A New Upper Bound on the Error Probability of Binary Linear Codes

Abstract

Performance evaluation of maximum-likelihood (ML) decoded binary linear codes is usually carried out using bounding techniques. In this paper, a new upper bound is presented to improve existing union bounds. The proposed upper bounding is based on probabilities of correct events, while the traditional union bound (UB) is on pair-wise error probabilities. Moreover, the improved upper bounding uses the intersection instead of the union of basic events. The theoretical and simulation results show that the proposed bound is tight than UB.