Use of Time-Frequency Analysis and Neural Networks for Mode Identification in a Wireless Software-Defined Radio Approach

Abstract

The use of time-frequency distributions is proposed as a nonlinear signal processing technique that is combined with a pattern recognition approach to identify superimposed transmission modes in a reconfigurable wireless terminal based on software-defined radio techniques. In particular, a software-defined radio receiver is described aiming at the identification of two coexistent communication modes: frequency hopping code division multiple access and direct sequence code division multiple access. As a case study, two standards, based on the previous modes and operating in the same band (industrial, scientific, and medical), are considered: IEEE WLAN 802.11b (direct sequence) and Bluetooth (frequency hopping). Neural classifiers are used to obtain identification results. A comparison between two different neural classifiers is made in terms of relative error frequency

A Distributed Wireless Sensor Network for Radio Scene Analysis

“…2). A situation with CS = 1 and dim(X) = 1 was considered in [17], where a feature vector v 1 (t,x 1 ) based on time-frequency analysis of the observed spectrum, O 1 (t,x 1 ) with CS 1 at position x 1 , was analyzed. A problem with dim(RS) = 2, i.e., with two radio sources, was analyzed there with the additional constraint that and , i.e., the positions of the radio sources, were fixed.…”

“…Assuming the cost of sensor 1, making an error when H 0 is present, is more than the cost of classifying correctly regardless of the classification of sensor 2, i.e., C 0j0 < C 1j0 , and by considering that: (15) can be expressed as a likelihood ratio test [24]: The previous equation (17) shows that the right-hand side is a function not only of the observation for sensor 1, i.e., y 1 , but it is possible to note that it is a function of C 2 , i.e., the classification rule for sensor 2 also and this dependence appears under the form of p (C 2 |y 2 , x 2 ).…”

“…Also in this approach, no superposition of modes is taken into account and, moreover, the solution is studied for a stand alone sensor. The first procedure for sensing and identification of overlapping modes is presented in [17], where a time frequency analysis is combined with neural network to classify spread spectrum interfaces, such as frequency hopping and direct sequence. The use of time frequency methods allows the study in the time and the frequency plane of spectrum in order to evaluate the so-called white spaces (or spectrum holes) also in time domain and, moreover, to discriminate two air interfaces using the same band.…”

Performance Analysis and Comparative Study of Cognitive Radio Spectrum Sensing Schemes

“…These two features are fed to a Bayesian classifier for determining the active primary user and for identifying spectrum opportunities. The standard deviation of the instantaneous frequency and the maximum duration of a signal are extracted using time-frequency analysis in [22], [23], [36], [37] and neural networks are used for identification of active transmissions using these features. Cycle frequencies of the incoming signal are used for detection and signal classification in [30].…”

A Pattern Construction Scheme for Neural Network-Based Cognitive Communication

“…Palicot et al [9] have managed to successfully determine the bandwidth and its shape by using RBNN (Radial-based Neural Network). In another study, Gandetto et al [10] have used Feed-Forward Back Propagation Neural Networks (FFBPNN) and Support Vector Machine (SVM) with Radial Bases Function (RBF) to make time-frequency analysis. On the other hand, Tsagkaris et al [11] have introduced and evaluated the learning schemes those are based on artificial neural networks and their study could be used to predict the capabilities (e.g., data rate) of a specific radio configuration.…”