Deep Learning-Based Relay Selection In D2D Millimeter Wave Communications

Abdelreheem, Ahmed; Omer, Osama A.; Esmaiel, Hamada; Mohamed, Usama S.

doi:10.1109/iccisci.2019.8716458

Cited by 35 publications

(25 citation statements)

References 10 publications

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“…Using Equations (1), (5), and (7)-(9), we see that the EE function is intractable, i.e., there is no closed form of the first derivative of EE with respect to P r . One can immediately consider a one-dimensional numerical search algorithms, such as golden section search, quadratic interpolation method, and inexact line searches, to find the local optimal solution [40], or machine learning based algorithm, e.g., reinforcement learning [41][42][43]. However, since the average achievable rate needs to be fed back from DN to RN for each iteration with adapted P r to estimate the EE, the iterative approaches require significant overhead of the networks.…”

Section: Proposed Power Control Methods For Energy Efficient Rnmentioning

confidence: 99%

Power Control Method for Energy Efficient Buffer-Aided Relay Systems

et al. 2019

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In this paper, a power control method is proposed for a buffer-aided relay node (RN) to enhance the energy efficiency of the RN system. By virtue of a buffer, the RN can reserve the data at the buffer when the the channel gain between an RN and a destination node (DN) is weaker than that between SN and RN. The RN then opportunistically forward the reserved data in the buffer according to channel condition between the RN and the DN. By exploiting the buffer, RN reduces transmit power when it reduces the transmit data rate and reserve the data in the buffer. Therefore, without any total throughput reduction, the power consumption of RN can be reduced, resulting in the energy efficiency (EE) improvement of the RN system. Furthermore, for the power control, we devise a simple power control method based on a two-dimensional surface fitting model of an optimal transmit power of RN. The proposed RN power control method is readily and locally implementable at the RN, and it can significantly improve EE of the RN compared to the fixed power control method and the spectral efficiency based method as verified by the rigorous numerical results.

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Section: Proposed Power Control Methods For Energy Efficient Rnmentioning

confidence: 99%

Power Control Method for Energy Efficient Buffer-Aided Relay Systems

et al. 2019

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“…In [65], the authors proposed a deep learning model for smart communication systems with high density D2D mmWave environments using beamforming. The model selects the best relay node taking into account multiple reliability metrics in order to maximize the average system throughput.…”

Section: Resource Allocation/managementmentioning

confidence: 99%

“…Most of the works that used fully connected layers addressed problems related to the physical medium in 5G systems [2,11,[15][16][17]21,22,24,26,56,60,[62][63][64][65]68,72,74,76]. This can be justified because physical information usually can be structured (e.g., CSI, channel quality indicator (CQI), radio condition information, etc.).…”

Section: Fully Connected Modelsmentioning

confidence: 99%

When 5G Meets Deep Learning: A Systematic Review

et al. 2020

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This last decade, the amount of data exchanged on the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high-speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. However, for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements, and each of these technologies brings its own design issues and challenges. In this context, deep learning models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contribution, this paper presents a systematic review about how DL is being applied to solve some 5G issues. Differently from the current literature, we examine data from the last decade and the works that address diverse 5G specific problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using deep learning models in 5G scenarios and identify several issues that deserve further consideration.

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“…= Mean squared error of ( * , ), In this section, we evaluate the performance of the proposed D2D mmWave communications scheme using numerical simulations compared to the standard IEEE 802.11ad [5] and in [11]. Deep learning model be implemented based on Python libraries and TensorFlow backend [12], [13]. For fair comparisons, we used the same simulation parameters given in [5] and [11] as follows: assuming simulation area equal to 5m ~ 100m and the number of devices = 10 ~ 200 with equiprobable uniform distribution, both 60GHz and 5GHz carrier frequency are considered, Rayleigh fading channel is considered with =3, AoAs/AoDs are assumed to be continuous in [0,2 ] and assuming several cases of blockage Probability ( ) = 0.9, 0.5 and 0.1.…”

Section: B Training Phase Of Proposed Deep Learning Schemementioning

confidence: 99%

Improved D2D Millimeter Wave Communications for 5G Networks Using Deep Learning.pdf

abdelreheem¹,

Mubarak²,

Omer³

et al. 2020

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Mode selection is normally used in conjunction with Device-to-Device (D2D) millimeter wave (mmWave) communications in 5G networks to overcome the low coverage area, poor reliability and vulnerable to path blocking of mmWave transmissions. Thus, producing a high-efficient D2D mmWave using mode selection based on select the optimal mode with low complexity turns to be a big challenge towards ubiquitous D2D mmWave communications. In this paper, low complexity and high-efficient mode selection in D2D mmWave communications based on deep learning is introduced utilizing the artificial intelligence. In which, deep learning is used to estimate the optimal mode y in the case of blocking of mmWave transmission or low coverage area of mmWave communications. Then, the proposed deep learning model is based on training the model with almost use cases in offline phase to predict the optimal mode for data relaying high-reliability communication in online phase. In mode selection process, the potential D2D transmitter select the mode to transmit the data either based on dedicated D2D communication or through the cellular uplink using the base station (BS) as a relay based on several criteria. The proposed deep learning model is developed to overcome the challenges of selected the optimal mode with low complexity and high efficiency. The simulation analysis show that the proposed mode selection algorithms outperform the conventional techniques in D2D mmWave communication in the spectral efficiency, energy efficiency and coverage probability.

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Deep Learning-Based Relay Selection In D2D Millimeter Wave Communications

Cited by 35 publications

References 10 publications

Power Control Method for Energy Efficient Buffer-Aided Relay Systems

Power Control Method for Energy Efficient Buffer-Aided Relay Systems

When 5G Meets Deep Learning: A Systematic Review

Improved D2D Millimeter Wave Communications for 5G Networks Using Deep Learning.pdf

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