In the past few decades, wireless networks based on different standards have been developed quickly, and the coexistence of multiple communication systems has turned into reality. Due to the need for mobile computing or pervasive computing, quick and accurate protocol identification and analysis technology is required. Rather than employing the traditional demodulation-based method that requires to implement all known protocol modules in one single device, in this paper, we fully analyze the features in both time domain and frequency domain of physical (PHY) layer signals that can be used to describe a protocol, and present a new method of identifying and analyzing protocols without demodulation, which uses PHY layer signals only. This method can be used in battlefield and other situations where demodulation is impractical. To validate the feasibility of our method, we implement a system using GNU Radio and Universal Software Radio Peripheral (USRP). The results show that the system can successfully identify three different signals (including Wi-Fi, Bluetooth and Zigbee signals) with frequency domain features, and detect the period of beacons in Wi-Fi networks with the help of time domain features.
In order to conduct performance analysis of IEEE 802.11 protocol in which priority backoff arithmetic was adopted and to provide for protocol optimization,a new analysis model based on the classical probability model was proposed.The capacity was computed by estimating the average period of packets sending. The two-level priority protocol was examined using this model.Simulation results coincide with the theoretical analysis,which proves the accuracy of this priority backoff arithmetic.
The 802.11 standard supports multiple data transmitting rates. The "throughput fairness" characteristic of the standard DCF appears undesirable, because of the high rate nodes has same channel access probability with the low rate ones. COT-based fairness is proposed with the purpose of guaranteeing fairness and improving the aggregate throughput, but the COT fairness is achieved by adjusting stations' packet length and sending multiple back-to-back packets at one time, it not suitable for all applications. The main contribution of this paper is the proposal of a PCAP(Proportional Channel Access Probability) fairness index. The index evaluate the fairness by the CAP which is in proportion to the nodes' transmission rates. This paper also proposed a PCAP-based fairness algorithm. With our algorithm, the stations in WLAN can achieve PCAP-based fairness which is independent with nodes' packet length. Through exclusive simulations, we show that the PCAP-based fairness index and algorithm leads to significant improvements in aggregate throughput and all stations have proportional fairness under multi-rate condition.
A cross traffic estimator is proposed based on the loss ratio of the probe packets. This method aims to estimate the future networks' available bandwidth such as optical switching networks. In these networks, node's processing time other than tight link becomes the bottleneck. Most popular estimate techniques neglect it. In the scheme, probe traffic intensity and its drop ratio are collected and fitted using the 1st order ordinary least squares (OLS) algorithm. OLS adopts probe traffic intensity as dependent variable and its drop ratio as independent variable. Then the intercept difference between fitted line and empirical line is just the cross traffic. Simulation shows that our scheme can obtain the bottleneck node cross traffic appropriately and outperforms the minimal backlogging algorithm which is also based M/G/1 model.
Cooperative spectrum sensing can improve the detection reliability in cognitive radio network. How to report the local sensing results to the fusion center has not aroused much interest. Since OFDM has been widely recognized as a promising candidate for cognitive radio in opportunistic spectrum access scenarios, a OFDMA-based sensing reporting scheme is designed. Correspondingly, several fusion rules at fusion center side are proposed according to the knowledge of the reporting channel and local sensing performance indices. It is shown by simulations that the fusion with the perfect reporting channel state information is optimum. If the fusion center has no knowledge of the reporting channel, soft fusion outperforms hard decision combination. Fusion with the statistic knowledge of the reporting channel is valid for practical use.
An efficient method based on SSNF algorithm and threshold de-noising to remove the noise in the image is proposed. To the disadvantages of the unified threshold denoising method, which causes the image fuzzy distortion because of “over-killed”, by using inter-scale dependency of wavelets coefficients, some edge information that “overkilled” by the unified threshold are extracted and reserved, Thereby, more image details are reserved. The experiment results show that the algorithm can effectively weaken the unified threshold's tendency of “over-killed”, and reduce the image fuzzy distortion.
In the scenario of dynamic spectrum access application for cognitive radio (CR), the spectrum-sidelobe problem of OFDM (orthogonal frequency division multiplexing) must be considered. A precoding ahead of IDFT (inverse discrete Fourier transformation) is presented to suppress the in-band-out-of-subband (IBOSB) radiation. Its design is based on generalized eigenvalue problem. In order to improve its performance for practical application, one column of the precoding matrix can be inverted to reduce the signal peak-to-average-power-ratio (PAPR) to a lower level. At the receiver, iterative soft detection with interference cancelation is adopted to detect the precoded data and further employs the frequency diversity. Both methods introduce some additional transceiver complexity compared with pure precoding, whereas simulations show that joint design not only provides improved PAPR statistics, but also achieves the markedly gain of Bit-Error-Rate (BER) performance over multipath fading channel.
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