An upper bound on the error probability (first error event) of product convolutional codes over a memoryless binary symmetric channel, and the resulting error exponent are derived. The error exponent is estimated for two decoding procedures. It is shown that, for both decoding methods, the error probability exponentially decreasing with the constraint length of product convolutional codes can be attained with nonexponentially increasing decoding complexity. Both estimated error exponents are similar to those for woven convolutional codes with outer and inner warp.
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