论文标题
概括最近的邻居解码
Generalized Nearest Neighbor Decoding
论文作者
论文摘要
众所周知，对于高斯通道（Gaussian Channels），最接近的邻居解码规则是最大的可能性解决方案，因此寻求代码字和接收到的通道输出向量之间的最小欧几里得距离，因此可以实现容量。最近的邻居解码仍然是通用通道的方便但不匹配的解决方案，本文的关键信息是，可以通过概括其解码度量以结合通道状态依赖性输出处理和密码字量表来改善最近的邻居解码的性能。使用广义的共同信息，该信息是独立且分布的代码书集合下的不匹配能力的下限，作为性能度量，本文在高斯通道输入下建立了最佳的最佳概括最近邻居解码规则。也得出了几种{限制形式的{限制形式，并将其与现有解决方案进行了比较。通过几个案例研究来说明结果，这些案例研究具有不完美的接收器通道状态信息以及具有量化效果的通道。
It is well known that for Gaussian channels, a nearest neighbor decoding rule, which seeks the minimum Euclidean distance between a codeword and the received channel output vector, is the maximum likelihood solution and hence capacity-achieving. Nearest neighbor decoding remains a convenient and yet mismatched solution for general channels, and the key message of this paper is that the performance of the nearest neighbor decoding can be improved by generalizing its decoding metric to incorporate channel state dependent output processing and codeword scaling. Using generalized mutual information, which is a lower bound to the mismatched capacity under independent and identically distributed codebook ensemble, as the performance measure, this paper establishes the optimal generalized nearest neighbor decoding rule, under Gaussian channel input. Several {restricted forms of the} generalized nearest neighbor decoding rule are also derived and compared with existing solutions. The results are illustrated through several case studies for fading channels with imperfect receiver channel state information and for channels with quantization effects.
