MIMO Receiver List Search Block for Signal Detection Complexity
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Solution Overview
Problem
Current MU-MIMO communication systems face challenges in reducing computational complexity while maintaining near-optimal error performance, especially under varying channel conditions, due to the high complexity of traditional signal detection algorithms and the impracticality of sphere decoding for commercial implementation.
Innovation Solution
A receiver system utilizing a List Search Block (LSB) configured with a Machine Learning (ML) algorithm to determine an ordered list of candidate constellation points, reducing the need for calculating distances to all constellation points by using a pre-trained ML model to identify the closest points, thereby decreasing computational complexity and improving block error rate (BLER) performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional MU-MIMO signal detection algorithms (MRC, ZF, LMMSE) are used, then computational complexity is reduced, but error performance deteriorates significantly under most channel conditions
Solution Approach 1:
The patent introduces an intermediary mechanism (ordered list of candidate constellation points) between the received signal and final detection decision. Instead of directly applying simple linear detectors or complex sphere decoding, the system generates and maintains an ordered list of candidate points that guides the detection process, achieving a balance between complexity and performance by exploring only the most promising candidates.
Solution Approach 2:
The patent employs dynamic adaptation by adjusting the number of candidate points in the ordered list based on channel conditions and performance requirements. The system can dynamically modify the search depth and candidate selection criteria to optimize the trade-off between computational complexity and error performance for different operational scenarios.
2Reliability
If optimal joint decoding algorithm is used, then error performance is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the joint decoding process into manageable stages by maintaining an ordered list of candidate constellation points at each detection stage. Instead of performing exhaustive joint decoding of all symbols simultaneously, the system processes symbols sequentially while maintaining and updating the candidate list, dividing the complex problem into smaller sub-problems that are computationally tractable.
Solution Approach 2:
The patent implements partial action by generating an ordered list of candidate points rather than evaluating all possible constellation points. The system performs detection on a selected subset of the most promising candidates from the ordered list, achieving near-optimal performance with significantly reduced computational effort compared to exhaustive search.
3Reliability
If sphere decoding is used, then near-optimal error performance is achieved, but implementation becomes impractical for commercial systems due to high computational requirements
Solution Approach 1:
The patent employs a computationally efficient approach that generates candidate constellation points on-demand and processes them through the ordered list mechanism. Rather than implementing the full complexity of sphere decoding algorithms, the system uses simpler operations to maintain and search the ordered list, achieving comparable performance with much lower computational cost suitable for commercial deployment.
4Productivity
If the number of antennas is increased, then spectral efficiency is enhanced, but multi-user interference increases and computational complexity at receiver increases
Solution Approach 1:
The patent segments the detection of multiple user signals by maintaining separate ordered lists of candidate points for different spatial streams or layers. The system processes each layer's candidate points independently while accounting for interference from other layers, dividing the complex multi-user detection problem into manageable per-layer sub-problems that scale better with the number of antennas.
Data Source
AI summary
A method and an apparatus for decoding a signal at a receiver in a MIMO communication system is described. A signal y is obtained over a channel from a plurality of transmitters in communication with the receiver, the signal y includes data signals transmitted on a plurality of layers N. A concatenated matrix R representing the channel between the plurality of transmitters and the receiver is obtained based on an estimated channel matrix H. An ordered list is determined based at least on the signal y and the obtained concatenated matrix R. The ordered list is a list of N-dimensional vectors and each vector is a candidate constellation point for the transmitted data signal based on a predefined metric, and is determined using a list search block configured to implement a machine learning algorithm.