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Error probability in binary baseband system

Short answer

For binary baseband signals s1(t), s2(t) over T_b with AWGN, the matched filter output sample y = a_i + n has Gaussian noise variance σ². Optimal threshold λ = (a1+a2)/2 gives P_e = ½erfc((a1-a2)/(2√2σ)) = Q((a1-a2)/(2σ)). With matched filter, P_e = ½erfc(√(E_d/4N_0)) where E_d = ∫(s1-s2)²dt is the difference signal energy.

Derive the expression for evaluating error probability in Binary baseband system.

In a binary baseband system, bits are sent as pulses (e.g. polar NRZ ±A\pm A) over a channel adding white Gaussian noise. The receiver samples the filtered signal once per bit and compares it with a threshold; an error occurs when noise pushes the sample across the threshold.

Model. In a binary system, symbol 1 is sent as s1(t)s_1(t) and 0 as s2(t)s_2(t), each lasting TbT_b, with equal probability. Channel adds white Gaussian noise w(t)w(t) of PSD N0/2N_0/2. The receiver filter output is sampled at t=Tbt=T_b:

 s_i+w  +--------+ y(t)  sample  y  +---------+
 ------>| filter |------>-/ ------->| y > lam?|--> 1/0
        +--------+       t=Tb       +---------+

The sample is y=ai+ny=a_i+n, where a1,a2a_1, a_2 are the noise-free outputs (a1>a2a_1>a_2) and nn is Gaussian with zero mean and variance σ2\sigma^2. Conditional pdfs:

f(y∣1)=12πσe−(y−a1)2/2σ2,f(y∣0)=12πσe−(y−a2)2/2σ2f(y\mid1)=\frac{1}{\sqrt{2\pi}\sigma}e^{-(y-a_1)^2/2\sigma^2},\qquad f(y\mid0)=\frac{1}{\sqrt{2\pi}\sigma}e^{-(y-a_2)^2/2\sigma^2}
   f(y|0)            f(y|1)
     .--.             .--.
    /    \     |     /    \
   /      \    |    /      \
  /     ...\...|.../...     \
 --------a2---lam---a1----------> y
        Pe0 = area right of lam under f(y|0)
        Pe1 = area left  of lam under f(y|1)

Decision rule: choose 1 if y>λy>\lambda, else 0. For equal priors the optimum threshold is midway, λ=a1+a22\lambda=\frac{a_1+a_2}{2}.

Error when 0 is sent (y>λy>\lambda): with u=(y−a2)/2σu=(y-a_2)/\sqrt2\sigma,

Pe0=∫λ∞12πσe−(y−a2)2/2σ2dy=1π∫(λ−a2)/2σ∞e−u2du=12erfc⁡(a1−a222 σ)\begin{aligned} P_{e0} &= \int_{\lambda}^{\infty}\frac{1}{\sqrt{2\pi}\sigma}e^{-(y-a_2)^2/2\sigma^2}dy=\frac{1}{\sqrt{\pi}}\int_{(\lambda-a_2)/\sqrt2\sigma}^{\infty}e^{-u^2}du \\ &= \frac12\operatorname{erfc}\left(\frac{a_1-a_2}{2\sqrt2\,\sigma}\right) \end{aligned}

By symmetry Pe1P_{e1} (1 sent, y<λy<\lambda) is the same. The average error probability is

Pe=12Pe0+12Pe1=12erfc⁡(a1−a222 σ)=Q(a1−a22σ)P_e=\tfrac12P_{e0}+\tfrac12P_{e1}=\frac12\operatorname{erfc}\left(\frac{a_1-a_2}{2\sqrt2\,\sigma}\right)=Q\left(\frac{a_1-a_2}{2\sigma}\right)

where erfc⁡(x)=2π∫x∞e−u2du\operatorname{erfc}(x)=\frac{2}{\sqrt\pi}\int_x^{\infty}e^{-u^2}du and Q(x)=12erfc⁡(x/2)Q(x)=\frac12\operatorname{erfc}(x/\sqrt2).

With a matched filter (matched to s1−s2s_1-s_2), the maximum of (a1−a2)2σ2\frac{(a_1-a_2)^2}{\sigma^2} is 2EdN0\frac{2E_d}{N_0}, where Ed=∫0Tb[s1(t)−s2(t)]2dtE_d=\int_0^{T_b}[s_1(t)-s_2(t)]^2dt. Therefore

Pe=12erfc⁡(Ed4N0)P_e=\frac12\operatorname{erfc}\left(\sqrt{\frac{E_d}{4N_0}}\right)

PeP_e depends only on the energy of the difference signal relative to N0N_0, not on pulse shape. Making the two signals as different as possible (antipodal) gives the lowest PeP_e.

Polar NRZ example. Levels a1=+Aa_1=+A, a2=−Aa_2=-A, threshold 0: Pe=12erfc⁡(A2σ)P_e=\frac12\operatorname{erfc}\left(\frac{A}{\sqrt2\sigma}\right); with matched filter, Pe=12erfc⁡Eb/N0P_e=\frac12\operatorname{erfc}\sqrt{E_b/N_0}. Unipolar (AA, 0) needs twice the average power for the same PeP_e.

✓ Answer checked by Claude Opus 5.5 (high effort)

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