论文标题
猜测通过突发错误通道传输的网络编码数据的随机加性噪声解码
Guessing Random Additive Noise Decoding of Network Coded Data Transmitted over Burst Error Channels
论文作者
论文摘要
我们考虑使用网络编码和广播编码数据包编码数据包的发射器。如果收集了足够数量的无错误编码数据包，则采用网络解码的接收器将恢复数据包。如果网络解码不成功，接收器不会放弃其恢复数据包的努力；取而代之的是，它采用综合征解码（SD）来修复错误的接收到的编码数据包，然后Reattempts网络解码。包括SD在内的大多数解码技术都假定错误是在接收到的编码数据包中独立且相同分布的。通过猜测随机添加噪声解码（Grand）框架的猜测，我们提出了横向宏伟（T-Grand）：一种算法，该算法利用了错误的统计依赖性，对错误的发生，补充网络解码并以比SD更高的概率恢复所有数据包。 T型物会根据出现和更改传输数据包的可能性顺序检查错误向量。所有错误向量的可能性值的计算和排序是一个简单但计算昂贵的过程。为了降低t戒指的复杂性，我们利用了似然函数的特性并开发了一种有效的方法，该方法可以识别最可能的误差向量，而无需计算和订购所有可能性值。
We consider a transmitter that encodes data packets using network coding and broadcasts coded packets. A receiver employing network decoding recovers the data packets if a sufficient number of error-free coded packets are gathered. The receiver does not abandon its efforts to recover the data packets if network decoding is unsuccessful; instead, it employs syndrome decoding (SD) in an effort to repair erroneous received coded packets, and then reattempts network decoding. Most decoding techniques, including SD, assume that errors are independently and identically distributed within received coded packets. Motivated by the guessing random additive noise decoding (GRAND) framework, we propose transversal GRAND (T-GRAND): an algorithm that exploits statistical dependence in the occurrence of errors, complements network decoding and recovers all data packets with a higher probability than SD. T-GRAND examines error vectors in order of their likelihood of occurring and altering the transmitted packets. Calculation and sorting of the likelihood values of all error vectors is a simple but computationally expensive process. To reduce the complexity of T-GRAND, we take advantage of the properties of the likelihood function and develop an efficient method, which identifies the most likely error vectors without computing and ordering all likelihood values.
