This paper presents the first orthogonal frequency-division
multiplexing(OFDM)-based digital over-the-air computation (AirComp) system for
wireless federated edge learning, where multiple edge devices transmit model
data simultaneously using non-orthogonal wireless resources, and the edge
server aggregates data directly from the superimposed signal. Existing analog
AirComp systems often assume perfect phase alignment via channel precoding and
utilize uncoded analog transmission for model aggregation. In contrast, our
digital AirComp system leverages digital modulation and channel codes to
overcome phase asynchrony, thereby achieving accurate model aggregation for
phase-asynchronous multi-user OFDM systems. To realize a digital AirComp
system, we develop a medium access control (MAC) protocol that allows
simultaneous transmissions from different users using non-orthogonal OFDM
subcarriers, and put forth joint channel decoding and aggregation decoders
tailored for convolutional and LDPC codes. To verify the proposed system
design, we build a digital AirComp prototype on the USRP software-defined radio
platform, and demonstrate a real-time LDPC-coded AirComp system with up to four
users. Trace-driven simulation results on test accuracy versus SNR show that:
1) analog AirComp is sensitive to phase asynchrony in practical multi-user OFDM
systems, and the test accuracy performance fails to improve even at high SNRs;
2) our digital AirComp system outperforms two analog AirComp systems at all
SNRs, and approaches the optimal performance when SNR $\geq$ 6 dB for two-user
LDPC-coded AirComp, demonstrating the advantage of digital AirComp in
phase-asynchronous multi-user OFDM systems.

本文提出了首个基于正交频分复用（OFDM）的数字化无线计算（AirComp）系统，该系统适用于多个边缘设备同时传输模型数据，使用非正交无线资源，并且边缘服务器直接从叠加信号聚合数据。

无线联合边缘学习的宽带数字无线计算

Broadband Digital Over-the-Air Computation for Wireless Federated Edge  Learning

Deep learning based channel code designs have recently gained interest as an
alternative to conventional coding algorithms, particularly for channels for
which existing codes do not provide effective solutions. Communication over a
feedback channel is one such problem, for which promising results have recently
been obtained by employing various deep learning architectures. In this paper,
we introduce a novel learning-aided code design for feedback channels, called
generalized block attention feedback (GBAF) codes, which i) employs a modular
architecture that can be implemented using different neural network
architectures; ii) provides order-of-magnitude improvements in the probability
of error compared to existing designs; and iii) can transmit at desired code
rates.

本文介绍了一种深度学习为基础的信道编码设计框架，称为广义块注意力反馈（GBAF）编码，该编码可以显著改善反馈信道传输的误码率，可以在所需的码率下传输。

反馈说话：使用块注意力反馈代码进行通信

All you need is feedback: Communication with block attention feedback codes

Designing codes that combat the noise in a communication medium has remained
a significant area of research in information theory as well as wireless
communications. Asymptotically optimal channel codes have been developed by
mathematicians for communicating under canonical models after over 60 years of
research. On the other hand, in many non-canonical channel settings, optimal
codes do not exist and the codes designed for canonical models are adapted via
heuristics to these channels and are thus not guaranteed to be optimal. In this
work, we make significant progress on this problem by designing a fully
end-to-end jointly trained neural encoder and decoder, namely, Turbo
Autoencoder (TurboAE), with the following contributions: ($a$) under moderate
block lengths, TurboAE approaches state-of-the-art performance under canonical
channels; ($b$) moreover, TurboAE outperforms the state-of-the-art codes under
non-canonical settings in terms of reliability. TurboAE shows that the
development of channel coding design can be automated via deep learning, with
near-optimal performance.

本文介绍了一种新型神经编码器和解码器 TurboAE，该编码器可以通过深度学习来自动化设计通道编码，以提高非规范通道模型的可靠性，并在中等块长度下接近规范通道模型的最新性能水平。

Turbo Autoencoder：基于深度学习的点对点通信信道编码

Turbo Autoencoder: Deep learning based channel codes for point-to-point  communication channels

Designing channel codes under low-latency constraints is one of the most
demanding requirements in 5G standards. However, a sharp characterization of
the performance of traditional codes is available only in the large
block-length limit. Guided by such asymptotic analysis, code designs require
large block lengths as well as latency to achieve the desired error rate.
Tail-biting convolutional codes and other recent state-of-the-art short block
codes, while promising reduced latency, are neither robust to channel-mismatch
nor adaptive to varying channel conditions. When the codes designed for one
channel (e.g.,~Additive White Gaussian Noise (AWGN) channel) are used for
another (e.g.,~non-AWGN channels), heuristics are necessary to achieve
non-trivial performance.
In this paper, we first propose an end-to-end learned neural code, obtained
by jointly designing a Recurrent Neural Network (RNN) based encoder and
decoder. This code outperforms canonical convolutional code under block
settings. We then leverage this experience to propose a new class of codes
under low-latency constraints, which we call Low-latency Efficient Adaptive
Robust Neural (LEARN) codes. These codes outperform state-of-the-art
low-latency codes and exhibit robustness and adaptivity properties. LEARN codes
show the potential to design new versatile and universal codes for future
communications via tools of modern deep learning coupled with communication
engineering insights.

本文针对 5G 标准下信道编码的低延迟要求，基于深度学习和通信工程知识，提出了一种端到端学习的神经编码器，称为 LEARN 编码器。相比传统卷积码，LEARN 编码器在低延迟条件下表现更好，具有强鲁棒性和适应性，表现出设计用于未来通信的新型多功能通用编码的潜力。