Opportunistic Channel Access Using Reinforcement Learning in Tiered CBRS Networks

Tonnemacher, Matthew; Tarver, Chance; Chandrasekhar, Vikram; Chen, Hao; Huang, Pengda; Ng, Boon Loong; Zhang, Jianzhong Charlie; Cavallaro, Joseph R.; Camp, Joseph

doi:10.1109/dyspan.2018.8610474

Cited by 21 publications

(15 citation statements)

References 9 publications

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“…This algorithm applies Q-learning to determine the state and action of [172] Q-value in deep-RL with URLLC for immediate reward and to discount the reward from the output of the DNN [205]. However, the efficient handover management, high capacity, and guaranteed high QoS in heterogeneous networks that using The Q-learning algorithm for reinforcement learning achieved [173] related to load balancing [174]- [177], mobility management [178]- [181], user association [182]- [184], and resource allocation [177], [185], [160], [186]- [190], [191].…”

Section: Reinforcement Learningmentioning

confidence: 99%

“…Successfully processing big data that stems from several sources depends on using a DL system with URLLC, which enables the real-time connection in 6G networks. URLLC improves network management based on using reaction analysis and the correlated prediction [87], [191], [199]- [202]. The URLLC design focuses on enabling precise predictions of channels and the high speed of traffic data, which is essentially predictive, and controlling the new development in DL.…”

Section: Deep Learning Platform For Mobile Networking With Urllcmentioning

confidence: 99%

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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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Section: Reinforcement Learningmentioning

confidence: 99%

Section: Deep Learning Platform For Mobile Networking With Urllcmentioning

confidence: 99%

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

et al. 2021

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“…Its gradient is obtained and optimized by reducing the error between the target and actual function value. Finally, weight θ is updated by utilizing (8). The Algorithm 1 shows the dynamic multichannel sensing algorithm based on DQN. )…”

Section: {1 2 }mentioning

confidence: 99%

“…In [8], reinforcement learning was utilized to adapt an energy detection threshold for secondary nodes in a shared spectrum environment and achieve opportunistic channel access. In [9], a partially observable Markov decision process (POMDP) was applied to achieve the dynamic spectrum allocation in CR.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Multichannel Sensing in Cognitive Radio: Hierarchical Reinforcement Learning

Liu

Jiayun

2021

IEEE Access

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Efficient use of spectral resources is critical in wireless networks and has been extensively studied in recent years. Dynamic spectrum access (DSA) is one of the key techniques on utilizing the spectral resources. Among them, reinforcement learning (RL) for DSA has gained great attention due to the excellent performance. Limited by the large state space in RL, obtaining the best solution to the spectrum access problem is often computationally expensive. Besides, it is hard to balance multiple objectives of the reward function in RL. To tackle these problems, we explore deep reinforcement learning in a layered framework and propose a hierarchical deep Q-network (h-DQN) model for DSA. The proposed approach divides the original problem into separate "sub problems", each of which is solved using its own reinforcement learning agent. This partitioning simplifies each individual problem, enables modularity, and reduces the complexity of the whole optimization process in the multi-objective case. The performance of Q-learning for dynamic sensing(QADS), deep reinforcement learning for dynamic access (DRLDA), and the proposed h-DQN model is evaluated through simulations. The simulation results show that h-DQN yields better performance with the faster convergence and higher channel utilization than the other two compared methods.INDEX TERMS Dynamic multichannel sensing, deep Q-network, hierarchical reinforcement learning, cognitive radio.

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“…FCC also proposed Citizens Broadband Radio Service (CBRS) to overcome spectrum scarcity issues. CBRS is a three-tiered spectrum-sharing scheme for the 3550-3700 MHz band [178].…”

Section: ) Spectrum Sensingmentioning

confidence: 99%

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

et al. 2020

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Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and lowlatency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driverless vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed. INDEX TERMS Machine learning, VANET, cognitive radio, autonomous vehicles, smart transportation system. performance of CR-VANET [15]. Security enhancement is one of the major issues in CR-VANET. Here, a vehicle can pretend to be a PU and propagate false information to obtain spectrum access selfishly. ML can be used to detect such actions and enhance security [16], [17]. ML also provides an optimum route to CR-VANET users to avoid traffic jams and road accidents. ML can also play a vital role in the best infotainment experience in CR-VANET. It can be used for appropriate scheduling, selecting the best channel, and prioritizing messages. CR and ML can play a major role in the next-generation driverless car system. The role of CR in the next-generation transportation system has been presented in previous discussions. This survey shows how ML can be applied to reduce road accidents and traffic congestion. CR can be used to accommodate the spectrum required to support massive data communication among automated driverless vehicles and networks. ML can be an integral part of this driverless or automated vehicle system. Similar to a robot, an autonomous vehicle (AV) can learn the surrounding environment and communicate with increased safety, reliability, QoS, and energy efficiency by applying such learning. This paper presents the dynamic usages of ML in CR-VANET elaborately. Several of the benefits of CR in VANETs and ML in VANETs and CR-VANETs are presented in Table 1. B. CONTRIBUTIONS OF THIS SURVEY ARTICLE Many survey articles describe CR, VANET, ML, and CR-VANETs individually or describe a few aspects of their amalgamation. To the best of our knowledge, surveys that cover the usage of ML in CR-VANET...

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Opportunistic Channel Access Using Reinforcement Learning in Tiered CBRS Networks

Cited by 21 publications

References 9 publications

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

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

Dynamic Multichannel Sensing in Cognitive Radio: Hierarchical Reinforcement Learning

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

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