 is assumed to be the primary limitation on anti-jam systems. 3.10 DISCUSSION In this chapter we have stated basic assumptions and defined key system parameters for anti-jam communication systems and jamming signals. Concepts such as coding, interleaving, and diversity were illustrated with examples. Even simple repeat codes (diversity) were shown to effectively neutralize the over 40 dB of degradation at 10-6 bit error probabilities that some jammers can cause in uncoded anti-jam systems. Also illustrated was the important fact that, in designing a coded anti-jam communication system, one ought to be careful in choosing the decoding metric. There is no single metric that is optimum for all types of jammers. We have seen that the maximum-likelihood metrics for the additive white Gaussian noise channel can cause the anti-jam system to be vulnerable to certain jammers and that a hard decision metric can yield much better per- formance than a soft decision metric. Also, having a decision rule that uses side information about when a channel is jammed or not can improve per- formance significantly. A good decision rule and associated metric is one that is robust in the sense that no jammer can degrade it much more than the baseline jammer. Except for these very simple examples, coded spread-spectrum systems under attack by various jamming signals are difficult to analyze. Analysis becomes even more difficult when unconventional decoding metrics are used. In the next chapter, a general approach to the analysis of spread-spec- trum communication systems is presented. It provides a means for handling these more difficult cases as well as placing the basic concepts of this chap- ter on firmer ground. 3.11 REFERENCES [1] A. J. Viterbi and I. M. Jacobs, "Advances in coding and modulation for non- coherent channels affected by fading, partial band, and multiple access inter- ference," in Advances in Communication Systems, vol. 4, New York: Academic Press, 1975, pp. 279-308. [2] S. W. Houston, "Modulation techniques for commu