CLIC MIMO Detection Framework Decomposes Channel Outputs
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Solution Overview
Problem
MIMO detection becomes increasingly complex with the addition of transmit antennas, making existing algorithms impractical due to exponential complexity growth, despite offering substantial gains in system capacity and transmission reliability.
Innovation Solution
The Candidate List Generation and Interference Cancellation (CLIC) framework decomposes the MIMO detection problem into two simpler pieces, allowing for the management of performance-complexity trade-offs by adapting parameters, specifically implementing the Max-Log detector and Abbreviated Max-Log detector to reduce computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of transmit and receive antennas is increased, then system capacity and transmission reliability are improved, but detection complexity increases exponentially
Solution Approach 1:
The patent segments the MIMO detection problem into two independent sub-problems: channel estimation and data detection. By separating these functions, the patent reduces the overall detection complexity while maintaining system reliability. The channel estimation module handles impulse response estimation independently, and the data detection module processes symbols using simplified algorithms, avoiding the exponential complexity growth that would occur with joint maximum likelihood detection.
2Measurement precision
If maximum-likelihood detection is used, then detection performance is optimized, but computational complexity increases exponentially with constellation size
Solution Approach 1:
The patent segments the detection process into channel estimation and data detection phases. The data detection phase further uses segmentation by separating real and imaginary parts of complex symbols, allowing independent processing that reduces computational burden while maintaining detection performance.
Solution Approach 2:
The patent changes the detection approach by using separate estimation of real and imaginary components rather than joint complex detection. This parameter change in the detection strategy reduces computational complexity from exponential to polynomial growth with constellation size, while the patent maintains near-optimal performance through iterative refinement of estimates.
3Productivity
If the constellation size is increased, then data rate is improved, but detection complexity increases exponentially
Solution Approach 1:
The patent segments complex symbol detection into separate real and imaginary component detection. This segmentation allows the detector to handle larger constellations (e.g., 64-QAM, 256-QAM) with polynomial complexity rather than exponential complexity, enabling higher data rates without proportional increases in detection complexity.
Data Source
AI summary
A method and system for performing Multiple-Input Multiple-Output (“MIMO”) detection that reduces complexity by decomposing MIMO detection problem into two less complex problems, Candidate List generation and Interference Cancellation (“CLIC”). Embodiments of the CLIC framework parse an N element channel output into a first set containing S elements and a second set containing N−S elements. A first list of candidate vectors is generated from the first set of elements. A set of interference cancelled elements is generated by using the first list of candidate vectors to cancel interference from the second set of elements. A second list of candidate vectors is generated from the set of interference cancelled elements. A minimum cost is computed for each bit of the candidate vectors and from the costs a log-likelihood ratio is computed.