Spiking Neural Networks for Communication Systems: Encoding Schemes, Learning Algorithms, and Equalization~Techniques

Machine learning with artificial neural networks (ANNs), provides solutions for the growing complexity of modern communication systems. This complexity, however, increases power consumption, making the systems energy-intensive. Spiking neural networks (SNNs) represent a novel generation of neural networks inspired by the highly efficient human brain. By emulating its event-driven and energy-efficient mechanisms, SNNs enable low-power, real-time signal processing. They differ from ANNs in two key ways: they exhibit inherent temporal dynamics and process and transmit information as short binary signals called spikes. Despite their promise, major challenges remain, e.g., identifying optimal learning rules and effective neural encoding. This thesis investigates the design of SNN-based receivers for nonlinear time-invariant frequency-selective channels. Backpropagation through time with surrogate gradients is identified as a promising update rule and the novel quantization encoding (QE) as promising neural encoding. Given the model of the intensity modulation with direct detection link, we compare two different receiver architectures based on equalization performance and spike count. Using decision feedback and QE achieves both strong performance and low spike counts. Notably, SNN-based receivers significantly outperform ANN-based counterparts. We furthermore introduce policy gradient-based update (PGU), an reinforcement learning-based update algorithm that requires no backpropagation. Using PGU, encoding parameters are optimized, drastically reducing runtime, complexity, and spikes per inference while maintaining performance. This thesis contributes a successful design and optimization framework for SNN-based receivers. By addressing key challenges in SNN optimization, it facilitates future advances in the design and deployment of energy-efficient SNN receivers.

Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware

Spiking neural networks (SNNs) emulated on dedicated neuromorphic accelerators promise to offer energy-efficient signal processing. However, the neuromorphic advantage over traditional algorithms still remains to be demonstrated in real-world applications. Here, we describe an intensity-modulation, direct-detection (IM/DD) task that is relevant to high-speed optical communication systems used in data centers. Compared to other machine learning-inspired benchmarks, the task offers several advantages. First, the dataset is inherently time-dependent, i.e., there is a time dimension that can be natively mapped to the dynamic evolution of SNNs. Second, small-scale SNNs can achieve the target accuracy required by technical communication standards. Third, due to the small scale and the defined target accuracy, the task facilitates the optimization for real-world aspects, such as energy efficiency, resource requirements, and system complexity.

Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications

In this paper, we highlight recent advances in the use of machine learning for implementing equalizers for optical communications. We highlight both algorithmic advances as well as implementation aspects using conventional and neuromorphic hardware.

Spiking Neural Belief Propagation Decoder for Short Block Length LDPC Codes

Spiking neural networks (SNNs) are neural networks that enable energy-efficient signal processing due to their event-based nature. This paper proposes a novel decoding algorithm for low-density parity-check (LDPC) codes that integrates SNNs into belief propagation (BP) decoding by approximating the check node update equations using SNNs. For the (273,191) and (1023,781) finite-geometry LDPC code, the proposed decoder outperforms sum-product decoder at high signal-to-noise ratios (SNRs). The decoder achieves a similar bit error rate to normalized sum-product decoding with successive relaxation. Furthermore, the novel decoding operates without requiring knowledge of the SNR, making it robust to SNR mismatch.

Energy-efficient Spiking Neural Network Equalization for IM/DD Systems with Optimized Neural Encoding

We propose an energy-efficient equalizer for IM/DD systems based on spiking neural networks. We optimize a neural spike encoding that boosts the equalizer's performance while decreasing energy consumption.

Spiking Neural Network Decision Feedback Equalization for IM/DD Systems

A spiking neural network (SNN) equalizer with a decision feedback structure is applied to an IM/DD link with various parameters. The SNN outperforms linear and artificial neural network (ANN) based equalizers.