# Robust Wireless Local Area Networks Based on Compressed Sensing
## 1 Introduction  
A wireless computer network can use either infrared or radio frequency technology to transmit and receive information over the air, in order to link a set of computers using a wireless distributionmethod [1; 2; 3]. This type of network implements a flexible data communication system frequently augmenting, rather than replacing, a wired LAN within a building or campus. WLANs have become popular for use in many applications, such as the home, school, computer laboratory, industrial automation, and office building [4; 5]. One important advantage of WLANs is to simplify networking topologies by enabling multiple computer users or networks to simultaneously share resources, including software and hardware systems in a home or business without additional or intrusive wiring [6; 7]. In order to improve the performance of a WLAN, we need to minimize network traffic, load of sampling rate, and power consumption. RTNTM and RTNTC are two important algorithms to improve the QoS for all users. Traditional RTNTM techniques rely on Shannon Nyquist Sampling (SNS) procedure, which suffer from huge load of sampling and power consumption [8]. Classification of network traffic in a WLAN is a strong tool that helps identify different applications and protocols that exist in a wireless network. Various actions, such as monitoring, discovery, control, and optimization, can then be performed on the identified network classes for improving the network performance [8]. Typically, once the packets are classified (identified) as belonging to a particular path or wireless device, different paths of a wireless network, or protocol, they are marked or flagged [8]. These markings or flags help the wireless router to determine appropriate path, routing matrix, routing vector, routing service, routing table, routing approach, and policies to be applied for those flows [8]. A WLAN is a type of different wireless network, such as wireless personal area networks, wireless body area networks, wireless metropolitan area networks, wireless campus area networks, and wireless storage area networks. In the WLANs, the infrared or radio frequency technology can be used to transmit and receive information over the air, in order to link a set of computers using a wireless distribution method [8]. This type of network implements a flexible data communication system frequently augmenting, rather than replacing, a wired LAN within a building or campus. WLANs have become popular for use in many applications, such as the healthcare, business, e-health, e-learning, e-business, home, school, computer laboratory, industrial automation, and office building. One important advantage of WLANs is to simplify networking topologies, such as star and bus, by enabling multiple computer users or networks to simultaneously share resources, including software and hardware systems, and wireless networking devices in a home or business without additional or intrusive wiring. Real-time network traffic classification technique is an essential tool for wireless networks in complex applications, such as wireless local area networks, wireless personal area networks (WPANs), wireless metropolitan area networks (WMANs), wireless campus area networks (WCANs), wireless storage area networks (WSANs), and wide area networks (WANs), in order to improve the quality of the wireless networks and wireless network performance. In this paper, a new architecture based on the following algorithms is presented for improving the QoS feature in WLANs: (1) RTNTC algorithm for WLANs based on CS; (2) RTNTM approach based on CS. In addition, a new QoS tomography based on RTNTM method is presented that enables us to identify the cause of QoS degradation when such degradation is detected in a WLAN. This architecture enables continuous data acquisition and compression of WLAN's signals that are suitable for a variety of other wireless networking applications.