Methods and systems are described for optimizing a predictive model for mobile network communications based on historical context information. In one aspect, historical context information is collected including at least one of communication environment, communication parameter estimates, mobile device statistics, mobile device transmit settings, base station receiver settings, past network statistics and settings, and adjacent network node information statistics and settings, the historical context information including data from communications of at least one mobile device. A predictive model for network communications is determined based on the historical context information. A communication context for a first mobile device different than the at least one mobile device is determined. The first device is scheduled and/or network parameters are set based on the determined predictive model. Actual to expected results are compared and the predictive model is adjusted based on the comparison.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for optimizing a predictive model for mobile network communications based on historical context information, the method comprising: collecting historical context information including at least one of communication environment, communication parameter estimates, mobile device statistics, mobile device transmit settings, base station receiver settings, past network statistics and settings, and adjacent network node information statistics and settings, the historical context information including data from communications of at least one mobile device; determining a predictive model for network communications based on the historical context information; determining a communication context for a first mobile device different than the at least one mobile device; at least one of scheduling the first mobile device and setting network parameters based on the determined predictive model; comparing actual to expected results of the at least one of scheduling the first mobile device and setting network parameters based on the determined predictive model; and adjusting the predictive model based on the comparison of actual to expected results. 2. The method of claim 1 wherein the communication environment comprises factors including weather factors, time of day, day of week, season, geography, and obstructions in a communication path. 3. The method of claim 1 wherein the communication parameter estimates comprises at least one of: delay spread, channel taps, channel power, received signal strength, number of channel taps, channel coherence time, and channel coherence bandwidth. 4. The method of claim 1 wherein the mobile device statistics include at least one of signal to noise ratio, signal to interference plus noise ratio, interference plus noise ratio, bit error rate, channel bit error rate, packet error rate, block error rate, location information, mobile device velocity, mobile device acceleration, number of antennas, traffic types, and buffer size. 5. The method of claim 1 wherein the mobile device transmit settings comprise at least one of transmit power, bandwidth, modulation, coding, spreading length, spreading code, scheduling information, frequency, power control, beam patterns, and precoding matrices. 6. The method of claim 1 wherein the base station receiver settings comprise at least one of receiver algorithm selection and receiver parameter settings. 7. The method of claim 1 wherein the collected historical communication context includes a sequence of historical communication contexts. 8. The method of claim 1 comprising collecting the historical communication context via at least one of a drive test, from a test mobile device, from one or more active mobile devices, from a simulation, from a test base station, from one or more active base stations, and through central processing via a cloud radio access network, via coordinated multi-point, or via high throughput, low latency links between cells. 9. The method of claim 1 comprising collecting historical communication context for at least one of intra-cell and inter-cell mobile devices. 10. The method of claim 1 wherein a type of historical context information collected is based on a communication throughput between a base station serving the first mobile device and at least one other base station. 11. The method of claim 1 wherein determining a predictive model includes determining a predictive model based on detected patterns in the collected historical communication contexts. 12. The method of claim 11, wherein the detected patterns indicate a trend and determining a predictive model includes at least one of scheduling the first mobile device and setting network parameters into the future in accordance with the indicated trend. 13. The method of claim 1 wherein determining a predictive model for network communications based on the historical context information includes determining a different predictive model for each cell in a cellular communication system. 14. The method of claim 1 wherein determining the predictive model includes determining the predictive model to at least one of maximize capacity of an associated communication network, minimize latency in the network, provide fairness as between users of the network, maximize a number of users of the network, maximize a number of users of the network with a minimum rate constraint, minimize power consumption of a user device, minimize power consumption of a network device, minimize an error rate, and determine an optimal error rate. 15. The method of claim 1 wherein at least one of scheduling the first mobile device and setting network parameters based on the determined predictive model includes performing real time network operations (RTNO) including setting at least one of scheduled time, scheduled frequency, modulation, coding, spreading code, number of layers, precoding matrix, transmit power level, closed loop power adjustments, antenna selection, and random backoff timer adjustments. 16. The method of claim 15 wherein the RTNO are related to at least one of uplink and downlink communications with the first mobile device. 17. The method of claim 1 wherein adjusting the predictive model includes adjusting the predictive model substantially in real time. 18. The method of claim 1 wherein the network communications include network communications via at least one of a cellular network, a Wi-Fi network, a WiMAX network, a satellite communications network, and an ad-hoc network. 19. The method of claim 1 comprising at least one of imposing persistence and imposing controlled evolution of settings within a network including the first mobile device. 20. A system for optimizing a predictive model for mobile network communications based on historical context information, the system comprising: means for collecting historical context information including at least one of communication environment, communication parameter estimates, mobile device statistics, mobile device transmit settings, base station receiver settings, past network statistics and settings, and adjacent network node information statistics and settings, the historical context information including data from communications of at least one mobile device; means for determining a predictive model for network communications based on the historical context information; means for determining a communication context for a first mobile device different than the at least one mobile device; means for at least one of scheduling the first mobile device and setting network parameters based on the determined predictive model; means for comparing actual to expected results of the at least one of scheduling the first mobile device and setting network parameters based on the determined predictive model; and means for adjusting the predictive model based on the comparison of actual to expected results. 21. A system for optimizing a predictive model for mobile network communications based on historical context information, the system comprising system components including: a context monitor component configured to determine a communication context for a first mobile device and to collect historical context information including at least one of communication environment, communication parameter estimates, mobile device statistics, mobile device transmit settings, base station receiver settings, past network statistics and settings, and adjacent network node information statistics and settings, the historical context information including data from communications of at least one other mobile device; a predictive model component configured to determine a predictive model for network communications based on the historical context information; a communication parameter component configured to at least one of schedule the first mobile device and set network parameters based on the determined predictive model; a validation component configured to compare actual to expected results of the at least one of schedule the first mobile device and set network parameters based on the determined predictive model; and a network learning component configured to adjust the predictive model based on the comparison of actual to expected results. 22. The system of claim 21 wherein the context monitor component is configured to collect the communication environment factors that include at least one of weather factors, time of day, day of week, season, geography, and obstructions in a communication path. 23. The system of claim 21 wherein the context monitor component is configured to collect communication parameter estimates that include at least one of: delay spread, channel taps, channel power, received signal strength, number of channel taps, channel coherence time, and channel coherence bandwidth. 24. The system of claim 21 wherein the context monitor component is configured to collect mobile device statistics that include at least one of signal to noise ratio, signal to interference plus noise ratio, interference plus noise ratio, bit error rate, channel bit error rate, packet error rate, block error rate, location information, mobile device velocity, mobile device acceleration, number of antennas, traffic types, and buffer size. 25. The system of claim 21 wherein the context monitor component is configured to collect mobile device transmit settings that include at least one of transmit power, bandwidth, modulation, coding, spreading length, spreading code, scheduling information, frequency, power control, beam patterns, and precoding matrices. 26. The system of claim 21 wherein the context monitor component is configured to collect base station receiver settings that include at least one of receiver algorithm selection and receiver parameter settings. 27. The system of claim 21 wherein the context monitor component is configured to collect historical communication context as a sequence of historical communication contexts. 28. The system of claim 21 wherein the context monitor component is configured to collect historical communication context via at least one of a drive test, from a test mobile device, from one or more active mobile devices, from a simulation, from a test base station, from one or more active base stations, and through central processing via a cloud radio access network, via coordinated multi-point, or via high throughput, low latency links between cells. 29. The system of claim 21 wherein the context monitor component is configured to collect historical communication context for at least one of intra-cell and inter-cell mobile devices. 30. The system of claim 21 wherein the context monitor component is configured to determine a type of historical context information collected based on a communication throughput between a base station serving the first mobile device and at least one other base station. 31. The system of claim 21 wherein the predictive model component is configured to determine a predictive model based on detected patterns in the collected historical communication contexts. 32. The system of claim 31, wherein the detected patterns indicate a trend and the predictive model includes at least one of scheduling the first mobile device and setting network parameters into the future in accordance with the indicated trend. 33. The system of claim 21 wherein the predictive model component is configured to determine a predictive model for network communications based on the historical context information by determining a different predictive model for each cell in a cellular communication system. 34. The system of claim 21 wherein the predictive model component is configured to determine the predictive model to at least one of maximize capacity of an associated communication network, minimize latency in the network, provide fairness as between users of the network, maximize a number of users of the network, maximize a number of users of the network with a minimum rate constraint, minimize power consumption of a user device, minimize power consumption of a network device, minimize an error rate, and determine an optimal error rate. 35. The system of claim 21 wherein the communication parameter component is configured to at least one of schedule the first mobile device and set network parameters based on the determined predictive model by performing real time network operations (RTNO) including setting at least one of scheduled time, scheduled frequency, modulation, coding, spreading code, number of layers, precoding matrix, transmit power level, closed loop power adjustments, antenna selection, and random backoff timer adjustments. 36. The system of claim 35 wherein the RTNO are related to at least one of uplink and downlink communications with the first mobile device. 37. The system of claim 21 wherein the network learning component is configured to adjust the predictive model substantially in real time. 38. The system of claim 21 wherein the network communications include network communications via at least one of a cellular network, a Wi-Fi network, a WiMAX network, a satellite communications network, and an ad-hoc network. 39. The system of claim 21 wherein at least one of persistence and controlled evolution of settings is imposed within a network including the first mobile device. 40. A computer readable medium storing a computer program, executable by a machine, for optimizing a predictive model for mobile network communications based on historical context information, the computer program comprising executable instructions for: collecting historical context information including at least one of communication environment, communication parameter estimates, mobile device statistics, mobile device transmit settings, base station receiver settings, past network statistics and settings, and adjacent network node information statistics and settings, the historical context information including data from communications of at least one mobile device; determining a predictive model for network communications based on the historical context information; determining a communication context for a first mobile device different than the at least one mobile device; at least one of scheduling the first mobile device and setting network parameters based on the determined predictive model; comparing actual to expected results of the at least one of scheduling the first mobile device and setting network parameters based on the determined predictive model; and adjusting the predictive model based on the comparison of actual to expected results.