PREDICTIVE SPECTRAL ALLOCATION IN MOBILE NETWORKS

• US 20140293955A1
• Filed: 06/24/2013
• Published: 10/02/2014
• Est. Priority Date: 03/26/2013
• Status: Active Grant

• Current Assignee: Empire Technology Development LLC (Allied Inventors Management, LLC)
• Original Assignee: Empire Technology Development LLC (Allied Inventors Management, LLC)
• Inventors: Keerthi, Arvind Vijay
• Granted Patent: US 9,204,448 B2
• Time in Patent Office: Days
• US Class Current: 370/330
• 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 employ predictive spectral allocation in wireless networks, the method comprising:

receiving a request for sub-carrier allocation from a mobile device, the request including a timestamp and a location of the mobile device;

identifying a particular cluster based on the timestamp and the location;

selecting a time-frequency vector from the particular cluster;

transmitting information pertaining to the sub-carrier allocation to the mobile device, wherein the information is based on the time-frequency vector; and

in response to receiving an indication from the mobile device that the sub-carrier allocation is unacceptable, switching to a default sub-carrier allocation and forwarding a bad quality indication associated with the unacceptable sub-carrier allocation to an analysis server.

2. The method according to claim 1, wherein transmitting the information comprises transmitting a sequence of frequencies to be used in consecutive time slots.

5. The method according to claim 1, wherein identifying the particular cluster based on the timestamp and the location comprises:

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

grouping the time slots by device location to form analysis intervals;

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; and

identifying one or more clusters of time-frequency vectors for each analysis interval and associated location as good clusters and bad clusters.

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

employing a machine learning technique to identify clusters of time-frequency vectors with substantially similar “

goodness”

indicator value.

7. The method according to claim 6, wherein employing the machine learning technique comprises using one or more of a neural network, a support vector machine (SVM), and a Bayes classifier.

8. The method according to claim 1, wherein receiving the request comprises receiving the request over a wireless communication technology that utilizes a multiple access technology.

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

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

3-4. -4. (canceled)

10-20. -20. (canceled)

21. 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 one or more of a time of day, a day of week, a day of month, a season, the device location, and/or an expected population change within a geographic area;

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.

22. The method according to claim 21, wherein the categorizing the clusters into two or more categories comprises labeling the clusters as good clusters or bad clusters.

23. The method according to claim 21, further comprising:

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.

25. The method according to claim 21, wherein computing the goodness indicator comprises:

computing the goodness indicator based on one or more of;

a channel quality indicator from the mobile device, 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/or a user requested power-up.

27. The method according to claim 21, 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/or a selected frequency of communication.

28. The method according to claim 27, further comprising:

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

29. The method according to claim 27, wherein receiving the information from a plurality of mobile devices comprises receiving the information over a wireless communication technology that utilizes a multiple-access technology.

24. (canceled)

26. (canceled)

30-31. -31. (canceled)

32. 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;

a processor coupled to the memory, the processor configured to execute the instructions to perform or cause to be performed;

evaluate information on device location, time slots, at least one sub-carrier allotted for each time slot, 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 one or more of a time of day, a day of week, a day of month, a season, and/or an expected population change within a geographic area;

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

compute a goodness indicator for each time-frequency vector;

identify clusters of time-frequency vectors; and

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

38. The analysis server according to claim 32, wherein the processor is further configured to execute the instructions to perform or cause to be performed:

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/or a selected frequency of communication.

39. The analysis server according to claim 38, wherein the processor is further configured to perform or cause to be performed:

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

40. The analysis server according to claim 38, wherein the communication technology of the wireless network are one of frequency division multiple access (FDMA), orthogonal frequency division multiple access (OFDMA), or a narrow band allocation technology.

41. The analysis server according to claim 32, wherein the processor is further configured to perform or cause to be performed:

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

42. The analysis server according to claim 32, wherein the processor is further configured to perform or cause to be performed:

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

43. The analysis server according to claim 32, wherein the analysis server is part of a base station.

44. The analysis server according to claim 32, wherein the analysis server is configured to receive the information from the plurality of mobile devices via one or more base stations.

45. The analysis server according to claim 32, 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.

33-37. -37. (canceled)

46-47. -47. (canceled)