nd-convolutional codes. I. Performance analysis

A noncoherent coded system, which incorporates convolutional codes in conjunction of multiple symbol noncoherent detection, is presented in this two-part paper, where Part I focuses on the performance analysis of the system and Part II deals with the structural properties of the underlying convolutional codes. These convolutional codes are referred to as nd-convolutional codes. It is shown that nd-convolutional codes provide a general framework for various noncoherent coding systems, including differential systems. Two models of the carrier phase are examined and the relationships between them is established. For the first one, the carrier phase remains constant for L channels signals, whereas for the second one, it unvaries throughout the transmission period. The regular structure of nd-codes facilitates the evaluation of a simple upper bound on the pairwise and bit error probabilities, as well as a simple expression for the generalized cutoff rate. The exponential rate of the error probability, which is the single parameter governing the error performance at large signal-to-noise ratios, is identified via large deviations techniques. This parameter leads to the interesting conclusion that increasing L does not necessarily monotonically improve the error performance of the noncoherent system. The same conclusion is reached by examining upper bounds and computer simulation results of several interesting examples. These examples also reveal that optimal codes for coherent detection are not necessarily optimal for noncoherent detection and a search for good codes, some of which are tabulated in Part II of the paper, is required.

[1]  S. Varadhan Large Deviations and Applications , 1984 .

[2]  Dariush Divsalar,et al.  The performance of trellis-coded MDPSK with multiple symbol detection , 1990, IEEE Trans. Commun..

[3]  D. Varberg Convex Functions , 1973 .

[4]  Harry Leib,et al.  Optimal noncoherent block demodulation of differential phase shift keying (DPSK) , 1991 .

[5]  Robert G. Gallager,et al.  The random coding bound is tight for the average code (Corresp.) , 1973, IEEE Trans. Inf. Theory.

[6]  J. Modestino,et al.  Performance of DPSK with Convolutional Encoding on Time-Varying Fading Channels , 1977, IEEE Trans. Commun..

[7]  Dariush Divsalar,et al.  Multiple-symbol differential detection of MPSK , 1990, IEEE Trans. Commun..

[8]  Stephen G. Wilson,et al.  Multi-symbol detection of M-DPSK , 1989, IEEE Global Telecommunications Conference, 1989, and Exhibition. 'Communications Technology for the 1990s and Beyond.

[9]  S.G. Wilson,et al.  Noncoherent sequence demodulation for trellis coded M-DPSK , 1991, MILCOM 91 - Conference record.

[10]  S. Shamai,et al.  On the achievable information rates of DPSK , 1992 .

[11]  Franz Edbauer Bit error rate of binary and quaternary DPSK signals with multiple differential feedback detection , 1992, IEEE Trans. Commun..

[12]  Laurence B. Milstein,et al.  Spread-Spectrum Communications , 1983 .

[13]  L. L. Campbell,et al.  Trellis coded MDPSK in correlated and shadowed Rician fading channels , 1991 .

[14]  Harry Leib,et al.  Module-phase codes with non-coherent detection , 1993, Proceedings of ICC '93 - IEEE International Conference on Communications.

[15]  Fumiyuki Adachi,et al.  Decision feedback multiple-symbol differential detection for M-ary DPSK , 1993 .

[16]  Pooi Kam,et al.  Maximum-Likelihood Digital Data Sequence Estimation Over the Gaussian Channel with Unknown Carrier Phase , 1987, IEEE Trans. Commun..

[17]  Osama M. El-Ghandour,et al.  Differential detection in quadrature-quadrature phase shift keying (Q2PSK) systems , 1991, IEEE Trans. Commun..

[18]  David G. Daut,et al.  New short constraint length convolutional code constructions for selected rational rates , 1982, IEEE Trans. Inf. Theory.

[19]  K. Yu,et al.  Trellis coded modulation with multiple symbol differential detection , 1993, Proceedings of ICC '93 - IEEE International Conference on Communications.