scholarly journals A Deep Learning Based Transmission Algorithm for Mobile Device-to-Device Networks

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1361 ◽  
Author(s):  
Tae-Won Ban ◽  
Woongsup Lee
Keyword(s):  
Deep Learning ◽  
Mobile Networks ◽  
Spectral Efficiency ◽  
Radio Resource Management ◽  
Cellular Communications ◽  
D2d Communications ◽  
Radio Resource ◽  
Next Generation Mobile Networks ◽  
Device To Device ◽  
Mobile Caching

Recently, device-to-device (D2D) communications have been attracting substantial attention because they can greatly improve coverage, spectral efficiency, and energy efficiency, compared to conventional cellular communications. They are also indispensable for the mobile caching network, which is an emerging technology for next-generation mobile networks. We investigate a cellular overlay D2D network where a dedicated radio resource is allocated for D2D communications to remove cross-interference with cellular communications and all D2D devices share the dedicated radio resource to improve the spectral efficiency. More specifically, we study a problem of radio resource management for D2D networks, which is one of the most challenging problems in D2D networks, and we also propose a new transmission algorithm for D2D networks based on deep learning with a convolutional neural network (CNN). A CNN is formulated to yield a binary vector indicating whether to allow each D2D pair to transmit data. In order to train the CNN and verify the trained CNN, we obtain data samples from a suboptimal algorithm. Our numerical results show that the accuracies of the proposed deep learning based transmission algorithm reach about 85%∼95% in spite of its simple structure due to the limitation in computing power.

scholarly journals A Survey on Relay Selection in Cooperative Device-to-Device (D2D) Communication in 5G Cellular Networks

2017 ◽  
Author(s):  
Shamganth K ◽  
Said Shafi Abdullah Al-Shabibia
Keyword(s):  
Cellular Networks ◽  
Spectral Efficiency ◽  
Relay Selection ◽  
Relay Node ◽  
Cooperative Networks ◽  
Vital Role ◽  
D2d Communication ◽  
Destination Node ◽  
D2d Communications ◽  
Device To Device

Device-to-device (D2D) communications underlayed to a cellular infrastructure has recently been proposed to increase spectrum and energy efficiency. Relay selection plays a vital role in cooperative networks. In D2D communication, if the chosen relay is not the best relay, then the whole communication will not be successful from source node to destination node. Also to choose the optimal relays, if more feedback and time delay exists between the source nodes and relay node then it leads to degradation of spectral efficiency.  A survey on the relay selection techniques used with D2D communications and the challenges and design issues associated with the integration of D2D in 5G cellular network is presented.

scholarly journals A Deep Learning Approach for Maximum Activity Links in D2D Communications

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2941 ◽  
Author(s):  
Bocheng Yu ◽  
Xingjun Zhang ◽  
Francesco Palmieri ◽  
Erwan Creignou ◽  
Ilsun You
Keyword(s):  
Deep Learning ◽  
Schedule Problem ◽  
Computation Time ◽  
Maximum Activity ◽  
Traffic Load ◽  
Link Scheduling ◽  
Cellular Communications ◽  
D2d Communications ◽  
Computational Overhead ◽  
Link Schedule

Mobile cellular communications are experiencing an exponential growth in traffic load on Long Term Evolution (LTE) eNode B (eNB) components. Such load can be significantly contained by directly sharing content among nearby users through device-to-device (D2D) communications, so that repeated downloads of the same data can be avoided as much as possible. Accordingly, for the purpose of improving the efficiency of content sharing and decreasing the load on the eNB, it is important to maximize the number of simultaneous D2D transmissions. Specially, maximizing the number of D2D links can not only improve spectrum and energy efficiency but can also reduce transmission delay. However, enabling maximum D2D links in a cellular network poses two major challenges. First, the interference between the D2D and cellular communications could critically affect their performance. Second, the minimum quality of service (QoS) requirement of cellular and D2D communication must be guaranteed. Therefore, a selection of active links is critical to gain the maximum number of D2D links. This can be formulated as a classical integer linear programming problem (link scheduling) that is known to be NP-hard. This paper proposes to obtain a set of network features via deep learning for solving this challenging problem. The idea is to optimize the D2D link schedule problem with a deep neural network (DNN). This makes a significant time reduction for delay-sensitive operations, since the computational overhead is mainly spent in the training process of the model. The simulation performed on a randomly generated link schedule problem showed that our algorithm is capable of finding satisfactory D2D link scheduling solutions by reducing computation time up to 90% without significantly affecting their accuracy.

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