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

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 Unified Multi-Functional Dynamic Spectrum Access Framework: Tutorial, Theory and Multi-GHz Wideband Testbed

Dynamic spectrum access is a must-have ingredient for future sensors that are ideally cognitive. The goal of this paper is a tutorial treatment of wideband cognitive radio and radar—a convergence of (1) algorithms survey, (2) hardware platforms survey, (3) challenges for multi-function (radar/communications) multi-GHz front end, (4) compressed sensing for multi-GHz waveforms—revolutionary A/D, (5) machine learning for cognitive radio/radar, (6) quickest detection, and (7) overlay/underlay cognitive radio waveforms. One focus of this paper is to address the multi-GHz front end, which is the challenge for the next-generation cognitive sensors. The unifying theme of this paper is to spell out the convergence for cognitive radio, radar, and anti-jamming. Moore’s law drives the system functions into digital parts. From a system viewpoint, this paper gives the first comprehensive treatment for the functions and the challenges of this multi-function (wideband) system. This paper brings together the inter-disciplinary knowledge

Spectrum Sensing: A Distributed Approach for Cognitive Terminals

Cognitive Radios is emerging in research laboratories as a promising wireless paradigm, which will integrate benefits of software defined radio with a complete aware communication behavior. To reach this goal many issues remain still open, such as powerful algorithms for sensing the external environment. This paper presents a further step in the direction of allowing cooperative spectrum sensing in peer-to-peer cognitive networks by using distributed detection theory. The approach aims at improving the radio awareness with respect to stand alone scenario as it is shown with theoretical and experimental results

A distributed approach to mode identification and spectrum monitoring for cognitive radios

In this paper a distributed approach to mode identification and spectrum monitoring is studied. A Wireless Network composed by Cognitive Terminals is used to classify air interfaces present in the radio scene. The use of cooperative strategies and an advanced signal processing tool, Time Frequency analysis, allows to improve the radio awareness of device. Results in the terms of error probability, modeling the probability density function of considered features as Asymmetric Generalized and Generalized Gaussian functions, are compared to error rate showing good performance and coherence of theoretical model with experimental results

Video and radio attributes extraction for heterogeneous location estimation: A context-based ambient intelligence architecture

A Context-based Ambient Intelligence Architectur

"A Mode Identificaton Procedure for Software Defined Radio Terminals in Case of Superimposed Signals"

In this work, a mode identification system for superimposed signals in the same band is presented. More precisely, a signal processing technique, namely the Wigner-Ville distribution, combined with non parametric (k-Nearest Neighbors and Parzen) and Neural Network classifiers is proposed for identifying the transmission modes in an indoor wireless environment. A reconfigurable terminal based on Software Defined Radio technology is considered aiming at the identification of the presence of two co-existent communication modes such as Bluetooth, based on Frequency Hopping -Code Division Multiple Access, and IEEE WLAN 802.11b, based on Direct Sequence -Code Division Multiple Access. Results in terms of error classification probability, expressed as relative error frequency, will be provided with a comparison among the classifiers

A Distributed Wireless Sensor Network for Radio Scene Analysis

In this paper, a distributed approach to radio scene analysis is considered. A Wireless Sensor Network, composed by Software Defined and Cognitive terminals, is used to classify air interfaces present in the radio scene. Two modes, namely Frequency Hopping Code Division Multiple Access and Direct Sequence Code Division Multiple Access, are identified, employing a signal processing technique. Time Frequency analysis, and a distributed decision theory. Advantages given by distributed detection are used to improve the performance of a Mode Identification module. Results in terms of error probability are obtained by modelling the probability density function of considered features as Asymmetric Generalized and Generalized Gaussian functions