mmWave Receiver Architecture With Spatial Compression Interface
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Solution Overview
Problem
Current mmWave receiver architectures face challenges in power efficiency due to high power consumption in wide bandwidth and high throughput I/O interfaces, with existing methods like CPRI and analog beamforming not effectively addressing signal correlation and sparsity of the mmWave channel, leading to limited spatial compression and increased computational complexity.
Innovation Solution
Implementing a spatial compression block in the digital domain to reduce the dimensionality of the channel using properties of the mmWave channel's sparsity and directionality, selecting significant receive signals to reduce the number of I/O links and enhance signal-to-noise ratio, while eliminating sector sweeping latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wide bandwidth and high throughput I/O interfaces are used to support next generation communication systems, then data rate and throughput are improved, but power dissipation increases significantly
Solution Approach 1:
The patent segments the high-dimensional digital receive signals into multiple lower-dimensional subspaces using spatial compression. The received signals from multiple antennas are divided into different spatial groups, each processed separately with reduced dimensionality, thereby reducing the total number of I/O links while maintaining the overall data throughput capability.
Solution Approach 2:
The patent applies spatial compression to transform the problem from high-dimensional spatial domain to lower-dimensional compressed spatial domain. By exploiting the sparsity of mmWave channels in the angular domain, the patent projects the received signals onto a reduced set of spatial basis vectors, effectively reducing the dimensionality of the I/O interface requirements while preserving the essential signal information.
2Quantity of substance
If existing compression methods like CPRI are used, then some data reduction is achieved, but they do not effectively address signal correlation and sparsity of the mmWave channel
Solution Approach 1:
The patent changes the compression approach from generic data compression (CPRI) to channel-specific spatial compression that exploits mmWave channel properties. By adapting the compression basis to the actual channel sparsity and correlation structure, the patent achieves more effective data reduction with lower computational complexity, as the compression is performed in the spatial domain where the channel exhibits structured sparsity.
3Use of energy by moving object
If the number of I/O links is reduced through spatial compression, then power consumption decreases, but signal dimensionality must be reduced
Solution Approach 1:
The patent creates a compressed spatial representation that captures the essential signal information in a lower-dimensional form. By projecting the full-dimensional receive signals onto a reduced set of spatial basis vectors derived from channel sparsity patterns, the patent creates a compressed copy of the signal that retains the critical information while requiring fewer I/O links for transmission.
Data Source
AI summary
A receiver circuit associated with a communication device is disclosed. The receiver circuit comprises a digital data compression circuit configured to receive a plurality of digital receive signals derived from a plurality of analog receive signals respectively associated with the receiver circuit. The digital data compression circuit is further configured to compress the plurality of digital receive signals to form one or more compressed digital data signals based thereon, to be provided to an input output (I/O) interface associated therewith. In some embodiments, a compressed digital signal dimension associated with the one or more compressed digital data signals is less than a digital signal dimension associated with the plurality of digital receive signals.