Signal processing in parallel
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Solution Overview
Problem
Existing signal processing methods struggle to efficiently and accurately estimate channels for wireless signals, particularly in environments with time-selective and frequency-selective artifacts, noise, and interference, without adequate utilization of parallel processing capabilities.
Innovation Solution
Implementing parallel channel estimation and equalization techniques using a plurality of graphics processing units (GPUs) and central processing units (CPUs) to perform channel estimation and equalization in parallel, utilizing algorithms like MMSE and applying matrix operations such as Cholesky or LU factorization to minimize estimation error and achieve low latency and high reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional sequential signal processing methods are used, then device complexity is reduced, but processing speed and latency increase
Solution Approach 1:
The patent divides the channel estimation and equalization processing into multiple independent parallel tasks that can be executed simultaneously on different processing units. Each processing unit handles a specific portion of the signal processing workload, enabling concurrent execution and significantly improving processing speed while distributing system complexity across multiple units.
Solution Approach 2:
The patent transitions from sequential single-threaded processing to parallel multi-threaded processing across multiple CPU cores and GPU units. This dimensional shift from one-dimensional sequential execution to multi-dimensional parallel execution space enables simultaneous processing of multiple signal streams, achieving lower latency without proportionally increasing individual processor complexity.
2Measurement precision
If parallel processing with multiple GPUs and CPUs is implemented, then processing speed and accuracy improve, but device complexity increases
Solution Approach 1:
The patent segments the computationally intensive channel estimation algorithm into multiple independent processing streams that can be executed in parallel. Each processing unit applies the same estimation algorithm to different portions of the data, improving overall accuracy through increased computational precision while distributing the algorithmic complexity across multiple units rather than requiring a single complex processor.
Solution Approach 2:
The patent combines the processing capabilities of multiple CPUs and GPUs into a unified parallel processing system. The CPU handles control logic and coordinate the processing tasks, while multiple GPU units simultaneously perform computationally intensive matrix operations for channel estimation. This merging of different processor types creates a synergistic system that achieves high precision through coordinated parallel computation.
3Loss of time
If parallel processing is used to meet latency requirements, then processing speed improves, but energy consumption increases
Solution Approach 1:
The patent implements periodic task scheduling where processing units alternate between active computation phases and idle states. During active phases, multiple processors work in parallel to complete channel estimation quickly, reducing latency. During idle phases, processors enter low-power states, reducing overall energy consumption. This periodic activation pattern balances speed and energy efficiency.
Solution Approach 2:
The patent employs dynamic parallel processing where the degree of parallelism is adjusted based on real-time requirements. When latency constraints are stringent, more processing units are activated to provide excessive processing capacity. When latency requirements are relaxed, fewer units are activated, reducing energy consumption. This partial activation strategy optimizes the trade-off between speed and energy usage.
Data Source
AI summary
Apparatuses, systems, and techniques to perform channel estimation on one or more signals. In at least one embodiment, channel estimation on one or more wireless signals is performed in parallel based on one or more frequencies of one or more signals.