  This paper considers the problem of code design for a channel where
communications and radar systems coexist, modeled as having both Additive White
Gaussian Noise (AWGN) and Additive Radar Interference (ARI). The issue of how
to adapt or re-design convolutional codes (decoded by the Viterbi algorithm)
and LDPC codes (decoded by the sum-product algorithm and optimized by using the
EXIT chart method) to effectively handle the overall non-Gaussian ARI noise is
investigated. A decoding metric is derived from the non-Gaussian ARI channel
transition probability as a function of the Signal-to-Noise Ratio (SNR) and
Interference-to-Noise Ratio (INR).
  Two design methodologies are benchmarked against a baseline "unaltered legacy
system", where a code designed for AWGN-only noise, but used on the
non-Gaussian ARI channel, is decoded by using the AWGN-only metric (i.e., as if
INR is zero). The methodologies are: M1) codes designed for AWGN-only noise,
but decoded with the new metric that accounts for both SNR and INR; and M2)
codes optimized for the overall non-Gaussian ARI channel. Both methodologies
give better average Bit Error Rate (BER) in the high INR regime compared to the
baseline. In the low INR regime, both methodologies perform as the baseline
since in this case the radar interference is weak. Interestingly, the
performance improvement of M2 over M1 is minimal. In practice, this implies
that specifications in terms of channel error correcting codes for commercially
available wireless systems need not be changed, and that it suffices to use an
appropriate INR-based decoding metric in order to effectively cope with the
ARI.
