Deep-learning Based Adaptive Beam Management Technique for Mobile High-speed 5G mmWave Networks

Na, Woongsoo; Bae, Byungjun; Cho, Sukhee; Kim, Nayeon

doi:10.1109/icce-berlin47944.2019.8966183

Cited by 11 publications

(14 citation statements)

References 2 publications

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“…Although the beam switching for a phased-array antenna can be carried out almost instantaneous (50 ns) [68], increase in training overhead caused by beam switching results in the beamforming delay [69]. In order to make data-driven decisions by analysing information about vehicle movement and surrounding environments as well as channel quality, ML is applied to find best beams [50], [51], to manage beam switching [52]- [54], and to alleviate the training overheads [55]- [59]. Moreover, ML is adopted to effectively coordinate BS and intelligent reflecting surface (IRS) [60] and to manage increase of computational complexity of UE-BS association in a scenario of multiple moving UE and multiple BSs [61].…”

Section: Ml-based Mobility Management In Mmwave Networkmentioning

confidence: 99%

“…The proactive handover management of mmWave bands is investigated by using ML to aim to minimize link disruption and signalling overhead in [52]- [54]. Firstly, the fact that identification of exact UE's location would be difficult is considered in [52].…”

Section: Ml-based Mobility Management In Mmwave Networkmentioning

confidence: 99%

“…Especially, in the scenario of single UE with two BSs, it shows that the proposed approach successfully predicts blockage/handoff in close to 95% of the times, resulting to reduction of session disconnection for high reliability and low latency. The authors in [54] propose a ML-based beam management scheme to mitigate such disconnection time caused by beam misalignment. In their approach, various information collected from UEs such as location, mobility vectors, received SNR from BSs, and the current beam index are utilized to describe UE's context.…”

Section: Ml-based Mobility Management In Mmwave Networkmentioning

confidence: 99%

“…Fig. 9: A proactive beam hand-off scheme proposed in [54] While beam training overhead is one of challenges to use mmWave bands, ML-based approaches to mitigate such overhead are studied in literature. In [55], for the scenario of multiple distributed BSs for one UE, the perspective of training/coordination overhead is considered.…”

Section: Ml-based Mobility Management In Mmwave Networkmentioning

confidence: 99%

See 3 more Smart Citations

Beam-Based Mobility Management in 5G Millimetre Wave V2X Communications: A Survey and Outlook

Kose¹,

Lee

Foh

et al. 2021

IEEE Open J. Intell. Transp. Syst.

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It is envisaged that 5G can enable many vehicular use cases that require high capacity, ultra-low latency and high reliability. To support this, 5G proposes the use of dense small cells technology as well as and highly directional mmWave systems deployment, among many other new advanced communication technologies, to boost the network capacity, reduce latency and provide high reliability. In such systems, enabling vehicular communication, where the nodes are highly mobile, requires robust mobility management techniques to minimise signalling cost and interruptions during frequent handovers. This presents a major challenge that communication system engineers need to address to realise the promise of 5G systems for V2X and similar applications. In this paper, we provide an overview of recent progresses in the development of handover and beam management techniques in 5G communication systems. We conduct a critical appraisal of current research on beam level and cell level mobility management in 5G mmWave networks considering the ultra-reliable and low-latency communication requirements within the context of V2X applications. We also provide an insight into the open challenges and the emerging trends as well as the possible evolution beyond the horizon of 5G.

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Section: Ml-based Mobility Management In Mmwave Networkmentioning

confidence: 99%

Section: Ml-based Mobility Management In Mmwave Networkmentioning

confidence: 99%

Section: Ml-based Mobility Management In Mmwave Networkmentioning

confidence: 99%

Section: Ml-based Mobility Management In Mmwave Networkmentioning

confidence: 99%

See 2 more Smart Citations

Beam-Based Mobility Management in 5G Millimetre Wave V2X Communications: A Survey and Outlook

Kose¹,

Lee

Foh

et al. 2021

IEEE Open J. Intell. Transp. Syst.

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show abstract

“…The authors in [7] proposed an adaptive beam management scheme based on deep learning. Also, the authors in [8] developed a dynamic network slicing technique for short term traffic prediction, applying deep learning techniques.…”

Section: B Deep Learning For 5gmentioning

confidence: 99%

Signal Compression for Efficient Partitioning of Deep Neural Networks

Brito¹,

Nascimento²,

Gonçalves³

et al. 2020

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Fifth generation (5G) mobile networks are adopting several techniques to provide higher data rates while meeting strict latency requirements. Advanced compression techniques and distributed data processing are among them supporting cost efficient network deployments. Also, the usage of machine learning techniques to optimize telecommunication systems is gaining momentum, specially after promising results with deep learning models. Following this trend, there are investigations towards splitting the processing of such models between different network nodes, making these applications more suitable and adaptable to scenarios with low processing capacity nodes. Therefore, in this work we investigate and propose different compression techniques for efficient partitioning of deep neural networks. We combine several splits with quantization and Huffman coding compression algorithms, providing insights on the configurations with the best performance. We compress the output score of the model to reduce the overhead of transmitting such scores through the network and evaluate how the accuracy is affected by this compression, and which compression technique provides the best performance.

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Ultra‐reliability and low‐latency communications on the internet of things based on 5G network: Literature review, classification, and future research view

Sefati

Halunga

2023

Trans Emerging Tel Tech

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A new technology known as the Internet of Things (IoT) uses several sensor devices and communication protocols. By implementing cutting‐edge and modern equipment, people use IoT to make their lives easier. Home automation is one of them, and it works with actuators and sensors. However, increasing the number of devices in the IoT network could degrade the Quality of Service (QoS). Therefore, an appropriate framework in software and hardware can improve the Quality of Experience (QoE) and QoS for all users. One of the critical QoS measures in IoT is called Ultra Reliability and Low Latency Communication (URLLC). URLLC is essential in the IoT network released from the third Generation Partnership Project (3GPP) cellular. However, a systematic and comprehensive investigation of the practical procedures for URLLC in IoT needs to be done. This paper comprehensively investigates the existing methodologies in this subject. All the chosen techniques are separated into four categories to obtain a complete picture of the topic: structure‐based, diversity‐based, metaheuristic algorithm‐based, and channel state information. In this paper, we also investigate more benefits and drawbacks of other QoS when URLLC is applied in the IoT network. This paper highlights the challenges of URLLC in IoT networks and describes future open issues in detail to provide an efficient way for researchers in this field.

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Deep-learning Based Adaptive Beam Management Technique for Mobile High-speed 5G mmWave Networks

Cited by 11 publications

References 2 publications

Beam-Based Mobility Management in 5G Millimetre Wave V2X Communications: A Survey and Outlook

Beam-Based Mobility Management in 5G Millimetre Wave V2X Communications: A Survey and Outlook

Signal Compression for Efficient Partitioning of Deep Neural Networks

Ultra‐reliability and low‐latency communications on the internet of things based on 5G network: Literature review, classification, and future research view

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