Iterative Receiver Structures for OFDM MIMO Systems
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Solution Overview
Problem
The computational complexity of near-optimal receiver structures for coded OFDM/MIMO systems with bit-interleaved coded modulation (BICM) grows exponentially with spectral efficiency, making it challenging to deploy reliable systems with high spectral efficiencies using practical receivers.
Innovation Solution
The implementation of iterative inner/outer decoder structures with a soft-output M-algorithm (SOMA) based inner decoder, which adapts and optimizes tree search parameters using extrinsic information from the outer decoder, reducing computational complexity while maintaining high bit-error rate performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If near-optimal receiver structures are used for coded OFDM/MIMO systems with BICM, then bit-error rate performance is improved, but computational complexity grows exponentially with spectral efficiency
Solution Approach 1:
The receiver is divided into two independent parts: a maximum likelihood sequence estimator (MLSE) that handles channel estimation and a soft-output M-algorithm (SOMA) decoder that handles error correction. This segmentation allows each component to operate independently with optimized complexity, avoiding the exponential complexity growth of joint decoding while maintaining good bit-error rate performance through iterative refinement.
Solution Approach 2:
The receiver employs iterative decoding where the SOMA decoder exchanges extrinsic information with the outer channel decoder multiple times. The tree search parameters and filtering operations are dynamically adjusted based on extrinsic information from previous iterations, allowing the system to adaptively optimize performance-complexity trade-offs without requiring excessive computational resources for any single iteration.
2Productivity
If spectral efficiency is increased to achieve higher data rates, then throughput per unit bandwidth is improved, but receiver complexity grows exponentially
Solution Approach 1:
By separating the receiver into MLSE and SOMA decoder components, the system can handle higher spectral efficiencies through the iterative refinement capability of the SOMA decoder without requiring proportional increases in computational complexity. The segmentation allows specialized optimization of each component for its specific function.
Solution Approach 2:
The receiver changes key parameters including the number of iterations, tree search depth, and filtering coefficients based on operating conditions and extrinsic information. This dynamic parameter adjustment enables the system to maintain acceptable complexity while adapting to different spectral efficiency requirements through iterative optimization rather than requiring complex fixed-structure receivers.
3Reliability
If iterative decoding with extrinsic information exchange is implemented, then performance-complexity trade-off is improved, but number of iterations increases computational overhead
Solution Approach 1:
The iterative decoding process uses feedback from the outer channel decoder in the form of extrinsic information to refine the SOMA decoder's estimates. This feedback mechanism allows progressive improvement of decoding accuracy across multiple iterations, achieving better performance-complexity trade-offs by utilizing intelligent feedback rather than brute-force repeated computation.
Solution Approach 2:
The receiver dynamically adjusts the number of iterations and tree search parameters based on the quality of extrinsic information and convergence characteristics. This dynamic adaptation allows the system to stop iterating when sufficient accuracy is achieved, reducing unnecessary computational overhead while maintaining good performance-complexity trade-off, rather than performing a fixed large number of iterations.
Data Source
AI summary
The techniques and components described herein may improve the performance for a class of reduced-complexity receiver designs for coded OFDM MIMO systems with bit interleaved coded modulation. The receiver structures described are soft-input soft-output inner/outer decoder receiver structures that include one or more of the following: 1) an inner decoder that includes a linear front-end followed by a limited tree-search based on a soft-output M-algorithm; 2) a conventional near-optimal or optimal decoder for the outer binary code; and 3) iterative decoding (ID), whereby decoding (output) information is passed from one decoder module as input to the other and used to refine and improve the inner/outer decoding module outputs.