Parameter Estimation Using Historical Context in Wireless Networks
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Solution Overview
Problem
Current parameter estimation methods in wireless communication networks face performance degradation due to errors, high overhead, and resource-intensive implementation, particularly in estimating channel and receiver parameters.
Innovation Solution
The method involves determining the communication context of a mobile device, comparing it to stored historical context information of other devices within the same cell, and using this comparison to estimate parameters for improved communication, thereby reducing the reliance on pilot sequences and enhancing estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pilot sequences are used for parameter estimation, then parameter estimates can be obtained, but overhead increases and performance degrades due to estimation errors
Solution Approach 1:
The system performs preliminary action by collecting and storing communication context information from multiple mobile devices in advance. Historical data including location, velocity, and channel parameters are pre-acquired and organized in a database, enabling rapid parameter estimation without requiring extensive pilot sequences during actual communication.
Solution Approach 2:
The system creates copies of communication context information from multiple mobile devices and uses these copied historical data sets to estimate parameters for current devices. Instead of directly measuring parameters using pilot sequences, the system copies relevant information from similar historical scenarios to derive accurate estimates.
2Reliability
If traditional parameter estimation methods are used, then parameters can be estimated, but implementation resource requirements increase
Solution Approach 1:
The system segments the parameter estimation process into distinct functional modules: context information collection, historical data storage, context matching, and parameter estimation. Each module performs a specific function, making the overall system more manageable and easier to implement while maintaining high reliability.
Solution Approach 2:
The system introduces an intermediary component - the communication context database - that mediates between raw historical data and parameter estimation requirements. This intermediary organizes and pre-processes information, reducing the computational burden on the estimation algorithm and simplifying implementation.
3Productivity
If pilot sequences are used for parameter estimation, then parameter estimates can be obtained, but performance decreases due to estimation errors
Solution Approach 1:
The system merges parameter estimation results from multiple historical contexts corresponding to different mobile devices in similar communication scenarios. By combining information from multiple sources rather than relying on a single pilot sequence measurement, the system achieves more accurate and reliable parameter estimates that improve throughput.
Data Source
AI summary
Methods and systems are described for parameter estimation in a wireless communication network based on historical context information. In one aspect, a communication context for a first mobile device is determined, wherein the determined communication context includes at least a determined current location of the first mobile device within a portion of a cell of the communications system. The communication context is compared to a stored historical communication context of at least one other mobile device that was determined to previously be located in the portion of the cell. Parameters for communicating with the first mobile device are estimated based on the comparison. The first mobile device is communicated with using the estimated parameters.