Sparse-Sensor Large Intelligent Surfaces for Low-Overhead Channel Estimation
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Solution Overview
Problem
Large intelligent surfaces (LISs) face challenges in channel estimation due to the massive number of elements, leading to huge training overhead when all elements are passive or prohibitive hardware complexity and power consumption when all elements are connected to the baseband.
Innovation Solution
The implementation of sparse channel sensors, where all LIS elements are passive except for a few active elements connected to the baseband, using compressive sensing and deep learning to design reflection matrices with negligible training overhead, allowing the LIS to approach the upper bound of achievable rates with less than 1% of elements being active.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If all LIS elements are passive reflecting elements, then hardware complexity and power consumption are reduced, but channel estimation requires huge training overhead
Solution Approach 1:
The LIS elements are segmented into two functional groups: passive reflecting elements (majority) and active channel sensing elements (minority, less than 1%). This segmentation allows the system to reduce power consumption by keeping most elements passive while using a small subset for channel estimation, thereby resolving the contradiction between low power consumption and reduced training overhead.
2Measurement precision
If all LIS elements are connected to the baseband through fully digital or hybrid architecture, then channel estimation accuracy is improved, but hardware complexity and power consumption become prohibitive
Solution Approach 1:
The active channel sensing capability is extracted from all LIS elements and concentrated in a small subset of active elements connected to the baseband. This extraction allows the system to maintain sufficient channel estimation accuracy using only a few active sensors, while the majority of elements remain simple passive reflectors, thereby resolving the contradiction between estimation accuracy and hardware complexity.
3Measurement precision
If a large number of active channel sensing elements are used, then channel estimation accuracy is improved, but the proportion of active elements increases beyond 1%
Solution Approach 1:
Different functional qualities are assigned to different parts of the LIS: most elements have passive reflecting quality while a small localized subset has active sensing quality. This local quality differentiation allows the system to achieve adequate channel estimation accuracy using only a small number of active elements (less than 1% of total), resolving the contradiction between estimation accuracy and the quantity of active elements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides an energy-efficient and spectral-efficient solution for channel estimation, achieving near-optimal achievable rates with reduced training overhead and lower hardware complexity.
Implementation Method 1
an array of passive reconfigurable reflecting elements
Implementation Method 2
a plurality of active channel sensing elements
Data Source
AI summary
Large intelligent surfaces (LISs) with sparse channel sensors are provided. Embodiments described herein provide efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. Consequently, an LIS architecture based on sparse channel sensors is provided where all LIS elements are passive reconfigurable elements except for a few elements that are active (e.g., connected to baseband). Two solutions are developed that design LIS reflection matrices with negligible training overhead. First, compressive sensing tools are leveraged to construct channels at all the LIS elements from the channels seen only at the active elements. These full channels can then be used to design the LIS reflection matrices with no training overhead. Second, a deep learning-based solution is deployed where the LIS learns how to optimally interact with the incident signal given the channels at the active elements, which represent the current state of the environment and transmitter/receiver locations.