OTFS SCMA Signal Detection With LCM-AMP for Multi-User Interference
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently detecting OTFS SCMA signals in high-mobility scenarios due to multi-dimensional interference such as ISI, IDI, and IUI, leading to increased processing complexity and poor performance in multi-user MIMO systems.
Innovation Solution
A Low-Complexity Memory Approximate Message Passing (LCM-AMP) detector is employed to recover OTFS SCMA signals from multiple users by using a factor graph with iterative processing between factor and variable nodes, applying Taylor expansion approximation and Gaussian message approximation to reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional detection methods are used for OTFS SCMA signals, then detection can be performed, but processing complexity increases significantly due to multi-dimensional interference
Solution Approach 1:
The detection process is segmented into iterative steps where the LCM-AMP detector processes signals in multiple iterations, breaking down the complex detection task into manageable stages. Each iteration refines the estimate of transmitted signals, progressively separating user signals from interference.
Solution Approach 2:
The LCM-AMP detector acts as an intermediary between the received mixed signal and the final detected symbols. It introduces intermediate variables and factor graphs to represent the detection problem, enabling complex multi-user detection through structured message passing while managing computational complexity.
2Reliability
If conventional detection methods are used for OTFS SCMA signals, then detection can be performed, but performance deteriorates in the presence of multi-user interference
Solution Approach 1:
The LCM-AMP detector converts the harmful effect of multi-user interference into a beneficial feature by exploiting the sparse structure of SCMA codebooks. The iterative message passing mechanism uses interference from other users as information to refine estimates, transforming the interference problem into a solvable structured estimation task through probabilistic inference.
Solution Approach 2:
The detector changes parameters iteratively by updating mean vectors and variances across multiple iterations. This dynamic parameter adjustment allows the system to adapt to varying interference conditions and converge to accurate signal estimates despite the presence of multi-user interference.
3Measurement precision
If sophisticated detection algorithms are used to handle multi-dimensional interference, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The LCM-AMP detector employs dynamic iterative processing where detection accuracy is progressively improved through multiple iterations. The algorithm dynamically adjusts its estimates and refines detection precision adaptively, allowing the system to achieve high accuracy while managing computational load through controlled iteration.
Solution Approach 2:
The detector applies partial action by performing a limited number of iterations rather than exhaustive processing. This approach achieves sufficient detection accuracy for practical applications while avoiding the prohibitive computational complexity that would result from complete or excessive processing, balancing precision and efficiency.
Data Source
AI summary
A method of detecting superimposed SCMA signals from multiple user equipment in a receiver of an OTFS communication system includes initializing an executing an iteration loop, in which a mean of all a posteriori estimates of the transmitted signal determined so far is calculated, which a posteriori estimates are based on the OTFS-demodulated received signal, the corresponding channel matrix, and the mean vectors and variances determined for each UE. The mean of all a posteriori estimates determined so far is used for determining vectors and variances for each UE, further using the probabilities of the non-zero elements of the respective UEs codebooks. The iteration is repeated until a termination criterion is met.