Wifi packet detector

Following the recent interest in data-based deep learning DL methods for physical layer signal processing, in this paper, we challenge the conventional methods with DL-based approach for Wi-Fi packet detection. Using one-dimensional Convolutional Neural Networks 1D-CNN , we present a detailed complexity vs performance analysis and comparison between conventional and DL-based Wi-Fi packet detection approaches.

Article :. These programs also do not usually resend information if it fails to get there, so if packets get lost in transmission, they are gone for good, which can also have a significant impact. Up until recently, web browsers have not been able to test this, as they have always retried everything until it works.

Recently however, WebRTC has been added to modern browsers, finally enabling this type of test. I could not find another site that has implemented this type of test, so I made one. This makes it very easy for anyone to test their packet loss also known as "packet drop" without downloading a more complicated tool like iPerf.

Now, you can just hit "Start Test" below, and then interpret your results. Also, you can read a bit more about the site at the dedicated About and Technology pages. We can also see that for the very first 20 samples or so, the correlation value is also very high. This is because the silence also repeats itself at arbitrary interval! A straight forward implementation would require both multiplication and division.

However, on FPGAs devision consumes a lot of resources so we really want to avoid it. In current implementation, we use a fixed threshold 0. In addition to the number of consecutive samples with correlation larger than 0.