Deep Learning Based MIMO Communications (1707.07980v1)

Abstract: We introduce a novel physical layer scheme for single user Multiple-Input Multiple-Output (MIMO) communications based on unsupervised deep learning using an autoencoder. This method extends prior work on the joint optimization of physical layer representation and encoding and decoding processes as a single end-to-end task by expanding transmitter and receivers to the multi-antenna case. We introduce a widely used domain appropriate wireless channel impairment model (Rayleigh fading channel), into the autoencoder optimization problem in order to directly learn a system which optimizes for it. We considered both spatial diversity and spatial multiplexing techniques in our implementation. Our deep learning-based approach demonstrates significant potential for learning schemes which approach and exceed the performance of the methods which are widely used in existing wireless MIMO systems. We discuss how the proposed scheme can be easily adapted for open-loop and closed-loop operation in spatial diversity and multiplexing modes and extended use with only compact binary channel state information (CSI) as feedback.

• The paper introduces a joint optimization framework using an autoencoder to integrate transmitter and receiver design under Rayleigh fading channels.
• It demonstrates that the deep learning system can outperform conventional MIMO techniques like STBC and SVD-based schemes at high SNR levels.
• The research effectively handles quantized CSI feedback, simplifying channel estimation and enhancing real-world deployment feasibility.

Overview of "Deep Learning-Based MIMO Communications"

This paper introduces an innovative approach to Multiple-Input Multiple-Output (MIMO) communications through the utilization of unsupervised deep learning via autoencoders. The authors present a novel physical layer scheme focusing on single-user scenarios, extending their scope from prior work centered on Single Input Single Output (SISO) systems to address the complexities of MIMO setups. The research integrates a Rayleigh fading channel model into the autoencoder optimization process, allowing the system to effectively learn and optimize transmitter and receiver processes tailored to this channel environment. This paper compares the performance of conventional MIMO systems against the proposed deep learning approach, showing promising results that surpass traditional methods under certain conditions.

Main Contributions

1. Joint Optimization Framework: The authors propose a unified framework that combines the traditionally separated tasks of estimation, feedback, encoding, and decoding into a single optimization problem. This holistic approach is key to improving throughput and minimizing the bit error rate, highlighting the strong potential for deep learning techniques in system-level optimizations.
2. Performance Metrics and Comparisons: The paper provides comprehensive simulations showing that a Deep Learning-based autoencoder system can exceed the performance of conventional MIMO systems, including both Space Time Block Codes (STBC) for spatial diversity and Singular Value Decomposition (SVD)-based schemes for spatial multiplexing, particularly when the Signal-to-Noise Ratio (SNR) is sufficiently high.
3. Handling Quantized Feedback: Addressing practical challenges related to the bandwidth-limited feedback channel for Channel State Information (CSI), the authors demonstrate that the system can perform robustly with compact binary representations of CSI, significantly enhancing feasibility for real-world deployment without sacrificing performance.

Results and Discussion

The simulations provide conclusive evidence that the autoencoder-based system outperforms classic MIMO configurations under certain SNR conditions. Notably, in a 2x1 MIMO setting focused on spatial diversity, the autoencoder surpasses the STBC scheme at SNR levels above 15 dB. Similarly, for a 2x2 MIMO scenario using spatial multiplexing under perfect CSI, the deep learning approach outpaces the established linear pre-coding solutions over all tested SNRs.

Furthermore, when quantized CSI is employed, the autoencoder-based system interestingly not only maintains performance but, in some configurations, outperforms the baseline system with perfect CSI. This intriguing outcome suggests that quantized CSI may serve as a beneficial constraint, simplifying the channel estimation task and potentially leading to more robust system implementations.

Implications and Future Directions

This research opens up several promising avenues in wireless communication systems, particularly in advancing more integrated and efficient physical layer designs. Its implications span enhancing computational efficiency, optimizing computational complexity, and promoting adaptive systems capable of dynamically learning from evolving channel conditions.

The findings encourage further exploration into scalable extensions, such as Massive MIMO and multi-user MIMO (MU-MIMO) systems. The paper hints at intriguing potential in tackling complex problems like channel estimation without CSI at the transmitter side, extending its approach to both multiple access and broadcast channels, and leveraging deep learning tools for more sophisticated beamforming strategies.

The journey towards embedding deep learning models into the core of communication systems suggests a transformative path for future telecommunications horizons, intertwining machine learning prowess with physical layer intricacies to yield systems that continuously learn and adapt for enhanced performance across diversified channel conditions.