5G shortcomings and Beyond-5G/6G requirements

Moussaoui, Meroua; Bertin, Emmanuel; Crespi, Noël

doi:10.1109/6gnet54646.2022.9830439

Cited by 14 publications

(5 citation statements)

References 64 publications

(124 reference statements)

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“…Finally, Section VII provides concluding remarks and envisioned future directions. [32]. Based on the latest preliminary proposal from the International Telecommunication Union (ITU) [33], the evolution of the fundamental services of wireless networks is summarized in Fig.…”

Section: Contributionmentioning

confidence: 99%

Toward Natively Intelligent Semantic Communications and Networking

Trevlakis,

Pappas,

Boulogeorgos

2024

IEEE Open J. Commun. Soc.

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As the process of envisioning, researching, and strategizing for 6G communications has commenced, we present our vision on the role of semantic communications (SemCom) in the beyond 5G era. In the following discourse, we delve into the factors that may impede the progress of research and implementation of 6G with the objective of catering to the semantically enriched communication requirements of the 2030s. In addition, we proceed to establish the fundamental attributes of SemCom and engage in an examination of the requisite technological modalities. In order to bolster this overarching vision, we introduce a semantic networking architecture that encapsulates and characterizes various application scenarios of SemCom. Expanding on the concept of point-to-point SemCom systems the semantic networking architecture focuses on extracting and filtering goal specific semantic information at the source before transmitting signals. It also involves decoding and post processing semantics at the destination. What sets this networking architecture apart is its transformation into a multi-user distributed, edge-to-cloud network with deadline constraint traffic. Given its complexity, implementing such an architecture necessitates frameworks for extracting and representing knowledge theoretical models to predict and manage multiple time varying deadline/delay constrained traffic flows that may cause significant congestion in the network as well, as innovative metrics infused with semantics to measure performance while encapsulating its inherent relevance. Finally, we provide research considerations and future directions towards the integration of semantics in the forthcoming 6G wireless systems.

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Section: Contributionmentioning

confidence: 99%

Toward Natively Intelligent Semantic Communications and Networking

Trevlakis,

Pappas,

Boulogeorgos

2024

IEEE Open J. Commun. Soc.

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“…In summary, the proposed method in this work will look for the best MCS(i) that supports the best video quality to users yet keeps the percentage of STAs receiving no video content correctly within the desired tolerated limits. (11) 121.9 1024-QAM, R = 5/6…”

Section: Adaptive Modulation Algorithmmentioning

confidence: 99%

“…There are many challenges for next-generation networks to improve communications capabilities. As stated in [11], these shortcomings include spectral efficiency, coverage, interference, security, and privacy. These concerns and those presented in this work contribute to developing next-generation networks that enable high-throughput, low-delay communications.…”

Section: Introductionmentioning

confidence: 99%

PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11bc

Gomez,

Camacho-Escoto,

Orozco-Barbosa

et al. 2023

Future Internet

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WiFi is a widely used wireless technology for data transmission. WiFi can also play a crucial role in simultaneously broadcasting content to multiple devices in multimedia transmission for venues such as classrooms, theaters, and stadiums, etc. Broadcasting allows for the efficient dissemination of information to all devices connected to the network, and it becomes crucial to ensure that the WiFi network has sufficient capacity to transmit broadcast multimedia content without interruptions or delays. However, using WiFi for broadcasting presents challenges that can impact user experience, specifically the difficulty of obtaining real-time feedback from potentially hundreds or thousands of users due to potential collisions of feedback messages. This work focuses on providing accurate feedback to the Access Point about the percentage of users not receiving broadcast traffic correctly so it can adjust its Modulation and Coding Scheme (MCS) while transmitting broadcast multimedia content to many users. The proposed method is comprised of two sequential algorithms. In order to reduce the probability of a collision after transmitting each message, an algorithm searches for the best probability value for users to transmit ACK/NACK messages, depending on whether messages are received correctly or not. This feedback allows the Access Point to estimate the number of STAs correctly/incorrectly receiving the messages being transmitted. A second algorithm uses this estimation so the Access Point can select the best MCS while maintaining the percentage of users not receiving broadcast content correctly within acceptable margins, thus providing users with the best possible content quality. We implemented the proposed method in the ns-3 simulator, and the results show it yields quick, reliable feedback to the Access Point that was then able to adjust to the best possible MCS in only a few seconds, regardless of the user density and dimensions of the scenario.

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“…The advent of fifth-generation (5G) and Beyond 5G (B5G) networks promises to revolutionize the telecommunications landscape, ushering in an era of unprecedented connectivity, ultra-low latency, and massive device proliferation [1]. As these networks evolve, their complexity increases exponentially, presenting substantial challenges in network management, performance optimization, and improving user experiences.…”

Section: Introductionmentioning

confidence: 99%

Modeling ML Efficiency in Telecommunications

Corona,

Teixeira,

Antunes

et al. 2024

Preprint

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Machine learning (ML) models are envisioned to play a fundamental role in Beyond 5G networks. However, training these models presents significant challenges due to the vast amounts of data involved and the complexity of the algorithms, requiring substantial training time and computing resources. This study offers a comprehensive analysis of how dataset characteristics, model complexity, and the number of iterations influence model performance and training time. Furthermore, we take pioneering steps towards modeling the performance and training time of ML models, specifically within the telecommunications sector. Our findings can pave the way for informed decision-making processes in telecommunications, enabling a comprehensive understanding of expected performance and training time requirements. Leading to reduced development times and optimized energy consumption and resource allocation.

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5G shortcomings and Beyond-5G/6G requirements

Cited by 14 publications

References 64 publications

Toward Natively Intelligent Semantic Communications and Networking

Toward Natively Intelligent Semantic Communications and Networking

PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11bc

Modeling ML Efficiency in Telecommunications

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