A study of G.711 and ILBC over WLAN 802.11n with EDCA

Chanthong, Rittichai; Wuttidittachotti, Pongpisit; Daengsi, Therdpong

doi:10.1109/icoin.2015.7057880

Cited by 2 publications

(2 citation statements)

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“…The proposed algorithm has the following advantages: (1) perform online clustering and classification; (2) establish strong background to expend the current WLANs to ultra-low-power WLANs. The proposed algorithm is defined based on the following assumptions: (1) Any WLAN signal D can be expressed as [32]:…”

Section: Rtntc Algorithmmentioning

confidence: 99%

“…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 distribution method [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.…”

Section: Introductionmentioning

confidence: 99%

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Robust Wireless Local Area Networks Based on Compressed Sensing

Balouchestani-Asli

2018

JSAN

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

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Section: Rtntc Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%