Literature Review - Software Defined Radio

In this section, first two methods to estimate signal to noise ratio (SNR) are reviewed and a few methods for modulation classification schemes are also studied after SNR estimation techniques. In most wireless communication systems, signals are usually corrupted by noise. SNR defined as the ratio of signal power to noise power, is commonly used as an essential metric in determining related system parameters, such as bit error rate (BER) and symbol error rate (SER). Moreover, various algorithms and system components require knowledge of the SNR for optimal performance. Thus, SNR is an important measure of channel quality in many modern wireless communication systems. proposes an SNR estimation algorithm for digital modulated signals in additive white Gaussian noise (AWGN) without any prior information about the signal parameters such as carrier frequency, bit rate and modulation scheme of the signals. First, the autocorrelation matrix is obtained by calculating the autocorrelation of the received signal.

Eigenvalues are calculated according to eigenvalue decomposition of the above matrix. Then the signal subspace dimension is calculated from minimum description length (MDL) criteria and then finally SNR is estimated. Simulation results are carried out for MPSK signals (M=2, 4, 8), MFSK signals (M=2, 4) and MQAM signals (M=16, 64, 128, 256). The results show that the estimated bias is within 1dB and the corresponding standard deviation (STD) is below 0.55 when the true SNR varies from -5dB to 25dB. presents different approach to the problem of the estimation of signal bandwidth and SNR estimation in this paper, which is based on the corner of power distribution function. The new method does not need to know the exact parameters such as the carrier frequency, symbol rate and the modulation pattern of the received signals and it makes no requirements on training sequence and the synchronization information. Simulation results show that new algorithm doesn't need any prior information, has lower computational complexity and higher precision, suitable for hardware implementation. In , Blind Modulation Recognition (BMR) algorithm using Hilbert transform was used to identify modulation types.

Computer simulation shows that the proposed method has strong capability for recognition of higher order modulation signals in the presence of additive white Gaussian noise (AWGN). A modulation classification algorithm using wavelet Transform and histogram calculation was used to identify QPSK and QAM with GMSK and M-ary FSK modulation type. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB and 12 dB for GMSK and QPSK respectively. When SNR is above 5 dB, the throughput of the proposed algorithm is more than 97.8%. In decision theoretic approach was proposed to identify digitally modulated schemes based on few key features and the threshold values determined for these key features. Simulations show that as SNR decreases, the percentage of correct classification with 100 % success rate also decreases. Instantaneous features such as amplitude phase and frequency and stochastic features such as amplitude mean, amplitude mean-square, phase mean were used to distinguish modulated signals for varying Signal to Noise Ratio (SNR). Three digital modulation classifiers for the application in blind modulation detection stage of adaptive OFDM modulation are presented and investigated in. These digital modulation classifiers are namely Maximum Likelihood Modulation Classification (MLMC), higher order statistics using fourth order cumulants and higher order statistics using sixth order cumulants.