Resource Allocation Based on Deep Neural Networks for Cognitive Radio Networks

Zhou, Fuhui; Zhang, Xiongjian; Hu, Rose Qingyang; Papathanassiou, Apostolos; Meng, Weixiao

doi:10.1109/iccchina.2018.8641220

Cited by 34 publications

(22 citation statements)

References 18 publications

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“…Energy-and spectrum-efficient RA strategies are required. Zhou et al [295] proposed an RA scheme for real-time performance with a simple implementation method. They designed their system by using DNN, and they presented a training method to train neural networks.…”

Section: E ML In Resource Allocation In Cr-vanetmentioning

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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Section: E ML In Resource Allocation In Cr-vanetmentioning

confidence: 99%

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

et al. 2020

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“…In [15], the authors have proposed a resource allocation technique for small cells by employing deep learning for dynamic channel selection, carrier aggregation, and fractional spectrum. In [16] Zhou et al have proposed an efficient DNN for resource allocation in cognitive radio networks aiming at the real-time performance to maximize the energy and spectral efficiency of the network. In [17], Li et al have proposed a model that utilizes a Hopfield neural network to predict the bit and power allocation in a multi-user OFDM system.…”

Section: A Supervised Learningmentioning

confidence: 99%

A Study on Deep Learning for Latency Constraint Applications in Beyond 5G Wireless Systems

2020

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The fifth generation (5G) of wireless communications has led to many advancements in technologies such as large and distributed antenna arrays, ultra-dense networks, software based networks and network virtualization. However, the need for a higher level of automation to establish hyper-low latency and hyper-high reliability for beyond 5G applications requires extensive research on machine learning with applications in wireless communications. Thereby, learning techniques will take a central stage in the sixth generation of wireless communications to cope up with the stringent application requirements. This paper studies the practical limitations of these learning methods in the context of resource management in nonstationary radio environment. Based on the practical limitations we carefully design and propose supervised, unsupervised, and reinforcement learning models to support rate maximization objective under user mobility. We study the effects of practical systems such as latency and reliability on the rate maximization with deep learning models. For common testing in the non-stationary environment, we present a generic dataset generation method to benchmark across different learning models versus traditional optimal resource management solutions. Our results indicate that learning models have practical challenges related to training limiting their applications. These models need an environment-specific design to reach the accuracy of an optimal algorithm. Such an approach is practically not realistic due to the high resource requirement needed for frequent retraining. INDEX TERMS Machine learning, low-latency application, learning model ageing, mobile communications, 6G.

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“…The aim was to provide less computational complexity and hence real-time processing at almost similar performance. In the CR context, Zhou et al [47] have implemented a deep neural network for resource allocation, namely spectral efficiency and energy efficiency maximizations. The training data have been obtained by specific conventional strategies in the literature.…”

Section: Resource Allocationmentioning

confidence: 99%

Attention-based deep convolutional neural network for spectral efficiency optimization in MIMO systems

Sun

Yaqot

Qiu

et al. 2020

Neural Comput & Applic

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Spectral efficiency (SE) optimization in massive multiple input multiple output (MIMO) antenna cognitive systems is a challenge originated from the coexistence restrictions. Traditional power allocation can optimize the SE; however, involving deep learning can meet real-time and fairness processing requirements. In unfair allocation problem, all power is possibly assigned to one or few antennas of a particular user. In this paper, we build a mathematical optimization model considering the fairness problem such that SE is optimized for all users. To implement the model, we propose an attentionbased convolutional neural network (Att-CNN), where h 0 and h k (i.e., cross-interference and direct channels) attention mechanisms are used to improve the SE. The convolutional neural network is applied to decrease the floating point operations (FLOPs) and number of network parameters. We conducted experiments from these aspects: Fair antenna power allocation, power allocation performance and computational performance. Heat maps with different interference thresholds show the fair allocation for all users. Analyses of SE validate the superiority of the Att-CNN compared with the equal power allocation and fully connected neural network (FNN) schemes. The analyses of the FLOPs and number of parameters show the superiority of the Att-CNN over the FNN.

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Resource Allocation Based on Deep Neural Networks for Cognitive Radio Networks

Cited by 34 publications

References 18 publications

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

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

A Study on Deep Learning for Latency Constraint Applications in Beyond 5G Wireless Systems

Attention-based deep convolutional neural network for spectral efficiency optimization in MIMO systems

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