Uplink Demapping System with Dynamic Processing Paths
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Solution Overview
Problem
Existing demapping systems for 4G and 5G uplink transmissions face challenges in efficiently processing data streams, particularly in handling reference signals and achieving fast and resource-efficient demapping.
Innovation Solution
The proposed demapping system employs three processing types to efficiently demap uplink transmissions. In the first type, reference signals are removed before layer demapping, followed by soft demapping and descrambling. In the second type, despread resource elements bypass reference signal removal and layer demapping. In the third type, resource elements are directly input to the soft mapper, bypassing despreading and layer demapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reference signals are removed before layer demapping, then processing efficiency is improved, but device complexity increases due to multiple processing paths
Solution Approach 1:
The system dynamically selects between multiple processing paths (first, second, and third processing types) based on the detected processing type parameter. This allows the equalizer to adapt its complexity to match the specific requirements of each transmission, improving efficiency when needed while avoiding unnecessary complexity when not required.
Solution Approach 2:
Different processing operations are applied to different portions of the received signal based on the detected processing type. Reference signals are removed only when necessary (first processing type), while other data streams bypass this operation. This localized application of processing steps optimizes overall system efficiency.
2Adaptability or versatility
If multiple processing paths are implemented, then adaptability is improved, but processing time increases
Solution Approach 1:
The system performs a detection step at the beginning to identify which processing type should be applied. This preliminary action allows the subsequent processing to follow a predetermined path, avoiding the need to evaluate multiple options during actual data processing and thus minimizing processing time while maintaining adaptability.
3Measurement precision
If reference signals are handled separately, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system dynamically adjusts whether to remove reference signals based on the detected processing type. When channel estimation precision is critical, reference signals are removed and processed separately. When speed is more important, the system can bypass reference signal removal entirely (third processing type), thus adapting the precision-speed tradeoff to specific operational requirements.
Data Source
AI summary
Method and apparatus for providing an equalizer that achieves a given precision for non-invertible matrices. The equalizer receives a plurality of symbols of an uplink transmission in a wireless communication system and performs an equalization operation on the plurality of received symbols of uplink transmission, wherein the equalization operation requires to perform an inversion of a matrix. The equalization operation on the plurality of received symbols is completed within a user-specified precision without adding any bit to the precision when the matrix is non-invertible. A gain normalizer performs a gain normalization operation on the plurality of received symbols following the equalization operation with certain values excluded from an βIRCaverage of gain normalization factors used for the gain normalization operation.