EbNo、EsNo和SNR之间的关系
摘自Matlab的Help文档
AWGN Channel Noise Level
The relative power of noise in an AWGN channel is typically described by quantities such as
-
Signal-to-noise ratio (SNR) per sample. This is the actual input parameter to the
awgn
function. -
Ratio of bit energy to noise power spectral density (EbNo). This quantity is used by
BER Analyzer
Tool and performance evaluation functions in this toolbox. -
Ratio of symbol energy to noise power spectral density (EsNo)
Relationship Between EsNo and EbNo
The relationship between EsNo and EbNo, both expressed in dB, is as follows:
Es/N0 (dB)=Eb/N0 (dB)+10log10(k)
where k is the number of information bits per symbol.
In a communication system, k might be influenced by the size of the modulation alphabet or the code rate of an error-control code. For example, if a system uses a rate-1/2 code and 8-PSK modulation, then the number of information bits per symbol (k) is the product of the code rate and the number of coded bits per modulated symbol: (1/2) log2(8) = 3/2. In such a system, three information bits correspond to six coded bits, which in turn correspond to two 8-PSK symbols.
Relationship Between EsNo and SNR
The relationship between EsNo and SNR, both expressed in dB, is as follows:
Es/N0 (dB)=10log10(Tsym/Tsamp)+SNR (dB) for complex input signalsEs/N0 (dB)=10log10(0.5Tsym/Tsamp)+SNR (dB) for real input signals
where Tsym is the symbol period of the signal and Tsamp is the sampling period of the signal.
For example, if a complex baseband signal is oversampled by a factor of 4, then EsNo exceeds the corresponding SNR by 10 log10(4).
Derivation for Complex Input Signals. You can derive the relationship between EsNo and SNR for complex input signals as follows:
Es/N0 (dB)=10log10((S⋅Tsym)/(N/Bn))=10log10((TsymFs)⋅(S/N))=10log10(Tsym/Tsamp)+SNR (dB)
where
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S = Input signal power, in watts
-
N = Noise power, in watts
-
Bn = Noise bandwidth, in Hertz
-
Fs = Sampling frequency, in Hertz
Note that Bn= Fs = 1/Tsamp.
Behavior for Real and Complex Input Signals. The following figures illustrate the difference between the real and complex cases by showing the noise power spectral densities Sn(f) of a real bandpass white noise process and its complex lowpass equivalent.