Patent Description:
In many countries, regulatory requirements may limit the number of <NUM> channels available or place additional restrictions on their use because the spectrum is shared with other technologies and services. For example, for parts of band <NUM>, there are regional requirements aiming at protecting radars from interference by other users of the spectrum.

DFS (Dynamic Frequency Selection) is a mechanism that allows a device to coexist with radar systems. DFS automatically selects a frequency that does not interfere with the radar systems. DFS allows you to use more channels. The DFS involves radar detection and selection of frequency without radar.

Since the coexistence between LTE and radar applications at the same frequency bands recently has been enabled through Licensed Assisted Access (LAA), the technical applications to be used as plugin to detect radar in LTE systems are limited. For LTE, such a plugin must be incorporated in the Radio Base Station between the Radio Unit and the medium access control (MAC) control layer with high demands on processing efficiency and accuracy. Since the frequency properties of a chirped signal are preferably analyzed through Fast Fourier Transforms (FFTs), the chirp detection algorithm will utilize the same FFT accelerators as regular traffic does in an Orthogonal Frequency Division Multiple Access (OFDMA) system such as, for example, LTE. As such, it is important to reduce the processing done in frequency domain while still maintaining good detection performance. <CIT> discloses a system for detection of radar signals in a radio communications system where radar pulses may be detected if an autocorrelation of a received signal is lower than the autocorrelation of noise. <CIT> discloses a wireless device for detecting radar signals. A radar signal is not detected if the received signal correlates with a standard preamble of legitimate wireless data packets. <CIT> discloses a receiver in a wireless communication system capable of detecting chirping radar pulses. A chirp is detected based on the rate of change of the frequency of a peak in a spectrum of the received signal.

To address the foregoing problems with existing solutions, disclosed are systems and methods that provide a tunable detector for detection of linear chirped radar signals in Orthogonal Frequency Division Multiple Access (OFDMA) based systems.

According to certain embodiments, a method by a network node for linear chirp detection includes obtaining a first number, N, of samples of a signal. The samples are divided into at least a first group and a second group, where the first group includes a second number, D, of the samples of the signal and the second group includes a third number, N-D, of the samples of the signal. A correlation is performed between the first group of samples and the second group of samples to generate a resultant group of samples of the signal. Within the resultant group of samples, a peak value is identified in the frequency domain. Based on at least one property associated with the peak value, it is determined whether there is a linear chirp within the signal.

According to certain embodiments, a network node for linear chirp detection includes memory storing instructions and a processor operable to execute the instructions to cause the network node to obtain a first number, N, of samples of a signal. The network node divides the samples into at least a first group and a second group, where the first group includes a second number, D, of the samples of the signal and the second group includes a third number, N-D, of the samples of the signal. The network node performs a correlation between the first group of samples and the second group of samples to generate a resultant group of samples of the signal. Within the resultant group of samples, a peak value is identified in the frequency domain. Based on at least one property associated with the peak value, the network node determines whether there is a linear chirp within the signal.

According to certain embodiments, a non-transitory computer-readable storage medium storing instructions is operable to be executed by a processor to cause the processor to obtain a first number, N, of samples of a signal. The sampels are divided into a first group and a second group. The first group includes a second number, D, of the samples of the signal, and the second group includes a third number, N-D, of the samples of the signal. Correlation is performed between the first group of samples and the second group of samples to generate a resultant group of samples of the signal. Within the resultant group of samples, a peak value is identified in the frequency domain. Based on at least one property associated with the peak value, it is determined whether there is a linear chirp within the signal.

Certain embodiments of the present disclosure may provide one or more technical advantages. For example, certain embodiments may provide effective utilization of the unlicensed band, which is a critical task in modern communications.

Radar detection plays a major role in the selection of the DFS required frequency band. Another advantage may be that certain embodiments detect the presence of a linear chirp in the received baseband signal. By analyzing the received radio signal for chirped like characteristics it is possible to distinguish a chirped like emitted radar signal from WiFi traffic and accordingly avoid false alarms with predominant WiFi interference scenarios.

Still another advantage may be that certain embodiments use the correlation along with the frequency analysis for the detection of the chirp characteristics.

Yet another advantage may be that certain embodiments provide the approximate band width of the chirp.

Other advantages may be readily apparent to one having skill in the art. Certain embodiments may have none, some, or all of the recited advantages.

Particular embodiments of the present disclosure may provide solutions providing a tunable detector for the detection of linear chirped radar signals in Orthogonal Frequency Division Multiple Access (OFDMA) based systems. Specifically, since no chirped signal characteristics are present in OFDMA based devices, an optimal chirp detection algorithm efficiently filters out all chirped like signal devices, like radars, from OFDM based devices in frequency bands where both systems shall co-exist without interfering each other.

According to certain embodiments described herein, a method is provided to facilitate an easy tunable detection algorithm for estimation and detection of linear chirps where the computation complexity in the algorithm is scalable and thus could be made more efficient in comparison to existing market solutions. The latter is made possible by decimation of received time domain chirp samples and by only requiring one Discrete Fourier Transform (DFT) to detect and estimate the chirp characteristics. By contrast, in current market solutions, several DFTs are utilized and executed at a sampling frequency covering the whole maximum bandwidth of the chirp. However, the methods and techniques described herein replace the multi-DFTs with one operation in time domain using correlation along with one DFT for frequency analysis. This provides a more efficient digital signal processor (DSP) implementation since it consists of complex multiplications followed by one DFT operation.

According to certain embodiments, the time domain operation may reduce the bandwidth of the chirp such that the sampling period may be decreased, which thereby reduces the computation complexity in the preceding calculation steps in the algorithm. The described time domain operation may also effectively filter out the bandwidth of the chirp in opposite to existing market solutions where several DFTs operations are followed by an estimation block where the DFTs are analyzed to detect if a linearly increase of the frequency is present.

Particular embodiments are described in <FIG> of the drawings, like numerals being used for like and corresponding parts of the various drawings. <FIG> illustrates an example context <NUM> for a dynamic frequency selection (DFS) algorithm in a Radio Access Technology (RAT) digital unit <NUM>, according to certain embodiments. In a particular embodiment, the RAT digital unit <NUM> is a RAT transceiver.

The Radar detector <NUM> performs an integral part of the DFS algorithm that is to be implemented in the RAT transceiver together with the native RAT channel estimation, modulator/demodulator and encoding/decoding modules. The radar detector operates on the digitalized complex radio frequency (RF) samples received over Common Public Radio Interface (CPRI) or an optical fiber link from the RF unit <NUM>. According to certain embodiments described herein, a detector is provided that detects whether an intercepted linear chirp is present in the RF samples. The detector <NUM> is preceded by a Pulse Detection block <NUM> that detects that energy has been detected for a short time duration. The short time duration of energy followed by a silent period that then repeat itself characterizes a potential intercepting radar signal in the RF samples.

<FIG> is a graph <NUM> demonstrating example time characteristics of a radar signal, according to certain embodiments. As depicted, the radar signal includes a burst <NUM> of pulses of a pulse width of <NUM>, which are repeated according to a pulse repetition interval <NUM>.

If the pulse widths <NUM> are around the transmission length in a time division duplex (TDD) system, the pulse detection block cannot distinguish whether the pulse train origin from a TDD system like WiFi or from a radar source. But for long pulse radar, the pulses are modulated by a linear chirp that characterizes the source. If a chirp is detected it can be used as condition to judge the type of intercepting source. According to certain embodiments, a linear chirp detector, as disclosed herein, provides an easily implementable and efficient detector to avoid false triggers on other sources than radar. This is crucial since a falsely detected radar source causes the radio access controller (RAC) portion of the DFS algorithm to shut down the operating carrier for thirty minutes according to FCC rules.

For example, and as depicted in <FIG>, the digital unit <NUM> in the RAT transceiver receives the complex RF samples and stores it for further processing. According to certain embodiments, the received complex signal, which may be a full radar pulse or a partial part of a radar pulse, is divided into at least two groups of signals. In a particular embodiment, each of the at least two groups of signals may be of the same duration and length. In another emboidment, the at least two groups of signals may have different durations. In this scenario, the resulting group of samples may equal the group with the shortest duration such that M - min(N-D, D).

In case of multi antenna configuration, the chirp detection can be performed on antenna combined sample, in a particular embodiment. Stated differently, the samples received on different antennas may be combined. The combined samples may then be used for further processing, according to certain particular embodiments. This may be done in the DU, and the combined samples may be fed to the radar detection. It is recognized, however, that this antenna combing is an optional step that is not mandatory.

Though the steps performed for pulse detection by pulse detector <NUM> are not described in detail, the pulse detector <NUM> may detect pulses by comparing the intercepted signal power against a threshold and once a pulse is found this is used as a trigger for the linear chirp detector <NUM>. The pulse detector <NUM> then delivers the samples of received pulses to the chirp detector <NUM>.

By performing correlation, the chirp detector <NUM> may then determine the phase change. For example, in a particular embodiment where the group of samples includes at least a first group of samples and a second of samples, the phase change may be determined by performing elementwise complex multiplication between the first group of samples and the conjugate of second group of samples. In case of linear chirp, the rate of change of phase is constant between the at least two groups of samples, which forms a sinusoidal wave with the half of the frequency change in the linear chip intercepted.

One way to detect the linear chirp radar is to identify the linearity in the frequency change in the intercepted signal. According to certain embodiments, a method is provided that uses the digitized time domain samples of the chirp signal. <FIG> illustrates an example block diagram <NUM> of a linear chirp detection method, according to certain embodiments. As shown, the method includes following steps in the process of chirp detection:.

A linearly chirped signal in time domain is defined as a complex signal with a linearly increasing frequency as shown in Equation <NUM>:
<MAT>.

As shown in Equation <NUM>, the phase of the chirped signal is a function of ω(t)t where its frequency is given by:
<MAT>.

By defining in Equation <NUM>:
<MAT>
where fc is the starting frequency, β is chirp width and T is the chirp duration, the frequency will be a linear function of t as shown in Equation <NUM>:
<MAT>.

From Equation <NUM>, it may be concluded that during the chirp duration, T, the frequency is sweeping from fc to fc + β.

<FIG> illustrates an example graph <NUM> of time domain of a linear chirp, according to certain embodiments. Specifically, <FIG> shows a linear chirp signal with start frequency, fc, of <NUM> and chirp duration of <NUM> microseconds.

<FIG> illustrates an example graph <NUM> of instantaneous phase of a linear chirp, according to certain embodiments, and <FIG> illustrates an example graph <NUM> of instantaneous frequency of a linear chirp, according to certain embodiments. The instantaneous frequency is started from <NUM> and ended at <NUM> as the chirp width is <NUM>.

<FIG> illustrates an example graph <NUM> of instantaneous frequency difference in a linear chirp, according to certain embodiments. More specifically, <FIG> illustrates the instantaneous frequency difference between two halves of the linear chirp. As the frequency is linearly incremented, the frequency difference between any two-time instants differed by same time is constant. The frequency between two points differed by half of the chip duration is constant and is equal to half of the chirp width of <NUM>.

<FIG> illustrates an example mathematical view <NUM> of linear chirp detection, according to certain embodiments. As seen in <FIG>, an elementwise conjugate multiplication is performed between, D, segments of the chirped pulse samples of length, T. The result is passed through an FFT and the estimated chirp width is the derived from the outputted FFT spectrum.

By using the convolution theorem in Equation <NUM> below, it can be shown that the operation between h(t) and g'(t) in <FIG> corresponds to convolution in frequency domain:
<MAT>.

As shown in Equations <NUM> and <NUM> below, the right-hand side could be recognized as the operation made in <FIG> where:
<MAT>
<MAT>
Where Ts is the sample time/duration.

As shown in Equations <NUM> and <NUM> below, the corresponding Fourier transforms will be:
<MAT>
<MAT>.

Recall the definition of the chirp, ^(t) is rewritten in terms of ω(t - TsD) in Equation <NUM>:
<MAT>.

Inserted for G{g*(t)} in Euqaiton <NUM>:
<MAT>.

Convolution is defined in Equation <NUM> as
<MAT>.

Replace H(τ) and G(ω - τ) in Equation <NUM> by using Equation <NUM> and Equation <NUM> the convolution between the dirac functions equal Equation <NUM>:
<MAT>
It may be seen that H(τ) ≠ <NUM> only for τ = ω(t - TτD). As shown in Equation <NUM>, solving corresponding ω using τ = ω(t - TsD) for G(ω - τ)) ≠ <NUM>:
<MAT>.

The relation in Equation <NUM> will hold for any t∈{TsD,. , T} where t<NUM> is the start time for the chirp. The continuous input signal to the FFT block in <FIG> may then be written in Equation <NUM> as:
<MAT>
where t∈{TsD,.

The time domain function is then passed through a FFT in Equation <NUM>:
<MAT>
where w is the window function used on the received chirp signal.

Assuming w(t) is rectangular its frequency spectrum W(ω) will be given by a sine function. The output from the FFT will thus be a sine located at
<MAT>
with <NUM>st crossing of the frequency axis at f = <NUM>/TsD. The model in Equation <NUM> could be used to derive wanted detection performance for arbitrary amplitude, A, and length, T, with selected window function, W, as a design choice in Equation <NUM>:
<MAT>
where A is the amplitude of y that for simplicity has been set to one in the conceptual outline.

As described above, <FIG> illustrates the discrete time domain. In a particular embodiment, the bandwidth of the chirp after time domain processing was shown by Equation <NUM> to be reduced by D. The sample rate in the algorithm can thus be decimated by D. As such, the parameter D facilitates the calculation complexity in the algorithm to be tuned based on the available DSP resources for the selected hardware platform, according to certain embodiments. In a particular embodiment, a decimation factor of two may be used.

<FIG> illustrates an example conceptual view <NUM> of chirp detection in discrete time, according to certain embodiments.

<FIG> illustrates an example of complex multiplication <NUM> in time domain, according to certain embodiments. Specifically, the digitized complex or real data is stored in the buffer for the linear chirp detection procedure. Consider there are N samples in the linear chirp. The buffer may contain full or part of the linear chirp radar pulse.

As shown in <FIG>, the linear chip data buffer may be divided into at least two groups of samples. For example, a first group may include a first number of D samples of the linear chirp and the second group may include the remaining N-D samples of the linear chirp. Correlation between the at least two groups of samples is performed by doing the complex multiplication of the samples. For example, elementwise complex multiplication of the D samples in the first group of samples may be performed with the conjugate of N-D samples in the second group of samples. The resultant group of samples is a number of M samples, which are stored in another buffer. The resultant is the change in the phase between the at least two groups of samples after (D/sample rate) duration.

In a particular embodiment, the length of buffer is equal to M and M is equal to D or nearest two powers. If M is greater than D, the (M - D) values are suffixed with zeros, as shown in <FIG>. In a particular embodiment, if the two more groups of samples have different durations, the shortest duration may be used to determine the number of samples to suffix.

In a particular embodiment, FFT is performed on the correlated data to find the frequency properties. Peak search may then be performed on the FFT output. Peak value to the noise floor ratio is calculated and this value is compared against the threshold to avoid the false alarms. If the peak to noise floor is greater than the threshold, declare as linear chirp found and chirp width as double the frequency corresponding to the peak.

According to certain embodiments, the method is tunable for different sample rates and different FFT sizes. If the load on the processor is critical, then the proposed algorithm may be run with reduced sample rate by selecting one sample for every D samples from the digitized sample buffer. In a particular embodiment, the length of FFT may also be configurable. For example, in a particular embodiment, the length of FFT may be inversely proportional to the accuracy of the chirp width detected.

<FIG> illustrates an example graph <NUM> of time domain correlation between two halves of a linear chirp, according to certain embodiments.

In case of sinusoidal signal, the value of β is zero and its constant phase difference is Φ. The frequency of the sinusoidal signal
<MAT>
can be detected using DFT. If required ρ will suffixed with zeros before FFT. The FFT of the ρ(m) is given in <FIG>, which illustrates an example frequency domain plot <NUM> of correlation between two halves of a linear chirp, according to certain embodiments. The frequency of the sinusoidal signal is estimated by find the peak power and its respective frequency. In <FIG>, the frequency component of the maximum power is <NUM>. The accuracy of the frequency is dependent on the number of FFT points used. The detected frequency <NUM> is approximately equal to the frequency difference (<NUM>) observed between two halves in <FIG>. The false alarms can be avoided by giving the threshold for difference between peak power to the noise floor.

As stated in Equation <NUM> and illustrated in <FIG>, the presence of a chirp may be a clear peak at half the chirp width in the DFT spectrum. The detection criteria in the DFT post processing is based on the SINR of the spectrum peak
<MAT>
where fpeaki is the indices to the peak in the spectrum and fnoisei is the indices to every other sample than the peak indices in the spectrum, Th is a constant design value.

<FIG> is a block diagram a wireless network <NUM> for linear chirp detection, in accordance with certain embodiments. Although the subject matter described herein may be implemented in any appropriate type of system using any suitable components, the embodiments disclosed herein are described in relation to a wireless network, such as the example wireless network illustrated in <FIG>. For simplicity, the wireless network of <FIG> only depicts network <NUM>, network nodes <NUM> and 1360b, and WDs <NUM>, 1310b, and 1310c. In practice, a wireless network may further include any additional elements suitable to support communication between wireless devices or between a wireless device and another communication device, such as a landline telephone, a service provider, or any other network node or end device. Of the illustrated components, network node <NUM> and wireless device (WD) <NUM> are depicted with additional detail. The wireless network may provide communication and other types of services to one or more wireless devices to facilitate the wireless devices' access to and/or use of the services provided by, or via, the wireless network.

<FIG> illustrates an example network node 1360for linear chirp detection, according to certain embodiments.

<FIG> illustrates an example wireless device (WD) for linear chirp detection, according to certain embodiments. As used herein, WD refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other wireless devices. Unless otherwise noted, the term WD may be used interchangeably herein with user equipment (UE). Communicating wirelessly may involve transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information through air. In some embodiments, a WD may be configured to transmit and/or receive information without direct human interaction. For instance, a WD may be designed to transmit information to a network on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the network. Examples of a WD include, but are not limited to, a smart phone, a mobile phone, a cell phone, a voice over IP (VoIP) phone, a wireless local loop phone, a desktop computer, a personal digital assistant (PDA), a wireless cameras, a gaming console or device, a music storage device, a playback appliance, a wearable terminal device, a wireless endpoint, a mobile station, a tablet, a laptop, a laptop-embedded equipment (LEE), a laptop-mounted equipment (LME), a smart device, a wireless customer-premise equipment (CPE). a vehicle-mounted wireless terminal device, etc. A WD may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-everything (V2X) and may in this case be referred to as a D2D communication device. As yet another specific example, in an Internet of Things (IoT) scenario, a WD may represent a machine or other device that performs monitoring and/or measurements and transmits the results of such monitoring and/or measurements to another WD and/or a network node. The WD may in this case be a machine-to-machine (M2M) device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the WD may be a UE implementing the 3GPP narrow band internet of things (NB-IoT) standard. Particular examples of such machines or devices are sensors, metering devices such as power meters, industrial machinery, or home or personal appliances (e.g. refrigerators, televisions, etc.) personal wearables (e.g., watches, fitness trackers, etc.). In other scenarios, a WD may represent a vehicle or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation. A WD as described above may represent the endpoint of a wireless connection, in which case the device may be referred to as a wireless terminal. Furthermore, a WD as described above may be mobile, in which case it may also be referred to as a mobile device or a mobile terminal.

Radio front end circuitry <NUM> is connected to antenna <NUM> and processing circuitry <NUM> and is configured to condition signals communicated between antenna <NUM> and processing circuitry <NUM>.

User interface equipment <NUM> is configured to allow input of information into WD <NUM> and is connected to processing circuitry <NUM> to allow processing circuitry <NUM> to process the input information. Using one or more input and output interfaces, devices, and circuits, of user interface equipment <NUM>, WD <NUM> may communicate with end users and/or the wireless network and allow them to benefit from the functionality described herein.

Network connection interface <NUM> may be configured to provide a communication interface to network 1643a. Network 1643a may encompass wired and/or wireless networks such as a local-area network (LAN), a wide-area network (WAN), a computer network, a wireless network, a telecommunications network, another like network or any combination thereof. For example, network 1643a may comprise a Wi-Fi network.

Storage medium <NUM> may allow UE <NUM> to access computer-executable instructions, application programs or the like, stored on transitory or non-transitory memory media, to offload data, or to upload data.

In <FIG>, processing circuitry <NUM> may be configured to communicate with network 1643b using communication subsystem <NUM>. Network 1643a and network 1643b may be the same network or networks or different network or networks. Communication subsystem <NUM> may be configured to include one or more transceivers used to communicate with network 1643b.

Network 1643b may encompass wired and/or wireless networks such as a local-area network (LAN), a wide-area network (WAN), a computer network, a wireless network, a telecommunications network, another like network or any combination thereof. For example, network 1643b may be a cellular network, a Wi-Fi network, and/or a near-field network.

In some embodiments, some signaling can be affected with the use of control system <NUM> which may alternatively be used for communication between the hardware nodes <NUM> and radio units <NUM>.

<FIG> illustrates a telecommunication network connected via an intermediate network to a host computer in accordance with some embodiments. Access network <NUM> comprises a plurality of base stations 1812a, 1812b, 1812c, such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 1813a, 1813b, 1813c. Each base station 1812a, 1812b, 1812c is connectable to core network <NUM> over a wired or wireless connection <NUM>. A first UE <NUM> located in coverage area 1813c is configured to wirelessly connect to, or be paged by, the corresponding base station 1812c. A second UE <NUM> in coverage area 1813a is wirelessly connectable to the corresponding base station 1812a.

Telecommunication network <NUM> is itself connected to host computer <NUM>, which may be embodied in the hardware and/or software of a standalone server, a cloudimplemented server, a distributed server or as processing resources in a server farm. Host computer <NUM> may be under the ownership or control of a service provider or may be operated by the service provider or on behalf of the service provider.

<FIG> illustrates a host computer communicating via a base station with a user equipment over a partially wireless connection in accordance with some embodiments.

It is noted that host computer <NUM>, base station <NUM> and UE <NUM> illustrated in <FIG> may be similar or identical to host computer <NUM>, one of base stations 1812a, 1812b, 1812c and one of UEs <NUM>, <NUM> of <FIG>, respectively.

Wireless connection <NUM> between UE <NUM> and base station <NUM> is in accordance with the teachings of the embodiments described throughout this disclosure. One or more of the various embodiments improve the performance of OTT services provided to UE <NUM> using OTT connection <NUM>, in which wireless connection <NUM> forms the last segment. More precisely, the teachings of these embodiments may improve the data rate, latency, and/or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, and/or extended battery lifetime.

In embodiments, sensors (not shown) may be deployed in or in association with communication devices through which OTT connection <NUM> passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above or supplying values of other physical quantities from which software <NUM>, <NUM> may compute or estimate the monitored quantities.

<FIG> depicts a method <NUM> by a network node <NUM> for linear chirp detection, according to certain embodiments. The method begins at step <NUM> when network node <NUM> obtains a first number, N, of samples of a signal. In a particular embodiment, for example, network node <NUM> may repeatedly detect energy for a sample duration, which is followed by a silent period where the energy is not detected. In another particular embodiment, network node <NUM> may select the first number, N, of the samples from a larger group of N x Z samples, wherein while selecting the first number, N, of the samples, every z-th sample is selected.

At step <NUM>, network node <NUM> divides the samples into at least a first group of samples and a second group of samples. The first group of samples includes a second number, D, of the samples of the signal, and the second group comprises a third number, N-D, of the samples of the signal.

At step <NUM>, network node <NUM> performs a correlation between the first group of samples and the second group of samples to generate a resultant group of samples of the signal. In a particular embodiment, the resultant group of samples may represent a change of phase between the first group of samples and the second group of samples after a duration. In a further particular embodiment, the duration is D divided by a sample rate.

In a particular embodiment, performing the correlation between the first group of samples and the second group of samples may include multiplying the second number, D, of the samples with a conjugate of the third number, N-D, of the samples.

In a further particular embodiment, performing the correlation between the first group of samples and the second group of samples may include performing elementwise complex multiplication of second number, D, of the samples with the third number, N-D, of the samples to generate the resultant group of samples. In a particular embodiment, the number of samples in the resultant group is M and M is equal to D. In another embodiment, the method may further include padding the resultant group of samples to a nearest two power such that the number of samples in the resultant group is M and M is greater than or equal to D.

At step <NUM>, network node <NUM> identifies, within the resultant group of samples, a peak value in the frequency domain.

Based on at least one property associated with the peak value, network node <NUM> determines whether there is a linear chirp within the signal, at step <NUM>.

In a particular embodiment, for example, the determination of whether there is a linear chirp within the signal may include comparing the peak value to a threshold. If the peak value is greater than or equal to the threshold, network node <NUM> may determine that there is a linear chirp within the signal. Conversely, if the peak value is not greater than or equal to the threshold, network node <NUM> may determine that there is not the linear chirp within the signal.

In another particular embodiment, the determination of whether there is a linear chirp within the signal may include calculating a peak value-to-noise floor ratio and comparing the peak value to noise floor ratio to the threshold. If the peak value-to-noise-floor ratio is greater than or equal to the threshold, network node <NUM> may determine that there is a linear chirp within the signal. Conversely, if the peak value-to-noise floor ratio is not greater than or equal to the threshold, network node <NUM> may determine that there is not a linear chirp within the signal.

In a particular embodiment, the at least one peak value includes a value of the peak, an absolute value, or a signal-to-noise ratio (SNR).

In a particular embodiment, the method may further include performing DFT or FFT on the resultant group of samples to find the peak value.

In a particular embodiment, network node <NUM> may determine that there is the linear chirp within the signal and a width of the linear chirp may be double the frequency corresponding to the peak value.

In a particular embodiment, network node <NUM> may determine that the linear chirp is associated with a radar signal and the network node <NUM> may abstain from transmitting on a channel associated with the radar signal for a radar duration. Conversely, in another embodiment, network node <NUM> may determine that there is not a linear chirp within the signal. In response to determining that there is not the linear chirp within the signal, network node <NUM> may transmit on a channel associated with the signal.

<FIG> illustrates a schematic block diagram of a virtual apparatus <NUM> in a wireless network (for example, the wireless network shown in <FIG>). The apparatus may be implemented in a wireless device or network node (e.g., wireless device <NUM> or network node <NUM> shown in <FIG>). Apparatus <NUM> is operable to carry out the example method described with reference to <FIG> and possibly any other processes or methods disclosed herein. It is also to be understood that the method of <FIG> is not necessarily carried out solely by apparatus <NUM>. At least some operations of the method can be performed by one or more other entities.

Virtual Apparatus <NUM> may comprise processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include digital signal processors (DSPs), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as read-only memory (ROM), randomaccess memory, cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and/or data communications protocols as well as instructions for carrying out one or more of the techniques described herein, in several embodiments. In some implementations, the processing circuitry may be used to cause obtaining module <NUM>, dividing module <NUM>, performing module <NUM>, identifying module <NUM>, determining module <NUM>, and any other suitable units of apparatus <NUM> to perform corresponding functions according one or more embodiments of the present disclosure.

According to certain embodiments, obtaining module <NUM> may perform certain of the obtaining functions of the apparatus <NUM>. For example, obtaining module <NUM> may obtain a first number, N, of samples of a signal.

According to certain embodiments, dividing module <NUM> may perform certain of the dividing functions of the apparatus <NUM>. For example, dividing module <NUM> may divide the samples into at least a first group of samples and a second group of samples.

According to certain embodiments, performing module <NUM> may perform certain of the performing functions of the apparatus <NUM>. For example, performing module <NUM> may perform a correlation between the first group of samples and the second group of samples to generate a resultant group of samples of the signal.

According to certain embodiments, identifying module <NUM> may perform certain of the identifying functions of the apparatus <NUM>. For example, identifying module <NUM> may identify, within the resultant group of sample, a peak value in the frequency domain.

According to certain embodiments, determining module <NUM> may perform certain of the determining functions of the apparatus <NUM>. For example, determining module <NUM> may determine whether there is a linear chirp within the signal based on at least one property associated with the peak value.

Modifications, additions, or omissions may be made to the systems and apparatuses described herein without departing from the scope of the disclosure. The components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses may be performed by more, fewer, or other components. Additionally, operations of the systems and apparatuses may be performed using any suitable logic comprising software, hardware, and/or other logic. As used in this document, "each" refers to each member of a set or each member of a subset of a set.

Modifications, additions, or omissions may be made to the methods described herein without departing from the scope of the disclosure. The methods may include more, fewer, or other steps. Additionally, steps may be performed in any suitable order.

Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the spirit scope of this disclosure, as defined by the following claims.

Claim 1:
A method performed by a network node (<NUM>) for linear chirp detection, the method comprising:
Obtaining (<NUM>) a first number, N, of samples of a signal;
Dividing (<NUM>) samples into at least a first group and a second group, the first group comprising a second number, D, of the samples of the signal, the second group comprising a third number, N-D, of the samples of the signal;
performing (<NUM>) a correlation between the first group of samples and the second group of samples to generate a resultant group of samples of the signal,
within the resultant group of samples, identifying (<NUM>) a peak value in the frequency domain; and
based on at least one property associated with the peak value, determining (<NUM>) whether there is a linear chirp within the signal.