In this paper, we introduce generalized tail biting encoding as a means to ameliorate the rate deficiency caused by zero-tail convolutional encoding. This technique provides an important link between quasi-cyclic block and convolutional codes. Optimum and suboptimum decoding algorithms for these codes are described and their performance determined by analytical and simulation techniques.

A design procedure is presented for synthesizing threshold decodable rate1/nand(n - 1)/nself-orthogonal block codes obtained from truncating convolutional codes by generalized tail-biting (GTB). When GTB is applied to an equal error-protection threshold decodable convolutional code, the result is a class of unequal error-protection threshold decodable block codes. These codes are related to unequal error-protection codes previously described by Mandelbaum [1]. Furthermore, the codes of Mandelbaum are generalized.

A sequential algorithm for decoding convolutional codes, the multiple stack algorithm or MSA devised by Chevillat and Costello, has been implemented and tested on a Zilog Z-80 microcomputer system. The results of this implementation, when compared to those of a (2, 1)8 Viterbi algorithm implemented on the same microcomputer system, show that the MSA obtains significantly higher data throughput. The results of the MSA implementation of a (2, 1)15 code suggest that the MSA is a practical choice for codes having longer constraint lengths than are normally used with Viterbi decoding. The comparative results may serve as guidelines for future implementation of the MSA algorithm on faster, more capable microprocessors.

It is shown in this paper that the performance of certain Rayleigh fast-fading channels can be accurately and efficiently evaluated using the elementary trapezoidal integration rule. The effects of intersymbol interference on such channels are analyzed.