is straightforward for most cod- ing channels encountered in anti-jam communication systems. The cutoff rate parameter is independent of the specific code used, but can now be used to evaluate the coded bit error probability of any code whose standard bit error probablity curve is available. For example, Figure 4.5 shows the coded bit error bounds for two codes commonly used over the additive white Gaussian noise channel. The basic modulation here is coherent BPSK or QPSK. Such curves are typically available in textbooks and published papers. For the stan- dard additive white Gaussian noise channel, the standard cutoff rates are shown in Figure 4.4 for coherent BPSK modulation. The soft decision case in Figure 4.4 is the p = 1 case in Figures 4.7 and 4.8. Suppose we now want to evaluate the coded bit error bound for the con- straint length K = 7 rate R = 1/2 convolutional code used in a DS/BPSK anti-jam system where there is a pulse jammer with p = .05. Also, suppose the detector used was a soft decision detctor and no jammer state informa- tion is available. Here we assume the metric used is simply that of (4.32). Figure 4.8 shows the R versus EJN1 for this case. We can now translate the standard curve for this code shown in Figure 4.5 to determine the bit error bound with this same code for the new pulse jammed channel described above. General Analysis of Anti-Jam Communication Systems 0.001 0.005 Es/N (dB) Figure 4.7. Soft decision with jammer state knowledge-DS/BPSK Note that for 10-6 coded bit error probability, the standard curve in Figure 4.5a shows a required Eb/No = 5.3 dB (4.53) or since Es = Eb/2 for R = 1/2, E/No = 2.3 dB. (4.54) Next, for this choice of EN, from Figure 4.4 we have that the cutoff rate required is (also see p = 1 curve in Figure 4.8) R1 = . 76 bits/symbol. (4.55) Recall that the coded bit error bound can always be expressed as a function of R as shown in (4.5) or, more specifically for this code, by (4.42) where D is given by (4.29). Thus, if we have another coding channel with the sa