TDD慢衰落系统中的预重传GBN-ARQ性能分析

Adaptive Predicted Repeat GBN-ARQ Algorithm in TDD Slowly Fading Channel

该文提出一种新的适于TDD时变慢衰落系统的预重传GBN-ARQ算法,并与NakamuraOsamu提出的预重传方案分析比较。比较的结果认为:吞吐率较低时该文提出的预重传方案改善明显,吞吐率较高时NakamuraOsamu方案增益较大,且该文算法对预测准确率的稳健性明显高于NakamuraOsamu预重传方案。仿真结果表明归一化吞吐率为0.6附近,该文预重传方案吞吐率相对于普通GBN提高大约4dB。

This paper presents a pre-repeat GBN ARQ scheme, which suits for slowly fading TDD system. The normalized throughputs also analysed and compared between the proposed scheme and Nakamura Osamu＇s. It turns out that, depending on the normalized throughput, the optimum scheme is one of the two following alternatives： （1） in the higher throughput, Nakamura Osamu＇s scheme is better; （2） in the lower cases, the proposed scheme works better and is more robust to the error prediction. Simulation results verify that the scheme improves the throughput efficiency： the gain is about 4 dB when the normalized throughput is around 0.6.