arXiv subjects

Haochuan Song

Publications and source records attributed to Haochuan Song.

4 recordsLinked to original sources

arXiv2023

Bit Error and Block Error Rate Training for ML-Assisted Communication

Even though machine learning (ML) techniques are being widely used in communications, the question of how to train communication systems has received surprisingly little attention. In this paper, we show that the commonly used binary cross-entropy (BCE) loss is a sensible choice in uncoded systems, e.g., for training ML-assisted data detectors, but may not be optimal in coded systems. We propose new loss functions targeted at minimizing the block error rate and SNR deweighting, a novel method that trains communication systems for optimal performance over a range of signal-to-noise ratios. The utility of the proposed loss functions as well as of SNR deweighting is shown through simulations in NVIDIA Sionna.

cs.IT↗

arXiv2021

Soft-Output Joint Channel Estimation and Data Detection using Deep Unfolding

We propose a novel soft-output joint channel estimation and data detection (JED) algorithm for multiuser (MU) multiple-input multiple-output (MIMO) wireless communication systems. Our algorithm approximately solves a maximum a-posteriori JED optimization problem using deep unfolding and generates soft-output information for the transmitted bits in every iteration. The parameters of the unfolded algorithm are computed by a hyper-network that is trained with a binary cross entropy (BCE) loss. We evaluate the performance of our algorithm in a coded MU-MIMO system with 8 basestation antennas and 4 user equipments and compare it to state-of-the-art algorithms separate channel estimation from soft-output data detection. Our results demonstrate that our JED algorithm outperforms such data detectors with as few as 10 iterations.

cs.IT↗

arXiv2021

Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems

We propose a joint channel estimation and data detection (JED) algorithm for densely-populated cell-free massive multiuser (MU) multiple-input multiple-output (MIMO) systems, which reduces the channel training overhead caused by the presence of hundreds of simultaneously transmitting user equipments (UEs). Our algorithm iteratively solves a relaxed version of a maximum a-posteriori JED problem and simultaneously exploits the sparsity of cell-free massive MU-MIMO channels as well as the boundedness of QAM constellations. In order to improve the performance and convergence of the algorithm, we propose methods that permute the access point and UE indices to form so-called virtual cells, which leads to better initial solutions. We assess the performance of our algorithm in terms of root-mean-squared-symbol error, bit error rate, and mutual information, and we demonstrate that JED significantly reduces the pilot overhead compared to orthogonal training, which enables reliable communication with short packets to a large number of UEs.

cs.IT↗

arXiv2018

Polar-Coded Forward Error Correction for MLC NAND Flash Memory Polar FEC for NAND Flash Memory

With the ever-growing storage density, high-speed, and low-cost data access, flash memory has inevitably become popular. Multi-level cell (MLC) NAND flash memory, which can well balance the data density and memory stability, has occupied the largest market share of flash memory. With the aggressive memory scaling, however, the reliability decays sharply owing to multiple interferences. Therefore, the control system should be embedded with a suitable error correction code (ECC) to guarantee the data integrity and accuracy. We proposed the pre-check scheme which is a multi-strategy polar code scheme to strike a balance between reasonable frame error rate (FER) and decoding latency. Three decoders namely binary-input, quantized-soft, and pure-soft decoders are embedded in this scheme. Since the calculation of soft log-likelihood ratio (LLR) inputs needs multiple sensing operations and optional quantization boundaries, a 2-bit quantized hard-decision decoder is proposed to outperform the hard-decoded LDPC bit-flipping decoder with fewer sensing operations. We notice that polar codes have much lower computational complexity compared to LDPC codes. The stepwise maximum mutual information (SMMI) scheme is also proposed to obtain overlapped boundaries without exhausting search. The mapping scheme using Gray code is employed and proved to achieve better raw error performance compared to other alternatives. Hardware architectures are also given in this paper.

cs.AR↗