Abstract: Cognitive radios have been recognized as one of the most promising technologies dealing with the scarcity of the radio spectrum. In cognitive radio systems, secondary users are allowed to utilize the frequency bands of primary users when the bands are idle. Hence, how to accurately detect the idle frequency bands has attracted many researchers’ interest. Detection performance is sensitive toward noise power and gain fluctuation. Since signal to noise ratio (SNR) between primary user and secondary users are not the same and change over the time, SNR and noise power estimation is essential. In this paper, we present a cooperative spectrum sensing algorithm using SNR estimation to improve detection performance in the real situation.
Abstract: People have the habitual pitch level which is used when people say something generally. However this pitch should be changed irregularly in the presence of noise. So it is useful to estimate SNR of speech signal by pitch. In this paper, we obtain the energy of input speech signal and then we detect a stationary region on voiced speech. And we get the pitch period by NAMDF for the stationary region that is not varied pitch rapidly. After getting pitch, each frame is divided by pitch period and the likelihood of closed pitch is estimated. In this paper, we proposed new parameter, NLF, to estimate the SNR of received speech signal. The NLF is derived from the correlation of near pitch periods. The NLF is obtained for each stationary region in voiced speech. Finally we confirmed good performance of the estimation of the SNR of received input speech in the presence of noise.
Abstract: The major problem that wireless communication
systems undergo is multipath fading caused by scattering of the
transmitted signal. However, we can treat multipath propagation as
multiple channels between the transmitter and receiver to improve
the signal-to-scattering-noise ratio. While using Single Input
Multiple Output (SIMO) systems, the diversity receivers extract
multiple signal branches or copies of the same signal received from
different channels and apply gain combining schemes such as Root
Mean Square Gain Combining (RMSGC). RMSGC asymptotically
yields an identical performance to that of the theoretically optimal
Maximum Ratio Combining (MRC) for values of mean Signal-to-
Noise-Ratio (SNR) above a certain threshold value without the need
for SNR estimation. This paper introduces an improvement of
RMSGC using two different issues. We found that post-detection and
de-noising the received signals improve the performance of RMSGC
and lower the threshold SNR.