MU-Massive MIMO ADC Bit Allocation Under Power Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large antenna arrays in millimeter wave (mmWave) communication systems face significant hardware power consumption due to excessive analog-to-digital front-end bit-rate, necessitating a reduction in power consumption with minimal performance degradation.
Innovation Solution
The method involves determining an error vector magnitude for analog signals received by multiple antennas and assigning different numbers of quantization bits to analog-to-digital converters (ADCs) within a base station, ensuring that higher gain signals are processed with more quantization bits, thereby reducing the error vector magnitude while optimizing bit resolutions for each ADC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large antenna arrays are used in mmWave systems, then beamforming gain and data rate are improved, but hardware power consumption increases significantly
Solution Approach 1:
The patent applies local quality by assigning different quantization bit depths to different ADCs based on their corresponding antenna signal characteristics. Specifically, ADCs processing high-gain antenna signals are allocated fewer quantization bits, while ADCs processing low-gain signals are allocated more bits. This non-uniform bit allocation optimizes the signal-to-quantization-noise ratio locally at each ADC, reducing overall power consumption while maintaining system performance.
2Device complexity
If uniform quantization bits are allocated to all ADCs, then implementation is simple, but error vector magnitude increases and power efficiency decreases
Solution Approach 1:
The patent changes the parameter of quantization bit depth from a uniform value across all ADCs to a non-uniform distribution. By calculating the error vector magnitude for each antenna and adjusting the quantization bit allocation accordingly, the system transforms a simple but inefficient uniform allocation into a complex but optimized non-uniform allocation that minimizes overall error while adapting to varying signal conditions.
Data Source
AI summary
A method includes determining an error vector magnitude for analog signals received by multiple antennas in an array of antennas of a base station, assigning quantization bits to a plurality of analog-to-digital converters (ADCs) of the base station such that some ADCs have different numbers of quantization bits allocated from a fixed total number of available quantization bits of the base station, and applying the analog signals to the ADCs with quantization bits assigned to reduce the error vector magnitude of the analog signals.