Although the jammer statistics can be approximated as Gaussian regard- less of the waveform used by the jammer, its power may be varied in time. This results in a Gaussian noise channel where the noise variance varies in time. Denote this variance sequence as {0n} where on is the noise variance during the n-th BPSK modulated data symbol. By concentrating its power on a few uncoded data bits, the jammer can dramatically increase the average bit error probability for the same time- averaged power level. To combat this jammer strategy some form of coding Discussion is required. For a sequence of L-coded symbols X1, X2, XL whre E {1, -1} the ideal decoder metric is (see [33]) (1.237) where , Y2, is the sampled BPSK correlator output. With this type of decoder metric the degradation due to time-varying jammer power (pulse jamming) can be effectively neutralized. The ideal soft decision metric (1.237) assumes complete knowledge of the jammer power level during each coded BPSK modulation symbol time. That is, it assumes knowledge of the noise variances {02} Section 1.7 examined various alternative metrics where this jammer state information is not avail- able or only partially available or estimated. Various quantized forms of this metric have also been considered in this chapter. DS/BPSK waveforms can also be used effectively in fading channels where a more complex receiver structure is required. Here, too, decision and decoding metrics are used that require complete or estimated information regarding the channel. The discussion of this chapter focussed on BPSK data modulation where coding consists of transmitting a constrained sequence of binary (+1) sym- bols each modulated on a BPSK waveform. In some applications it is use- ful to use orthogonal binary sequences. In particular, M = 2L orthogonal binary sequences of L bit length can be generated by defining a sequence of m X m binary matricese {Hm} as, (1.238) where (1.239) The M = 2L rows of HL are L bit length orthogonal binary sequences. Orthogonal binary sequence