Predictive spectral allocation in mobile networks

• US 9,204,448 B2
• Filed: 06/24/2013
• Issued: 12/01/2015
• 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:
  US14/123,199
• Publication Number:
  US 20140293955A1
• Time in Patent Office: 890 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 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.

3. 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.

4. 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.

5. The method according to claim 4, wherein employing the machine learning technique comprises using one or more of a neural network, a support vector machine (SVM), and a Bayes classifier.

6. 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.

7. 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.