Measuring direction of arrival in cognitive radio systems using variable step adaptive learning for spectrum sensing

Abstract

In wireless communications cognitive radios are rapidly growing technology and it is used in medical telemetry applications to provide treatment to remote located patients on time. Generally, interferences are occurred due to a shortage of frequency range and spectrums. To avoid these interferences new technologies are developed with cognitive radios. In the received signal there are some noisy signals are occurred due to signal estimation is not done properly. For estimating input signal primary user and secondary user signals direction of arrival is taken into consideration. To improve further output and to remove noise signals of received signal proposed an adaptive learning methodology. In this paper a regularization based variable step adaptive learning algorithm (VSALA) is developed for avoiding minimum disturbance constraint functions in cost function. Further regularization factor and step size combination results a faster convergence when compared to conventional normalized adaptive algorithm (NALA). The proposed learning methodology is analysed for different threshold values like 0.1, 0.5 and 1.0 for estimating the direction of arrival (DOA) and their corresponding beam forming patterns are shown.