MIMO Receiver Detection Complexity Reduction
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Solution Overview
Problem
Current MIMO communication systems face high computational complexity in detection and decoding processes, making them resource-intensive and impractical for many applications.
Innovation Solution
Implementing reduced complexity detection schemes that perform receiver spatial processing, log-likelihood ratio (LLR) computation independently for the best data streams, and jointly for the remaining streams, while reducing the number of hypotheses through techniques like list sphere detection or Markov chain Monte Carlo methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search is performed for all possible data bit sequences to achieve optimal detection performance, then detection accuracy is improved, but computational complexity becomes prohibitive
Solution Approach 1:
The patent segments the MIMO detection problem into two distinct parts: (1) per-stream LLR computation for individual data streams, and (2) joint LLR computation for multiple streams. This segmentation allows the receiver to process streams independently when possible, reducing the overall computational burden while maintaining detection accuracy through selective joint processing.
Solution Approach 2:
The patent applies partial action by performing joint LLR computation only for a subset of data streams (M-D streams) rather than all streams. The D streams with highest SNR are processed independently, while only the remaining streams require joint processing. This partial approach achieves near-optimal performance with significantly reduced complexity compared to exhaustive search.
2Reliability
If joint LLR computation is performed for all M data streams to maintain performance, then detection performance is preserved, but the number of hypotheses to evaluate increases exponentially
Solution Approach 1:
The patent applies local quality by differentiating the processing approach based on stream characteristics. Streams with higher SNR (D streams) are processed with simpler independent LLR computation, while streams with lower SNR (M-D streams) receive more sophisticated joint processing. This localized quality adjustment optimizes the balance between performance and complexity for each stream based on its specific conditions.
Solution Approach 2:
The patent changes the processing parameter (computation mode) based on stream quality metrics. By identifying streams with highest SNR and assigning them to independent processing mode, while assigning lower SNR streams to joint processing mode, the system dynamically adjusts computational parameters to achieve efficient performance.
Data Source
AI summary
Techniques for performing detection and decoding at a receiver are described. In one scheme, the receiver obtains R received symbol streams for M data streams transmitted by a transmitter, performs receiver spatial processing on the received symbols to obtain detected symbols, performs log-likelihood ratio (LLR) computation independently for each of D best data streams, and performs LLR computation jointly for the M−D remaining data streams, where M>D≧1 and M>1. The D best data streams may be selected based on SNR and/or other criteria. In another scheme, the receiver performs LLR computation independently for each of the D best data streams, performs LLR computation jointly for the M−D remaining data streams, and reduces the number of hypotheses to consider for the joint LLR computation by performing a search for candidate hypotheses using list sphere detection, Markov chain Monte Carlo, or some other search technique.