Neural Network Detection of Data Sequences in Communication Systems (1802.02046v3)

Abstract: We consider detection based on deep learning, and show it is possible to train detectors that perform well without any knowledge of the underlying channel models. Moreover, when the channel model is known, we demonstrate that it is possible to train detectors that do not require channel state information (CSI). In particular, a technique we call a sliding bidirectional recurrent neural network (SBRNN) is proposed for detection where, after training, the detector estimates the data in real-time as the signal stream arrives at the receiver. We evaluate this algorithm, as well as other neural network (NN) architectures, using the Poisson channel model, which is applicable to both optical and molecular communication systems. In addition, we also evaluate the performance of this detection method applied to data sent over a molecular communication platform, where the channel model is difficult to model analytically. We show that SBRNN is computationally efficient, and can perform detection under various channel conditions without knowing the underlying channel model. We also demonstrate that the bit error rate (BER) performance of the proposed SBRNN detector is better than that of a Viterbi detector with imperfect CSI as well as that of other NN detectors that have been previously proposed. Finally, we show that the SBRNN can perform well in rapidly changing channels, where the coherence time is on the order of a single symbol duration.

• The paper proposes a sliding bidirectional recurrent neural network (SBRNN) for model-free data sequence detection in communication systems without explicit channel state information.
• The research validates the SBRNN's effectiveness against traditional methods using theoretical Poisson channel models and experimental data from a molecular communication platform.
• This approach offers improved robustness, efficiency, and practical applicability in complex communication environments where channel models are unknown or difficult to characterize.

Overview of Neural Network Detectors in Communication Systems

The paper by Nariman Farsad and Andrea Goldsmith investigates the use of neural network-based detection techniques in communication systems, particularly under conditions where the channel models are either unknown or analytically intractable. Traditional approaches rely heavily on the knowledge of channel models and often require continuous estimation of channel state information (CSI), which can be impractical or challenging in dynamic and complex environments, such as optical and molecular communication systems.

This research proposes a sliding bidirectional recurrent neural network (SBRNN) for detection, which is capable of estimating transmitted data sequences in real-time without requiring explicit CSI or comprehensive knowledge of the channel models. The paper rigorously evaluates this approach across various channel conditions using both theoretical models (such as the Poisson channel model) and experimental data from a molecular communication platform where precise channel characterization is difficult.

Key Contributions

1. Model-Free Detection: The paper introduces a novel detection method that can function without detailed channel modeling. The SBRNN is trained to generalize across a spectrum of channel conditions, making it robust against variations that traditional model-based detectors struggle with, particularly under poorly characterized or rapidly fluctuating conditions.
2. Poisson Channel Testing: Using a Poisson channel model, which is relevant for both optical and molecular communications, the effectiveness of the SBRNN is benchmarked against traditional Viterbi-based detectors. The results indicate notable advantages, especially when the channel memory length is uncertain or when there is CSI estimation error.
3. Experimental Validation: The practicality of SBRNN is reinforced through empirical testing on a physical molecular communication platform. This demonstrates the SBRNN's applicability to real-world scenarios, showcasing its adaptability in experimental conditions where existing channel models might fail.
4. Computational Efficiency: The paper points out that the SBRNN, with its linear computational complexity relative to memory length, offers significant efficiency gains compared to the Viterbi detector's exponential growth with memory length, making it feasible for longer memory channels.

Implications and Future Directions

The implications of this work are profound for the future of ubiquitous intelligent communication systems. The shift from traditional model-based to data-driven approaches in signal detection opens up possibilities for applications in environments where channel models are difficult to formulate, or CSI is costly to estimate.

Practically, this could mean enhanced performance in wireless and molecular communication systems that operate in unpredictable and variable environments, such as under medical or biological constraints. The methodology could be extended to even more complex modulation schemes and larger symbol sets, providing a foundation for robust, adaptive detection in diverse communication technologies.

The research invites further exploration into integrating reinforcement learning techniques for better adaptability and the potential for transfer learning, where the SBRNN trained on one channel condition could be fine-tuned to rapidly adapt to new, unseen conditions. This avenue promises to enhance the resilience and efficiency of communication systems across numerous sectors.