![]() ![]() During years, a long list of results have appeared to precise and understand what is the complexity of the systems. The Impact Factor for Entropy is 2.494 (2019).Discrete dynamical systems are given by the pair ( X,f) where X is a compact metric space and f: X→ X is a continuous map. It is fully covered by the leading indexing and abstracting services, including Google Scholar, MathSciNet, Scopus and Science Citation Index Expanded (Web of Science). Because it is an online and open access journal, papers published in Entropy will receive high publicity. Is an open access journal which maintains a rigorous and fast peer-review system and accepted papers are immediately published online. Privacy and security aspects of NOMA and massive access.Īccepted papers are immediately published online.ĭr. Leveraging new advances in wireless communications, such as reconfigurable intelligent surfaces (RIS) and orbital angular momentum (OAM), to bolster multiple access and Methods for active user identification in massive access, including machine learning and compressed-sensing-based techniques Ĭoding and modulation schemes designed for NOMA and massive access Incorporating MIMO and massive MIMO with NOMA and massive access Machine learning and data-aided aspects in NOMA and massive access design Message-passing algorithms and sparse graph models for efficient NOMA and massive access įinite blocklength and URLLC aspects in NOMA and massive access ![]() Techniques for coordinated/uncoordinated (unsourced) and grant-based/grant-free multiple access Low-complexity transceiver design for code-domain and power-domain NOMA Performance limits of NOMA and massive access This Special Issue solicits unpublished original papers and comprehensive reviews on topics including, but not limited to: This Special Issue aims to present a broad information-theoretic perspective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. 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. In this framework, the notion of “massive access” has been recently considered as an alternative to the classical multiple-access channel setting. 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 particular, the focus has seemed to shift to new scaling laws for the number of users, code blocklengths, and number of receiving antennas. Nevertheless, some key features of massive machine-type communications may require new paradigms more suitable for the specific characteristics of such settings. 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. 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. ![]() 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. 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. The vision for future wireless networks encompasses a wide variety of applications, featuring dramatically higher throughputs as well as massive machine-type communications. ![]()
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