Link Adaptation Parameter Determination Using Channel Covariance
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently adapting link parameters for MIMO transmissions due to high computational complexity and memory requirements, especially in LTE systems, where calculating post-equalizer SINR for link adaptation involves extensive calculations and storage of complex channel coefficients.
Innovation Solution
A method is introduced to determine link adaptation parameters using a channel covariance matrix, approximating post-equalizer SINR and calculating an offset between the approximated and received SINR, which reduces computational load and memory requirements by using a simplified expression for future link adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search through all parameter combinations is performed to maximize throughput, then link adaptation accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent pre-calculates and stores channel covariance matrices from channel estimates before link adaptation is needed. This preliminary computation separates the heavy matrix processing from the real-time parameter selection, allowing the exhaustive search to operate on pre-processed data rather than raw channel coefficients, thus reducing real-time computational complexity while maintaining accuracy
Solution Approach 2:
The patent divides the link adaptation process into two independent stages: (1) channel covariance matrix computation from channel estimates, and (2) parameter combination search using the pre-computed covariance. This segmentation allows each stage to be optimized independently, with the first stage handling heavy computation offline and the second stage performing efficient search online
2Measurement precision
If all channel coefficients are stored to calculate post-equalizer SINR, then link adaptation accuracy is improved, but memory requirements increase significantly
Solution Approach 1:
The patent extracts only the essential statistical property of the channel (the covariance matrix) rather than storing all individual channel coefficients. The covariance matrix captures the second-order statistics needed for SINR calculation while requiring significantly less memory, especially as the number of antennas increases
Solution Approach 2:
The patent transforms the channel representation from raw complex coefficients to a derived statistical parameter (covariance matrix). This parameter transformation reduces the data dimensionality while preserving the information necessary for accurate SINR estimation and link adaptation
Data Source
AI summary
The teachings relate to a method 100 performed in a network node 2 for determining a link adaptation parameter, SINRLA,i, for a wireless device 3. The network node 2 supporting a multi-antenna transmission mode comprising spatial multiplexing layers for transmission of data on a channel between the wireless device 3 and the network node 2. The method 100 comprises: determining 110 a channel covariance matrix H HH for the channel, wherein H is the channel matrix for the channel; approximating 120 a post-equalizer signal to interference plus noise ratio SINRapprox., for a spatial multiplexing layer i using the channel covariance matrix H HH; determining 130 an offset SINRoffset, i to be the difference between a received signal to interference plus noise ratio SINRreceived and the approximated post-equalizer signal to interference plus noise ration SINRapprox.,i, and determining 140 the link adaptation parameter SINRLA,i to be the approximated post-equalizer signal to interference plus noise ratio SINRapprox.,i compensated by the determined offset SINRoffset. Corresponding network node, computer program and computer program products are also provided.