 0. In general, the ML metric requires knowledge of Pk(Z) or Pk while having JSI requires knowledge of the random variable Z in the partial-band case and J for a more general noise power distribution. For the ML metric with or without JSI the worst case partial-band noise jammer is again found to be the broadband noise jammer (p = 1). Figure 2.125 shows the cutoff rates for ML metrics (2.388) and (2.390) with worst case partial-band noise jamming and compares the results with no Rayleigh fading. Figures 2.126 to 2.130 compare cutoff rates for M = 2, 4, 8, 16, and 32 where the curves are labelled as follows: 1. Soft decision energy metric with JSI 2. Soft decision energy metric without JSI 3. ML list metric with JSI 4. ML list metric without JSI. Other Coding Metrics (3b) (4b) (3) WITH JSI (4) WITHOUT JSI (a) WITHOUT FADING (b) WITH FADING E/N, (dB) Figure 2.125. List metric R0 for FH/4FSK against worst case noise jammer. Although the unquantized soft decision energy metric with JSI gives bet- ter performance, its advantage over the list metrics decreases as the alpha- bet size increase. Furthermore, with other types of jamming or interference in the channel the list metric can outperform the soft decision energy met- ric. Multitone jamming and multiple access interference cases are where the (3) )(4) Figure 2.126. List and energy metrics R for FH/8FSK against worst case noise jammer. Non-Coherent Frequency-Hopped Systems (3)(4) E/N (dB) Figure 2.127. List and energy metrics R0 for FH/4FSK against worst case noise jammer. list metric can do better than the energy metric [30], [31] (see Chapter 5, Part 5). The results presented here generalize to arbitrary memoryless channels with FH/MFSK signals with list output as long as P is the probability that the transmitted symbol has a corresponding energy detector output sample (3)(4) E/NJ (dB) Figure 2.128. List and energy metrics R for FH/8FSK against worst case noise jam- Other Coding Metrics (3)(4) E/N, (dB) Figure 2.129. List and energy metrics R for FH/16FSK against