Massive MIMO Receiver Channel Estimation Using PDP Neural Networks
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Solution Overview
Problem
Existing channel estimation methods in MIMO systems face high computational complexity and inefficiency in power delay profile estimation, particularly in massive MIMO systems with a large number of antennas, and require extensive memory for storing filter coefficients.
Innovation Solution
Implementing a domain knowledge enhanced neural network (DKE-NN) with asymmetric inference and a look-up table (LUT) based LMMSE channel estimation approach, which splits neural network layers and avoids online matrix inversion, reducing computational complexity and memory footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional channel estimation methods are used in massive MIMO systems, then channel estimation accuracy can be maintained, but computational complexity increases significantly
Solution Approach 1:
The patent segments the channel estimation process into two distinct phases: (1) power delay profile estimation using a neural network, and (2) channel estimation using LMMSE filtering. This segmentation allows each phase to be optimized independently, with the neural network handling the complex PDP estimation and the LMMSE filter handling the channel estimation, thereby reducing overall computational complexity while maintaining accuracy
Solution Approach 2:
The patent performs preliminary estimation of the power delay profile using a neural network before performing the actual channel estimation. By pre-processing the signal to extract PDP information and storing it in a lookup table, the subsequent LMMSE channel estimation can proceed with reduced computational requirements, as the complex PDP analysis is already completed
2Measurement precision
If conventional channel estimation methods are used in massive MIMO systems, then channel estimation can be performed, but memory requirements increase significantly
Solution Approach 1:
The patent extracts the power delay profile information from the received pilot signals and stores it in a separate lookup table. By separating the PDP data from the main channel estimation process, the system reduces the memory burden on the main processor while maintaining access to the necessary channel characteristics for accurate estimation
Solution Approach 2:
The patent creates a lookup table that stores pre-computed power delay profile information based on received signal strength indicators. This copying of PDP data into a compact table format allows the system to reference channel characteristics without storing complete channel state information for all antennas, significantly reducing memory requirements
3Measurement precision
If neural network processing is applied to all receive antennas, then power delay profile estimation accuracy is improved, but processing time increases
Solution Approach 1:
The patent combines the power delay profile estimates from multiple receive antennas into a single combined PDP estimate. By merging the intermediate results from parallel neural network processing of multiple antennas, the system achieves accurate overall PDP estimation without requiring sequential processing, thus reducing total processing time while maintaining accuracy
Solution Approach 2:
The patent performs preliminary neural network processing on pilot signals to extract power delay profile information and stores these intermediate results in a lookup table. This pre-computation allows the main channel estimation process to proceed more quickly by referencing pre-analyzed PDP data rather than performing full neural network processing during actual channel estimation
Data Source
AI summary
According to various examples, a receiver is described, comprising a memory and a processor, the processor being configured to receive, for each of a plurality of receive antennas, a pilot signal via the receive antenna, wherein the pilot signal has a component for each of a plurality of subcarriers, generate, for each of the plurality of receive antennas, an intermediate power delay profile estimation processing result by processing the pilot signal received via the receive antenna by one or more first neural network layers combining the components of the pilot signal received via the receive antenna for the sub-carriers, generate a combined intermediate power delay profile estimation processing result by combining the intermediate power delay profile estimation processing results generated for the receive antennas and determine a power delay profile estimate by processing the combined intermediate power delay profile processing estimation result by one or more second neural network layers.