Wideband and Entropy-Aware Deep Soft Bit Quantization

Deep learning has been recently applied to physical layer processing in digital communication systems in order to improve end-to-end performance. In this work, we introduce a novel deep learning solution for soft bit quantization across wideband channels. Our method is trained end-to-end with quantization-and entropy-aware augmentations to the loss function and is used at inference in conjunction with source coding to achieve near-optimal compression gains over wideband channels. We prove and verify that a proper weight initialization scheme leads to a reduced feature variance, which allows us to use a fixed latent feature quantization scheme. When tested on channel distributions never seen during training, the proposed method achieves a compression gain of up to 10% in the high SNR regime versus previous state-of-the-art methods.

I. Introduction

Soft bit quantization [1], [2] is an important task in integrated, low-power digital communication platforms where memory is an expensive asset [3]. A critical application area for quantizing estimated soft bits is given by hybrid automatic repeat request (HARQ) schemes in, e.g., 5G networks [4], where information from a failed transmission is stored in order to boost the performance via soft combining methods [5]. Given that a cellular base station may communicate with hundreds of users simultaneously, storing soft bits from failed packets requires efficient and low-distortion quantization methods to avoid memory bottlenecks on the platform. Another application area where a flexible trade-off between compression rate and reconstruction distortion is desirable is given by compress-and-forward relaying schemes [6], [7], where estimated soft bits are forwarded to a receiver and compressed in order to lower relay channel resource utilization.