Metrics and Algorithms for Designing Convolutional Codes With Unequal Error Protection
Mohammad Karimzadeh, Mai H. Vu · IEEE Transactions on Vehicular Technology · 2021
We consider multi-input convolutional codes with unequal error protection (UEP) among the inputs. Using conventional metrics including the free input distance and free input sum weight as a measure for UEP degree, we design efficient and optimal algorithms to evaluate these metrics for any number of inputs. Next, we discuss several ambiguities arisen from using these metrics to estimate convolutional code UEP property and propose two new metrics, the average weight per path and the ant colony system UEP (ACS-UEP) index. The ACS-UEP index can accurately and consistently capture UEP properties over all ranges of SNR. Using these metrics, we propose an algorithmic process for designing convolutional codes with bounded external degree complexity to minimize a weighted sum of input error probabilities. We analyze the effectiveness of the design approach and show significant improvements over existing UEP convolutional code designing approach in terms of both the absolute and UEP input error protections. Our proposed algorithms for designing multi-input convolutional codes with UEP are flexible, adaptable, and provide high performance codes within the specified complexity.