Codeword Selection Lookup Table Reduces MIMO Search Space
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Solution Overview
Problem
Current MIMO systems face challenges with high computational intensity, power consumption, and delay in determining optimal beamforming codewords due to repeated matrix computations and inefficient feedback mechanisms, particularly in mobile devices.
Innovation Solution
A method using a lookup table to identify codewords based on similarity metrics, reducing the search space by organizing codewords in memory according to their similarity, allowing for efficient selection of precoding matrices that satisfy specific criteria such as maximizing SNR or minimizing chordal distance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional beamforming codeword selection methods are used, then communication performance can be maintained, but computational load and power consumption increase significantly
Solution Approach 1:
The patent pre-computes and stores codeword similarity metrics in a lookup table before actual communication occurs. This preliminary action allows the receiver to quickly identify suitable codewords without performing intensive matrix computations in real-time, thereby reducing power consumption while maintaining communication performance.
Solution Approach 2:
The patent segments the codebook into groups based on codeword similarity metrics. Instead of searching through the entire codebook, the receiver divides the search space into manageable segments and only evaluates codewords within relevant segments, reducing computational load and power consumption while preserving performance.
2Reliability
If exhaustive codeword search is performed, then optimal beamforming selection is achieved, but computational intensity and processing time increase
Solution Approach 1:
The patent performs partial search by evaluating only a subset of codewords that are most likely to be optimal, based on similarity metrics stored in the lookup table. This partial action approach achieves near-optimal beamforming selection without the full computational burden of exhaustive search.
Solution Approach 2:
Similarity metrics for all codeword pairs are pre-computed and stored in a lookup table. This preliminary computation eliminates the need for real-time matrix operations during codeword selection, significantly reducing computational intensity while maintaining selection accuracy.
3Reliability
If codebook search is performed without optimization, then all codewords can be evaluated, but processing delay increases
Solution Approach 1:
The codebook is segmented into groups based on similarity to the channel matrix. The receiver only needs to evaluate codewords within the relevant segment rather than the entire codebook, maintaining evaluation completeness for the optimal codeword while reducing processing delay through focused search.
Solution Approach 2:
The lookup table with pre-computed similarity metrics is prepared in advance, enabling the receiver to quickly identify which codewords warrant evaluation. This preliminary organization of data eliminates unnecessary computations and reduces processing delay while ensuring the optimal codeword is not missed.
4Measurement precision
If feedback information is transmitted with high precision, then channel quality is accurately communicated, but feedback overhead increases
Solution Approach 1:
The patent changes the parameter representation from transmitting full channel state information to transmitting only a codeword index selected from a predefined codebook. This parameter change reduces feedback overhead significantly while maintaining adequate channel quality indication accuracy through the codebook selection mechanism.
Data Source
AI summary
A method of determining indicators for matrix codewords in a matrix codeword codebook, where the matrix codewords are adapted for communicating information between a transmitter and a receiver. The method includes providing a lookup table that is associated with multiple codewords that are associated with a codebook. The lookup table has m rows, and each row in the lookup table has a one-to-one correspondence with a codeword in the multiple codewords. Each row has p entries and each of the p entries holds a codeword indicator that identifies a codeword in the multiple codewords. The (i,j)th entry in the lookup table holds the codeword indicator identifying the jth most similar codeword to the codeword associated with the ith row according to a similarity metric, where 1≦i≦m and 1≦j≦p, and the m rows include a row k. The method includes identifying a first indicator in the row k by testing each of the indicators in the first p entries of row k to determine which of the indicators in the first n entries identifies a reduced search codeword that best satisfies a selection criterion, wherein, n has a predetermined value and 1≦n≦p, and, in relation to the other remaining indicators in the first n entries of the first row, the first indicator identifies the reduced search codeword that best satisfies the selection criterion.