Predictive spectral allocation in mobile networks

• US 9,572,165 B2
• Filed: 08/22/2015
• Issued: 02/14/2017
• Est. Priority Date: 03/26/2013
• Status: Expired due to Fees

• Current Assignee: Empire Technology Development LLC (Allied Inventors Management, LLC)
• Original Assignee: Empire Technology Development LLC (Allied Inventors Management, LLC)
• Inventors: Keerthi, Arvind Vijay
• Primary Examiner(s): Jung, Min
• Application Number:
  US14/833,082
• Publication Number:
  US 20150365932A1
• Time in Patent Office: 542 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: H04B 17/373 Predicting channel quality or other radio frequency [RF] parameters
• CPC Class Codes: H04L 5/0007 the frequencies being orthogonal, e.g. OFDM(A), DMT
• CPC Class Codes: H04L 5/006 Quality of the received signal, e.g. BER, SNR, water filling
• CPC Class Codes: H04W 72/044 based on the type of the allocated resource
• CPC Class Codes: H04W 72/0446 Resources in time domain, e.g. slots or frames
• CPC Class Codes: H04W 72/0453 Resources in frequency domain, e.g. a carrier in FDMA
• CPC Class Codes: H04W 72/23 in the downlink direction of a wireless link, i.e. towards a terminal
• CPC Class Codes: H04W 72/51 based on terminal or device properties

Abstract

Technologies are generally described for discerning patterns in the “goodness” or “badness” of time-frequency slots to allow predictive allocation of spectral resources that may be appropriate for a wireless user. According to some examples, information on device location, time slots, sub-carrier(s) allotted for each time slot, and quality indicators may be received from mobile devices. The time slots may be grouped by location to form analysis intervals. A time-frequency vector may then be identified for each analysis interval and a unit of geographic grid. A “goodness” indicator may be computed for each time-frequency vector. Clusters of time-frequency vectors may be categorized for each analysis interval and associated unit of geographic grid such that mobile devices can be assigned “good” clusters through sub-carrier allocation.

Claims

1. A method to analyze sub-carrier allocation data to categorize clusters for predictive spectral allocation in a wireless network, the method comprising:

receiving information on device location, time slots, at least one sub-carrier allotted for each time slot, and quality indicators from a plurality of mobile devices;

grouping the time slots to form analysis intervals;

dynamically adjusting a length of the analysis intervals based on an expected population change within a geographic area that encompasses the plurality of mobile devices;

identifying a time-frequency vector for each analysis interval, wherein the time-frequency vector associates the analysis interval with the device location;

computing a goodness indicator for each time-frequency vector;

identifying clusters of time-frequency vectors; and

categorizing the clusters of time-frequency vectors into two or more categories.

2. The method according to claim 1, wherein categorizing the clusters into two or more categories comprises labeling the clusters as good clusters or bad clusters.

3. The method according to claim 1, further comprising:

employing a machine learning technique to identify the clusters of time-frequency vectors with substantially similar values of goodness indicators.

4. The method according to claim 1, wherein computing the goodness indicator comprises:

computing the goodness indicator based on one or more of;

channel quality indicators from the plurality of the mobile devices, a received signal strength indicator (RSSI), a bit error rate (BER), a number of packet retransmissions, a signal to noise ratio (S/N), a number of call failures, a user feedback, a user requested power-down at a same bit-rate, and a user requested power-up.

5. The method according to claim 1, wherein the device location is based on a plurality of units of a geographic grid, and the method further comprises:

dynamically adjusting a size of each unit of the geographic grid based on one or more of whether the device location is in a densely populated area, whether the device location is in a sparsely populated area, a communication technology of the wireless network, and a selected frequency of communication.

6. The method according to claim 5, further comprising:

further adjusting the size of each unit of geographic grid based on one or more of a time of day, a day of week, a day of month, a season, and an expected population change within a geographic area.

7. The method according to claim 5, wherein receiving the information from the plurality of mobile devices comprises receiving the information over a wireless communication technology that utilizes a multiple-access technology.

8. An analysis server to analyze sub-carrier allocation data to categorize clusters for predictive spectral allocation in a wireless network, the analysis server comprising:

a memory configured to store instructions; and

a processor coupled to the memory, the processor configured to execute a communication application in conjunction with the stored instructions, the communication application comprising;

an analysis module configured to;

evaluate information on device location, time slots, at least one sub-carrier allotted for each of the time slots, and quality indicators received from a plurality of mobile devices;

group the time slots by location to form analysis intervals;

dynamically adjust a length of the analysis intervals based on an expected population change within a geographic area that encompasses the plurality of mobile devices;

identify a time-frequency vector for each analysis interval, wherein the time-frequency vector associates the analysis interval with the device location; and

compute a goodness indicator for each time-frequency vector; and

a support vector machine (SVM) module configured to;

identify clusters of time-frequency vectors; and

categorize clusters of time-frequency vectors into two or more categories.

9. The analysis server according to claim 8, wherein the analysis module is further configured to:

dynamically adjust a size of each unit of a geographic grid based on one or more of whether the device location is in a densely populated area, whether the device location is in a sparsely populated area, a communication technology of the wireless network, and a selected frequency of communication.

10. The analysis server according to claim 9, wherein the analysis module is further configured to:

further adjust the size of each unit of the geographic grid based on one or more of a time of day, a day of week, a day of month, a season, and an expected population change within a geographic area.

11. The analysis server according to claim 8, wherein a communication technology of the wireless network is one of frequency division multiple access (FDMA), orthogonal frequency division multiple access (OFDMA), or a narrow band allocation technology.

12. The analysis server according to claim 8, wherein the analysis module is further configured to:

determine the time-frequency vector for one of a single sub-carrier frequency or a group of sub-carrier frequencies.

13. The analysis server according to claim 8, wherein the analysis module is further configured to:

compute the goodness indicator based on a type of communication associated with a user.

14. The analysis server according to claim 8, wherein the analysis server is part of a base station.

15. The analysis server according to claim 8, wherein the analysis module is configured to receive the information from the plurality of mobile devices via one or more base stations.

16. The analysis server according to claim 8, wherein the wireless network is one of an Evolved Universal Mobile Telecommunications System Terrestrial Radio Access Network (eUTRAN), a long term evolution (LTE) network, an LTE-Advanced network, a high speed packet access (HSPA) network, or an HSPA-Advanced network.

17. A computer readable memory device with instructions stored thereon to analyze sub-carrier allocation data to categorize clusters for predictive spectral allocation in a wireless network, the instructions being executable by a computing device to perform or cause to be performed:

identifying information on device location, time slots, at least one sub-carrier allotted for each time slot, and quality indicators from a plurality of mobile devices;

grouping the time slots to form analysis intervals;

dynamically adjusting a length of the analysis intervals based on an expected population change within a geographic area that encompasses the plurality of mobile devices;

identifying a time-frequency vector for each analysis interval, wherein the time-frequency vector associates the analysis interval with the device location;

computing a goodness indicator for each time-frequency vector;

identifying clusters of time-frequency vectors based on the computed goodness indicator for each time-frequency vector; and

categorizing the clusters of time-frequency vectors into two or more categories.

18. The computer readable memory device of claim 17, wherein the instructions are further executable by the computing device to perform or cause to be performed:

employing a machine learning technique to identify the clusters of time-frequency vectors with substantially similar values of goodness indicators, wherein the machine learning technique includes one or more of a neural network, a support vector machine (SVM), and a Bayes Classifier.

19. The computer readable memory device of claim 17, wherein

dynamically adjusting the length of the analysis intervals is further based on one or more of a time of day, a day of week, a day of month, a season, and a location of a mobile device.

20. The computer readable memory device of claim 17, wherein the instructions are further executable by the computing device to perform or cause to be performed:

determining the time-frequency vector for one of a single sub-carrier frequency or a group of sub-carrier frequencies.

21. The computer readable memory device of claim 17, wherein the instructions are further executable by the computing device to perform or cause to be performed:

computing the goodness indicator based on a type of communication associated with a user.