Special Issue "Information-Theoretic Aspects of Non-orthogonal and Massive Access for Future Wireless Networks"

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

Deadline for manuscript submissions: 20 December 2021.

Special Issue Editors

Dr. Benjamin M. Zaidel
E-Mail Website
Guest Editor
Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
Interests: information theory; wireless communications; random matrix theory; statistical mechanics; MIMO techniques; cellular communication
Dr. Ori Shental
E-Mail Website
Guest Editor
Qualcomm Inc., 5775 Morehouse Drive, San Diego, CA 92121, USA
Interests: wireless communications; information theory; statistical mechanics; inference in graphical models; machine learning

Special Issue Information

Dear Colleagues,

The vision for future wireless networks encompasses a wide variety of applications, featuring dramatically higher throughputs as well as massive machine-type communications. It manifests the evolution from human-centric communications, through the current epoch of “internet-of-things” (IoT), to the era of “internet-of-everything” (IoE) expected to evolve during the next decade. This motivates a paradigm shift from legacy orthogonal multiple access (OMA) to non-orthogonal multiple access (NOMA), where the number of simultaneously active users exceeds the number of available time–frequency–space resources. Information-theoretic analyses of NOMA schemes have been a fruitful ground for research in recent years; however, a full comprehension of the fundamental performance limits of the envisioned use-cases is still lacking. Obviously, classical Shannon-type approaches to deriving key performance measures, such as the achievable spectral efficiency, remain useful and of clear interest in certain settings. Nevertheless, some key features of massive machine-type communications may require new paradigms more suitable for the specific characteristics of such settings. In particular, the focus has seemed to shift to new scaling laws for the number of users, code blocklengths, and number of receiving antennas. Moreover, alternative performance measures, such as the recently introduced message-length capacity and per-user probability of error (PUPE), may be essential to a more insightful analytical foundation. In this framework, the notion of “massive access” has been recently considered as an alternative to the classical multiple-access channel setting. Essentially, the massive access model lets the number of active users scale with the blocklength and facilitates analysis of the impact of finite (and short) code blocklengths due to stringent delay constraints, user burstiness, and connectivity larger by orders of magnitude than in classical settings. This Special Issue aims to present a broad information-theoretic perspective of the state-of-the-art of non-orthogonal and massive access research and invites authors to present recent advances in the field that shed light on the challenges ahead in the design of future wireless networks.

This Special Issue solicits unpublished original papers and comprehensive reviews on topics including, but not limited to:

  • performance limits of NOMA and massive access;
  • low-complexity transceiver design for code-domain and power-domain NOMA;
  • techniques for coordinated/uncoordinated (unsourced) and grant-based/grant-free multiple access;
  • message-passing algorithms and sparse graph models for efficient NOMA and massive access;
  • finite blocklength and URLLC aspects in NOMA and massive access;
  • machine learning and data-aided aspects in NOMA and massive access design;
  • incorporating MIMO and massive MIMO with NOMA and massive access;
  • methods for active user identification in massive access, including machine learning and compressed-sensing-based techniques;
  • coding and modulation schemes designed for NOMA and massive access;
  • leveraging new advances in wireless communications, such as reconfigurable intelligent surfaces (RIS) and orbital angular momentum (OAM), to bolster multiple access; and
  • privacy and security aspects of NOMA and massive access.

Dr. Benjamin M. Zaidel
Dr. Ori Shental
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 papers will be 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 1800 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

  • information theory
  • signal processing
  • random matrix theory
  • statistical mechanics
  • multiple access
  • graph theory

Published Papers (5 papers)

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Research

Article
Optimization Algorithms for Joint Power and Sub-Channel Allocation for NOMA-Based Maritime Communications
Entropy 2021, 23(11), 1454; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111454 - 01 Nov 2021
Viewed by 284
Abstract
This paper investigates resource optimization schemes in a marine communication scenario based on non-orthogonal multiple access (NOMA). According to the offshore environment of the South China Sea, we first establish a Longley–Rice-based channel model. Then, the weighted achievable rate (WAR) is considered [...] Read more.
This paper investigates resource optimization schemes in a marine communication scenario based on non-orthogonal multiple access (NOMA). According to the offshore environment of the South China Sea, we first establish a Longley–Rice-based channel model. Then, the weighted achievable rate (WAR) is considered as the optimization objective to weigh the information rate and user fairness effectively. Our work introduces an improved joint power and user allocation scheme (RBPUA) based on a single resource block. Taking RBPUA as a basic module, we propose three joint multi-subchannel power and marine user allocation algorithms. The gradient descent algorithm (GRAD) is used as the reference standard for WAR optimization. The multi-choice knapsack algorithm combined with dynamic programming (MCKP-DP) obtains a WAR optimization result almost equal to that of GRAD. These two NOMA-based solutions are able to improve WAR performance by 7.47% compared with OMA. Due to the high computational complexity of the MCKP-DP, we further propose a DP-based fully polynomial-time approximation algorithm (DP-FPTA). The simulation results show that DP-FPTA can reduce the complexity by 84.3% while achieving an approximate optimized performance of 99.55%. This advantage of realizing the trade-off between performance optimization and complexity meets the requirements of practical low-latency systems. Full article
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Article
Spectrum Slicing for Multiple Access Channels with Heterogeneous Services
Entropy 2021, 23(6), 686; https://0-doi-org.brum.beds.ac.uk/10.3390/e23060686 - 28 May 2021
Cited by 1 | Viewed by 700
Abstract
Wireless mobile networks from the fifth generation (5G) and beyond serve as platforms for flexible support of heterogeneous traffic types with diverse performance requirements. In particular, the broadband services aim for the traditional rate optimization, while the time-sensitive services aim for the optimization [...] Read more.
Wireless mobile networks from the fifth generation (5G) and beyond serve as platforms for flexible support of heterogeneous traffic types with diverse performance requirements. In particular, the broadband services aim for the traditional rate optimization, while the time-sensitive services aim for the optimization of latency and reliability, and some novel metrics such as Age of Information (AoI). In such settings, the key question is the one of spectrum slicing: how these services share the same chunk of available spectrum while meeting the heterogeneous requirements. In this work we investigated the two canonical frameworks for spectrum sharing, Orthogonal Multiple Access (OMA) and Non-Orthogonal Multiple Access (NOMA), in a simple, but insightful setup with a single time-slotted shared frequency channel, involving one broadband user, aiming to maximize throughput and using packet-level coding to protect its transmissions from noise and interference, and several intermittent users, aiming to either to improve their latency-reliability performance or to minimize their AoI. We analytically assessed the performances of Time Division Multiple Access (TDMA) and ALOHA-based schemes in both OMA and NOMA frameworks by deriving their Pareto regions and the corresponding optimal values of their parameters. Our results show that NOMA can outperform traditional OMA in latency-reliability oriented systems in most conditions, but OMA performs slightly better in age-oriented systems. Full article
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Article
DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC
Entropy 2021, 23(5), 613; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050613 - 14 May 2021
Cited by 1 | Viewed by 711
Abstract
Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users [...] Read more.
Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL)-based algorithm was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) scheme. Full article
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Article
On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel
Entropy 2021, 23(5), 605; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050605 - 14 May 2021
Viewed by 506
Abstract
Motivated by applications in unsourced random access, this paper develops a novel scheme for the problem of compressed sensing of binary signals. In this problem, the goal is to design a sensing matrix A and a recovery algorithm, such that the sparse binary [...] Read more.
Motivated by applications in unsourced random access, this paper develops a novel scheme for the problem of compressed sensing of binary signals. In this problem, the goal is to design a sensing matrix A and a recovery algorithm, such that the sparse binary vector x can be recovered reliably from the measurements y=Ax+σz, where z is additive white Gaussian noise. We propose to design A as a parity check matrix of a low-density parity-check code (LDPC) and to recover x from the measurements y using a Markov chain Monte Carlo algorithm, which runs relatively fast due to the sparse structure of A. The performance of our scheme is comparable to state-of-the-art schemes, which use dense sensing matrices, while enjoying the advantages of using a sparse sensing matrix. Full article
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Article
Soft Interference Cancellation for Random Coding in Massive Gaussian Multiple-Access
Entropy 2021, 23(5), 539; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050539 - 28 Apr 2021
Viewed by 388
Abstract
In 2017, Polyanskiy showed that the trade-off between power and bandwidth efficiency for massive Gaussian random access is governed by two fundamentally different regimes: low power and high power. For both regimes, tight performance bounds were found by Zadik et al., in 2019. [...] Read more.
In 2017, Polyanskiy showed that the trade-off between power and bandwidth efficiency for massive Gaussian random access is governed by two fundamentally different regimes: low power and high power. For both regimes, tight performance bounds were found by Zadik et al., in 2019. This work utilizes recent results on the exact block error probability of Gaussian random codes in additive white Gaussian noise to propose practical methods based on iterative soft decoding to closely approach these bounds. In the low power regime, this work finds that orthogonal random codes can be applied directly. In the high power regime, a more sophisticated effort is needed. This work shows that power-profile optimization by means of linear programming, as pioneered by Caire et al. in 2001, is a promising strategy to apply. The proposed combination of orthogonal random coding and iterative soft decoding even outperforms the existence bounds of Zadik et al. in the low power regime and is very close to the non-existence bounds for message lengths around 100 and above. Finally, the approach of power optimization by linear programming proposed for the high power regime is found to benefit from power imbalances due to fading which makes it even more attractive for typical mobile radio channels. Full article
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