Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning

Abstract: Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise a massive number of nearly-passive elements that interact with the incident signals, for example by reflecting them, in a smart way that improves the wireless system performance. Prior work focused on the design of the LIS reflection matrices assuming full knowledge of the channels. Estimating these channels at the LIS, however, is a key challenging problem, and is associated with large training overhead given the massive number of LIS elements. This paper proposes efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. First, a novel LIS architecture based on sparse channel sensors is proposed. In this architecture, all the LIS elements are passive except for a few elements that are active (connected to the baseband of the LIS controller). We then develop two solutions that design the LIS reflection matrices with negligible training overhead. In the first approach, we leverage compressive sensing tools to construct the 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. In the second approach, we develop a deep learning based solution 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. We show that the achievable rates of the proposed compressive sensing and deep learning solutions approach the upper bound, that assumes perfect channel knowledge, with negligible training overhead and with less than 1% of the elements being active.

• The paper introduces a new architecture using few active sensors to design optimal reflection matrices with minimal training overhead.
• The compressive sensing method reconstructs full channel information from sparse observations, achieving near-optimal performance.
• The deep learning framework predicts effective reflection strategies from limited data, enhancing both energy and spectral efficiency.

Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning

The paper "Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning" explores the implementation and optimization of Large Intelligent Surfaces (LIS) in future wireless communication systems, particularly beyond 5G. It introduces innovative methods to efficiently design reflection matrices for LIS, aiming to improve the coverage and data rates without imposing significant training overhead.

Overview of Large Intelligent Surfaces

LIS systems consist of nearly passive elements that can intelligently reflect incident wireless signals to enhance communication performance. Existing research primarily assumes full channel knowledge to optimize reflection matrices. However, obtaining this channel knowledge incurs substantial training overhead or requires complex hardware when all LIS elements are connected to the baseband.

Proposed Solutions

The authors address these challenges by proposing a novel LIS architecture that leverages both compressive sensing and deep learning techniques:

1. Novel LIS Architecture: The paper introduces a system where most elements are passive, complemented by a few randomly distributed active elements serving as channel sensors. These active elements are utilized to design LIS reflection matrices with minimal training overhead.
2. Compressive Sensing Approach: This technique reconstructs the full channel information from partial observations made by a small number of active elements. By employing sparse sensing methods, the authors show that comprehensive channel estimation is feasible even when a small fraction of LIS elements are active.
3. Deep Learning Approach: The authors propose a deep learning framework enabling the LIS to predict optimal reflection strategies based on limited channel information, referred to as environment descriptors. This method forgoes traditional channel estimation in favor of a predictive model trained on historical data to optimize interaction with incident signals.

Numerical Results and Implications

The paper presents strong numerical results demonstrating that both compressive sensing and deep learning methods can achieve near-optimal performance. Notably, the proposed solutions approach the upper bound of achievable rates with less than 1% of the LIS elements being active. These results underscore significant enhancements in energy and spectral efficiency.

Practical and Theoretical Implications

Practically, the proposed methodologies offer a path toward deploying high-performance LIS systems with reduced energy consumption and complexity, making them suitable for real-world applications, such as vehicular communications where mobility constraints exist. Theoretically, the integration of deep learning in wireless system optimization showcases the potential of machine learning to transform traditional communication paradigms.

Future Research Directions

Future research may explore optimizing the distribution of active sensors or expanding the deep learning models to adapt to dynamic environments and diverse channel conditions. Additionally, the application of reinforcement learning could be investigated to continuously adapt and refine the interaction strategies in real-time, providing robustness against environmental variations.

This paper contributes significantly to the body of knowledge in LIS systems by providing scalable and efficient solutions that bridge compressive sensing techniques and modern machine learning, paving the way for their practical deployment in future wireless networks.