Cognitive Neural Network Delay Predictor for High Speed Mobility in 5G C-RAN Cellular Networks

Mahmood, Ali Majeed; Al-Yasiri, Adil; Alani, Omar

doi:10.1109/5gwf.2018.8516715

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…At present, the majority of the research related to 5G presents theoretical analysis [8][9][10][11][12] and simulation studies [11][12][13][14][15][16]. In general, various theoretical state of the art and open issues are presented in [8], the effects of ultra-densification are investigated in [9], various network architectures, medium access mechanisms, and open issues are presented in [10], as well as routing algorithms to achieve lower interference and a balanced traffic load amoung routes in 5G environment are investigated in [11,12], respectively.…”

Section: Our Contributionsmentioning

confidence: 99%

“…In general, various theoretical state of the art and open issues are presented in [8], the effects of ultra-densification are investigated in [9], various network architectures, medium access mechanisms, and open issues are presented in [10], as well as routing algorithms to achieve lower interference and a balanced traffic load amoung routes in 5G environment are investigated in [11,12], respectively. In addition, traffic offloading from backbone routes and the central controller to distributed nodes is investigated in [13,14], the transmission delay is predicted based on channel states in [15], and the feasiblity of D2D in 5G environment is investigated in [16]. Some researchers conduct proof of concept experiments; however, the focus is primarily on the physical layer, particularly spectrum management in [2], interference mitigation in [17], channel sensing in [18], as well as on the data link layer, particularly channel hopping (or switches) in [19].This study focuses on the networking aspect over a 5G-based platform using universal software radio peripheral with GNU radio (USRP/GNU radio) units and Raspberry Pi3 B+ (RP3) processors [20].…”

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A Distributed Testbed for 5G Scenarios: An Experimental Study

Chamran

Yau

Noor

et al. 2019

Sensors

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This paper demonstrates the use of Universal Software Radio Peripheral (USRP), together with Raspberry Pi3 B+ (RP3) as the brain (or the decision making engine), to develop a distributed wireless network in which nodes can communicate with other nodes independently and make decision autonomously. In other words, each USRP node (i.e., sensor) is embedded with separate processing units (i.e., RP3), which has not been investigated in the literature, so that each node can make independent decisions in a distributed manner. The proposed testbed in this paper is compared with the traditional distributed testbed, which has been widely used in the literature. In the traditional distributed testbed, there is a single processing unit (i.e., a personal computer) that makes decisions in a centralized manner, and each node (i.e., USRP) is connected to the processing unit via a switch. The single processing unit exchanges control messages with nodes via the switch, while the nodes exchange data packets among themselves using a wireless medium in a distributed manner. The main disadvantage of the traditional testbed is that, despite the network being distributed in nature, decisions are made in a centralized manner. Hence, the response delay of the control message exchange is always neglected. The use of such testbed is mainly due to the limited hardware and monetary cost to acquire a separate processing unit for each node. The experiment in our testbed has shown the increase of end-to-end delay and decrease of packet delivery ratio due to software and hardware delays. The observed multihop transmission is performed using device-to-device (D2D) communication, which has been enabled in 5G. Therefore, nodes can either communicate with other nodes via: (a) a direct communication with the base station at the macrocell, which helps to improve network performance; or (b) D2D that improve spectrum efficiency, whereby traffic is offloaded from macrocell to small cells. Our testbed is the first of its kind in this scale, and it uses RP3 as the distributed decision-making engine incorporated into the USRP/GNU radio platform. This work provides an insight to the development of a 5G network.Sensors 2020, 20, 18 2 of 23 either as a licensed user (or a primary user, PU) to utilize its licensed channels (or cellular channels), or as an unlicensed user (or a secondary user, SU) to explore and utilize white spaces, which are the underutilized licensed channels (or cognitive channels) [4]. D2D enables nodes to access both cellular and cognitive channels to improve spectrum efficiency in order to improve data transmission rate and quality of service (QoS) [5-7]. Our ContributionsAt present, the majority of the research related to 5G presents theoretical analysis [8][9][10][11][12] and simulation studies [11][12][13][14][15][16]. In general, various theoretical state of the art and open issues are presented in [8], the effects of ultra-densification are investigated in [9], various network architectures, medium access mechanisms, and open issues a...

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Section: Our Contributionsmentioning

confidence: 99%

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confidence: 99%

A Distributed Testbed for 5G Scenarios: An Experimental Study

Chamran

Yau

Noor

et al. 2019

Sensors

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“…Deep learning (DL) is more similar to the human brain. It is a subgroup of machine learning methods, which is learning the many levels of representations of data with different levels of abstraction at each stage [6]. DL is an efficient data feature extraction algorithm because it can overcome the problem of extracting Int J Artif Intell ISSN: 2252-8938  features that are involved in nonlinear big data by shallow learning.…”

Section: Introductionmentioning

confidence: 99%

Predictive maintenance of electromechanical systems based on enhanced generative adversarial neural network with convolutional neural network

Abood

Nasser

Al-Khazraji

2023

IJ-AI

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<p>Predictive maintenance (PdM) is a cost-cutting method that involves avoiding breakdowns and production losses. Deep learning (DL) algorithms can be used for defect prediction and diagnostics due to the huge amount of data generated by the integration of analog and digital systems in manufacturing operations. To improve the predictive maintenance strategy, this study uses a hybrid of the convolutional neural network (CNN) and conditional generative adversarial neural network (CGAN) model. The proposed CNN-CGAN algorithm improves forecast accuracy while substantially reducing model complexity. A comparison with standalone CGAN utilizing a public dataset is performed to evaluate the proposed model. The results show that the proposed CNN-CGAN model outperforms the conditional GAN (CGAN) in terms of prediction accuracy. The average F-Score is increased from 97.625% for the CGAN to 100% for the CNN-CGAN.</p>

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“…As another example, in [4], a routing algorithm is proposed to select routes based on the CC's network-wide information, including intermediate nodes, the traffic of each link, and the number of hops of a route, to reduce interference among routes and achieve better load balancing. Meanwhile, simulation has been conducted in [3,4,[9][10][11][12]. In [3,12], traffic offload from the CC and backbone routes [Backbone route is not defined yet prior to this] to SC nodes has shown to reduce access and end-to-end delays.…”

Section: Introductionmentioning

confidence: 99%

“…In [9], a multihop D2D communication to deliver a high-quality video stream to a destination has shown to increase network capacity at the expense of a higher end-to-end delay. In [10], a virtual BS is proposed to determine the available channels, so that a source node can use this information to reduce the delay of a route. In [11], CC manages D2D communication among user devices in different channels, and it has shown to improve data transmission.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios

et al. 2021

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This paper demonstrates a route selection mechanism on a testbed with heterogeneous device-to-device (D2D) wireless communication for a 5G network scenario. The source node receives information about the primary users’ (PUs’) (or licensed users’) activities and available routes from the macrocell base station (or a central controller) and makes a decision to select a multihop route to the destination node. The source node from small cells can either choose: (a) a route with direct communication with the macrocell base station to improve the route performance; or (b) a route with D2D communication among nodes in the small cells to offload traffic from the macrocell to improve spectrum efficiency. The selected D2D route has the least PUs’ activities. The route selection mechanism is investigated on our testbed that helps to improve the accuracy of network performance measurement. In traditional testbeds, each node (e.g., Universal Software Radio Peripheral (USRP) that serves as the front-end communication block) is connected to a single processing unit (e.g., a personal computer) via a switch using cables. In our testbed, each USRP node is connected to a separate processing unit, i.e., raspberry Pi3 B+ (or RP3), which offers three main advantages: (a) control messages and data packets are exchanged via the wireless medium; (b) separate processing units make decisions in a distributed and heterogeneous manner; and (c) the nodes are placed further apart from one another. Therefore, in the investigation of our route selection scheme, the response delay of control message exchange and the packet loss caused by the operating environment (e.g., ambient noise) are implied in our end-to-end delay and packet delivery ratio measurement. Our results show an increase of end-to-end delay and a decrease of packet delivery ratio due to the transmission of control messages and data packets in the wireless medium in the presence of the dynamic PUs’ activities. Furthermore, D2D communication can offload 25% to 75% traffic from macrocell base station to small cells.

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Cognitive Neural Network Delay Predictor for High Speed Mobility in 5G C-RAN Cellular Networks

Cited by 8 publications

References 18 publications

A Distributed Testbed for 5G Scenarios: An Experimental Study

A Distributed Testbed for 5G Scenarios: An Experimental Study

Predictive maintenance of electromechanical systems based on enhanced generative adversarial neural network with convolutional neural network

An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios

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