem. This algorithm has been applied to degraded broadcast channels, in which the supporting hyperplanes of the capacity region are again cast as maximization problems. In this work, we consider general broadcast channels and extend this algorithm to compute inner and outer bounds on the capacity regions. Our main contributions are as follows: first, we show that the optimization problems are max–min problems and that the exchange of minimum and maximum holds; second, we design Blahut–Arimoto algorithms for the maximization part and gradient descent algorithms for the minimization part; third, we provide convergence analysis for both parts. Numerical experiments validate the effectiveness of our algorithms. Full article

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

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18 pages, 823 KiB

Open AccessFeature PaperArticle

Age of Synchronization Minimization Algorithms in Wireless Networks with Random Updates under Throughput Constraints

by Yuqiao He, Guozhi Chen, Yuchao Chen, Jintao Wang and Jian Song

Entropy 2023, 25(9), 1331; https://doi.org/10.3390/e25091331 - 14 Sep 2023

Viewed by 918

Abstract

This study considers a wireless network where multiple nodes transmit status updates to a base station (BS) through a shared bandwidth-limited channel. Considering the random arrival of status updates, we measure the data freshness with the age of synchronization (AoS) metric; specifically, we use the time elapsed since the latest synchronization as a metric. The objective of this study is to minimize the weighted sum of the average AoS of the entire network while meeting the minimum throughput requirement of each node. We consider both the central scheduling scenario and the distributed scheduling scenario. In the central scheduling scenario, we propose the optimal stationary randomized policy when the transmission feedback is unavailable and the max-weight policy when it is available. In the distributed scenario, we propose a distributed policy. The complexity of the three scheduling policies is significantly low. Numerical simulations show that the policies can satisfy the throughput constraint in the central controlling scenario and the AoS performance of the max-weight policy is close to the lower bound. Full article

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

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21 pages, 387 KiB

Open AccessFeature PaperArticle

Design of Multi-User Noncoherent Massive SIMO Systems for Scalable URLLC

by Zheng Dong, He Chen and Jian-Kang Zhang

Entropy 2023, 25(9), 1325; https://doi.org/10.3390/e25091325 - 12 Sep 2023

Cited by 1 | Viewed by 749

Abstract

This paper develops and optimizes a non-orthogonal and noncoherent multi-user massive single-input multiple-output (SIMO) framework, with the objective of enabling scalable ultra-reliable low-latency communications (sURLLC) in Beyond-5G (B5G)/6G wireless communication systems. In this framework, the huge diversity gain associated with the large-scale antenna array in the massive SIMO system is leveraged to ensure ultra-high reliability. To reduce the overhead and latency induced by the channel estimation process, we advocate for the noncoherent communication technique, which does not need the knowledge of instantaneous channel state information (CSI) but only relies on large-scale fading coefficients for message decoding. To boost the scalability of noncoherent massive SIMO systems, we enable the non-orthogonal channel access of multiple users by devising a new differential modulation scheme to ensure that each transmitted signal matrix can be uniquely determined in the noise-free case and be reliably estimated in noisy cases when the antenna array size is scaled up. The key idea is to make the transmitted signals from multiple geographically separated users be superimposed properly over the air, such that when the sum signal is correctly detected, the signal sent by each individual user can be uniquely determined. To further enhance the average error performance when the array antenna number is large, we propose a max–min Kullback–Leibler (KL) divergence-based design by jointly optimizing the transmitted powers of all users and the sub-constellation assignments among them. The simulation results show that the proposed design significantly outperforms the existing max–min Euclidean distance-based counterpart in terms of error performance. Moreover, our proposed approach also has a better error performance compared to the conventional coherent zero-forcing (ZF) receiver with orthogonal channel training, particularly for cell-edge users. Full article

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

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22 pages, 372 KiB

Open AccessArticle

Lossy State Communication over Fading Multiple Access Channels

by Viswanathan Ramachandran

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

Viewed by 926

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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12 pages, 515 KiB

Open AccessArticle

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

by Zhaopeng Xie, Pingping Chen and Yong Li

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

Viewed by 1118

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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16 pages, 4943 KiB

Open AccessArticle

Rank-Adaptive Tensor Completion Based on Tucker Decomposition

by Siqi Liu, Xiaoyu Shi and Qifeng Liao

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

Cited by 1 | Viewed by 1304

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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