This paper demonstrates the application of binary convolutional coding applied to the bandwidth efficient, constant envelope modulation developed by Resnikoff and Tigerman [1], [2]. The modulation waveform and phaseform are presented with the corresponding bit error performance expression for uncoded M -ary modulation. Probability of information bit error performance analysis is provided for M -ary modulation with binary convolutional coding with hard and soft decision Viterbi decoding. Expected performance bounds and coding gains are determined using the probability of information bit error performance analysis for M = 8 modulation with rate 1/3 binary convolutional codes, and are validated by numerical simulations. Near optimum path metrics are devised for M -ary modulation with binary convolutional codes with soft decision decoding.
I. INTRODUCTIONBandwidth efficient modulation usually comes at the expense of large information bit error rates when compared to less efficient modulation schemes. As a result, higher order Mary modulations schemes where M > 2 are often employed with forward error correction to achieve high bandwidth efficiency with low information bit error rates while only moderately impacting required channel bandwidths. This paper presents the application of binary convolutional coding to the bandwidth efficient, constant envelope modulation originally devised by Resnikoff and Tigerman [1] when M = 8. Probability of information bit error performance analysis is provided for M = 8 modulation with rate 1/3 binary convolutional coding for both hard and soft decision Viterbi decoding. Near optimum path metrics are devised for M > 2 modulation with binary convolutional codes with soft decision decoding. Numerical simulations validate the expected performance analysis and demonstrate the utility of near optimum path metrics for soft decision decoding.