Parameterized Sphere Detector MIMO Complexity Reduction
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Solution Overview
Problem
MIMO detection algorithms face increasing complexity with the number of transmit antennas, making it impractical to achieve optimal detection performance, as existing detectors either sacrifice performance for reduced complexity or require excessive computational resources.
Innovation Solution
The Parameterized-Sphere (P-SPH) detector operates in initialization and completion modes to enumerate a fixed number of candidate vectors, using a greedy search and QR decomposition to reduce complexity while maintaining performance, approximating detectors like the truncated-sphere, max-log, or decision-feedback detectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of transmit and receive antennas is increased to increase data throughput and diversity, then system capacity increases linearly and fading probability decreases exponentially, but detection complexity becomes increasingly complex
Solution Approach 1:
The patent segments the detection process into two distinct phases: an initialization phase that enumerates a fixed number of best candidate vectors, and a completion phase that uses greedy search to enumerate additional candidates. This segmentation allows the system to handle high-dimensional MIMO detection by breaking down the complex exhaustive search into manageable stages, thereby reducing overall detection complexity while maintaining throughput benefits
Solution Approach 2:
The patent applies partial action by enumerating a predetermined number of candidate vectors rather than performing exhaustive search over all possible combinations. The detector initializes with a fixed number of candidates and then performs greedy search to find additional candidates up to a predetermined threshold, achieving near-optimal performance without the exponential complexity of complete enumeration
2Measurement precision
If the maximum-likelihood detector is used to achieve optimal detection performance, then detection accuracy is maximized, but complexity increases exponentially with the number of input channels
Solution Approach 1:
The patent performs preliminary action by initializing the detector with a fixed number of best candidate vectors before the main detection process. This initialization phase pre-computes and stores promising candidate vectors, which are then used as starting points for the greedy search. This preliminary preparation reduces the computational burden during the main detection phase while maintaining detection accuracy
Solution Approach 2:
The patent changes the parameter of candidate enumeration from exhaustive (all possible combinations) to a predetermined finite number. By controlling the number of candidate vectors enumerated in both initialization and completion phases, the system transforms the exponential complexity problem into a polynomial complexity solution, achieving near-ML performance with significantly reduced computational requirements
3Device complexity
If existing detectors are used to reduce complexity, then computational resources are reduced, but performance is sacrificed
Solution Approach 1:
The patent ensures continuity of useful action by maintaining the greedy search process that continuously refines the candidate list. After initialization with a fixed number of candidates, the completion phase continues to enumerate additional candidates through greedy search until a predetermined number is reached. This continuous refinement process maintains detection performance while keeping computational complexity manageable throughout the entire detection process
Data Source
AI summary
A Multiple-input Multiple-Output (MIMO) receiver is provided. The MIMO receiver comprises a parameterized sphere detector having two search modes. During a first search mode, the parameterized sphere detector enumerates a number of best candidate vectors up to a fixed parameter value. During a second search mode, the parameterized sphere detector enumerates additional candidate vectors using a greedy search until a predetermined number of candidate vectors have been enumerated.