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1、自我介绍Thank you, Mr./Ms. Chair. /professorMy name is sang qian. I am very honored to be here to do oral presentation.I am a Master student from Hohai University and I am currently doing some research on physical layer security.Today, I would like to share with you some of my research on relay selectio
2、n in cooperative communication. (external /ekstrnl; kstrnl/)内容安排：My presentation includes these five parts.First, some background information about this research;Second, system model we have done;Third, NN-based relay selection scheme we have proposedForth, Simulation and results analysisAnd last, s
3、ome conclusions we have got P4Part one, introductionFirstly, I would like to give you a bit of background.Differing from the traditional cryptographic techniques based on secret keys, we can make use of wireless channel characteristics to enhance physical layer security.Cooperative communication has
4、 been widely recognized as an effective way to combat wireless fading and provide diversity gain which is one of the research hot spots.Machine learning as an emerging technology has been widely applied in image processing, cancer prediction, stock analysis and other fields. So why not try it in wir
5、eless communication?P5:Next, I want to talk a little bit about present studyRecent studies on deep learning for wireless communication systems have proposed alternative approaches to enhance certain parts of the conventional communication system such as modulation recognition、 channel encoding and d
6、ecoding、channel estimation and detection and an autoencoder which can replace the total system with a novel architecture【modulation recognition:An NN architecture for modulation recognition that consists of a 4-layer NN and two two-layer NNs。channel encoding and decoding：A plain DNN architecture for
7、 channel decoding to decode k bitsmessages from N bits noisy codewords。channel estimation and detection ：A dense-Net for symbol-to-symbol detection can adopt long short-term memory (LSTM) to detect an estimated symbol. Autoencoder：the autoencoder can represent the entire communication system and joi
8、ntly optimize the transmitter and receiver over an AWGN channel.】P6So why did we conduct this research? Well, we want to exploit the potential benefits of deep learning in enhancing physical layer security in cooperative(/kprtv/ wireless communication and reduce the feedback overhead in limited spec
9、trum resouce by our our proposed scheme.P8 Now let me move onto part two -system modelHere, you can see a figure which is a system model.This is the source ; these are the relay nodes and this is the destination ,this is the eavesdropperThe whole process of cooperative wireless communication can be
10、divided into two phases.In the first phase, the source broadcasts the signal to the optimal relay which guarantees perfect security. As shown in Fig 1,represents a fading coefficient of the channel from the source to the relay node( . )In the second phase, the optimal relay forwards a scaled version
11、 of its received signal to the destination in the presence of the eavesdropper, where the optimal relay is considered to adopt amplify-and-forward (AF) relay scheme. In this figure,represents a fading coefficient of the channel from the relay to the destination represents a fading coefficient of the
12、 channel from the relay to the eavesdropper. P9: Here you can see some following expressions. I am not going to waste our precious time on the lengthy derivation. I would like to invite you to directly take a look at the equation in its final form.This is the optimal index of the selected relay with
13、 the conventional relay selection scheme.Amaong this expression represents the achievable secrecy rate of system model when the relay is selected.P11Now let me move to part three -NN-based Relay SelectionHere you can see a figure which shows conventional 3-layer neural network . It consists of input
14、 layer, hidden layer 1, hidden (/hdn/)layer 2 and output layer. Neural network can learn features from raw data automatically and adjust parameters（/prmt(r)z/）flexibly(/fleksbli/) such as weights and biases.In complex (/kmpleks/)conditions (scenarios(/snr /),) Neural network has promising applicatio
15、ns in relay selection for several reasons.First, the deep network has superior(/supr/) learning ability despite(/dspat/)the complex channel conditions.Second, Neural network can handle large data sets because of distributed(/dstrbjtd/) and parallel(/prlel/)computing(/kmpjut/s, which ensure computation(/kmpjte()n/)speed and processing capacity(/kpst/).Third, various libraries or frameworks, such as TensorFlow, Theano,and Caffe give it wide applicationsIn this paper, the problem of the relay selection is modeled as a multi(/mlt/,ao)-classification problem. We adopt simple neural network(
