Title :
Performance of robust metrics with convolutional coding and diversity in FHSS systems under partial-band noise jamming
Author :
Cheun, Kyungwhoon ; Stark, Wayne E.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA
fDate :
1/1/1993 12:00:00 AM
Abstract :
The performance of robust metrics (metrics that can be computed from the outputs of the matched filters only) with convolutional coding and diversity under worst-case partial-band noise jamming is analyzed. Both binary and dual-k convolutional codes employing these metrics with diversity are compared via Union-Chernoff bounds. The performances of metrics considered in the literature that assume perfect side-information are given for comparison purposes. It is found that there exist very good robust metrics that provide performance comparable to metrics using perfect side-information. Among the robust metrics considered, the self-normalized metric offers the best performance and achieves performance practically identical to that of the square-law-combining metric with perfect side-information for M=8
Keywords :
convolutional codes; diversity reception; jamming; spread spectrum communication; FHSS systems; Union-Chernoff bounds; binary convolutional codes; convolutional coding; diversity; dual-k convolutional codes; error correction coding; frequency hop spread spectrum communication; partial-band noise jamming; perfect side-information; robust metrics; self-normalized metric; square-law-combining metric; Convolution; Convolutional codes; Decoding; Diversity reception; Hamming distance; Jamming; Matched filters; Noise robustness; Spread spectrum communication; Viterbi algorithm;
Journal_Title :
Communications, IEEE Transactions on