MIMO Maximum Likelihood Detector Accelerator Using Parallel Branch Prediction
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Solution Overview
Problem
Current MIMO systems face significant challenges in decoding spatial multiplexed signals efficiently due to the enormous number of candidate transmit vectors, leading to slow decoding speeds and high computational complexity.
Innovation Solution
An iterative method is introduced that uses a tree graph search to find the most likely transmit signal, employing branch prediction and parallel evaluation of accumulated distances to accelerate the decoding process, allowing for near-maximum likelihood performance at faster speeds and increased search complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MIMO decoding methods are used to evaluate all candidate transmit vectors, then decoding accuracy is maintained, but decoding speed becomes extremely slow due to the enormous number of candidates
Solution Approach 1:
The patent segments the decoding process into two independent parallel operations: a branch prediction unit that selects candidate nodes based on distance increments, and a confirmation unit that validates selections by computing accumulated distances. This segmentation allows simultaneous execution of prediction and validation, dramatically improving decoding speed while maintaining accuracy through the confirmation step.
2Measurement precision
If all candidate transmit vectors are evaluated to ensure maximum likelihood performance, then decoding accuracy is maximized, but computational complexity becomes prohibitively high
Solution Approach 1:
The branch prediction unit performs preliminary action by pre-selecting candidate nodes based on distance increments before full validation. This preliminary filtering reduces the number of candidates that require complete accumulated distance computation, thereby reducing computational complexity while the confirmation unit ensures maximum likelihood performance is maintained.
3Ease of operation
If sequential processing of branch decisions is used, then implementation is simpler, but decoding speed is limited by the sequential nature of the process
Solution Approach 1:
The patent transitions from sequential single-dimensional processing to parallel two-dimensional processing by implementing independent branch prediction and confirmation units that operate simultaneously. This dimensional change in processing architecture enables both units to work in parallel, dramatically increasing decoder speed while maintaining implementation feasibility through modular design.
Data Source
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AI summary
A decoder to search a tree graph to decode a received signal y. The tree graph may have a plurality of levels, each level having a plurality of nodes and each node representing a different value of an element of a candidate transmit signal s corresponding to the received signal y. The decoder may include a first module to execute a branch prediction at each branch node of the tree graph to select one of a plurality of candidate nodes stemming from the branch node that has a smallest distance increment. The decoder may include a second module, running in parallel to the first module, to evaluate the branch prediction made by the first module at each branch node by computing an accumulated distance of the selected node. If the accumulated distance of the selected node is greater than or equal to a search radius, the first module may override the branch prediction and select an alternative candidate node.