Device to Device Communications with Carrier Aggregation
US-2015156693-A1 · Jun 4, 2015 · US
US9429647B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-9429647-B2 |
| Application number | US-201314097188-A |
| Country | US |
| Kind code | B2 |
| Filing date | Dec 4, 2013 |
| Priority date | Dec 4, 2013 |
| Publication date | Aug 30, 2016 |
| Grant date | Aug 30, 2016 |
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The present disclosure discloses a system and method for. classifying Wi-Fi signals from Fourier transform samples. Generally, classifying Wi-Fi signals from Fourier transform samples includes: collecting and dividing Fourier transform samples into frequency blocks; determining the bandwidth for the Fourier transform sample; and determining whether the Fourier transform sample corresponds to a narrowband signal. Further, if a determination is made that the Fourier transform sample does not correspond to a narrowband signal, channel utilization is calculated based on a determination that the FFT sample corresponds to a Wi-Fi signal. If it is determined that the Fourier transform sample corresponds to a narrowband signal, then a determination is made that the FFT sample corresponds to a Wi-Fi signal based on certain criteria. The certain criteria may include one or more of a slope value, a number of sub-peak bins, an analysis of adjacent channels, characteristic matching, or other criteria.
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What is claimed is: 1. A non-transitory computer readable medium comprising instructions which, when executed by one or more hardware processors, causes performance of operations comprising: determining, for each of a plurality of frequency bins in a Fourier Transform sample, a corresponding signal strength, the Fourier Transform sample corresponding to a wireless signal as detected at a first time period; identifying a first frequency bin, from the plurality of frequency bins, with a first signal strength, the first signal strength being a highest signal strength in the signal strengths corresponding to each of the plurality of frequency bins; determining a decrease in signal strength between (a) the first signal strength corresponding to the first bin and (b) a second signal strength corresponding to a second bin in the plurality of bins; and classifying the Fourier Transform sample based at least on the decrease in signal strength and a frequency difference between the first bin and the second bin, and on an average difference between sets of signal strengths corresponding to bins in the plurality of frequency bins. 2. The medium of claim 1 , wherein classifying the Fourier Transform sample further comprises classifying the Fourier Transform sample as corresponding to one of a Wi-Fi signal or a non-Wi-Fi signal. 3. The medium of claim 1 , wherein classifying the Fourier Transform sample further comprises classifying the Fourier Transform sample based on a burst duration exceeding a predetermined threshold value, wherein the wireless signal is detected during the burst duration. 4. The medium of claim 1 , wherein classifying the Fourier Transform sample further comprises: for each set in a plurality of sets of frequency bins: identifying a signal strength difference and a frequency difference; computing a slope value based on the signal strength difference and the frequency difference; determining if the slope value meets a corresponding threshold value; identifying a number of sets in the plurality of sets of frequency bins for which the computed slope value meets the corresponding threshold value; classifying the Fourier Transform based on the number of sets in the plurality of sets of frequency bins for which the computed slope value meets the corresponding threshold value. 5. The medium of claim 1 , wherein classifying the Fourier Transform sample is further based on one or more difference values, a difference value being computed by comparing (a) a signal strength corresponding to a particular frequency bin in the plurality of frequency bins with (b) an average of signal strength values for two or more frequency bins other than the particular frequency bin. 6. The medium of claim 5 , wherein the two or more frequency bins comprise a first number of frequency bins preceding the particular frequency bin and a second number of frequency bins following the particular frequency bin, wherein the first number and the second number are equal. 7. A non-transitory computer readable medium comprising instructions which, when executed by one or more hardware processors, causes performance of operations comprising: determining, for each of a plurality of frequency bins in a Fourier Transform sample, a corresponding signal strength, the Fourier Transform sample corresponding to a wireless signal as detected at a first time period; identifying a first frequency bin, from the plurality of frequency bins, with a first signal strength, the first signal strength being a highest signal strength in the signal strengths corresponding to each of the plurality of frequency bins; identifying a subset of the plurality of frequency bins, each particular bin in the subset of frequency bins corresponding to signal strengths that is (a) lower than the first signal strength, (b) higher than at least the signal strength for n consecutive frequency bins in the plurality of frequency bins preceding the particular bin, and (c) higher than at least the signal strength for m consecutive frequency bins in the plurality of frequency bins subsequent to the particular bin, wherein n and m are positive integers greater than or equal to two; and based on a number of frequency bins in the subset of frequency bins, classifying the Fourier Transform sample. 8. The medium of claim 7 , wherein classifying the Fourier Transform sample comprises classifying the Fourier Transform sample as corresponding to one of a Wi-Fi signal or a non-Wi-Fi signal. 9. The medium of claim 7 , wherein the operations further comprise classifying the Fourier Transform sample based on a burst duration during which the wireless signal is detected. 10. The medium of claim 7 , wherein the classifying operation is based on whether the number of frequency bins in the subset of frequency bins exceeds a threshold number. 11. The medium of claim 7 , wherein n is different than m. 12. The medium of claim 7 , wherein n is same as m. 13. The medium of claim 7 , wherein the subset of plurality of frequency bins is further defined to include one of: (a) bins that are subsequent to the first bin or (b) bins that are preceding the first bin. 14. A non-transitory computer readable medium comprising instructions which, when executed by one or more hardware processors, causes performance of operations comprising: detecting a plurality of wireless signals on a corresponding plurality of frequency channels; classifying each of the plurality of wireless signals as Wi-Fi signals or non-Wi-Fi signals based on a burst duration exceeding a predetermined threshold value, wherein a wireless signal of the plurality of wireless signals is detected during the burst duration, or an average difference between sets of signal strengths corresponding to bins in frequency bins in a sample corresponding to the wireless signal of the plurality of wireless signals; and analyzing information for the wireless signals from the plurality of wireless signals that were classified as Wi-Fi signals to identify at least two of the wireless signals detected on two corresponding channels as a portion of a same Wi-Fi transmission from a same source device. 15. The medium of claim 14 , wherein a channel width of each of the plurality of frequency channels is 20 Mhz. 16. The medium of claim 14 , wherein identifying at least two of the wireless signals as a portion of the same Wi-Fi transmission comprises: determining that the at least two wireless signals are detected with a same signal strength range. 17. The medium of claim 14 , wherein identifying at least two of the wireless signals as a portion of the same Wi-Fi transmission comprises: determining that the at least two wireless signals are detected within a same time period.
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