reshold levels, but the disadvantage of this is that optimum threshold settings require AGC, which is difficult to maintain in a jamming environment. An example of a detector that does not require AGC is one that makes hard M-ary decisions based on the largest chip energy detector output or that outputs an erasure symbol [105]-[108]. Reed-Solomon codes have been proposed for these systems with M-ary symbols that can correct twice as many erasures as hard decision errors. Various techniques for the detector to select a hard decision or make an erasure decision have been proposed including taking advantage of the fact that for asynchronous radios the inter- fering tone usually does not overlap completely in time with the intended signal [109], [110]. An alternative approach, called list detection, was discussed in Section 2.7.3 in Chapter 2 of Volume II. With this technique, demodulator energy outputs are ranked in magnitude from the highest to the lowest, and met- rics are assigned according to position in the ranking rather than the mag- nitude. The process of rank-ordering is equivalent to partitioning the M-dimensional observation space of demodulator outputs into regions cor- responding to the different ordered lists. In this sense list detection is like soft-decision quantization because it creates a discrete memoryless channel with more outputs than inputs as seen by the encoder/decoder. Note that list detection does not depend on thresholds which are difficult to maintain dur- ing jamming. List detection has been considered previously [111], [112] for the AWGN channel where performance is inferior to threshold quantization. More recently, Viterbi [113] has presented an interference mitigation technique which is variant of list detection, and results show that robust performance is achieved against tone jamming with simple receiver implementation (see Section 2.7.2, Chapter 2, Part 2). Following the analysis of Section 2.7.3 in Chapter 2 of Part 2, we present results of Crepeau [114] and Crepeau et al. [115]