Capacity optimization for ultra-reliable low-latency communication in 5G — The SON perspective

Roth-Mandutz, Elke; Waswa, Abubaker-Matovu; Mitschele‐Thiel, Andreas

doi:10.23919/cnsm.2017.8255978

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…5. Radio resource  It failed to enhance the parameter optimization at the user-centric and service network (Roth-Mandutz et al, 2017). management techniques  The fairness of service and the data traffic explosion was not resolved in (Dighriri et al, 2016) and the resource model in (Rony et al, 2018) was not effective in traffic load condition.…”

Section: Scheduling Basedmentioning

confidence: 99%

A Review on Ultra Reliable and Low Latency Communications (PHY and MAC Layer Perspectives)

Philip¹,

Abdel-Hafez²

2020

Journal of Computer Science

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Fifth Generation (5G) wireless network plays major attention in the communication scenario due to the advanced features in data transmission. 5G is designed to overcome the issues, such as data rate, reliability, throughput, latency and capacity in the wireless network domains. The 5G wireless services aim to increase the data rate through massive communication services. In this survey, well known research papers based on low latency communication at the Physical layer (PHY) and Medium Access Control layer (MAC) are reviewed based on the Ultra-Reliable Low Latency Communication (URLLC) perspective. Here, the communication techniques are categorized as, structure-based model, resource reuse-based techniques, radio resource management techniques, scheduling based approach, diversity-based techniques and network slicing model based on the purpose of data communication. The issues and challenges faced by the existing research works are reviewed and discussed in the research gaps and issues section. Moreover, the analysis carried out in this survey is based on the evaluation parameters, network layer, network parameters, system, purpose and simulation toolsets. The future work of the URLLC research is based on the issues collected from the recent research papers reviewed in this study.

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Section: Scheduling Basedmentioning

confidence: 99%

A Review on Ultra Reliable and Low Latency Communications (PHY and MAC Layer Perspectives)

Philip¹,

Abdel-Hafez²

2020

Journal of Computer Science

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“…ideas for the communication of robot swarms. Therefore, the research and development of distributed networking mode is imperative [2].…”

Section: Introduction a Motivationmentioning

confidence: 99%

Research on Election Interval of Distributed Wireless Ad Hoc Networks

Peng

2020

IEEE Access

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The distributed scheduling mode possesses numerous advantages over the centralized scheduling mode in scenarios where the topology changes rapidly, such as low operation and maintenance costs, high robustness and improved delay performance, and thus has broad application prospects in robot swarms, device-to-device networks, industrial sensor networks, etc. The distributed election mechanism can effectively reduce collisions and improve the success probability of random access, which is one of the most commonly used channel access methods in distributed networks. The size of election interval directly affects the probability of successful election and the network performance. Specifically, with the expansion of the network scale, smaller election intervals will lead to more election nodes and reduce the probability of successful elections. Although expanding the election interval can reduce collisions to a certain extent, the number of legal election nodes will increase, which reduces the probability of successful elections. Therefore, the choice of the election interval needs to be explored in detail. This paper analyzes the relationship between the election interval and the number of effective election nodes, and establishes the analytical model of the election success probability, scheduling delay and effective throughput. In addition, the election interval size and the number of control slots are optimized to improve effective throughput and delay performance. Finally, the impact of the election interval on the network performance is verified by simulation, which provides guidance for parameter selection in realistic scenarios.

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A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

et al. 2021

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The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-toend behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.INDEX TERMS Artificial neural networks, artificial intelligence, Internet of Things, sixth-generation wireless communication and network architecture, URLLC.

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Capacity optimization for ultra-reliable low-latency communication in 5G — The SON perspective

Cited by 7 publications

References 8 publications

A Review on Ultra Reliable and Low Latency Communications (PHY and MAC Layer Perspectives)

A Review on Ultra Reliable and Low Latency Communications (PHY and MAC Layer Perspectives)

Research on Election Interval of Distributed Wireless Ad Hoc Networks

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

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