Antenna Selection via Stochastic Approximation for Millimeter Wave Systems
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Solution Overview
Problem
Existing high giga-Hertz communications systems, such as 60 GHz millimeter wave communications, face challenges in antenna selection due to the lack of access to the MIMO channel matrix, as signals are combined in the analog domain, making it difficult to devise an antenna selection method based solely on scalar outputs rather than the channel matrix.
Innovation Solution
An iterative antenna selection process using discrete stochastic approximation and an adaptive transmit and receive beamforming selection process, which exploits the strong line-of-sight property of high giga-Hertz channels, allowing for the quick locking onto a near-optimal antenna subset and utilizing a low-rate feedback channel to inform the transmitter about selected beams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If antenna selection techniques are used to reduce hardware complexity, then the number of RF chains is reduced, but the difficulty of detecting and measuring channel information increases because the receiver only has access to scalar output rather than the full channel matrix
Solution Approach 1:
The patent implements a feedback mechanism where the receiver computes an objective function based on the scalar output from the analog beamformer and feeds back antenna index information to the transmitter. This allows the transmitter to select antennas based on feedback from the receiver without requiring the receiver to have access to the full channel matrix, thus resolving the contradiction between reduced hardware complexity and channel information access.
Solution Approach 2:
Instead of requiring the receiver to process and access the full channel matrix (excessive action), the patent uses only the scalar output from the analog beamformer (partial action) to compute the objective function and provide feedback for antenna selection. This partial information approach is sufficient to achieve effective antenna selection while maintaining reduced hardware complexity.
2Device complexity
If analog beamforming is used to combine signals in the analog domain, then hardware complexity is reduced, but the reliability of channel matrix estimation deteriorates because the receiver cannot access the full channel matrix
Solution Approach 1:
The patent introduces an intermediary objective function that bridges the gap between the scalar output from the analog beamformer and the antenna selection decision. The objective function serves as a mediator that captures the essential channel information needed for antenna selection without requiring direct access to the full channel matrix, thus maintaining reliability while using simplified analog beamforming architecture.
3Reliability
If iterative antenna selection processes are implemented to achieve near-optimal antenna subsets, then the received signal-to-noise ratio is improved, but the loss of time increases due to multiple iterations of pilot symbol transmission and objective function computation
Solution Approach 1:
The patent performs preliminary actions by having the transmitter send pilot symbols through different antenna subsets in advance of actual data transmission. The receiver uses these preliminary pilot transmissions to compute objective functions and provide feedback for antenna selection before the main communication begins, allowing the system to converge to near-optimal antenna subsets without delaying the actual data transmission.
Data Source
AI summary
Systems that: (a) select a current antenna subset; (b) receive signals in response to a transmission of pilot symbols using the current antenna subset: (c) determine a current objective function for the current antenna subset; (d) replace an antenna in the current antenna subset with another antenna not, in the current antenna subset, to form a next antenna subset; (e) receive signals in response to a transmission of pilot symbols using the next antenna subset; (f) determines a next objective function for the current antenna subset; (g) determines whether the next objective function is better than the current objective function, and if so creates a corresponding next occupation probability vector entry; and (h) determines whether the value for the corresponding next occupation probability vector entry is larger than a value for an optimal occupation probability vector entry, and if so sets the second antenna subset as the optimal antenna subset.


