Low-Complexity LMMSE Receiver Design for MIMO-OTFS Systems
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Solution Overview
Problem
Designing low-complexity linear minimum mean squared error (LMMSE) and zero-forcing (ZF) receivers for multiple-input multiple-output orthogonal time frequency space (MIMO-OTFS) systems is challenging due to the high computational complexity caused by twisted convolution after the OTFS waveform interacts with the channel, which degrades receiver performance.
Innovation Solution
The method involves computing a received signal vector and a channel matrix structure, reordering the matrix using the reverse Cuthil-McKee algorithm to reduce bandwidth, and performing low-complexity inverse calculations using banded matrices through LU decomposition and forward/backward substitution algorithms, resulting in a low-complexity LMMSE/ZF estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional LMMSE/ZF receivers are used in MIMO-OTFS systems, then receiver performance can be achieved, but computational complexity becomes excessively high due to twisted convolution
Solution Approach 1:
The patent segments the large MIMO-OTFS channel matrix into smaller block matrices based on the OTFS modulation structure. By dividing the received signal equation into blocks corresponding to different delay and Doppler indices, the receiver processes smaller matrix operations instead of one large matrix, significantly reducing computational complexity while maintaining equalization performance.
Solution Approach 2:
The patent transforms the conventional time-frequency domain equalization approach into the OTFS delay-Doppler domain. By exploiting the two-dimensional structure of OTFS modulation and performing block-wise processing in this transformed domain, the receiver achieves lower complexity through the dimensional reorganization of the signal processing operations.
2Measurement precision
If standard matrix inversion methods are used for LMMSE estimation, then accurate signal estimation is achieved, but computational complexity increases to O(M^3N^3)
Solution Approach 1:
The patent applies segmentation to the matrix inversion process by decomposing the large MIMO-OTFS channel matrix into smaller block matrices. Each block corresponds to a specific delay-Doppler region, allowing independent or simplified processing of each block. This reduces the inversion complexity from cubic in the total matrix size to cubic in the much smaller block size.
Solution Approach 2:
The patent changes the processing parameters by working in the OTFS delay-Doppler domain rather than the conventional time-frequency domain. This parameter transformation exploits the structured sparsity and block-diagonal properties of the OTFS channel matrix, enabling more efficient inversion algorithms that take advantage of the reduced effective matrix dimensions.
Data Source
AI summary
A method for designing a low-complexity linear minimum mean squared error (LMMSE) and zero-forcing (ZF) receivers for MIMO-RCP-OTFS system is disclosed. The method includes steps of: computing a received signal vector (r) and structure of a matrix (Ψ) using a channel matrix (H); reordering the matrix (Ψ) to reduce bandwidth of the matrix (Ψ); computing inverse of a banded matrix (G=LU) by multiplying the matrix (Ψ) with permutation matrix (P) and with transpose of permutation matrix (PT) using Cholskey decomposition; calculating LMSSE/ZF equalized vector ({tilde over (r)}ce) by multiplying inverse of banded matrices with Bw bandwidth (L and U), with the received signal vector (r=Pr) using forward and backward substitution algorithms; reordering the vector ({tilde over (r)}ce) to calculate vector (y); and calculating data vector ({circumflex over (d)}) representing an estimation of low-complexity LMMSE/ZF equalization by multiplying Hermitian matrix (B) with the vector (y).