An approach to classification and under-sampling of the interfering wireless signals

Classification of interfering signals that belong to different wireless standards is important topic in wireless communications. In this paper, we propose a procedure for separation and classification of wireless signals belonging to the Bluetooth and to the IEEE 802.11b standards. These signals operate in the same frequency band and may interfere with each other. The procedure is made of a few steps. In the first step, the separation of signal components is done using the eigenvalue decomposition approach. The second stage is based on the compressive sensing approach, used to reduce the number of transmitted samples. A suitable transform domain is chosen for each separated component using ℓ1-norm as a measure of sparsity. Since the Bluetooth signals are less sparse compared to the IEEE 802.11b signals, after choosing sparse domain, additional sparsification needs to performed to further enhance the sparsity. In the last step of the procedure, the classification is performed by observing the time-frequency characteristics of the reconstructed separated components. The theory is proved by the experimental results.