This paper describes a receiver that uses an innovative method to predict,
according to history of receiver operating metrics (packet lost/well received),
the optimum automatic gain control (AGC) index or most appropriate variable
gain range to be used for next packet reception, anticipating an interferer
appearing during the payload reception. This allows the receiver to have higher
immunity to interferers even if they occur during the gain frozen payload
reception period whilst still ensuring an optimum sensitivity level. As a
result, the method allows setting the receiver gain to get an optimum trade-off
between reception sensitivity and random interferer immunity.

这篇论文描述了一种使用创新方法来预测接收机操作指标（丢包率 / 接收良好率）历史数据的接收机，以确定下一个数据包接收时最佳的自动增益控制（AGC）指数或最合适的可变增益范围，预计在有效负载接收期间出现干扰信号。这使得接收机对干扰具有更高的免疫力，即使它们出现在增益冻结的有效负载接收期间，同时仍保证最佳灵敏度水平。因此，该方法允许设置接收机增益，以在接收灵敏度和随机干扰免疫力之间实现最佳权衡。

拥挤无线环境下 AGC 指数管理算法

Algorithm for AGC index management against crowded radio environment

Robust and far-field speech recognition is critical to enable true hands-free
communication. In far-field conditions, signals are attenuated due to distance.
To improve robustness to loudness variation, we introduce a novel frontend
called per-channel energy normalization (PCEN). The key ingredient of PCEN is
the use of an automatic gain control based dynamic compression to replace the
widely used static (such as log or root) compression. We evaluate PCEN on the
keyword spotting task. On our large rerecorded noisy and far-field eval sets,
we show that PCEN significantly improves recognition performance. Furthermore,
we model PCEN as neural network layers and optimize high-dimensional PCEN
parameters jointly with the keyword spotting acoustic model. The trained PCEN
frontend demonstrates significant further improvements without increasing model
complexity or inference-time cost.

该研究介绍了一种新的前端 Technique 叫作 PCEN，它使用基于自动增益控制的动态压缩来替代广泛使用的静态压缩以提高在远场条件下的语音识别性能并进一步优化具有高维参数的 PCEN，其在关键字检测任务中的表现显示了显著的性能提升。