5G-U: Conceptualizing Integrated Utilization of Licensed and Unlicensed Spectrum for Future IoT

Lu, Xi; Petrov, Vitaly; Moltchanov, Dmitri; Andreev, Sergey; Mahmoodi, Toktam; Dohler, Mischa

doi:10.1109/mcom.2019.1800663

Cited by 52 publications

(29 citation statements)

References 12 publications

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“…In the next task, we design a more far-sighted RL process to replace the main part of Algorithm 1. The purpose of RL is to maximize the expected discounted return (10)…”

Section: Policy Gradient Updatementioning

confidence: 99%

“…Especially with a resources shortage (such as spectrum and power), efficient URLLC resource allocation methods are particularly important. Current technologies, such as power amplification and millimeter-wave (mmWave), can temporarily alleviate the dilemma caused by the shortage of radio resources [8][9][10]. But for the rapid growth of communication data, even mmWave with abundant spectrum resources still faces a shortage of spectrum.…”

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confidence: 99%

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Reinforcement learning-based hybrid spectrum resource allocation scheme for the high load of URLLC services

Huang

Xie

Cheriet

2020

J Wireless Com Network

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Ultra-reliable and low-latency communication (URLLC) in mobile networks is still one of the core solutions that require thorough research in 5G and beyond. With the vigorous development of various emerging URLLC technologies, resource shortages will soon occur even in mmWave cells with rich spectrum resources. As a result of the large radio resource space of mmWave, traditional real-time resource scheduling decisions can cause serious delays. Consequently, we investigate a delay minimization problem with the spectrum and power constraints in the mmWave hybrid access network. To reduce the delay caused by high load and radio resource shortage, a hybrid spectrum and power resource allocation scheme based on reinforcement learning (RL) is proposed. We compress the state space and the action space by temporarily dumping and decomposing the action. The multipath deep neural network and policy gradient method are used, respectively, as the approximater and update method of the parameterized policy. The experimental results reveal that the RL-based hybrid spectrum and the power resource allocation scheme eventually converged after a limited number of iterative learnings. Compared with other schemes, the RL-based scheme can effectively guarantee the URLLC delay constraint when the load does not exceed 130%.

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“…In the next task, we design a more far-sighted RL process to replace the main part of Algorithm 1. The purpose of RL is to maximize the expected discounted return (10)…”

Section: Policy Gradient Updatementioning

confidence: 99%

mentioning

confidence: 99%

Reinforcement learning-based hybrid spectrum resource allocation scheme for the high load of URLLC services

Huang

Xie

Cheriet

2020

J Wireless Com Network

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“…3GPP TR 38.889 [16] presents NR-U and Wi-Fi coexistence results in the 5 GHz bands that are obtained by multiple companies, but the simulators are not publicly available, and the models are not described with much insight. Also, authors in [29] present results of a 5G system that integrates the 28 GHz licensed bands and the 60 GHz unlicensed bands, while coexisting with WiGig therein, but few details are revealed about the proprietary simulator that is used. As such, the obtained results in [16], [29] are not easily reproducible, and system performance metrics are presented without much detail revealed about the underlying models and assumptions.…”

Section: Introductionmentioning

confidence: 99%

“…Also, authors in [29] present results of a 5G system that integrates the 28 GHz licensed bands and the 60 GHz unlicensed bands, while coexisting with WiGig therein, but few details are revealed about the proprietary simulator that is used. As such, the obtained results in [16], [29] are not easily reproducible, and system performance metrics are presented without much detail revealed about the underlying models and assumptions. We believe that reproducibility and model openness are crucial to perform research in the area of coexistence of multi-RAT technologies in unlicensed spectrum.…”

Section: Introductionmentioning

confidence: 99%

NR-U and IEEE 802.11 Technologies Coexistence in Unlicensed mmWave Spectrum: Models and Evaluation

et al. 2020

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New Radio-based access to Unlicensed spectrum (NR-U) intends to expand the applicability of 5G NR access technology to support operation in unlicensed bands by adhering to Listen-Before-Talk (LBT) requirement for accessing the channel. As the NR-U specification is being developed, simulations to assess the performance of NR-U and IEEE 802.11 technologies coexistence in unlicensed spectrum bands are crucial. In this paper, we report on extensions to a popular and open source network simulator, ns-3, to build an NR-U system-level simulator and to model the NR-U and IEEE 802.11 technologies coexistence in the currently available unlicensed spectrum bands. The proposed NR-U model capitalizes on an NR Release-15 based model that has been extended to operate in unlicensed bands, while meeting its regulatory requirements. For the coexistence analysis, we pay particular attention to the millimeter-wave bands and provide a complete set of simulation campaigns evaluating the coexistence of NR-U and IEEE 802.11ad Wireless Gigabit (WiGig) in the 60 GHz bands. In particular, we focus on determining whether NR-U fulfills its coexistence objective in terms of a fairness criterion by testing different NR-U parameters, such as the numerology, the bandwidth, the channel access scheme, the energy detection threshold, and the beamforming method.

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“…the latest release of the 5G NR specifications is a significant milestone in the development of next-generation mobile cellular technology. By specifying the air interface that is capable of operating in the mmWave frequency bands and thus having the unprecedented capacity, 5G systems support bandwidth-hungry user applications, such as ultrahigh-definition video streaming, AR/VR broadcasting, and proximate gaming [5].…”

Section: Introductionmentioning

confidence: 99%

Applying Blockchain Technology for User Incentivization in mmWave-Based Mesh Networks

et al. 2020

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Wireless traffic produced by modern mobile devices displays high temporal and spatial dynamics as users spontaneously engage in collective applications where a significant portion of generated data remains localized. As a result, conventional service provisioning approaches may no longer be sufficient in beyond fifth generation (B5G) systems. The challenge of increased dynamics on the access networks can be mitigated with moving cells. However, the deployment time of these temporary serving entities may lag behind the service demand lifetime. Another viable solution to offload excessive cellular traffic is to rely upon locally available radio resources offered by user devices via direct mmWave-based mesh interworking. An important challenge in such systems is related to the incentivization of users to partake in collaborative resource sharing. To leverage multi-hop mesh capabilities, we propose the use of emerging blockchain technology that offers cryptographically-strong accounting while maintaining the anonymity of the participants. With system-level evaluations, we demonstrate that the utilization of mobile blockchain methods allows for a non-incremental improvement in the offloading gains. This demonstrates the potential of the outlined proposal for becoming a successful mechanism in the emerging B5G systems. INDEX TERMS Mesh networks, millimeter wave communication, blockchain, Ad hoc networks, 5G mobile communication, multimedia communication.

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5G-U: Conceptualizing Integrated Utilization of Licensed and Unlicensed Spectrum for Future IoT

Cited by 52 publications

References 12 publications

Reinforcement learning-based hybrid spectrum resource allocation scheme for the high load of URLLC services

Reinforcement learning-based hybrid spectrum resource allocation scheme for the high load of URLLC services

NR-U and IEEE 802.11 Technologies Coexistence in Unlicensed mmWave Spectrum: Models and Evaluation

Applying Blockchain Technology for User Incentivization in mmWave-Based Mesh Networks

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