An electronic device with wireless communication capabilities, such as a desktop or notebook computer, tablet computer, or smart device, may wirelessly connect to a computer network via a wireless access point (WAP). In such examples, the electronic device may connect to the network via the WAP when it is within the transmission range of the WAP. In some examples, a plurality of WAPs may be placed around a site, such as an office or school, in order to provide wireless access to the network from many locations around the site.
The following detailed description references the drawings, wherein:
As noted above, a plurality of wireless access points (WAPs) may be placed around a site, such as an office or school. The respective locations of the WAPs may be chosen such that the WAPs provide wireless coverage to each desired location within the site. In some scenarios, certain locations within the site may lose wireless coverage if one of the WAPs fails or otherwise malfunctions. In some examples, if a WAP is malfunctioning, the respective coverage areas of other WAPs at the site may be increased to compensate for the malfunctioning WAP. In such examples, the coverage area of a WAP may be increased by increasing the transmit power of the WAP's radios, for example.
However, in some scenarios, the placement of WAPs around a site may be such that the coverage areas of the functioning WAPs cannot be increased to completely compensate for the malfunctioning WAP. In other examples, increasing the coverage area of the functioning WAPs to compensate for the malfunctioning WAP may cause at least one of the WAPs to provide wireless coverage to a portion of the site where wireless coverage is undesirable, such as a parking lot. Additionally, an administrator for the site, for example, may not be aware that, in a given arrangement, the WAPs would not be able to acceptably compensate for a particular WAP if it were to malfunction.
To address these issues, examples described herein may determine coverage predictions for a plurality of WAP objects representing WAPs in a site model, and determine whether the coverage predictions satisfy parameters of coverage monitors indicating the desired coverage for different regions of a site model including the WAP objects. Some examples may determine, from the coverage predictions and coverage monitors, whether a WAP object is a primary WAP object representing a WAP for which other WAPs may not be able to appropriately compensate if the WAP were to malfunction. Some examples described herein may generate a report identifying the primary WAP objects of the site model. In this manner, examples described herein may determine how tolerant a proposed or actual placement of WAPs would be to WAPs malfunctioning. In some examples, a user (e.g., a site administrator) may use this information to determine whether to change the proposed or actual WAP placement.
Additionally, some examples described herein may propose WAP configurations to use when a WAP is malfunctioning. Such WAP configurations may include, for example, the transmit power settings for each of the functioning WAPs. In such examples, the WAP configurations may include compensating WAP configurations that may be used to completely compensate for a malfunctioning WAP and substitute WAP configurations that may be used to partially compensate for a malfunctioning WAP. Additionally, in some examples, a user (e.g., an administrator) may configure a WAP manager to apply a proposed compensating or substitute WAP configuration if a corresponding WAP is determined to be malfunctioning. In this manner, examples described herein may allow a user to configure a WAP manager to at least partially compensate for a malfunctioning WAP.
Referring now to the drawings,
As used herein, a “processor” may be at least one of a central processing unit (CPU), a semiconductor-based microprocessor, a graphics processing unit (GPU), a field-programmable gate array (FPGA) configured to retrieve and execute instructions, other electronic circuitry suitable for the retrieval and execution instructions stored on a machine-readable storage medium, or a combination thereof. Processor 110 may fetch, decode, and execute instructions stored on storage medium 120 to implement the functionalities described below. In other examples, the functionalities of any of the instructions of storage medium 120 may be implemented in the form of electronic circuitry, in the form of executable instructions encoded on a machine-readable storage medium, or a combination thereof.
As used herein, a “machine-readable storage medium” may be any electronic, magnetic, optical, or other physical storage device to contain or store information such as executable instructions, data, and the like. For example, any machine-readable storage medium described herein may be any of Random Access Memory (RAM), flash memory, a storage drive (e.g., a hard disk), a Compact Disc Read Only Memory (CD-ROM), and the like, or a combination thereof. Further, any machine-readable storage medium described herein may be non-transitory. In the example of
In some examples, instructions 122 may store, in memory 115, a site model including a plurality of WAP objects. As used herein, a “site model” is a collection of information including a plurality of site characteristics defining a representation of an actual environment or an environment design. In some examples, site characteristics may include a definition of the outer boundaries of the environment of the site model. In some examples, site characteristics may also include boundaries of at least one inner portion of the environment. Site characteristics may also include, for example, at least one of the predicted signal attenuation for at least a portion of the environment, and the location and signal attenuating properties of at least one object in the environment. In some examples, site characteristics may further define a plurality of regions within the outer boundaries of the environment, and each of the regions may have at least one associated site characteristic. In some examples, the site model may be stored in memory 115.
Additionally, as used herein, a “wireless access point (WAP) object” of a site model is a collection of information representing a wireless access point. In some examples, a WAP object may specify characteristics of the WAP object, such as its current location within the site model, predicted signal transmission and reception capabilities, and at least one WAP setting, such as a channel and a transmit power for the WAP object. In some examples, a WAP object may represent an actual or designed WAP and the characteristics defined by the WAP object may represent corresponding characteristics of the WAP. As used herein, a “wireless access point (WAP)” is an electronic device comprising at least one radio for wirelessly sending and receiving communications to and from a remote electronic device to wirelessly connect the remote electronic device to a computer network. In some examples, the WAP objects of a site model may represent a proposed or actual placement of WAPs in an environment represented by the site model.
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In some examples, instructions 126 may generate a coverage prediction for the initial WAP configuration. As used herein, a “coverage prediction” for a WAP configuration for a plurality of WAP objects is a prediction of at least the coverage area that would be provided by a plurality of WAPs represented by the WAP objects, respectively, and having the WAP setting values specified by the WAP configuration for the WAP objects. In some examples, the coverage prediction for a WAP configuration for a plurality of WAP objects may include a predicted coverage area for each of the WAP objects included in the WAP configuration. The predicted coverage area for a WAP object may be a prediction of at least the coverage area that would be provided by a WAP represented by the WAP object and having the WAP setting values specified by the WAP configuration for the WAP object. In some examples, WAP setting values of the WAP objects may be used to generate the coverage prediction if values for those WAP settings are not specified in the WAP configuration. Additionally, as used herein, a “coverage area” represents an area within which a WAP may communicate wirelessly with another electronic device. In some examples, a coverage prediction for a WAP configuration may further include a prediction of the quality of service provided within the coverage area. Instructions 128 may determine whether a coverage prediction for a WAP configuration for the WAP objects satisfies each coverage parameter 143 of each coverage monitor 142.
As used herein, a “coverage parameter” for a coverage monitor is information identifying a wireless coverage characteristic to be met in the region associated with the coverage monitor. Example coverage parameters 143 include coverage type parameters, which specify a type of coverage to be provided in the associated region of the coverage monitor to satisfy the coverage parameter. Such coverage type parameters may include, for example, a full-coverage parameter, a no-coverage parameter, and a selective coverage parameter. Other example coverage parameters 143 include signal quality parameters specifying, for example, at least one of a minimum, maximum, or average signal level to be met in the associated region 141 of the coverage monitor 142.
As used herein, a “full-coverage parameter” is a coverage parameter that is satisfied by any coverage prediction having a predicted coverage area including the entire region associated with a coverage monitor including the full-coverage parameter. As used herein, a “no-coverage parameter” is a coverage parameter that is satisfied by any coverage prediction having a predicted coverage area excluding the entire region associated with a coverage monitor including the no-coverage parameter. As used herein, a “selective coverage parameter” is a coverage parameter that is satisfied by any coverage prediction. For example, the selective coverage parameter may be satisfied if a predicted coverage area includes some, all, or none of the region associated with the coverage monitor including the selective coverage parameter. In this manner, the selective coverage parameter may be associated with a region in which coverage may be provided, and in which partial or no coverage is also acceptable.
In some examples, instructions 128 may compare the coverage prediction to each coverage parameter 143 of each coverage monitor 142 for the site model to determine whether the coverage prediction satisfies each coverage parameter 143 of each coverage monitor 142. In such examples, instructions 128 may determine that a coverage prediction satisfies a full-coverage parameter of a coverage monitor if a predicted coverage area of the coverage prediction includes the entire region associated with the coverage monitor. In some examples, instructions 128 may determine that a coverage prediction satisfies a no-coverage parameter of a coverage monitor if a predicted coverage area of the coverage prediction excludes the entire region associated with the coverage monitor. Instructions 128 may determine that any coverage prediction satisfies a selective coverage parameter of a coverage monitor regardless of whether the coverage prediction includes any of the region associated with the coverage monitor.
Additionally, in some examples, instructions 128 may determine that a coverage prediction satisfies a minimum signal parameter if the coverage prediction indicates that the entire region associated with the coverage parameter is predicted to receive at least the minimum signal level (e.g. −70 dBm, −80 dBm, etc.) included in the minimum signal parameter. Instructions 128 may also determine that a coverage prediction satisfies a maximum signal parameter if the coverage prediction indicates that the entire region associated with the coverage parameter is predicted to receive at most the signal level included in the maximum signal parameter. Instructions 128 may determine that a coverage prediction satisfies an average signal parameter if the coverage prediction indicates that the average signal level predicted for the region associated with the coverage parameter is at least the signal level included in the average signal parameter.
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In some examples, instructions 126 may generate, with processor 110, a coverage prediction for each of the trial WAP configurations generated by instructions 124. In such examples, instructions 128 may determine, with processor 110, that a first one of the WAP objects is a primary WAP object if none of the coverage predictions, for the trial WAP configurations in which the first WAP object is disabled, satisfies each coverage parameter 143 of each coverage monitor 142. As used herein, a “primary WAP object” is a given WAP object of a plurality of WAP objects of a site model for which the at least one other WAP object of the plurality may not be able to compensate, while satisfying each coverage parameter of each coverage monitor of the site model, if the given WAP object is disabled.
In some examples, instructions 124 may generate the trial WAP configurations iteratively. For example, for a selected WAP object, instructions 124 may iteratively generate a plurality of trial WAP configurations in which the selected WAP object is disabled in each of the trial WAP configurations. In such examples, for each iteration, instructions 126 may generate a coverage prediction for the trial WAP configuration generated in that iteration and determine whether the coverage prediction satisfies each coverage parameter of each of the coverage monitors for the site model. In some examples, the iterations with the selected WAP object disabled may continue until either instructions 128 determine that a coverage prediction for one of the trial WAP configurations satisfies each coverage parameter of each coverage monitor, or a threshold number of iterations for the selected WAP object have been completed without determining that a coverage prediction for any of the generated trial WAP configurations satisfies each coverage parameter. In such examples, instructions 128 may determine that the selected WAP object is a primary WAP object if the threshold number of iterations is reached and none of the coverage predictions for the generated trial WAP configurations satisfies each coverage parameter of each coverage monitor for the site model. In some examples, the iterative process described above in relation to instructions 124, 126, and 128 may be performed for each WAP object as the selected WAP object. In such examples, instructions 128 may evaluate trial WAP configurations to determine, for each WAP object, whether the WAP object is a primary WAP object.
In some examples, instructions 124 may generate new WAP configurations by adjusting at least one WAP setting value of preceding WAP configurations. For example, instructions 124 may generate a new WAP configuration from a preceding WAP configuration by adjusting at least one setting value (e.g., a transmit power value) for at least one WAP object included in the preceding WAP configuration. In some examples, instructions 124 may begin generating WAP configurations from a preliminary or initial WAP configuration. For example, to generate a first trial WAP configuration, instructions 124 may generate a trial WAP configuration that is the same as the initial WAP configuration except that one of the WAP objects of the site model is disabled in the first trial WAP configuration. In such examples, instructions 124 may generate a second trial WAP configuration in which at least one of the WAP settings for at least one of the non-disabled WAP objects is adjusted relative to the first trial WAP configuration. Any non-adjusted WAP setting may be the same in the first and second trial WAP configurations.
In such examples, each WAP setting value adjustment between sequential WAP configurations may be an increase or decrease of the value within the allowed range for the value (e.g., between 0 and 22 dBm for transmit power). In some examples, the amount of the adjustment between sequential WAP configurations may be determined heuristically. For example, an adjusted value may be determined by randomly or pseudo-randomly selecting an increment or decrement value in a range that would keep the adjusted value within the allowed range for the setting. In some examples, the increment value may be scaled based on how close a coverage prediction for the preceding WAP configuration is to satisfying each coverage parameter of each coverage monitor. In other examples, the iterative process of generating trial WAP configurations and evaluating coverage predictions for the trial WAP configurations may be implemented using a hill-climbing optimization technique (e.g., utilizing various processes described above), a genetic algorithm technique, or another heuristic-based iterative technique.
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Examples described herein may determine that a WAP object of a plurality of WAP objects is a primary WAP object if none of the coverage predictions for the trial WAP configurations in which the WAP object is disabled satisfies each coverage parameter of each coverage monitor. In this manner, examples described herein may inform an administrator or other user that a set of WAPs represented by the WAP objects of a site model may not be able to compensate for the WAP represented by the primary WAP object if the WAP malfunctions. In some examples, this information may be used to determine whether to use or change a proposed or actual WAP placement represented by the WAP objects of the site model.
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In some examples, instructions 128 may determine whether the generated coverage prediction 251A satisfies each coverage parameter of each of the coverage monitors 240. In the example illustrated in
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In some examples, at this iteration, instructions 128 may determine whether the generated coverage prediction 251B satisfies each coverage parameter of each of coverage monitors 240. In the example illustrated in
As used herein, a “compensating WAP configuration” for a plurality of WAP objects of a site model is a WAP configuration having a coverage prediction that satisfies each coverage parameter of each coverage monitor for the site model with one of the WAP objects is disabled. For example, if a site model includes a plurality of WAP objects, a WAP configuration excluding one of the WAP objects and having a coverage prediction that satisfies each of the coverage parameters of each of the coverage monitors may be considered a compensating WAP configuration. In such examples, the compensating WAP configuration may be considered to compensate for the excluded WAP object, which may be excluded to simulate the malfunctioning of a corresponding WAP. In other examples, a WAP configuration in which the transmit power of one of the WAP objects is set to zero and which has a coverage prediction that satisfies each of the coverage parameters of each of the coverage monitors may also be considered a compensating WAP configuration. In such examples, setting the transmit power to zero may simulate the malfunctioning of a corresponding WAP.
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In some examples, at this iteration, instructions 128 may also determine whether the generated coverage prediction 251C satisfies each coverage parameter of each of coverage monitors 240. In the example illustrated in
In some examples, if the threshold number of iterations with WAP object 252 disabled has been reached without determining that one of the trial WAP configurations is a compensating WAP configuration (i.e., satisfies each coverage parameter of each coverage monitor 240), then instructions 128 may select one of the trial WAP configurations as a substitute WAP configuration. As used herein, a “substitute WAP configuration” for a plurality of WAP objects of a site model is a WAP configuration in which one of the WAP objects is disabled and wherein a coverage prediction for the WAP configuration fails to satisfy at least one coverage parameter of at least one coverage monitor for the site model. For example, instructions 128 may select the trial WAP configuration having coverage prediction 251C as a substitute WAP configuration associated with WAP object 252 being disabled. In some examples, instructions 126 may select the substitute WAP configuration from among the trial WAP configurations based on at least one of a plurality of different criteria. For example, instructions 126 may select the trial WAP configuration that most nearly satisfies all of the coverage parameters.
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Instructions 122 may also store site model 201 in memory 115. In the example of
In some examples, coverage monitors 240 may comprise a plurality of coverage monitors for site model 201, including at least a coverage monitor 342 and a coverage monitor 346. Alternatively, coverage monitors 240 may include one coverage monitor, such as coverage monitor 342. In other examples, coverage monitors 240 may include more than two coverage monitors. In the example of
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Coverage monitor 346 may include region information 347 defining the region of site model 201 associated with coverage monitor 346, and at least one coverage parameter 348 for the associated region defined by region information 347. Coverage parameter 348 may include at least a coverage type parameter. In some examples, region information 347 may also include site characteristics, such as signal attenuation information for the region defined by region information 347. In the example of
Instructions 124 may generate a plurality of trial WAP configurations 360 for WAP objects 352 of site model 201, wherein one of WAP objects 352 is disabled in each of the trial WAP configurations, as described above in relation to
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In other examples, a particular WAP object may be disabled in a WAP configuration by excluding the WAP object from the WAP configuration. In some examples, instructions 124 may generate, for each WAP object 352, at least one trial WAP configuration in which the WAP object 352 is disabled. Although
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In some examples, instructions 126 may generate each coverage prediction based in part on at least one of site characteristics 305 specified by site model 201 and site characteristics specified by at least one of coverage monitors 240. For example, in making coverage predictions, instructions 126 may take into account at least one predicted signal attenuation specified in site characteristics 305, such as at least one of the predicted signal attenuation for a particular region of the site model and the predicted signal attenuation effects of objects in site model 201. Instructions 126 may also take into account at least one of site characteristics (e.g., predicted signal attenuation) specified by coverage monitors 240, and co-channel interference.
In some examples, instructions 128 may determine that one of trial WAP configurations 361 is a compensating WAP configuration if the coverage prediction for the trial WAP configuration 361 satisfies each coverage parameter of each of coverage monitors 240. In such examples, the trial WAP configuration 361 may be determined to be a compensating WAP configuration when the first WAP object 352 is disabled. Additionally, in some examples, instructions 128 may determine that one of trial WAP configurations 371 is a compensating WAP configuration if the coverage prediction for the trial WAP configuration 371 satisfies each coverage parameter of each of coverage monitors 240. In such examples, the trial WAP configuration 371 may be determined to be a compensating WAP configuration when the second WAP object 352 is disabled.
In some examples, instructions 124, 126, and 128 may implement the above-described functionalities in accordance with the iterative process described above in relation to
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Instructions 334 may receive a selection 382 of at least one of the compensating WAP configurations identified in the fault tolerance report. In some examples, instructions 334 may receive a plurality of selections 382, each identifying at least one of the identified compensating WAP configurations. In the example of
In such examples, configuring WAP manager 316 to apply a selected compensating WAP configuration may include associating the compensating WAP configuration with the WAP represented by the WAP object 352 disabled in the compensating WAP configuration. In such examples, instructions 336 may configure WAP manager 316 such that, in response to determining that the WAP associated with the compensating WAP configuration is malfunctioning, WAP manager 316 may apply the compensating WAP configuration to the WAPs. WAP manager 316 may apply the compensating WAP configuration by setting the WAP settings of the WAPs to the values specified in the compensating WAP configuration for the WAP objects representing the WAPs, respectively. In some examples, functionalities described herein in relation to
In some examples, instructions 122 may receive a site model 201 including a plurality of WAP objects 352 via network interface 318 and store the site model 201 on memory 415. Additionally, in some examples, instructions 122 may receive a plurality of coverage monitors 240 for site model 201 in a communication 381 via network interface 318. Instructions 122 may store the received coverage monitors 240 on memory 415. Coverage monitors 240 may be the same as coverage monitors 240 described above in relation to
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In some examples, instructions 124 may further generate a plurality of trial WAP configurations 460 for WAP objects 352, wherein each of trial WAP configurations 460 excludes one of WAP objects 352. In the example of
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In some examples, instructions 124 may generate, for each WAP object 352, at least one trial WAP configuration 460 in which the WAP object 352 is excluded. In such examples, instructions 126 may generate a respective coverage prediction for each of trial WAP configurations 460 and instructions 128 may compare each of the generated coverage predictions to each coverage parameter of each of coverage monitors 240. In such examples, instructions 128 may determine, for each WAP object 352, that the WAP object 352 is a primary WAP object if the respective coverage predictions for each trial WAP configuration 460 excluding the WAP object 352 fails to satisfy at least one coverage parameter of at least one of coverage monitors 240.
In some examples, instructions 124 may, for each WAP object 352, iteratively generate trial WAP configurations 460 excluding the WAP object 352, until either a coverage prediction for one of the generated trial WAP configurations 460 excluding the WAP object 352 satisfies each coverage parameter of each of coverage monitors 240 or a threshold number of trial WAP configurations 460 excluding the WAP object 352 is generated. Instructions 124 may iteratively generate these trial WAP configurations 460 with the iterative process described above in relation to
In some examples, to perform this iterative process, instructions 124, 126, and 128 may perform an iterative WAP configuration generating process using a plurality of variables, such as coverage monitor incompliance level (CMIL) and site coverage incompliance level (SCIL). As used herein, a “coverage monitor incompliance level (CMIL)” for a given coverage monitor and coverage prediction is the percentage of the region associated with the coverage monitor for which the coverage prediction fails to satisfy at least one coverage parameter of the coverage monitor. Additionally, as used herein, a “site coverage incompliance level (SCIL)” for a given site and coverage prediction is the average CMIL for all of the coverage monitors of the site for the given coverage prediction. In some examples, instructions 128 may determine that a coverage prediction satisfies each coverage parameter of each of coverage monitors 240 if the SCIL for the coverage prediction is zero.
In some examples, the iterative WAP configuration generating process may begin with a first WAP configuration. The first WAP configuration may be, for example, a preliminary WAP configuration for a plurality of WAP objects 352 or a trial WAP configuration generated by instructions 124 by removing one of WAP objects 352 from an initial WAP configuration, for WAP objects 352, having a coverage prediction satisfying each coverage parameter. In the iterative WAP configuration generating process, instructions 126 may determine a coverage prediction for the first WAP configuration and instructions 128 may determine an SCIL for the coverage prediction. If the SCIL is zero, then the process ends. Otherwise, instructions 124 may iteratively generate new WAP configurations from preceding WAP configurations. At each iteration, instructions 126 may generate a coverage prediction for the new WAP configuration and instructions 128 may determining the SCIL for the coverage prediction.
In such examples, a new WAP configuration is generated from a preceding WAP configuration by adjusting at least one WAP setting value of the preceding WAP configuration. New WAP configurations may be iteratively generated until instructions 128 determine that the SCIL is zero at the current iteration, or until a threshold number of iterations is reached. In some examples, the new WAP configuration is generated from a preceding WAP configuration, which is either a WAP configuration generated at a previous iteration, or the first WAP configuration if there was no previous iteration.
In some examples, the WAP setting values adjusted by instructions 124 may be the transmit power values for the WAP objects. In such examples, instructions 124 may choose an adjusted value for the transmit power for at least one of the included WAP objects 352 via a random or pseudo-random process. For example, to adjust a transmit power value of a preceding WAP configuration, instructions 124 may randomly or pseudo-randomly select an increment value in the range [−C, (M−C)], where “C” is the current transmit power value, and “M” is the maximum valid transmit power value (e.g., 22 dBm). In such examples, applying this increment value to the current value will not adjust the transmit power value outside of a valid range (e.g., 0 dBm-22 dBm).
Instructions 124 may also generate an adjusted increment value by multiplying the increment value by the current SCIL, where the SCIL is normalized to be in the range [0, 1]. In such examples, smaller increments may be applied when the SCIL is closer to zero. Instructions 124 may generate the adjusted transmit power value by adding the adjusted increment value to the current transmit power value. In other examples, other WAP setting values may be adjusted in addition to or as an alternative to transmit power values. Additionally, in other examples, the iterative process of generating trial WAP configurations and evaluating coverage predictions for the trial WAP configurations may be implemented using a hill-climbing optimization technique (e.g., utilizing various processes described above), a genetic algorithm technique, or another heuristic-based iterative technique. For example, a hill-climbing optimization technique may include an assessment procedure, a selection procedure, and a modification procedure. In such examples, utilizing a hill-climbing optimization technique may include determining an SCIL for a coverage prediction in the assessment procedure, selecting a preceding WAP configuration having a coverage prediction with the lowest SCIL among the preceding WAP configurations in the selection procedure, and generating a new WAP configuration from the selected WAP configuration (i.e., the “preceding” WAP configuration) in the modification procedure.
In some examples, trial WAP configurations 460 excluding a given WAP object 352 may be generated by performing the above-described iterative WAP configuration generating process starting from a first WAP configuration equivalent to the initial WAP configuration for WAP objects 352 with the given WAP object 352 excluded (or otherwise disabled). In such examples, the process ends when a coverage prediction for a trial WAP configuration 460 excluding the given WAP object 352 satisfies the coverage parameters (i.e., when the SCIL is zero) or when a threshold number of iterations is reached. In such examples, when a trial WAP configuration 460 having a coverage prediction that satisfies the coverage parameters is found, then instructions 128 may determine that the trial WAP configuration 460 is a compensating WAP configuration for the given WAP object 352. Alternatively, when the threshold number of iterations is reached, instructions 128 may determine the given WAP object 352 to be a primary WAP object, and may determine the generated trial WAP configuration having the lowest SCIL to be a substitute WAP configuration for the given WAP object 352.
In some examples, the above-described iterative WAP configuration generating process may be performed for each WAP object 352 as the disabled WAP object 352. In this manner, instructions 124 may iteratively generate, for each WAP object 352, trial WAP configurations 460 excluding the WAP object 352, until either a coverage prediction for one of the generated trial WAP configurations 460 excluding the WAP object 352 satisfies each coverage parameter of each of coverage monitors 240 or a threshold number of trial WAP configurations 460 excluding the WAP object 352 is generated.
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At 505 of method 500, computing device 300 may receive, with a network interface 318, a plurality of coverage monitors 240, each defining at least one coverage parameter for an associated region of a site model 201 including a plurality of WAP objects 352. At 510, processor 110 may store coverage monitors 240 in memory 115 of computing device 300.
At 515, computing device 300 may iteratively adjust WAP settings for a first set of WAP objects 352, wherein the first set excludes a first one of WAP objects 352, to generate a plurality of first trial WAP configurations (e.g., trial WAP configurations 461 of
At 520, computing device 300 may determine that one of the first trial WAP configurations is a first compensating WAP configuration if a coverage prediction for the one of the first trial WAP configurations satisfies each coverage parameter of each of coverage monitors 240. In some examples, the coverage prediction may be generated as described above in relation to instructions 126, and may be compared to the coverage parameters as described above in relation to instructions 128. At 525, computing device 300 may determine that the first WAP object 352 is a primary WAP object if respective coverage predictions for a threshold number of the first trial WAP configurations each fail to satisfy at least one coverage parameter of at least one of the coverage monitors 240.
At 605 of method 600, computing device 300 may receive, with a network interface 318, a respective location for each of a plurality of WAP objects 352 within a site model 201. In such examples, processor 110 may store the location for WAP objects 352 in memory 115 as part of WAP objects 352. At 610, computing device 300 may receive, with network interface 318, a plurality of coverage monitors 240, each defining at least one coverage parameter for an associated region of site model 201 including WAP objects 352. At 615, processor 110 may store coverage monitors 240 in memory 115 of computing device 300.
At 620, computing device 300 may iteratively adjust WAP settings for a first set of WAP objects 352, wherein the first set excludes a first one of WAP objects 352, to generate a plurality of first trial WAP configurations (e.g., trial WAP configurations 461 of
At 625, computing device 300 may determine whether a coverage prediction for any of the first trial WAP configurations satisfies each coverage parameter for each of coverage monitors 240. If so, then method 600 may proceed to 630, where computing device 300 may determine that the first trial WAP configuration having the coverage prediction satisfying the coverage parameters is a first compensating WAP configuration. Otherwise, if it is determined that respective coverage predictions for a threshold number of the first trial WAP configurations each fail to satisfy at least one coverage parameter of at least one of the coverage monitors 240, then method 600 may proceed to 635, where method 600 may determine that the first WAP object 352 is a primary WAP object. Method 600 may then proceed to 640, where computing device 300 may determine a substitute WAP configuration excluding the first WAP object 352. For example, the first trial WAP configuration having the lowest SCIL may be determined to be the substitute WAP configuration, as described above in relation to
Also at 625, computing device 300 may determine whether a coverage prediction for any of the second trial WAP configurations satisfies each coverage parameter for each of coverage monitors 240. If so, method 600 may proceed to 630 where computing device 300 may determine that one of the second trial WAP configurations is a second compensating WAP configuration. Otherwise, if it is determined that respective coverage predictions for a threshold number of the second trial WAP configurations each fail to satisfy at least one coverage parameter of at least one of the coverage monitors 240, then method 600 may proceed to 635, where method 600 may determine that the second WAP object 352 is a primary WAP object. Method 600 may then proceed to 640, where computing device 300 may determine a substitute WAP configuration excluding the second WAP object 352.
At 645, WAP manager 316 of computing device 300 may monitor each of a plurality of WAPs corresponding to WAP objects 352, respectively. At 650, WAP manager 316 may apply one of the first compensating WAP configuration and the substitute WAP configuration to the WAPs in response to determining that a first WAP, associated with the first WAP object 352, is malfunctioning. For example, if the first WAP object 352 was determined to be a primary WAP object, WAP manager 316 may apply the substitute WAP configuration if it determines that the first WAP is malfunctioning. Alternatively, if the first WAP object 352 was not determined to be a primary WAP object, WAP manager 316 may apply the first compensating WAP configuration if it determines that the first WAP is malfunctioning.
Filing Document | Filing Date | Country | Kind | 371c Date |
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PCT/US2012/035733 | 4/30/2012 | WO | 00 | 10/23/2014 |