有噪DPC及其在认知信道中的应用
Noisy DPC and Application to a Cognitive Channel
摘要：
在本文中，我们首先考虑一个信道被两个独立的高斯噪声$S ~ N(0,Q)$和$Z_0 ~ N(0,N_0)$污染。当发射机和/或接收机知道独立的有噪声版本的$S$时，计算该信道的容量。它示出的信道容量大于容量时，$S$是完全未知的，但小于容量时，$S$是完全已知的发送器或接收器。例如，如果仅在发射机处已知$S$的一个噪声版本，则容量为$0.5log(1+P/(Q(N_1/(Q+N_1))+N_0))$，其中$P$是输入功率约束，而$N_1$是破坏$S$的噪声的功率。然后，我们考虑高斯认知干扰信道（IC），并提出了一个因果噪声脏纸编码（DPC）的战略。我们使用这种噪声DPC策略计算可实现的区域，并在达到速率上界时量化该区域。
英文摘要：
In this paper, we first consider a channel that is contaminated by two independent Gaussian noises $S ~ N(0,Q)$ and $Z_0 ~ N(0,N_0)$. The capacity of this channel is computed when independent noisy versions of $S$ are known to the transmitter and/or receiver. It is shown that the channel capacity is greater then the capacity when $S$ is completely unknown, but is less then the capacity when $S$ is perfectly known at the transmitter or receiver. For example, if there is one noisy version of $S$ known at the transmitter only, the capacity is $0.5log(1+P/(Q(N_1/(Q+N_1))+N_0))$, where $P$ is the input power constraint and $N_1$ is the power of the noise corrupting $S$. We then consider a Gaussian cognitive interference channel (IC) and propose a causal noisy dirty paper coding (DPC) strategy. We compute the achievable region using this noisy DPC strategy and quantify the regions when it achieves the upper bound on the rate.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
