Methods for detecting narrowband interference in wireless networks

Abstract

Methods for detecting narrowband interference in wireless networks, including: Forming (304) a threshold of each block of samples to generate thresholded samples, wherein the samples are normalized (303) frequency amounts obtained from a spectrum of a wireless signal in a channel; Summing (305) of each block of thresholded samples to produce a thresholded value for each block; and Autocorrelation (307) of the thresholded values ​​to determine whether a bandwidth of the wireless signal is consistent with narrowband interference, wherein the steps are performed in a processor, wherein the summation (305) produces the thresholded value according to y ( n ) = Σ j = 1 N fft ( X j , n − XS threshold ) + , where j indexes the number of samples in block n, and N fftX is a positive integer j,n the samples are, X Schwelle the threshold is, and an operator (x) + is defined by (x) + = { 0 x ≤ 0 xx ≻ 0, where the autocorrelation (307) is obtained according to r(m) = E[y(n)y(n+m)], where E[.] is an expectation and m represents a delay at which the autocorrelation (307) is evaluated.

Description

[Technical field]

[0001] The invention relates generally to the detection of interference in wireless communication networks and in particular to the detection of narrowband interference. [Technical background]

[0002] In wireless local area networks (WLANs), it is necessary to detect spectrum occupancy and the spatial orientation of radio transmitters. This is particularly important because WLANs typically use an unregulated band, for example 2.4 GHz, where many devices can operate simultaneously.

[0003] For example, many devices such as cordless phones, car alarms, baby monitors, video transmitters, wireless speakers, cameras, game controllers (Xbox and Wii), and ZigBee, wireless audio playback (WiFi), and Bluetooth devices use the 2.4 GHz frequency, the same frequency used by devices operating under the Wi-Fi standards 802.11a, 802.11g, and 802.11n, collectively known as AGN. This can cause a significant decrease in speed, or sometimes completely block the Wi-Fi signal where the interference is occurring.

[0004] It is known that microwave ovens (MWOs) emit radio frequencies in this band, which interfere with normal operation. A wireless communication (Wi-Fi) device (access point or client device) has the ability to detect and avoid interference from other Wi-Fi devices through the Multiple Access Control (MAC) protocol. However, in the case of non-Wi-Fi devices such as MWOs, there is no mechanism to coordinate the use of the unregulated wireless spectrum. Therefore, interference from non-Wi-Fi devices like MWOs reduces performance.

[0005] Without the ability to coordinate spectral usage across systems (WLAN and non-WLAN), some methods adapt the WLAN system to the presence of interference. For example, changing the WLAN's operating channel in response to the detection of interfering signals on a current operating channel, or adjusting the data rate or encoding scheme accordingly. In any case, WLAN devices must be able to detect the presence of interfering signals and respond accordingly.

[0006] It is preferred that interference detection be performed using existing, commercial WLAN hardware to minimize costs. Fortunately, some WLAN hardware provides access to some physical layer signal data that can be used for interference detection.

[0007] For example, the Qualcomm Atheros 9082 AGN WLAN chipset provides access to raw Fast Fourier Transform (FFT) data of the received signal. Specifically, the chip can provide the strength of the received signal in each FFT bin, which is typically used for data demodulation. Therefore, the device provides a rough estimate of the frequency spectrum of the received signal.

[0008] A system called Airshark uses Atheros WLAN hardware for passive detection of various non-WiFi devices (Rayanchu et al., "Airshark: Detection of Non-WiFi RF Devices Using Standard WiFi Hardware," Proceedings of the 2011 ACM SIGCOMM Internet Measurement Conference, ACM, 2011). However, this system requires near-continuous channel scanning by the hardware for detection purposes. Therefore, the system cannot be used as a conventional, standard WLAN device transmitting user data. [Summary of the invention]

[0009] The embodiments of the invention provide a method for detecting narrowband interference in wireless communication networks such as WiFi and WLANs. In particular, it is desirable to use the functionality of commercially available WiFi and WLAN devices to enable the detection of microwave oven interference.

[0010] The detection process scans wireless channels infrequently, so normal user communications are not affected. The sampled data is processed in such a way that only a single value is stored in memory to represent the bandwidth of a signal present in a channel during a scan. Therefore, the interference detection process uses minimal memory capacity to store data characterizing the interfering signals, such as FFT magnitude data.

[0011] Further processing of the values ​​can generate an estimate of the on-time of the interfering signals to classify the signals as originating from a narrowband interfering source, such as a microwave oven or similar interfering source with a known ON / OFF periodicity, if the estimated on-time falls within expected ranges.

[0012] One advantage of this method is its use of data already available from commercial wireless hardware capable of providing coarse signal spectrum measurements. By coarse, we mean that the audio reproduction of the spectral measurements is limited both to the number of frequencies at which the spectrum is measured and to the infrequency of the measurements. [Brief description of the drawings] [ Fig. 1] Fig. Figure 1 is a graph of a microwave oven interference signal as a function of time and frequency; [ Fig. 2] Fig. 2 is a scheme for data extraction from signal samples according to embodiments of the invention; [ Fig. 3] Fig. 3 is a method for detecting interference according to embodiments of the invention; and [ Fig. 4] Fig. Figure 4 is a scheme of a summation process used by embodiments of the invention. [Description of the embodiments]

[0013] The embodiments of our invention provide a method for detecting narrowband interference emitted by unregulated devices, for example an MWO, in a WLAN. Channel spectrogram

[0014] Fig. Figure 1 is a spectrogram of radio frequency (RF) signals in a wireless channel, obtained by scanning the channel. The spectrogram shows the magnitudes of the channel's frequency spectrum over a period of time (in milliseconds). The spectrogram shows signals 101, generated by the MWO, and some weaker Wi-Fi signals 102. The MWO signals 101 span frequencies and cause interference, disrupting the normal operation of the Wi-Fi devices.

[0015] Interference can vary depending on a number of factors, such as device specifications, environment, location within a building, and the like. However, we can reliably detect the bandwidth and periodicity of the interference to identify its source.

[0016] Fig. Figure 1 shows that the bandwidth is relatively narrow, occupying only a few hundred kilohertz. This figure also shows that the emissions are discontinuous, with active and inactive periods of approximately 8 ms, when the MBO is connected to alternating current (AC). The AC generates microwaves for one half, either the positive or negative portion, of the AC current duration. For example, if the nominal frequency of the AC mains is 60 Hz, the ON / OFF duration is 1 / 60 Hz, or 16.6 ms.

[0017] Using this observation to determine the narrowband nature and periodicity of MWO interference, we can detect the interference using only the limited spectral capabilities of commercial WLAN devices.

[0018] We assume that the WLAN device provides spectral samples, that is, information about the signal power received in each of the subcarriers of an 802.11 OFDM-based system. For example, the widely used Qualcomm Atheros AR9280 AGN wireless card can provide information from 56 OFDM subcarriers. In each 20 MHz channel, the subcarriers are separated by 312.5 kilohertz, and data carrier signals are transmitted on 56 of these subcarriers. The other 8 subcarriers are unused and serve as safety bands. Each spectral sample (FFT) generated by the wireless card includes the power received in 56 subcarriers (FFT bin), which corresponds to a 17.5 MHz (56 * 0.3125 MHz) portion of the spectrum. Signal processing

[0019] As in Fig. As shown in Figure 2, our method detects narrowband interference with a predetermined periodicity. Fig. Figure 2 shows a received signal 201, which is sampled for further processing. Signal 21 represents the samples of either the real or imaginary in-phase or quadrature components (I, Q signal) of a complex baseband signal. We assume that for each M sample of the received signal, a block X j,n 202 out of sixty-four samples (j = 1, 2, ..., 64) is selected for further processing. In other words, M specifies an extraction frequency of the sampling block, or equivalently, the time interval between successive interference scans. As can be seen, the signal is sampled relatively infrequently and therefore does not unduly affect the normal operation of the device. That is, a WLAN device can normally be used to transmit and receive data, and only rarely can it be used to scan the channel for interference. Recognition methods

[0020] Fig. Figure 3 shows the individual steps of our interference detection method, which uses a summation function ƒ(.) to detect spikes. This method can be executed in a 350 processor. Block X j,n 202 is extracted 301 from a received signal 201. Such blocks are typically available from commercial WLAN devices 351 as described above.

[0021] An FFT 302 is applied to the block, and the magnitudes of the FFT samples are normalized 303 so that the sum of the magnitudes is 1.

[0022] The j te element of n te Vector (Blocks) is X j,n We assign a single value to n. te Block by comparing 304 of the normalized FFT values ​​X j,n for j = 1, ..., 64 to a threshold X Schwelle 401 here to generate a thresholded value y(n).

[0023] As in Fig.As shown in section 4, the function f(.) sums the amounts greater than the threshold as y(n)=Σj=164(Xj,n−Xthreshold)+, where an operator (x) + (x)+={0 x≤0x x≻0. is.

[0024] The 401 threshold can be adjusted according to the background noise of the WLAN device, which is also easily accessible, and also according to the bandwidth of the interfering signal. For example, for very narrowband signals, the threshold can be set to relatively high values.

[0025] The threshold y(n) specifies the bandwidth of the signal. If the bandwidth is relatively wide, then the values ​​become smaller after normalization, so that only a small number of values ​​are greater than the threshold.

[0026] However, if a bandwidth is narrow, then the peak is significantly larger than the threshold after normalization, resulting in a large threshold value of y(n). In other words, the FFT blocks X j,n The values ​​are compressed to a single threshold y(n). The values ​​are stored in memory 306. This memory is used to determine an autocorrelation r(m) = E[y(n)y(n + m)] of the sequence of values ​​y(n), where E[.] is an expected value.

[0027] We locate a maximum value for r(n), that is, a peak. Although r(n) has a maximum value at r(0), we locate a specific interval of the function r(n) where we expect an additional peak if interference is present. The interval depends on the value of M, which is chosen for the periodic scanning and reporting of the frequency spectrum, the frequency of the AC power, and the bandwidth of the WLAN receiver. In particular, we expect a peak in r(n) at a certain point. n=Tn=BW∗1ACf∗1M, where BW is the WLAN receiver bandwidth, and AC f The AC power frequency. Typically, BW is 20 MHz for WLAN channels. For MWO operation, AC is used. f In North America, 60 Hz. Assuming M = 640 and using the above typical values ​​for BW and AC. f , we calculate T n = 542.53 (samples).

[0028] Therefore, we expect the peak of r(n) to occur at either sample 542 or 543. After determining r(n) for n > T n We only need to determine the position of the peak in an area near T n Check 308. If a peak is present (yes 309), the peak is consistent with narrowband interference, for example from an MWO, and otherwise not.

[0029] Another processing method 310 can be used to determine the interference periodicity, for example 1 / 60 Hz or 16.6 ms for AC-operated microwave ovens, and to classify other types of interference signals, 311.

[0030] The method described above can be used to detect the presence of MWO interference and relies solely on spectral data readily available from commercial WLAN hardware. It requires minimal memory (only a single value for each spectral scan) and simple processing. We also note that, although we have described this invention in terms of MWO interference, the method can also be used to detect any interference pattern that is narrowband and exhibits on / off periodicity.

Claims

[1] A method for detecting narrowband interference in wireless networks, comprising: forming (304) a threshold of each block of samples to generate thresholded samples, the samples being normalized (303) frequency magnitudes obtained from a spectrum of a wireless signal in a channel; summing (305) each block of thresholded samples to produce a thresholded value for each block; and Autocorrelating (307) the thresholded values to determine whether a bandwidth of the wireless signal is consistent with narrowband interference, the steps being performed in a processor, wherein the summation (305) establishes the thresholded value according to y(n)=Σj=1Nfft(Xj,n−Xthreshold)+, where j indicates the number of samples in the block n, and N fft is a positive integer, X j,nthe samples are, X Schwelle is the threshold, and an operator (x) + is defined by (x)+={0 x≤0x x≻0, where the autocorrelation (307) is obtained according to r(m)=E[y(n)y(n+m)], where E[.] is an expectation and m represents a lag at which the autocorrelation (307) is evaluated. [2] The method of claim 1, wherein the spectrum is uncontrolled. [3] The method of claim 1, wherein the interference is radiated from a microwave oven. [4] The method of claim 1, further comprising: Determining (310) an interference periodicity to characterize a device type causing the interference. [5] The method of claim 1, wherein a fast Fourier transform (302) is applied to the samples prior to normalization (303). [6] The method of claim 1, wherein the summation (305) produces the thresholded value according to y(n)=Σj=164(Xj,n−Xthreshold)+, where j is the number of samples in the block n, X j,n the samples are, X Schwelle is the threshold, and an operator (x) + is defined by (x)+={xx>00x≤0⋅ [7] The method of claim 1, wherein the processor (350) is included in a wireless device and the threshold is set according to a noise floor of the device. [8] The method of claim 1, further comprising: Storing the thresholded values in a buffer (306) for autocorrelation (307). [9] Method according to claim 6, wherein the autocorrelation (307) is obtained according to r(m)=E[y(n)y(n+m)], where E[.] is an expectation and m represents a lag at which the autocorrelation (307) is evaluated. [10] The method of claim 1, wherein the blocks are acquired periodically.

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