Distributed Digital Pre-Distortion Training for Wireless Base Stations
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Solution Overview
Problem
Current digital pre-distortion training methods in wireless communications, particularly in base stations, face challenges such as limited computation capability in user equipment (UEs) and inaccurate non-linearity coefficient determination for wide bandwidths, leading to inefficiencies and increased latency.
Innovation Solution
A distributed digital pre-distortion training approach where a base station transmits training parameters to multiple UEs, which calculate and aggregate training values to determine non-linearity coefficients, reducing computational burden on individual UEs and enhancing bandwidth coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single UE performs digital pre-distortion training computations, then the computational burden is concentrated, but the UE's limited computation capability results in inaccurate non-linearity coefficient determination and increased latency
Solution Approach 1:
The patent divides the digital pre-distortion training process into multiple segments performed by different UEs. Each UE computes a portion of the non-linearity coefficients based on training parameters, and the base station aggregates these partial results. This segmentation allows the computational task to be distributed across multiple UEs, reducing the complexity burden on any single UE while maintaining or improving coefficient determination accuracy through combined results.
2Area of stationary object
If training is performed over wide bandwidths, then more comprehensive channel characterization is achieved, but the computational complexity and latency increase
Solution Approach 1:
The patent segments the wide bandwidth training process across multiple UEs, where each UE handles a portion of the computational workload for non-linearity coefficient determination. This parallel segmentation enables the base station to aggregate results from multiple UEs simultaneously, achieving comprehensive wide bandwidth coverage while reducing the time required compared to sequential processing by a single UE.
3Productivity
If multiple UEs are used for distributed training computations, then the computational burden is distributed and bandwidth coverage is enhanced, but the system complexity increases
Solution Approach 1:
The base station serves as an intermediary that coordinates the distributed training process. It transmits training parameters to multiple UEs, collects the computed training values from each UE, and aggregates these values to determine the final non-linearity coefficients. This intermediary role simplifies the system architecture by centralizing the coordination function, thereby managing complexity while maintaining the productivity benefits of distributed computation.
Solution Approach 2:
The patent merges the partial non-linearity coefficient results from multiple UEs at the base station to produce the final set of coefficients. This merging process combines the computational outputs from distributed UEs into a unified result, achieving improved training efficiency and bandwidth coverage while managing system complexity through a straightforward aggregation operation.
4Measurement precision
If a single UE determines non-linearity coefficients, then the system architecture is simpler, but the coefficients are inaccurate for wide bandwidths due to limited UE computation capability
Solution Approach 1:
The patent segments the coefficient determination task across multiple UEs, with each UE computing a portion of the non-linearity coefficients. The base station then aggregates these segmented results to produce accurate coefficients for wide bandwidths. This segmentation approach resolves the accuracy limitation of single-UE computation while managing architectural complexity through a systematic division of computational labor.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. A base station may transmit, to each user equipment (UE) of a set of UEs, a different respective set of one or more training parameters for a digital pre-distortion procedure. Some or all of the UEs may determine one or more respective training values based on the one or more training parameters, where the training values from the UEs support the base station computing a set of non-linearity coefficients for the digital pre-distortion procedure. The base station thus may receive training parameters from some or all of the UEs and determine the set of non-linearity coefficients based thereon. The base station use the non-linearity coefficients to perform the digital pre-distortion procedure for one or more downlink transmissions, to one or more UEs of the group of UEs used for training, or to one or more other UEs.