Patent Publication Number: US-2023147824-A1

Title: Systems and methods for identifying a source of radio frequency interference in a wireless network

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This patent application is a continuation of U.S. patent application Ser. No. 16/896,323, filed on Jun. 9, 2020, and titled “INTERNET OF THINGS DEVICE CONNECTIVITY REAL TIME NOTIFICATION SYSTEMS AND METHODS FOR IDENTIFYING A SOURCE OF RADIO FREQUENCY INTERFERENCE IN A WIRELESS NETWORK,” the disclosure of which is hereby incorporated by reference in their entirety. 
    
    
     BACKGROUND 
     Wireless telecommunications networks may operate on portions of the radio frequency (RF) spectrum. In some situations, interference may be caused in such a way that is detrimental to the performance of a given wireless telecommunications network. For example, external interference may occur when a device external to the network site transmits a signal in a spectrum that overlaps the RF spectrum of the network. In some instances, interference events are irregular, affecting sites on a particular day of the week or specific business hours, which can make it difficult to identify the cause or source of the interference. Furthermore, the conventional process for identifying a source of interference requires significant human capital and specialized equipment. For example, even after field engineers manage to determine that an interference event is occurring or has occurred for a particular network site, the engineers must physically canvass the area proximate to the network site with a directional antenna to identify fluctuations of the interference levels until the source of the interference is identified. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an overview of an environment in which systems and methods consistent with embodiments are used; 
         FIG.  2    illustrates an example environment in which one or more embodiments, described herein, may be implemented; 
         FIG.  3    is a block diagram illustrating example components of a computer device  400  according to one embodiment; 
         FIG.  4    is a flow diagram illustrating an example process for estimating a location of an unknown interference source, consistent with implementations described herein; 
         FIG.  5    is a graph of exemplary uplink radio power for a wireless station on a per-physical resource block (PRB) basis; 
         FIG.  6    illustrates an exemplary main wireless station and a number of neighboring wireless stations; 
         FIG.  7    is a flow diagram illustrating one implementation of a process for identifying candidate interference source locations consistent with embodiments described herein; 
         FIG.  8    is a graphical depiction of an exemplary boundary selection for the example of  FIG.  6   ; 
         FIG.  9    illustrates an exemplary heat map based on the example of  FIG.  6   ; 
         FIG.  10    is a view of a portion, of the map of  FIG.  8   , illustrating sector boundaries for a wireless station; and 
         FIG.  11    is a flow diagram illustrating one implementation of a process for sector analysis vector generation consistent with embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The following detailed description refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. The following detailed description does not limit the invention, which is defined by the claims. 
     Telecommunications service providers may operate wireless networks (e.g., cellular or other types of wireless networks) at a given set of frequencies (or frequency bands) of the Radio Frequency (RF) spectrum. While these frequencies are often licensed (e.g., by a governmental agency and/or by some other authority) for exclusive use by one entity or operator, some bands may be shared by multiple different entities. For instance, a portion of the RF spectrum may be designated for “shared access,” or a portion of the RF spectrum that was previously licensed for access by one entity may be licensed for additional entities. In situations where the same portion of the RF spectrum is licensed for use by multiple entities, the use of the portion of the RF spectrum by one entity may negatively impact the use of that portion of the RF spectrum by other entities. 
     For example, and as shown in  FIG.  1   , an entity may cause excessive RF interference (referred to herein simply as “interference” or “noise”). In some cases, there may be multiple sources causing RF interference. For example, assume that a wireless network provider operates wireless stations  100 - 1  to  100 - 2  within a particular frequency band, and that wireless stations  100  service a user equipment device (UE)  105 . Further assume that another device  110  (also referred to as broadcast source  110 ), which is associated with another entity, also operates within the same frequency band, and emits an RF interference signal into that band because of intermodulation, excessive power, poor filter design, or for other reasons. Such third-party broadcast sources may negatively impact the operation of wireless station  100  (and/or of UE device  105  that communicate with wireless station  100 , such as mobile telephones, Internet of Things (“IoT”) devices, Machine-to-Machine (“M2M”) devices, etc.), by introducing RF interference or noise. Because the broadcast source is associated with an entity that is separate from the entity that owns and operates wireless stations  100 , it may be difficult to coordinate the operation of wireless station  100  to account for the unexpected and unpredictable interference caused by the broadcast source. 
     Consistent with implementations described herein, an interference source location determination tool may be provided to more quickly and accurately identify a likely location of an interference source. In particular, interference may be determined based on a particular frequency range within which it is occurring. Wireless stations  100  are configured to operate in accordance with various frequency bands and time slots, arranged in physical resource blocks (PRBs). A PRB denotes the most granular aspect of a wireless station&#39;s capabilities and includes both a frequency component and a time component. As described herein, interference may be experienced and analyzed on a per-PRB basis. 
     For example, as described herein, interference-indicating data, such as uplink power measurements data (i.e., uplink signal power) for particular PRBs may be received and stored by the wireless stations  100 . When a wireless station experiences external interference, interference-indicating data for the neighboring wireless stations are retrieved and analyzed to determine whether similar interference is perceived by any neighboring wireless stations. Once wireless stations that are not experiencing a similar external interference are filtered out, a heat map indicating a likely location(s) of the interference source may be generated. 
     For example, when an affected wireless station is  100  is identified (referred to herein as main wireless station  100 - 1 ), either autonomously by an interference detection system or via external (e.g., manual) reporting, other wireless stations  100  that are proximate (i.e., geographic neighbors) to the main wireless station  100 - 1  are examined for similar interference experiences on a particular PRB or PRBs affecting main wireless site  100 - 1 . 
     Consistent with embodiments described herein, the likely location(s) may be determined by calculating error, such as root mean square error (RMSE) by using Free Space Path Loss (FSPL) calculations based on a number of interference source location guesses. The process is iteratively repeated until minimum values of FSPL are determined. The heat map is generated based on the calculations for each of a number of guessed locations. The generated heat map is provided to field engineers to assist in expediting manual identification of the interference source. 
       FIG.  2    illustrates an example environment in which one or more embodiments, described herein may be implemented. As shown in  FIG.  2   , environment  200  may include radio access network (RAN)  205  that includes a plurality of wireless stations  100 - 1  to  100 - x  (collectively referred to as wireless stations  100  and individually referred to as wireless station  100 ), a wireless station database  210 , an interference detection system  215 , an interference reporting system  220 , and one or more networks  225 . The number of devices and/or networks, illustrated in  FIG.  2   , is provided for explanatory purposes. In practice, environment  200  may include additional, fewer, different, or a different arrangement of devices and/or networks than illustrated in  FIG.  2   . 
     For example, while not shown, environment  200  may include devices that facilitate or enable communication between various components shown in environment  200 , such as routers, modems, gateways, switches, hubs, etc. Alternatively, or additionally, one or more of the devices of environment  200  may perform one or more functions described as being performed by another one or more of the devices of environments  200 . Devices of environment  200  may interconnect with each other and/or other devices via wired connections, wireless connections, or a combination of wired and wireless connections. In some implementations, devices of environment  200  may be physically integrated in, and/or may be physically attached to other devices of environment  200 . 
     RAN  205  may include a wireless telecommunications network (e.g., a Long-Term Evolution (LTE) RAN, a Third Generation Partnership Project (3GPP) a Fifth Generation (5G) RAN, etc. As mentioned above, RAN  205  may include one or more wireless stations  100 , via which devices (e.g., user equipment (UE), such as mobile telephones, IoT devices, M2M devices, etc.) may communicate with one or more other elements of environment  200 . RAN  205  may communicate with such devices via an air interface. For instance, RAN  205  may receive traffic (e.g., voice call traffic, data traffic, messaging traffic, signaling traffic, etc.) from a UE via the air interface, and may forward the traffic to network  225 . Similarly, RAN  205  may receive traffic intended for a UE from network  225  and may forward the traffic to the UE via the air interface. RAN  205  may operate at a set of frequencies (e.g., a set of licensed spectra). In some embodiments, one or more of the bands, at which RAN  205  operates, may be shared with an entity other than the entity that owns and/or operates RAN  205 . 
     Wireless station database  210  may include one or more devices (e.g., a server device, or a collection of server devices) for storing wireless station-related information. For example, wireless station database  210  may receive, store, and/or output information relating to various wireless stations  100  in RAN  205 . Such information may include, among other data elements, identification information, geographic location information, and performance information relating to performance characteristics of each wireless station  100 . 
     Interference detection system  215  may include one or more devices (e.g., a server device, or a collection of server devices) to determine likely locations of interference sources. For example, interference detection system  215  may identify likely locations of interference sources detected in RAN  205 . For example, as briefly described above, interference detection system  215  may generate geographic heat maps that identify the likely locations of sources of interference based on data collected from wireless stations  100  within RAN  205 . Consistent with embodiments described herein, the heat map may be generated based on statistical minimization of free space loss calculations at various geographic locations proximate to affected wireless stations. Interference detection system  215  may further take administrative or corrective actions when detecting unique sources of interference, as described in greater detail below. 
     Interference reporting system  220  may include one or more devices (e.g., a server device, or a collection of server devices) to perform one or more functions described herein. For example, interference reporting system  220  may include messaging systems capable of generating and/or sending messages via network  225 . The messages may be emails, text messages, application-specific messages, and/or other types of messages related to alerts that a heat map of possible interference sources has been generated by interference detection system  215 . Consistent with implementations described herein, interference reporting system  220  may forward or otherwise notify network personnel (e.g., field engineers) about the identified interference and the generated heat map for use in ascertaining the source of the interference. Interference reporting system  220  may also maintain a history of interference determinations for use in determining patterns. 
     Network(s)  225  may include one or more wired and/or wireless networks. For example, network(s)  225  may include one or more core networks of a licensed wireless telecommunications system (e.g., an LTE core network, a 5G core network, etc.), an Internet Protocol (“IP”)-based PDN, a wide area network (“WAN”) such as the Internet, a private enterprise network, and/or one or more other networks. One or more of the devices or networks shown in  FIG.  2    may communicate, through network(s)  225 , with each other and/or with other devices that are not shown in  FIG.  2   . Network  225  may further include, or be connected to, one or more other networks, such as a public switched telephone network (“PSTN”), a public land mobile network (“PLMN”), and/or another network. 
       FIG.  3    is a block diagram illustrating example components of a computer device  300  according to one embodiment. Wireless stations  100 , wireless station database  210 , interference detecting system  215 , and interference reporting system  220  may include or may be included within one or more of computer device  300 . As shown in  FIG.  3   , computer device  300  may include a bus  310 , a processor  320 , a memory  330 , an input device  340 , an output device  350 , and a communication interface  360 . 
     Bus  310  includes a path that permits communication among the components of computer device  300 . Processor  320  may include any type of single-core processor, multi-core processor, microprocessor, latch-based processor, and/or processing logic (or families of processors, microprocessors, and/or processing logics) that executes instructions. In other embodiments, processor  320  may include an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and/or another type of integrated circuit or processing logic. 
     Memory  330  may include any type of dynamic storage device that may store information and/or instructions, for execution by processor  320 , and/or any type of non-volatile storage device that may store information for use by processor  320 . For example, memory  330  may include a random access memory (RAM) or another type of dynamic storage device, a read-only memory (ROM) device or another type of static storage device, a content addressable memory (CAM), a magnetic and/or optical recording memory device and its corresponding drive (e.g., a hard disk drive, optical drive, etc.), and/or a removable form of memory, such as a flash memory. 
     Input device  340  may allow an operator to input information into device  300 . Input device  340  may include, for example, a keyboard, a mouse, a pen, a microphone, a remote control, an audio capture device, an image and/or video capture device, a touch-screen display, and/or another type of input device. In some embodiments, device  300  may be managed remotely and may not include input device  340 . In other words, device  300  may be “headless” and may not include a keyboard, for example. 
     Output device  350  may output information to an operator of device  300 . Output device  350  may include a display, a printer, a speaker, and/or another type of output device. For example, output device  350  may include a display, which may include a liquid-crystal display (LCD) for displaying content to the customer. In some embodiments, device  300  may be managed remotely and may not include output device  350 . In other words, device  300  may be “headless” and may not include a display, for example. 
     Communication interface  360  may include a transceiver that enables device  300  to communicate with other devices and/or systems via wireless communications (e.g., radio frequency, infrared, and/or visual optics, etc.), wired communications (e.g., conductive wire, twisted pair cable, coaxial cable, transmission line, fiber optic cable, and/or waveguide, etc.), or a combination of wireless and wired communications. Communication interface  360  may include a transmitter that converts baseband signals to RF signals and/or a receiver that converts RF signals to baseband signals. Communication interface  360  may be coupled to one or more antennas/antenna arrays for transmitting and receiving RF signals. 
     Communication interface  360  may include a logical component that includes input and/or output ports and/or other input and output components that facilitate the transmission of data to other devices. For example, communication interface  360  may include a network interface card (e.g., Ethernet card) for wired communications and/or a wireless network interface (e.g., a WiFi) card for wireless communications. Communication interface  360  may also include a universal serial bus (USB) port for communications over a cable, a Bluetooth wireless interface, a radio-frequency identification (RFID) interface, a near-field communications (NFC) wireless interface, and/or any other type of interface that converts data from one form to another form. 
     Device  300  may perform various operations in response to processor  320  executing software instructions contained in a computer-readable medium, such as memory  330 . A computer-readable medium may be defined as a non-transitory memory device. A memory device may be implemented within a single physical memory device or spread across multiple physical memory devices. The software instructions may be read into memory  330  from another computer-readable medium or from another device. The software instructions contained in memory  330  may cause processor  320  to perform processes described herein. Alternatively, hardwired circuitry may be used in place of, or in combination with, software instructions to implement processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software. 
     Although  FIG.  3    shows exemplary components of device  300 , in other implementations, device  300  may include fewer components, different components, additional components, or differently arranged components than depicted in  FIG.  3   . Further, in some embodiments, one or more of the components described above may be implemented as virtual components, such as virtual processors, virtual memory, virtual interfaces, etc. Additionally, or alternatively, one or more components of device  300  may perform one or more tasks described as being performed by one or more other components of device  300 . 
       FIG.  4    illustrates an example process  400  for estimating a location of an unknown interference source, consistent with implementations described herein. In some embodiments, process  400  may be performed by interference detection system  215 . In some embodiments, process  400  may be performed by, or in conjunction with, one or more other devices or systems, such as wireless station database  210 , and/or interference reporting system  220 .  FIG.  4    is described in conjunction with  FIGS.  5 - 10   . Some of these figures include graphs or other graphical representations of data, which may be generated by interference detection system  215 . In some embodiments, the figures graphically illustrate calculations, aggregation, analysis, and/or other types of operations that may be performed by interference detection system  215 . 
     Process  400  may include identifying one or more wireless stations that are experiencing unexpected interference, particularly when compared to surrounding wireless stations (block  405 ). Consistent with embodiments described herein, interference may be determined based on a particular frequency range within which it is occurring. Wireless stations  100  are configured to operate in accordance with various frequency bands and time slots, arranged in physical resource blocks (PRBs). A PRB denotes the most granular aspect of a wireless station&#39;s capabilities and includes both a frequency component and a time component. For long term evolution (LTE) wireless stations (e.g., eNodeB&#39;s) or 5G New Radio (5G) wireless stations (e.g., gNodeB&#39;s), each wireless station  100  may have a set number of PRBs across its available frequency spectrum, each of which comprise approximately 180 KHz of bandwidth. Accordingly, for a wireless station  100  operating in a 10 MHz band, the wireless station will generally include 50 PRB s, each having a discrete frequency and time allocation. Thus, for a given sector (e.g., where “sector” refers to a particular geographic region, which may approximately or precisely correspond to the coverage area of a particular wireless station  100 , or a set of wireless stations  100 , of RAN  205 ) and over a given time window (e.g., one minute, one hour, one day, one week, etc.), the received (i.e., uplink) radio power, per PRB, may be measured or otherwise retrieved. 
     For instance,  FIG.  5    includes a graph that shows an example of uplink radio power, on a per-PRB basis, at a given sector and within a given time window. Each bar on the plot may indicate, in some embodiments, an average of the received uplink radio power measured over a time window. In some embodiments, the plot may indicate a different aspects of the received radio power (e.g., the maximum uplink radio power measured over the time window, the minimum uplink radio power measured over the time window, etc.). As shown, the uplink radio power measured at PRB  6  and PRB  10 , at the sector and over the time window, may be relatively high, as compared to the radio power at the other PRBs. The relatively high uplink radio power may indicate a likely interference event. 
     Consistent with implementations described herein, PRB uplink power measurements or other related measurements for wireless stations  100  may be aggregated or otherwise maintained in wireless station database  210  on a periodic basis, such as every minute, every 10 minutes, every hour, etc. For example, wireless stations  100  may be configured to report various elements of performance metrics (i.e., key performance indicators (KPIs)) on a periodic basis. The reported KPIs may include uplink power measurements for each PRB in the wireless station  100 . Interference detection system  215  may monitor the PRB uplink power measurements for each wireless station  100  and may determine instances of likely interference based thereon. For example, continued disrupted (i.e., reduced) PRB uplink power measurements over a period of time may be a strong indication of interference. In some embodiments, autonomous systems, such as artificial intelligence or machine learning systems may be implemented in interference detection system  215  to identify interference-experiencing wireless stations  100  based on the available historical data. In other implementations, interference detection system  215  may receive indications of interference experiencing wireless stations  100  via a manual reporting system. For example, wireless interference detection system may receive a wireless station identifier and date/time of the interference from an operator. 
     When an affected wireless station is  100  is identified (e.g., wireless station  100 - 1 ), either autonomously by interference detection system  215  or via external (e.g., manual) reporting, wireless stations  100  that are proximate (i.e., neighbors) to the identified wireless station  100 - 1  (also referred to as the “main wireless station”  100 - 1 ) are examined for similar interference experiences (block  410 ). For example, interference detection system  215  may identify neighboring wireless stations  100  within an initial distance from the main wireless site  100 - 1 , based on the geographic location of the main wireless site  100 - 1 , the PRB(s) that are experiencing the interference, and the timeframe(s) during which the PRB(s) experienced the interference. As described above, wireless station database  210  may include information regarding wireless stations in RAN  205 , such as location information (e.g., longitude and latitude information) and performance metrics (e.g., PRB KPIs). Using the collected information regarding wireless stations  100  in RAN  205 , interference detection system  215  may ascertain the identities and locations of neighboring wireless stations  100  that are experiencing similar interference during similar timeframes. 
       FIG.  6    illustrates the main wireless station  100 - 1  and a number of neighboring wireless stations  100 - 2 ,  100 - 3 ,  100 - 4 ,  100 - 5 , and  100 - 6 . Assume that main wireless station  100 - 1  has experienced interference from an unknown source during at least some point in time. Consistent with embodiments described herein, performance data for neighboring wireless stations  100 - 2  to  100 - 6  that exhibit a similar interference, may be obtained. 
     In some implementations, wireless stations  100 - 2  to  100 - 6 , which may experience interference may be determined in an expanding step-wise manner based on a location from main wireless station  100 - 1 . For example, interference on neighboring wireless stations  100  may be initially determined for neighboring wireless stations that are within distances of about 3-4 kilometers (km) from the initial or main wireless station  100 - 1 . For example, as shown in  FIG.  6   , wireless stations  100 - 2 ,  100 - 4 , and  100 - 5  are within the initial range. If none of the stations are in the initial range, the range may be expanded incrementally, until a maximum range is reached. For example, the range may be expanded in 2 km increments until at least one other neighbor is determined or a maximum of 10 km from the main wireless station  100 - 1  is reached, though other smaller or larger increments are contemplated herein. Neighbors at the shortest distance are more likely to experience the same interference as the main site and also offer data for enabling better accuracy when generating a heat map. 
     Referring back to  FIG.  4   , after identifying neighboring wireless stations  100 , wireless stations  100  that are experiencing similar interference effects are determined (block  415 ). As described above, external interference typically affects a small number of PRBs at a wireless station  100 . To filter out wireless stations that are not experiencing the same interference, the PRB interference-related KPI data (e.g., uplink signal level values) for the candidate wireless stations  100  for the same time period as the main wireless station has detected interference, are retrieved and compared to the corresponding interference-related KPI data on the affected PRBs. For example, using uplink signal levels as an interference-related KPI, values in a −115 dB to −120 dB range generally indicate a low interference signal. In contrast, a high interference signal is usually indicated my uplink signal level ranging from approximately −75 dB to −105 dB. It should be noted that these ranges may be different, depending on the environment and traffic each wireless station is handling. 
     By way of example, assume main wireless station  100 - 1  has identified an uplink signal level of −90 dB on PRB  30  and an uplink signal level of −85 dB on PRB  20 , as indications of possible interference at PRBs  20  and  30 . When identifying relevant neighbors, wireless stations having normal (e.g., −115 dB to −120 dB) uplink signals for PRBs  20  or  30  are excluded or filtered out, even if those wireless stations exhibit higher signals level on different PRBs. To focus the analysis on particular interference signals, data that may indicate other possible interference signals or factors are excluded. For the following discussion, assume that wireless stations  100 - 2 ,  100 - 4 , and  100 - 6  are identified as experiencing interference on the same PRBs during the same timeframe as main wireless station  100 - 1 . 
     After identifying neighboring wireless stations  100  as sites that may have experienced similar interference as main wireless station  100 - 1 , an analysis of the PRB data for those wireless stations is performed to identify likely locations for the source of the interference (block  420 ). For example, to determine candidate interference source locations, path loss calculations, such as free space path loss (FSPL) calculations may be performed for each of a plurality of location approximations based on the distance between the wireless station and the selected location approximation, the RF frequency of the PRB under investigation, and the estimated or expected uplink signal value at the wireless station. Minimization calculations may be performed to increase the accuracy of the obtained coordinates. For example, an indication of the accuracy of the selected location approximation may be calculated for each of the wireless stations experiencing interference based on the FSPL calculations and the actual observed uplink power signal level, and the interference source location approximation may be iteratively adjusted until further adjustment does not result in an increased level of accuracy. Although FSPL is provided as an exemplary path loss calculation methodology, it should be understood that additional methods of path loss determination may also be used, consistent with implementations described herein. 
       FIG.  7    is a flow diagram illustrating one implementation of a process  700  for determining candidate interference source locations consistent with embodiments described herein. Process  700  may be performed by interference detection system  215 . However, in some embodiments, process  700  may be performed by, or in conjunction with, one or more other devices or systems, such as wireless station database  210 , and/or interference reporting system  220 . 
     Process  700  may include identifying the neighboring wireless station  100  whose PRB data shows the highest interference (block  705 ). For clarity, the identified wireless station  100  may be referred to as the “strongest correlating station.” For example, using the information retrieved from wireless station database  205 , interference detection system  215  may compare the uplink power levels for the particular timeframe under investigation for each of the wireless stations identified in block  410 . As the result of the comparison, interference detection system  215  may conclude that wireless station  100 - 1  is the strongest correlating station. The strongest correlating station may not be the main wireless station, since various factors may go into an initial identification of an interference condition, for example the identification may be made manually in response to customer complaints, effects on other network equipment, diagnostics, etc. 
     Next, the geographic boundary for the likely interference sources is determined based on all possible combinations of the strongest correlating station with all other interference-affected neighbors (block  710 ), where each combination may correspond to a portion of the boundary. For example, if block  410  above identified three interference-affected wireless stations ( 100 - 1  (referred to as A),  100 - 2  (referred to as B),  100 - 4  (referred to as C), and  100 - 6  (referred to as D)), with the strongest correlating station being wireless station A, the remaining combinations would include wireless station A-B, A-C, A-D, A-B-C, A-B-D, A-C-D, and A-B-C-D.  FIG.  8    graphically depicts an example of such a boundary selection over the map of  FIG.  6   . As shown, wireless stations  100 - 1 ,  100 - 2 ,  100 - 4 , and  100 - 6  form the vertices along the outer boundary  800  within which the interference source is likely to be found. 
     Next, an initial interference source location within the geographic boundary is selected (block  715 ). An exemplary location is depicted in  FIG.  8    at location  810 - 1 , within boundary  800 . In some implementations, an initial location may be set equal to the location of the strongest correlating station, although any other location with boundary  600  may be selected. 
     Using the selected location, expected interference-related KPI values for each wireless station on each combination from the initially selected location are determined (block  720 ). For example, expected uplink signal level values may be calculated using free space path loss as expressed by equations (1) and (2) below: 
         d= 10 (20log     10     (frequency)−SignalLevel−27.55)/20 ,   (1)
 
     where d is the distance between wireless station and the selected location (in km), frequency refers to the RF frequency (in megahertz) of the PRB under investigation, SignalLevel refers to the estimated or expected uplink signal value at the wireless station (in decibels), and 27.55 is a constant relating to the spherical wave front of the RF signal and the units selected for the computation (e.g., km and MHz) in this example. The distances between the selected location and the respective wireless stations A-D are depicted as d A  to d D  in  FIG.  8   . Solving equation (1) for SignalLevel results in: 
       SignalLevel=20 log 10 (frequency)+20 log 10 ( d )−27.55   (2)
 
     Once expected values for uplink signal levels have been calculated for each wireless station  100 , these values are compared to the observed or actual values to determine the accuracy of the selected location (block  725 ). In one implementation, the comparison may include calculating root mean squared error for each interference experiencing wireless station. The root mean squared error may be expressed as: 
       RMSE=√{square root over ([Σ i≤1   n (Expected i −Actual i ) 2 )}/ n ],   (3)
 
     where n is the number of interference-experiencing wireless stations, expected is the uplink signal level calculated in equation (2), and actual is the observed uplink signal level at the time of the interference, whose value was retrieved from wireless station database  210  at block  410  above. A lower value for RMSE indicates that the expected value is closer to the actual value over the range of data. Although RMSE is described as an accuracy determining methodology herein, other statistical calculations for error may be used, such as mean square error (MSE), mean absolute scaled error (MASE), mean absolute percentage error (MAPE), symmetric MAPE (SMAPE), etc. 
     A minimization process is performed for RMSE (block  730 ). For example, interference detection system  215  may iteratively select additional estimated locations and calculate expected and RMSE values for each location, until a minimum RMSE is obtained. Process  700  may result in determining a number of locations and their corresponding RMSE values. 
     Returning to  FIG.  4   , scores are generated for each of the identified locations (block  425 ). For example, interference detection system  215  determines a score for use in generating the heat map of possible interference source locations briefly described above. In one exemplary implementation, the scores may be based on the RMSE values as well as a statistical constant reflecting the number or count of wireless stations that are possibly experiencing the interference. For example, each score may be weighted 70% based on the number of wireless stations being analyzed and 30% based on the RMSE for the particular location. 
     Next, a heat map is generated based on the identified locations and their relative scores (block  430 ). For example, interference detection system  215  generates a map that indicates the identified locations and provides graphical indications of the probabilities that the interference source is proximate to the identified locations. 
       FIG.  9    is an example of a heat map  900  generated using the process of block  420 . As shown, heat map  900  includes a geographical map of the affected locations and identifies the specific locations  810 - 1  through  810 - x.  In addition, heat map  900  includes graphical indicia  905  based on the relative scores and aggregate proximity for each location that indicates a relative probability that the interference source would be found in a particular area. In some embodiments, as shown in  FIG.  9   , graphical indicia  905  may be provided as an overlay of varying color or opacity to indicate higher and lower probability. 
     The generated heat map may be used to ascertain the actual location of the interference source and to initiate remediation (block  435 ). For example, interference detection system  215  may provide or forward the heat map to interference reporting system  220  for delivery to relevant field personnel or other entities associated with the service provider of RAN  205 . 
     In the embodiments described above, sector azimuth (i.e., angle of orientation of the antenna) and beam width are not taken into consideration in identifying a possible interference source. This may be the case for wireless stations that broadcast omnidirectional signals having a beam width of 360 degrees. However, in some circumstances, particular sectors of wireless stations may transmit signals in different, selected directions. To account for the antenna directions, the FSPL determined at block  420  may be adjusted by identifying boundaries (e.g., polygons) for each one of the sectors for each wireless station. The FSPL calculation may then be adjusted based on whether a guessed location falls within the boundary for the particular sector. If the guessed location is within the boundary, no adjustments are necessary. However, if the point is not within the boundary, an adjustment is made to the FSPL calculation for the particular location. For example, a +3 dB adjustment may be made to reflect that the particular wireless station is not detecting the interference directly within its transmission beam and detects a lower level of interference than the one calculated without the adjustment. 
       FIG.  10    illustrates a portion of  FIG.  8    in which wireless station  100 - 1  cover sectors  1000 - 1  to  1000 - 3 . Sector  1000 - 2  is under investigation for the PRBs discussed above and is shaded in gray. In this example, several of locations  810  are not within the boundaries of sector  1000 - 2 . Consistent with embodiments described herein, the FSPL calculations for these guessed locations may be adjusted by +3 dB for each wireless station, for which the location falls outside of the sector boundary. In this way, lower interference values for locations outside of a particular sector do not unnecessarily impact the RMSE minimization process. The adjustments may result in more accurate location determination. 
     In some instances, various portions of the heat map may have similar intensities (based on the scores generated in block  425  above), rendering it difficult to identify a particular field search starting point without field expertise or any additional information. Consistent with embodiments described herein, a collocated sector analysis may be performed to estimate a direction in which the interference source is more likely to be located on a per-wireless station basis. 
       FIG.  11    illustrates an example process  1100  for determining a starting search location based on a collocated sector analysis. In some embodiments, process  1100  may be performed by interference detection system  215 . In some embodiments, process  1100  may be performed by, or in conjunction with, one or more other devices or systems, such as wireless station database  210 , and/or interference reporting system  220 .  FIG.  11    is described in conjunction with  FIG.  12   . 
     In addition to, or in lieu of the heat map described above (e.g., heat map  900 ), a cluster map may be generated (block  1105 ) that clusters to possible interference source locations based on a predetermined cluster distance (e.g., 800 meters). Next, it is determined whether multiple clusters have been identified (block  1110 ). For example, based on K-means clustering, various clusters and related cluster centroids may be generated. If optimized clustering results in a single cluster being identified (block  1110 —NO), field searching may be targeted based on the centroid of the identified cluster (block  1115 ). However, if multiple clusters are identified (block  1110 —YES), an initial sector analysis vector may be generated for each wireless station  100  that is experiencing similar interference effects (block  1120 ). 
     As described above in relation to  FIG.  10   , each wireless station  100  that is experiencing similar interference effects (as identified in block  415  above) may have more than one sector pointing in different directions (azimuth) and with different beam widths. Taking this into account, it can be assumed that, like the embodiment of  FIG.  10   , any sector that is seeing the interference source directly (i.e., within its azimuth and beam width) will most likely show a higher interference power level. Accordingly, for a site that has more than one sector affected by interference, a vector can be initially determined in the direction of the most affected sector&#39;s azimuth (e.g., that sector showing the highest interference power level; also referred to as the “main” sector). In the example, of  FIG.  10   , this vector  1050  (shown as a dashed line) would be directed along the azimuth angle of sector  1000 - 2 , which may be referred to as the main sector. 
     Next, consistent with implementations described herein, the angle of the initial sector analysis vectors may be steered (i.e., adjusted) based on the interference power levels on the collocated sectors (block  1125 ). For example, consider wireless station wireless station  100 - 1  having sectors  1000 - 1  to  1000 - 3 , as shown in  FIG.  10   . As discussed above, each sector  1000 - 1  to  1000 - 3  has a particular interference power reading for a particular PRB that is experiencing interference effects. A resultant vector  1055  may be generated based on the azimuth, beam width, and interference power level in the main sector (the one with the highest reading) as well as each of the other sectors. In one implementation, the angle adjustment from the main sector azimuth angle is based on a ratio of the main sector interference power level to each of the remaining sector power levels, which may be referred to as the intensity ratio for each of the remaining sectors. 
     Using the sector example of  FIG.  10   , assume that for PRB  40  under analysis, sector  1000 - 2  is the main sector and has an azimuth of 0° and a beam width of 120° and an interference power level of −83 dB, sector  1000 - 3  has an interference power level of −102 dB, and sector  1000 - 1  has an interference power level of −90 dB. Using this information, an intensity ratio of 1.23 is calculated for sector  1000 - 3  (−102/083) and an intensity ratio of 1.08 is calculated for sector  1000 - 1  (−90/−83). The angle of adjustment (denoted as θ in  FIG.  10   ) may be calculated using the difference between the intensity ratios for sectors  1000 - 3  and  1000 - 1 , which is 0.15 in this case (1.23−1.08). This difference in ratios is then multiplied with beam width of main sector  1000 - 2  (120°), resulting in an angle adjustment of 18° (120×0.15) toward sector  1000 - 1 , as represented by adjusted vector  1050  in  FIG.  10   . Note that if sector  1000 - 3  had a higher intensity ratio than sector  1000 - 1 , the difference would be negative 0.15, which would result in a −18° angle adjustment toward sector  1000 - 3 . 
     Once the sector analysis vectors have been adjusted for all wireless stations experiencing interference effects, the vectors may be applied (e.g., overlaid) on the heat map and/or cluster map to help target a likely interference source from among a number of candidate locations or clusters (block  1130 ). 
     The foregoing description of implementations provides illustration and description but is not intended to be exhaustive or to limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention. For example, while series of blocks and signal messages have been described with respect to  FIGS.  5  and  7   , the order of the blocks and signal messages may be varied in other implementations. Moreover, non-dependent blocks may be performed in parallel. 
     Certain features described above may be implemented as “logic” or a “unit” that performs one or more functions. This logic or unit may include hardware, such as one or more processors, microprocessors, application specific integrated circuits, or field programmable gate arrays, software, or a combination of hardware and software. 
     No element, act, or instruction used in the description of the present application should be construed as critical or essential to the invention unless explicitly described as such. Also, as used herein, the article “a” is intended to include one or more items. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. 
     To the extent the aforementioned embodiments collect, store, or employ personal information of individuals, it should be understood that such information shall be collected, stored, and used in accordance with all applicable laws concerning protection of personal information. Additionally, the collection, storage, and use of such information can be subject to consent of the individual to such activity, for example, through well known “opt-in” or “opt-out” processes as can be appropriate for the situation and type of information. Storage and use of personal information can be in an appropriately secure manner reflective of the type of information, for example, through various encryption and anonymization techniques for particularly sensitive information. 
     In the preceding specification, various preferred embodiments have been described with reference to the accompanying drawings. It will, however, be evident that various modifications and changes may be made thereto, and additional embodiments may be implemented, without departing from the broader scope of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative rather than restrictive sense.