Ensuring extremely high reliability is essential for channel coding in 6G
networks. The next-generation of ultra-reliable and low-latency communications
(xURLLC) scenario within 6G networks requires a frame error rate (FER) below
10-9. However, low-density parity-check (LDPC) codes, the standard in 5G new
radio (NR), encounter a challenge known as the error floor phenomenon, which
hinders to achieve such low rates. To tackle this problem, we introduce an
innovative solution: boosted neural min-sum (NMS) decoder. This decoder
operates identically to conventional NMS decoders, but is trained by novel
training methods including: i) boosting learning with uncorrected vectors, ii)
block-wise training schedule to address the vanishing gradient issue, iii)
dynamic weight sharing to minimize the number of trainable weights, iv)
transfer learning to reduce the required sample count, and v) data augmentation
to expedite the sampling process. Leveraging these training strategies, the
boosted NMS decoder achieves the state-of-the art performance in reducing the
error floor as well as superior waterfall performance. Remarkably, we fulfill
the 6G xURLLC requirement for 5G LDPC codes without the severe error floor.
Additionally, the boosted NMS decoder, once its weights are trained, can
perform decoding without additional modules, making it highly practical for
immediate application.

在 6G 网络中，确保极高的可靠性对于信道编码至关重要。我们提出一种创新的解决方案，即增强型神经最小和（NMS）解码器，通过采用新颖的训练方法，在 5G 低密度奇偶校验（LDPC）码中实现了优越的性能，并满足了 6G 低误码要求，同时具备高度的实际应用性。

增强型神经解码器：实现 6G 网络中 LDPC 码的极高可靠性

Boosted Neural Decoders: Achieving Extreme Reliability of LDPC Codes for  6G Networks

Ultra-Reliable and Low-Latency Communications (URLLC) services in vehicular
networks on millimeter-wave bands present a significant challenge, considering
the necessity of constantly adjusting the beam directions. Conventional methods
are mostly based on classical control theory, e.g., Kalman filter and its
variations, which mainly deal with stationary scenarios. Therefore, severe
application limitations exist, especially with complicated, dynamic
Vehicle-to-Everything (V2X) channels. This paper gives a thorough study of this
subject, by first modifying the classical approaches, e.g., Extended Kalman
Filter (EKF) and Particle Filter (PF), for non-stationary scenarios, and then
proposing a Reinforcement Learning (RL)-based approach that can achieve the
URLLC requirements in a typical intersection scenario. Simulation results based
on a commercial ray-tracing simulator show that enhanced EKF and PF methods
achieve packet delay more than $10$ ms, whereas the proposed deep RL-based
method can reduce the latency to about $6$ ms, by extracting context
information from the training data.

本论文研究汽车网络中毫米波频段的超高可靠低延迟通信服务，通过修改经典控制算法以适应非稳态场景，并提出一种基于强化学习的方法，从训练数据中提取上下文信息来实现 URLLC 要求，模拟结果表明，强化学习方法可以将延迟缩短至约 6ms。

基于深度强化学习的车载网络低延迟服务波束跟踪

Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services  in Vehicular Networks

Supporting ultra-reliable and low-latency communications (URLLC) is one of
the major goals for the fifth-generation cellular networks. Since spectrum
usage efficiency is always a concern, and large bandwidth is required for
ensuring stringent quality-of-service (QoS), we minimize the total bandwidth
under the QoS constraints of URLLC. We first propose a packet delivery
mechanism for URLLC. To reduce the required bandwidth for ensuring queueing
delay, we consider a statistical multiplexing queueing mode, where the packets
to be sent to different devices are waiting in one queue at the base station,
and broadcast mode is adopted in downlink transmission. In this way, downlink
bandwidth is shared among packets of multiple devices. In uplink transmission,
different subchannels are allocated to different devices to avoid strong
interference. Then, we jointly optimize uplink and downlink bandwidth
configuration and delay components to minimize the total bandwidth required to
guarantee the overall packet loss and end-to-end delay, which includes uplink
and downlink transmission delays, queueing delay and backhaul delay. We propose
a two-step method to find the optimal solution. Simulation and numerical
results validate our analysis and show remarkable performance gain by jointly
optimizing uplink and downlink configuration.

为了支持超可靠低延迟通信（URLLC），我们首先提出了适用于 URLLC 的分组传递机制，然后联合优化上下行带宽配置和延迟成分，以最小化保证总数据包丢失和端到端延迟所需的总带宽。