Changes between Version 5 and Version 6 of 802.11/Benchmarks/Pkt_Det_Min_Power_Char


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Timestamp:
Dec 18, 2014, 5:10:27 PM (9 years ago)
Author:
chunter
Comment:

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  • 802.11/Benchmarks/Pkt_Det_Min_Power_Char

    v5 v6  
    3131The above figure shows a 10% PER curve and a 1% PER curve as a function of minimum packet detection power. In the leftmost region of the plot where the PER curves "flatten out," the PER of the link is dominated by noise. In other words, the minimum packet detection power is set at powers lower than the PHY can receive a packet anyway. At a certain point, an increased minimum packet detection power does begin to affect reception because packets received weaker than that threshold are ignored. As such, the PER curve increases with a slope of 1dB per dB.
    3232
    33 The 1% PER curve is plotted alongside the 10% PER curve in order to demonstrate
     33In the rightmost linear region, you can see about a ~2dB gap between the 1% PER curve and 50% PER curve. This is saying that you need 2dB of headroom above your minimum packet detection power setting in order to move from ignoring half of all packets to ignoring 1 in 100 packets. Even 1 in 100 packets is a substantial PER, so a good practice for this parameter would be to give even more headroom. For example, suppose a fixed topology produces an average receive power of -60 dBm and we want to set the minimum packet detection power to a value that does not adversely affect the reception of our average -60 dBm packets, but still ignores weak receptions. It would be unwise to set the minimum packet detection power to -60 dBm -- the design would ignore a substantial number of packets that would otherwise be decodable. Instead, the parameter should be considerably lower. As the above figure shows, we should ''at least'' drop the threshold to -62 dBm to ignore 1% of packets instead of 50% of packets. Even then, we probably want to reduce it even more to account for wireless fading, which can easily reduce the received power of the packets that we care about. In the end, a good practice in experiment design is to iterate on this parameter and establish at what level it begins to adversely affect your results.