HARNESSING PREDICTIVE MODELS OF DURATIONS OF CHANNEL AVAILABILITY FOR ENHANCED OPPORTUNISTIC ALLOCATION OF RADIO SPECTRUM

• US 20090003201A1
• Filed: 06/29/2007
• Published: 01/01/2009
• Est. Priority Date: 06/29/2007
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Horvitz, Eric J.
• Granted Patent: US 8,254,393 B2
• Time in Patent Office: Days
• US Class Current: 370/229
• CPC Class Codes: H04L 41/0896 Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities flow or congestion control using dynamic resource allocation, e.g. in-call renegotiation, H04L47/76
• CPC Class Codes: H04L 41/147 for predicting network behaviour

Abstract

A proactive adaptive radio methodology for the opportunistic allocation of radio spectrum is described. The methods can be used to allocate radio spectrum resources by employing machine learning to learn models, via accruing data over time, that have the ability to predict the context-sensitive durations of the availability of channels. The predictive models are combined with decision-theoretic cost-benefit analyses to minimize disruptions of service or quality that can be associated with reactive allocation policies. Rather than reacting to losses of channel, the proactive policies seek switches in advance of the loss of a channel. Beyond determining durations of availability for one or more frequency bands statistical machine learning also be employed to generate price predictions in order to facilitate a sale or rental of the available frequencies, and these predictions can be employed in the switching analyses The methods can be employed in non-cooperating distributed models of allocation, in centralized allocation approaches, and in hybrid spectrum allocation scenarios.

Claims

1. A system that optimizes usage of resources in a communication network comprising:

a bandwidth sensing component that learns from monitoring communication channels over time and identifies one or more communication channels to use in an opportunistic manner based at least upon a prediction of duration of availability of the bandwidth; and

a bandwidth allocation component that adaptively determines one or more of time allocations or price predictions for the one or more identified communication channels.

2. The system of claim 1, the bandwidth sensing component utilizes statistical machine learning to infer one or more of availability of the one or more identified channels and a duration of availability of the one or more free channels.

3. The system of claim 2, bandwidth sensing component predicts the duration of availability in a cellular telephone network, by estimating time until at least one of an arrival of a new call or a call handed off from another cell which would occupy the one or more identified channels.

4. The system of claim 2, the bandwidth sensing component further comprises a statistical classifier in order to sense the availability and duration of the one or more identified channels.

5. The system of claim 4, the classifier is explicitly trained based at least on historical data wherein the availability and duration of availability of the one or more identified channels are considered.

6. The system of claim 3, the classifier is implicitly trained based at least on current inferences regarding the availability and duration of availability of the one or more identified channels.

7. The system of claim 1, the bandwidth allocation component employs at least frequency demand to facilitate determination of the allocations.

8. The system of claim 1, the bandwidth allocation component further predicts the prices utilizing, at least in part, frequency demand.

9. The system of claim 8, the bandwidth allocation component further predicts the prices based on a geographic location that generates the frequency demand.

10. The system of claim 8, the bandwidth allocation component further comprises a classifier to at least one of allocate the one or more identified channels or generate the price predictions.

11. The system of claim 10, the classifier is explicitly trained based at least on historical data wherein the allocations and the price predictions of the one or more identified channels were correctly inferred.

12. The system of claim 10, the classifier is implicitly trained based at least on current inferences regarding the allocations and the price predictions of the one or more identified channels.

13. The system of claim 1, the bandwidth allocation component allocates the one or more identified channels in a cellular communication network based at least upon co-channel interference.

14. The system of claim 1, the bandwidth allocation component further comprises a preference model that guides decisions regarding proactive switching of channels versus an imposed reactive switch.

15. The system of claim 14, the preference model maximizes duration of usage of a first channel by monitoring time of first channel usage and switching to a second channel pre-selected from the one or more communication channels based on a decision-theoretic, considering the likelihoods, and the costs and benefits of different switching times.

16. The system of claim 14, the preference model proactively switches channels prior to an imposed reactive switch upon rising probability of being forced into a reactive switch.

17. A method to optimize the usage of communication resources within a network comprising:

monitoring frequency bands in a network;

identifying one or more frequency bands to utilize for communication based at least upon detected bandwidth usage and prediction for duration of availability; and

adaptively determining one or more of allocations or price predictions for the one or more unused frequency bands.

18. A system for optimizing usage of communication network resources comprising:

means for monitoring frequency bands in a network;

means for identifying one or more channels based at least upon detected bandwidth usage and prediction for duration of availability of the one or more channels;

means for adaptively determining at least one of allocations or price predictions for the one or more free channels.

19. The system of claim 18, the means for identifying identifies the one or more free channels by employing automated decision making harnessing statistical models of channel availability.

20. The system of claim 19, the means for allocation employs one of an automatic decision making in order to determine at least one of allocations or price predictions.