Patent Description:
In a world of preassigned spectrum frequency use, such as frequency use of certain government agencies or for other particular commercial use, allowing the resharing of bands that are not in use is possible only if the previous incumbents are not using them. In some cases, the use of the radio frequency bands may be infrequent in a given location. Detecting the use of these radio frequencies in real time to reassign to new users so as to not interfere with the incumbents is a very difficult problem particularly on large bands and with narrow and infrequent uses. For example, government agencies may have been granted use of certain spectrum. One method of managing spectrum access for use by another set of users is to move the incumbent government users out of the spectrum such as to a different frequency band, or to detect when frequency bands are not in use and then temporarily assign the spectrum for use by others.

Existing radio frequency sniffers typically look for frequency uses on a single frequency at a time which can be too slow and too costly for attempting to detect unused frequencies in a timely manner to allow use by others. <CIT> discloses a system and method for real-time spectrum analysis in a radio device.

The disclosure will be more readily understood in view of the following description when accompanied by the below figures and wherein like reference numerals represent like elements, wherein:.

In one example, environmental radio frequency sensors (ERFS), also referred to as detectors, detect a large dynamic range of radio frequencies in real time without requiring complicated automatic gain control operations. In one example, a signal conditioner, such as an RF signal strength separator, breaks the incoming signal into two or more overlapping dynamic frequency ranges based on signal strength, thus allowing detection of a large dynamic range of radio frequencies. Each path associated with each overlapping dynamic range uses a series of transformations to detect frequency patterns such as both slow and fast pulses and chirps over a wide frequency range. The detected frequency patterns are compared to expected frequency patterns (e.g., fingerprints) and if a match is found frequency use is determined in the environment. Stated another way, each of the detectors performs a type of pattern matching of a broadband of frequencies divided in both low and high signal strength levels (or other signal strength levels). For example, one signal strength window may be -89dBm to -34dBm, while the other is -35dBm to 20dBm, giving 119dB of dynamic range. Each detection over an aggregate noise threshold is then converted into a detection event which is output to a host unit. The host analyzes the edges as detected and looks for certain fingerprints by analyzing each on a case by case basis, which converts into a known pulse train. Each pulse train, from each antenna (e.g., left or right antenna) that detected the signal is broadcast to a cloud spectrum decision unit such as a spectrum access server (SAS) or other cloud component which compares against all other detectors. Using a logic map to aggregate the signals together, the detection is then declared and acted upon as designed.

For example, a coarse location of a transmitting RF signal source can be determined, and/or when government assigned frequencies are not being used, the frequencies can be assigned to other commercial users. Such a system may be employed as part of a spectrum access system (SAS). The spectrum access system may be, for example, part of the citizens broadband radio service (CBRS) to facilitate shared wireless broadband use of the <NUM>-<NUM> band (<NUM> gigahertz band), or with any other suitable radio frequency sharing system.

Each detector in one example includes a left and right antenna and performs real time edge up and edge down detection of frequencies over a large band. Each detector in one example, includes memory that stores fingerprints of signals of interest and determines whether a detected signal is a signal of interest by comparing the detected signal to the stored fingerprints. For example, one fingerprint may be created to look for a pulse of lMHz that lasts from <NUM>. 5microseconds to <NUM> microseconds and repeats <NUM> times with a pulse repetition rate between <NUM> and <NUM> times per second. However any suitable fingerprint information may be employed.

In some embodiments, if a fingerprint match of a signal of interest is detected, it is determined that the frequencies of interest are currently in use. A respective detector then sends the notification of the match to a cloud component as part of a spectrum access system. The spectrum access system obtains similar data from other detectors and if a number of detectors have detected the use of the same frequencies, the spectrum access server determines that the radio frequency is in use. Comparing fingerprint matches from multiple detectors allows for a reduction in false positives. If no match occurs, the detector continues to analyze frequencies and does not need to send information to the SAS.

In one example, the spectrum decision unit, such as a spectrum access server, uses the detected frequency information from each of the detectors to detect frequency usage in a portion of a geographic area. The geographic area may be, for example, within a city, along a coastline, within a rural area, or any other suitable geographic area. The system provides a coarse grain area detection of signal transmitters of given frequencies. Using the location of the detectors, the SAS locates a geographic location of a transmitter of the frequency such as a mobile or non-mobile base station if desired. Using redundant frequency detection results from the differing detectors also accommodates a situation where one of the detectors, has an error in detection, is out of service or for other reasons is unavailable. Using multiple detections from multiple detectors allows the SAS to validate that a particular detector obtained good data versus bad data. If bad data is determined to be coming from a detector repeatedly, the detector can be tagged as potentially defective and require movement or maintenance.

In some embodiments, an environmental frequency sensing device, includes logic that performs signal strength (SS) level separation on a received band of frequencies (e.g., <NUM>-<NUM>) to produce SS level separated frequencies. The logic is also operative to perform frequency grouping on the SS level separated frequencies for each signal strength level to produce magnitude information for each grouping. The logic generates peak data by detecting peaks of the produced magnitude information. The logic generates an edge event indicating a signal edge based on arrival or departure of a given peak and compares, on a frequency basis, generated edges to stored fingerprint data of a signal of interest. Based on the comparison, the logic provides detected signal data indicating current use of a range of frequencies in an environment. In some embodiments the logic provides the detected signal data to a spectrum analysis access server.

In some embodiments a server, such as a cloud server, includes a spectrum decision unit operative to evaluate, from multiple environmental radio frequency (RF) sensors, the detected frequency data which is data representing that one or more RF frequencies has been detected by each of the multiple environmental radio frequency (RF) sensors in use. The server determines a geographic area corresponding to a source device transmitting the RF frequency detected to be in use using the multiple environmental radio frequency (RF) sensors and prevents user equipment located in the geographic area from using the RF frequency detected.

In some embodiments the server includes one or more processors and memory containing executable instructions that when executed by the one or more processors cause the one or more processors to perform the evaluation, determination and preventing noted above. The one or more processors also compare the data representing one or more RF frequencies detected by each of the multiple environmental radio frequency (RF) sensors to be in use, to each other and determine whether at least one of the environmental radio frequency (RF) sensors provided data containing error.

In some embodiments, a server determines a geographic area corresponding to a source device that is transmitting the RF frequency detected to be in use by the multiple environmental radio frequency (RF) sensors. In some embodiments, the server identifies frequencies that are not in use based on those that are detected to be in use and uses the information to facilitate use of the unused frequencies by user equipment in the area.

<FIG> illustrates one example of a spectrum access system <NUM> that employs one or more environmental frequency sensing devices <NUM>, <NUM> and <NUM>. The environmental radio frequency sensors <NUM>-<NUM> may be in communication with one or more spectrum analysis access servers <NUM> or any other suitable cloud component through one or more networks <NUM>, such as but not limited to, the Internet and/or wireless wide area network and/or wireless local area network or any other suitable network or networks. The environmental frequency sensing devices may be linked to the spectrum analysis access server <NUM> through the network <NUM> via backhaul links generally shown as <NUM> or through any suitable wireless or wired connection as desired. Spectrum analysis access server <NUM> is also operably in communication with the network <NUM> through any suitable network interface generally shown as <NUM>. A wireless spectrum transmitter <NUM> such as one or more base stations, mobile base stations or any other suitable radio frequency transmission device that wirelessly transmits over a band of frequencies is sensed by the sensing devices <NUM>-<NUM>. In this example and the following figures, the system <NUM> will be made with reference to a CBRS system. However, any other suitable frequency bands may be employed. The wireless spectrum source <NUM> may be in communication with the network <NUM> through any suitable network communication link or links generally shown as <NUM>.

The environmental frequency sensing devices <NUM>-<NUM> are positioned, for example, along a coastline, within any particular city location, rural location or any other suitable location in an effort to detect radio frequency transmissions emanating from the wireless spectrum source <NUM>. The spectrum access system <NUM> may also include one or more user equipments <NUM> and <NUM> such as smartphones, laptops, wearables or any other suitable wireless devices that can use the frequencies of the broadband employed by wireless spectrum source <NUM> when the frequencies are available for use. As shown in this example, the UEs <NUM> and <NUM> are currently in communication with a different wireless spectrum source <NUM> but can be instructed to use the frequencies of the source <NUM> if the spectrum analysis access server <NUM> determines that the frequencies are available. UEs that are currently not in communication with any base station may also be candidates for use of the wireless spectrum employed by the wireless spectrum source <NUM>.

In this example, each of the environmental radio frequency sensors <NUM>-<NUM> employ a left antenna <NUM> and right antenna <NUM>. As used herein, an antenna can include any suitable antenna structure and the left and right antennas may include more than one antenna. In one example, each ERFS includes three antennas: a pair of receive antennas and a transmit antenna such as with a -<NUM> dB coupling to the two receive antennas. The antenna pattern is designed to maximize the redundancy between ERFS sites by setting the gain maxima at an angle of <NUM>° off bore site (e.g., for <NUM>, BW=<NUM> deg. and for <NUM>, BW=<NUM> deg. This may be useful if a single ERFS is allowed to determine which quadrant of an antenna pattern a signal is arriving from and can do this cost effectively. The single ERFS performs this operation by doing a simplified two antenna angle of arrival detection by comparing amplitude of the two signals in the incoming phase. The gain would be approximately <NUM> dBi. By way of example, the disclosed system can accommodate a wide range of signal strength levels such as -<NUM> dB to +<NUM> dB or approximately <NUM> dB of range.

Referring also to <FIG>, an example of environmental RF sensing device <NUM> (i.e., sensor) is shown. The environmental RF sensing device <NUM> includes in this example, an RF signal strength separator <NUM>, a controller <NUM>, a host unit (e.g., processor) <NUM> and a power supply <NUM>. The antennas <NUM> and <NUM> are connected to the RF signal strength separator <NUM> through, in this example, a coaxial cable illustrated as <NUM> and <NUM>, respectively. The RF signal strength separator <NUM> can be implemented in any suitable fashion including, but not limited to, one or more processors and accompanying memory, interface logic, FPGAs, state machines, or any suitable logic. The controller <NUM> may also be implemented in any suitable fashion including but not limited to field programmable gate arrays, programmable processors, state machines or any other suitable logic. The environmental frequency sensing device <NUM> includes suitable memory such as RAM and ROM that stores data including thresholds and other information as well as executable instructions that when executed cause one or more processors to execute in a manner consistent with the disclosure. Any suitable apparatus may be employed.

In one example, the RF signals strength separator <NUM>, for each of the left and right antenna, performs signal strength level separation on the received band of frequencies from each antenna. The received band of frequencies is shown as signals <NUM> and <NUM>, respectively. The output from the RF signal strength separator <NUM> is signal strength level separated frequency information that indicates those frequencies within the wide band of incoming frequencies from the left antenna that are above a high signal strength threshold shown as <NUM> and those frequencies within the same band that have a signal strength above a low threshold shown as <NUM>, as illustrated in <FIG> for example. Similar signal strength level separation is performed resulting in right antenna high frequencies <NUM> and right antenna low frequencies <NUM> that exceed a low signal strength threshold. The controller <NUM> produces detected frequency information <NUM> for the host <NUM>. The host <NUM> which performs a signal fingerprint analysis using stored fingerprint data of frequencies of interest on the detected frequency information <NUM> from each antenna and determines if frequencies of interest have been detected. The detected signal data <NUM> is sent to the spectrum analysis access server <NUM>. The detected signal data <NUM> indicates current use of one or more frequencies in an environment, which is any geographic area of interest. In one example, out of a <NUM> window, a <NUM> signal can be detected.

Referring to <FIG>, the signal strength separator <NUM> in this example includes a signal strength separator <NUM> for the left antenna and a signal strength separator <NUM> for the right antenna. <FIG> is one example of a signal strength separator <NUM> for the left antenna. In this example, the input range of -34dBm to -89dBm is amplified to -16dBm to -71dBm, while the high side takes the range of 20dBm to -35dBm and attenuates it to -5dBm to -60dBm. The band of frequencies <NUM> are received by the signal strength separator <NUM> and input through a cavity filter <NUM>. As shown in this example, the received broadband of frequencies is over a range of <NUM>-<NUM>. However any suitable range may be employed. The filtered output <NUM> is then input to another directional coupler <NUM>. The output <NUM> from the directional coupler <NUM> is input to a limiter circuit <NUM>. If desired, an accommodation of cable loss may be employed as shown in block <NUM>. The output <NUM> is the left antenna high signal strength output in this example <NUM> dB to -<NUM> dB of a signal that ranges from <NUM> dB to -<NUM> dBm. The signal strength level separation resulting in output signal <NUM> which in this example has an overlapping signal strength of -<NUM> dBm to -<NUM> dBm includes passing the output <NUM> through a limiter circuit <NUM>. The output <NUM> serves as input to linear noise amplifier circuit <NUM>. Cable losses are accounted for as shown in block <NUM>. The frequencies are the same for both the signal strength separator output signals <NUM> and <NUM> but only those that meet the thresholds for the signal strength levels are output. In this example, the high side provides a signal too quiet below - 35dBm.

<FIG> illustrates an example of the controller <NUM> as well as a prior art controller <NUM>. As can be seen, the controller <NUM> includes an environmental sensing capability (ESC) signal processor <NUM> in addition to an analog to digital converter <NUM> that has an output <NUM> provided to a direct digital controller <NUM>. In this example, the ESC signal processor <NUM> operates at <NUM> so the <NUM> clock DDC output is fed into a FIFO <NUM> at <NUM> and read from the FIFO at <NUM>. However, the FIFOs need not be employed if the ESC signal processor can operate at <NUM> in this example. The output <NUM> from the ESC signal processor <NUM> is fed into a FIFO <NUM> to send data to the host <NUM> through a direct memory access block (DMA) <NUM>.

<FIG> illustrates one example of the ESC signal processor <NUM> which uses a sub-band range, in this example, <NUM> shown as <NUM> of the band of frequencies (in this example, <NUM>-<NUM>). In this example, additional FIFO buffers <NUM> and <NUM> are employed to buffer read data and write data. However, they need not be employed if desired. The ESC signal processor <NUM> includes an STFT processor <NUM>, a peak extraction processor <NUM> and a pulse edge detector <NUM>. Arrows <NUM> and <NUM> represent an embodiment where no FIFOs are used.

In some implementations, input data is sampled at 100MSps or higher (higher in the case of the x310 where the fractional decimation filter doesn't have enough alias rejection) on <NUM> input channels.

Referring to <FIG>, <FIG> and <FIG>, in some examples, the IQ data cannot directly be used to detect thresholds because the noise power over the <NUM> bandwidth may provide an inadequate signal to noise ratio (SNR). Fast Fourier transforms (FFTs) <NUM> and <NUM> are employed to increase the SNR for any frequency bin which may contain a radar frequency range in the case where radar frequencies are frequencies of interest. The P0N type radars want a frequency bandwidth of approximately <NUM> so a <NUM> point FFT is used which at a sample rate of 112MSps gives a frequency resolution of <NUM> with a time resolution of <NUM>. As the shortest radar is 500ns this leads to an SNR degradation of worst case -<NUM>. 6dB with no degradation under most of the radar test conditions. The following criteria was taken into account for a radar example, however, any suitable frequency range or suitable wireless transmitter can be employed:.

The next stage <NUM> interleaves the two time offsets of each of the <NUM> FFT streams (<NUM> channels x <NUM> FFT lengths). The order of interleaving must be carefully done to prevent a single pulse at a certain frequency in time to look like <NUM> pulses. The delayed stream data counterintuitively comes first. This is because the delay works by inserting zeros at the front of the data stream so the first FFT it is calculating the spectrum of only the first half of the time data so that means that in time this FFT comes first.

The goal of the peak finder process is to make a streaming peak finder which outputs the integer log value and the frequency bin index for the top <NUM> local maxima for each FFT data stream and sums up the linear values and count for all the remaining elements of the FFT.

To accomplish this goal the program uses a struct with the following elements (FFT start index U8, FFT stop index U8, Max index U8, Max value FXP s16. <NUM>, count U8, linear sum FXP s64. The FFT start index represents the first element index of a given peak which occurs at the first rising element or first element of the FFT. The FFT stop index represents the last element that is part of the same peak (i.e. the last element before the next first rising element). The Max index and Max value represent the value from the input of the largest value between the start and stop indices. The count is a tally of the number of elements that are part of the peak and the linear sum is the sum of all the linear input values between the start and stop indices.

The program keeps <NUM> of these structs in memory. The first is the current_state struct. The other three structs represent the struct for the top <NUM> peaks (Peak1, Peak2, Peak3). In addition there is a fifth struct which has (count U8, linear sum FXP s64. <NUM>) called the noise_floor which is used to accrue the linear sum and count for any peak which is supplanted from the top <NUM> peak structs.

The program works by checking each new element to see if it is the first element of the FFT or the first rising element (i.e. a high value following a low value following a higher value). In either of these conditions the program assumes that the previous state struct is closed and it updates the <NUM> peak value structs and the noise_floor struct (this process is explained in the next paragraph). If it is the first element of the FFT then the <NUM> peak structs are output from this block as is the noise_floor struct. In the clock cycle after this the noise_floor struct and all <NUM> peak value structs are cleared.

The decision to update the <NUM> peak values and noise_floor structs are performed by first comparing the max value of the <NUM> peak value structs and then determining the struct with the smallest value. The peak value struct with the smallest value's Max value is then compared to the current_state struct's Max value. If the current value is smaller, the peak value structs are left alone and instead the count and linear sum elements of the current_state structs are added to the noise floor struct. If the current value is larger, then the count and linear sum from the peak value are taken and added to the count and linear sum of the noise_floor struct and afterwards that particular peak value struct is replaced by the current_value struct.

For the linear sum steps a signed <NUM> bit with <NUM> integer bits is used to accrue the linear sum values. When samples are output from this block they are output as two structs. The first struct takes the Max index cast as a U8 type and Max value cast as a U8 type for each of the top <NUM> peaks. The noise_floor struct is output without changing the data type.

<FIG> shows the flow for the noise floor calculation. The noise flor calculation effectively sums all of the values that are not in the top three.

Referring to <FIG>, <FIG> and <FIG>, the feature extractor <NUM> takes the form of a processor that performs an amplitude tracking algorithm which is operated on all <NUM> data streams. The results of all of these algorithms are reported to the final FIFO for sending to the host over a common DMA. The final packet includes a <NUM> bit timestamp representing the <NUM> bit FFT count (as an FFT is performed every ~142ns, this counter will roll over every <NUM> minutes so the host should be designed to handle this case), an <NUM> bit header representing the bit flags (to inform the host what algorithm, FFT length, channel and whether the event is the start of a pulse or end of a pulse), an <NUM> bit value representing the noise value at the last FFT of the peak, the peak index and the amplitude.

The goal of the amplitude algorithm is to identify rising and falling edges of peaks which exceed a threshold while making sure the events are not caused by an elevated noise floor. This algorithm works by using an array of Boolean 'state_array' equal to the length of the FFT on whose data it is operating with its values initialized to False.

When a new struct of peaks and noise floor is fed into the algorithm a check is made for each of the three peak values that the peak is above the threshold while the noise floor is below the threshold value. This Boolean condition is then stored in the 'state_array' at the index of the peak.

The algorithm keeps a copy of the 'state_array' from the previous iteration, along with the peak values and indices and the noise floor. We shall refer to them with the suffix '_old' here and the current values with the suffix 'new'. The algorithm keeps an array of length of the FFT and stores the maximum values and the corresponding noise floor values. These values are reset to -<NUM> on reaching a falling edge.

At each iteration a check is made at the <NUM> current peak indices and the <NUM> peak indices from the previous iterations on both 'state_array_new' and 'state_array_old'. If 'state_array _old' at a given index is True while 'state_array_new' shows a False, that indicates a falling edge of a peak and in this case the max value, index and noise floor from the stored array is output along with header and timestamp. If 'state_array_old' at a given index is False while 'state_array_new' shows a True, that indicates the rising edge of a peak and in this case the peak value, index and noise floor from the current iteration is output along with the header and timestamp. If 'state_array_old' at a given index is True while 'state_array_new' shows a True that indicates we are still in the middle of a peak and in this case we do not output anything. If 'state_array_old' at a given index is False while 'state_array_new' shows a False we do not output anything.

The header is defined by <NUM> conditions. Edge (Rising = <NUM>, Falling = <NUM>), Channel (Channel <NUM> = <NUM>, Channel <NUM> = <NUM>), FFT length (<NUM> point = <NUM>, <NUM> point = <NUM>), Algorithm type (Amplitude algorithm = <NUM>, Peak tracking algorithm = <NUM>). The header is stored in a U8. Bits <NUM>. <NUM> represent the edge. Bits <NUM>. <NUM> represent Channel. Bits <NUM>. <NUM> represent FFT length and Bits <NUM>. <NUM> represent Algorithm type.

Referring to <FIG>, the method for assessing radio frequency spectrum use as performed by the environmental frequency sensing device is shown. In this example, the RF signal strength separator <NUM>, the controller <NUM> and the host unit <NUM> perform operations as described. However, any suitable structure may be employed. As shown in block <NUM>, the method includes receiving, from one or more sources, such as base station <NUM>, an RF signal having a band of frequencies. Receiving may be carried out, for example, by the RF signal strength separator <NUM> as receiving the signals from the left and right antennas. As shown in block <NUM>, the method includes performing signal strength level separation on the received band of frequencies and producing signal strength level separated frequencies information <NUM>-<NUM>. This is done, for example, by the RF signal strength separator <NUM> having received RF signals in the <NUM>-<NUM> range. As shown in block <NUM>, the method includes, performing frequency grouping on the SS level separated frequencies that are associated with each signal strength level to produce magnitude information for each grouping. This is performed, for example, by the controller <NUM>. In one example, short duration pulses and long duration pulses are detected from the separated signal strength signals in the signal strength level separated frequencies information <NUM>-<NUM> output by the RF SS <NUM>. The pulse filtering in this example is performed by the STFT processor <NUM>.

As shown in block <NUM>, the method includes generating peak data shown as <NUM> by detecting peaks of the detected magnitude information. For example, peaks that are beyond a threshold for each pulse type, such as a detected short pulse or detected long pulse has its peak detected by the peak extraction processor <NUM> as described above. As shown in block <NUM>, the method includes generating an edge event indicating a signal edge based on arrival or departure of a given peak. This is performed by the pulse edge detector <NUM>. The process is carried out by the ESC signal processor <NUM> and is performed in real time and hence the system described performs a real time spectral analysis.

As shown in block <NUM>, the method includes comparing, on a frequency basis, the generated edges to stored fingerprint data of a signal of interest. This is done by the host unit <NUM> in this example. As noted above the fingerprint information can include any suitable criteria to determine whether a frequency or range of frequencies has been detected by the detectors.

As shown in block <NUM>, if a match exists, the detected signal data <NUM> is provided for the SAS server, however it will be recognized that the SAS server can perform operations of the sensor such as determining if a match occurs, as well as any other suitable operations. The detected signal data <NUM> indicates a current use of a range of frequencies by an incumbent device, base station, system or any other source of the wireless RF spectrum that has been detected. This is shown in block <NUM>. The process repeats for each <NUM> sub-band within a band of received signals until no other sub-bands are left. Referring back to block <NUM>, if no match is detected, the process proceeds to perform signal strength level separation on received frequencies to continue the process.

<FIG> is a block diagram of the SAS server <NUM> which in this example, includes one or more processors <NUM>, memory <NUM> that can serve as one or more databases, memory to store executable instructions that when executed by the one or more processors <NUM>, causes the one or more processors to carry out the operations described herein. A network interface <NUM> is also in communication with the processors to allow the processor to communicate with the environmental RF sensors and any other suitable network element.

Referring to <FIG>, an example of a method carried out by the SAS server <NUM> is shown. The method includes evaluating detected signal data from a plurality of antennas from each of multiple environmental radio frequency (RF) sensors, the detected signal data representing one or more RF frequencies that are in use as detected by each of the plurality of antennas from each respective environmental radio frequency (RF) sensor. This is shown in block <NUM>. As shown in block <NUM>, the method includes determining a defined protection region corresponding to a source device transmitting the RF frequencies detected to be in use using the multiple environmental radio frequency (RF) sensors. This may include, for example, employing location information from each of the antennas from the environmental RF sensors which may include GPS location information. For example, frequency use detection is performed at the environmental RF sensors in this example as described above. A cloud based decision engine (e.g., one or more programmed processors) in the SAS server <NUM> determines, for example, frequency use in a particular geographic area of interest by the source device. As shown in block <NUM> the method includes preventing user equipment located in the defined protection region from using the RF frequencies detected. The geographic area is then protected by not allowing use of the detected frequencies in a that geographic area. In this example, being protected includes not assigning use of the frequency to other devices because it has been determined that a government device or other incumbent device is already using the frequency or frequency range of interest. In one example, the detected signal data received from the environmental RF sensors includes signal data from each of a right and left antenna. The decision engine in the SAS sever identifies that the detected signal data is coming from multiple environmental RF sensors that are geographically adjacent to each other. In other examples the system detects that frequencies are not in use and causing commercial devices or other devices to be assigned use of the undetected frequencies.

<FIG> illustrates another system <NUM> that does not employ a spectrum analysis access server <NUM> but instead employs a device, such as a server, that is a spectrum decision unit <NUM>. In this example, frequency reassignment need not be employed. Instead, the spectrum decision unit <NUM> determines whether frequencies of interest are being produced by the source device <NUM>. The spectrum decision unit <NUM> may operate as previously described to determine the location of the source device unit <NUM> within a geographic area and the geographic area is protected as previously described. The spectrum decision unit <NUM> need not be a server, such as a web server, but may be any suitable device that is in communication with the environmental RF sensors <NUM>-<NUM>. As noted above, the source device <NUM> may be a mobile device or non-mobile device depending upon the particular system design.

In another example, the SAS server or spectrum decision unit compares the data representing the one or more frequencies detected via each of the multiple antennas from each of the multiple environmental radio frequency sensors to be in use, to each other, to determine whether at least one of the environmental radio frequency sensors provided data containing error. For example, if three sensors are employed, if one of the sensors detects use of a frequency but the two others do not and the location of the other two sensors is known, the SAS server can infer that the detection by the one sensor should not be given high weight or should be given no weight at all since the other sensors should have detected similar frequency use.

Referring to <FIG>, the RF source device <NUM> would potentially be detected by right-A, left-B and right-B antennas of environmental RF sensors <NUM> and <NUM>. The cloud decision engine determines that the detected signal is coming from between "A" and "B" and from there, the east-<NUM> geographic area is designated as protected. This results in not allowing any new devices to be granted access to the frequency spectrum provided by the source device <NUM> in this area. If the source device <NUM> is further south in the diagram, RF sensor <NUM> would indicate that both left-B and right-B antennas were detecting the signal and such a detection would result in the cloud decision engine protecting both east-<NUM> and east-<NUM> geographic areas. As such, when a source device location is known but no RF frequency use is detected and the frequencies have been previously assigned to government devices but the devices are not using the spectrum, other commercial devices, for example, are assigned to those frequencies that are not determined to be in use.

Claim 1:
An environmental frequency sensing device (<NUM>, <NUM>, <NUM>) comprising:
a plurality of co-located directional antennas (<NUM>, <NUM>); and
logic operative coupled to the plurality of co-located directional antennas (<NUM>, <NUM>), the logic configured to:
for each co-located directional antenna of the plurality of co-located directional antennas (<NUM>, <NUM>):
perform (<NUM>) signal strength (SS) level separation on a received band of radio frequency (RF) frequencies to produce SS level separated frequencies;
perform (<NUM>) frequency grouping on the SS level separated frequencies for each signal strength level to produce magnitude information for each grouping;
generate (<NUM>) peak data by detecting peaks of the produced magnitude information;
generate (<NUM>) an edge event indicating a signal edge based on arrival or departure of a given peak;
compare (<NUM>), on a frequency basis, generated edge events to stored fingerprint data of a signal of interest; and
based on the comparison, provide (<NUM>) detected signal data indicating current use of a range of frequencies in an environment.