A wireless local area network (WLAN) is an important type of wireless network which\nconnotes different wireless nodes in a local area network. Network traffic or data traffic in a WLAN is\nthe amount of network packets moving across a wireless network from each wireless node to another\nwireless node, which provide the load of sampling in a wireless network. WLAN�s network traffic\nis the main component for network traffic measurement, network traffic control, and simulation.\nIn addition, traffic classification technique is an essential tool for improving the Quality of Service\n(QoS) in different wireless networks in the complex applications, such as local area networks, wireless\nlocal area networks, wireless personal area networks, wireless metropolitan area networks, and wide\narea networks. Network traffic classification is also an essential component in the products for QoS\ncontrol in different wireless network systems and applications. Classifying network traffic in a WLAN\nallows one to see what kinds of traffic we have in each part of the network, organize the various\nkinds of network traffic in each path into different classes in each path, and generate network traffic\nmatrix in order to identify and organize network traffic, which is an important key for improving\nthe QoS feature. In this paper, a new architecture based on the following algorithms is presented\nfor improving the QoS feature in a wireless local area network: (1) Real-Time Network Traffic\nClassification (RTNTC) algorithm for WLANs based on Compressed Sensing (CS); (2) Real-Time\nNetwork Traffic Monitoring (RTNTM) approach based on CS. This architecture enables continuous\ndata acquisition and compression of WLAN�s signals that are suitable for a variety of other wireless\nnetworking applications. At the transmitter side of each wireless node, an analog CS framework is\napplied at the sensing step before an analog to digital converter in order to generate the compressed\nversion of the input signal. At the receiver side of the wireless node, a reconstruction algorithm is\napplied in order to reconstruct the original signals from the compressed signals with high probability\nand enough accuracy. The proposed architecture allows reducing Data Delay Probability (DDP) to\n15%, Bit Error Rate (BER) to 14% at each wireless node, False Detection Rate (FDR) to 25%, and Packet\nDelay (PD) to 15%, which are good records for WLANs. The proposed architecture is increased\nData Throughput (DT) to 22% and Signal to Noise (S/N) ratio to 17%, and 10% accuracy of wireless\ntransmission. The proposed algorithm outperforms existing algorithms by achieving a good level of\nQuality of Service (QoS), which provides a good background for establishing high quality wireless\nlocal area networks.
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