Semantic communication (SemCom) and edge computing are two disruptive solutions to address emerging requirements of huge data communication, bandwidth efficiency and low latency data processing in Metaverse. However, edge computing resources are often provided by computing service providers and thus it is essential to design appealingly incentive mechanisms for the provision of limited resources. Deep learning (DL)- based auction has recently proposed as an incentive mechanism that maximizes the revenue while holding important economic properties, i.e., individual rationality and incentive compatibility. Therefore, in this work, we introduce the design of the DLbased auction for the computing resource allocation in SemComenabled Metaverse. First, we briefly introduce the fundamentals and challenges of Metaverse. Second, we present the preliminaries of SemCom and edge computing. Third, we review various incentive mechanisms for edge computing resource trading. Fourth, we present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse. Simulation results demonstrate that the DL-based auction improves the revenue while nearly satisfying the individual rationality and incentive compatibility constraints.

Edge-assisted vehicle-to-everything (V2X) motion planning is an emerging paradigm to achieve safe and efficient autonomous driving, since it leverages the global position information shared among multiple vehicles. However, due to the imperfect channel state information (CSI), the position information of vehicles may become outdated and inaccurate. Conventional methods ignoring the communication delays could severely jeopardize driving safety. To fill this gap, this paper proposes a robust V2X motion planning policy that adapts between competitive driving under a low communication delay and conservative driving under a high communication delay, and guarantees small communication delays at key waypoints via power control. This is achieved by integrating the vehicle mobility and communication delay models and solving a joint design of motion planning and power control problem via the block coordinate descent framework. Simulation results show that the proposed driving policy achieves the smallest collision ratio compared with other benchmark policies.

可重新配置的智能表面（RIS）可以显着增强TERA-HERTZ大量多输入多输出（MIMO）通信系统的服务覆盖范围。但是，获得有限的飞行员和反馈信号开销的准确高维通道状态信息（CSI）具有挑战性，从而严重降低了常规空间分裂多次访问的性能。为了提高针对CSI缺陷的鲁棒性，本文提出了针对RIS辅助TERA-HERTZ多用户MIMO系统的基于深度学习的（DL）基于速率的多访问（RSMA）方案。具体而言，我们首先提出了基于DL的混合数据模型驱动的RSMA预编码方案，包括RIS的被动预编码以及模拟主动编码和基本站（BS）的RSMA数字活动预码。为了实现RIS的被动预码，我们提出了一个基于变压器的数据驱动的RIS反射网络（RRN）。至于BS的模拟主动编码，我们提出了一个基于匹配器的模拟预编码方案，因为BS和RIS采用了Los-Mimo天线阵列结构。至于BS的RSMA数字活动预码，我们提出了一个低复杂性近似加权的最小均方误差（AWMMSE）数字编码方案。此外，为了更好地编码性能以及较低的计算复杂性，模型驱动的深层展开的主动编码网络（DFAPN）也是通过将所提出的AWMMSE方案与DL相结合的。然后，为了在BS处获得准确的CSI，以实现提高光谱效率的RSMA预编码方案，我们提出了一个CSI采集网络（CAN），具有低飞行员和反馈信号开销，下行链接飞行员的传输，CSI在此处使用CSI的CSI反馈。 （UES）和BS处的CSI重建被建模为基于变压器的端到端神经网络。

集成感应和通信（ISAC）代表范式转移，以前竞争的无线传输是共同设计的，可通过共同使用硬件平台来提高光谱，能源和硬件效率来和谐地运行。但是，由于诸如褪色和堵塞之类的对抗性因素，ISAC无融合可能会遭受高感知不确定性的影响。本文提出了一个多点ISAC（MPISAC）系统，该系统通过利用多雷达数据冗余来融合来自多个ISAC设备的输出，以实现更高的感应性能。此外，我们建议通过功能选择模块有效地探索传感和通信之间的性能权衡，该功能选择模块可适应地确定ISAC设备的工作状态（即传感或通信）。我们方法的症结在于采用融合模型，该模型通过假设检验和最佳投票分析来预测融合精度。仿真结果表明，MPISAC优于各种基准方案，并表明所提出的方法可以有效地跨越ISAC系统中的权衡区域。

应用于设备的射频指纹〜（RFF）的深度学习（DL）由于其非凡的分类性能而引起了物理层认证的极大关注。传统的DL-RFF技术通过采用最大似然估计〜（MLE）训练，倾向于过度拟合培训数据集中嵌入的通道统计信息。这限制了他们的实际应用，因为收集足够的培训数据来捕获所有可能的无线渠道环境的特征是具有挑战性的。为了应对这一挑战，我们提出了一个DL表示的DL框架学习〜（DRL），该框架首先学会通过对抗学习将输入信号分解为相关的组件和设备 - iRretrelevant组件。然后，它通过在给定的培训数据集中洗牌以训练后续的RFF提取器来综合一组增强信号。所提出的框架中的隐式数据增强在RFF提取器上实施了正则化，以避免在不收集未知通道的其他数据的情况下，可能会过度拟合设备 - IRRELELERVENT的通道统计。实验验证了所提出的方法，称为DR-RFF，就不明复杂的传播环境的普遍性而言，均优于常规方法，例如，即使所有训练数据都在简单的直接线上收集，即使所有训练数据都收集到分散多径褪色通道，即使 - 见面〜（LOS）传播路径。

Realizing human-like perception is a challenge in open driving scenarios due to corner cases and visual occlusions. To gather knowledge of rare and occluded instances, federated learning assisted connected autonomous vehicle (FLCAV) has been proposed, which leverages vehicular networks to establish federated deep neural networks (DNNs) from distributed data captured by vehicles and road sensors. Without the need of data aggregation, FLCAV preserves privacy while reducing communication costs compared with conventional centralized learning. However, it is challenging to determine the network resources and road sensor placements for multi-stage training with multi-modal datasets in multi-variant scenarios. This article presents networking and training frameworks for FLCAV perception. Multi-layer graph resource allocation and vehicle-road contrastive sensor placement are proposed to address the network management and sensor deployment problems, respectively. We also develop CarlaFLCAV, a software platform that implements the above system and methods. Experimental results confirm the superiority of the proposed techniques compared with various benchmarks.

边缘联合学习（FL）是一种新兴范式，它基于无线通信从分布式数据集中列出全局参数模型。本文提出了一个单位模量的空中计算（UMAircomp）框架，以便于高效的边缘联合学习，它同时通过模拟波束形成更新本地模型参数并更新全局模型参数。所提出的框架避免了复杂的基带信号处理，导致通信延迟和实现成本低。推导Umaircomp FL系统的培训损失界限，并提出了两个低复杂性大规模优化算法，称为惩罚交替最小化（PAM）和加速梯度投影（AGP），以最小化非凸起的非运动损耗绑定。仿真结果表明，与PAM算法的提议Umaircomp框架达到了模型参数估计，训练丢失和测试错误的较小均方误差。此外，具有AGP算法的提议Umaircomp框架实现了令人满意的性能，而与现有优化算法相比，通过幅度的序列降低了计算复杂性。最后，我们展示了Umaircomp在车辆到一般的自主驾驶仿真平台中的实现。发现自主驾驶任务对模型参数误差比其他任务更敏感，因为自主驱动的神经网络包含稀疏模型参数。

自动扬声器验证（ASV）已在现实生活中广泛用于身份认证。但是，随着语音转换的快速发展，语音合成算法和记录设备质量的提高，ASV系统很容易受到欺骗攻击。近年来，有关合成和重播语音检测的许多作品，研究人员提出了许多基于手工制作的特征的反欺骗方法，以提高合成和重播语音检测系统的准确性和鲁棒性。但是，使用手工制作的功能而不是原始波形将丢失某些信息进行抗旋转，这将降低系统的检测性能。受图像分类任务中Convnext的有希望的性能的启发，我们将Convnext网络体系结构相应地扩展到SPOOF攻击任务，并提出了端到端的反欺骗模型。通过将扩展体系结构与频道注意块相结合，提出的模型可以专注于最有用的语音表示子频段，以改善反欺骗性的性能。实验表明，对于ASVSPOOF 2019 LA评估数据集和PA评估数据集，我们提出的最佳单个系统可以达到1.88％和2.79％的误差率，这证明了该模型的抗SpoFofing能力。

This paper studies the quantization of heavy-tailed data in some fundamental statistical estimation problems, where the underlying distributions have bounded moments of some order. We propose to truncate and properly dither the data prior to a uniform quantization. Our major standpoint is that (near) minimax rates of estimation error are achievable merely from the quantized data produced by the proposed scheme. In particular, concrete results are worked out for covariance estimation, compressed sensing, and matrix completion, all agreeing that the quantization only slightly worsens the multiplicative factor. Besides, we study compressed sensing where both covariate (i.e., sensing vector) and response are quantized. Under covariate quantization, although our recovery program is non-convex because the covariance matrix estimator lacks positive semi-definiteness, all local minimizers are proved to enjoy near optimal error bound. Moreover, by the concentration inequality of product process and covering argument, we establish near minimax uniform recovery guarantee for quantized compressed sensing with heavy-tailed noise.

We present Self Meta Pseudo Labels, a novel semi-supervised learning method similar to Meta Pseudo Labels but without the teacher model. We introduce a novel way to use a single model for both generating pseudo labels and classification, allowing us to store only one model in memory instead of two. Our method attains similar performance to the Meta Pseudo Labels method while drastically reducing memory usage.