Space-Time Code Decoding via Iterative Interference Cancellation
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Solution Overview
Problem
Existing decoding techniques for non-orthogonal space-time codes with multiple antennas are complex and inefficient, especially as the number of antennas or modulation states increases, due to exponential complexity and the need for maximum likelihood decoding, which becomes impractical for systems with more than two antennas.
Innovation Solution
A method involving diversity pre-encoding and iterative processing, including diagonalization and interference cancellation, to refine symbol estimation and eliminate transmission interference, using techniques like spread-spectrum or linear pre-encoding, and incorporating automatic gain control and channel-decoding steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maximum likelihood decoding is used for non-orthogonal space-time codes, then decoding accuracy is improved, but device complexity increases exponentially
Solution Approach 1:
The decoding process is segmented into multiple iterative steps: initial decoding using simplified criteria, followed by multiple iterations of interference cancellation and re-decoding. Each iteration refines the estimate by removing interference from previous estimates, breaking down the complex maximum likelihood problem into manageable steps that converge to accurate results without requiring exponential complexity.
Solution Approach 2:
The method implements feedback through iterative processing where the output of each iteration feeds back into the next iteration. The interference cancellation step uses previously decoded symbols to generate interference estimates that are subtracted from the received signal, and this process repeats multiple times, with each iteration using the refined estimates from the previous one to achieve progressively more accurate decoding.
2Reliability
If the number of antennas increases to improve diversity, then decoding performance is improved, but implementation complexity increases exponentially
Solution Approach 1:
The decoding algorithm is made dynamic through iterative processing, where the complexity of each individual processing step remains manageable but the overall system adapts by performing multiple iterations. The number of iterations can be adjusted based on the number of antennas and the required performance, allowing the system to handle multiple antennas without requiring a fixed exponential complexity structure.
3Device complexity
If iterative decoding methods are used to reduce complexity, then implementation complexity is reduced, but decoding time increases
Solution Approach 1:
The method applies partial action by performing a limited number of iterations rather than exhaustive search. Each iteration performs interference cancellation and re-decoding, but the process stops after a predetermined number of iterations that provides sufficient accuracy without requiring complete convergence. This partial iteration approach reduces decoding time while maintaining acceptable performance.
Data Source
AI summary
The disclosure relates to a method for decoding a received signal comprising symbols which are distributed in space and time with the aid of a space-time coding matrix, comprising a space-time decoding stage and at least two iterations, each of which comprising the following sub-stages: diversity pre-decoding, the opposite of diversity pre-decoding carried out when the signal is emitted, providing precoded data; estimation of symbols forming said signal on the basis of said pre-decoded data, providing estimated symbols; diversity preceding identical to diversity preceding carried out during emission, applied to the estimated symbols in order to provide an estimated signal.