The growing complexity of network traffic and demand for ultra-low latency communication require smarter packet traffic management. Existing Deep Learning-based queuing approaches struggle with dynamic network scenarios and demand high engineering effort. We propose AQM-LLM, distilling Large Language Models (LLMs) with few-shot learning, contextual understanding, and pattern recognition to improve Active Queue Management (AQM) [RFC 9330] with minimal manual effort. We consider a specific case where AQM is Low Latency, Low Loss, and Scalable Throughput (L4S) and our design of AQM-LLM builds on speculative decoding and reinforcement-based distilling of LLM by tackling congestion prevention in the L4S architecture using Explicit Congestion Notification (ECN) [RFC 9331] and periodic packet dropping. We develop a new open-source experimental platform by executing L4S-AQM on FreeBSD-14, providing interoperable modules to support LLM integration and facilitate IETF recognition through wider testing. Our extensive evaluations show L4S-LLM enhances queue management, prevents congestion, reduces latency, and boosts network performance, showcasing LLMs' adaptability and efficiency in uplifting AQM systems.

This paper investigates the significance of designing a reliable, intelligent, and true physical environment-aware precoding scheme by leveraging an accurately designed channel twin model to obtain realistic channel state information (CSI) for cellular communication systems. Specifically, we propose a fine-tuned multi-step channel twin design process that can render CSI very close to the CSI of the actual environment. After generating a precise CSI, we execute precoding using the obtained CSI at the transmitter end. We demonstrate a two-step parameters' tuning approach to design channel twin by ray tracing (RT) emulation, then further fine-tuning of CSI by employing an artificial intelligence (AI) based algorithm can significantly reduce the gap between actual CSI and the fine-tuned digital twin (DT) rendered CSI. The simulation results show the effectiveness of the proposed novel approach in designing a true physical environment-aware channel twin model.

Multi-agent reinforcement learning (MARL) has been widely adopted in high-performance computing and complex data-driven decision-making in the wireless domain. However, conventional MARL schemes face many obstacles in real-world scenarios. First, most MARL algorithms are online, which might be unsafe and impractical. Second, MARL algorithms are environment-specific, meaning network configuration changes require model retraining. This letter proposes a novel meta-offline MARL algorithm that combines conservative Q-learning (CQL) and model agnostic meta-learning (MAML). CQL enables offline training by leveraging pre-collected datasets, while MAML ensures scalability and adaptability to dynamic network configurations and objectives. We propose two algorithm variants: independent training (M-I-MARL) and centralized training decentralized execution (M-CTDE-MARL). Simulation results show that the proposed algorithm outperforms conventional schemes, especially the CTDE approach that achieves 50 % faster convergence in dynamic scenarios than the benchmarks. The proposed framework enhances scalability, robustness, and adaptability in wireless communication systems by optimizing UAV trajectories and scheduling policies.

The authors present a privacy-preserving protocol for learning text classifiers on short texts using secure multiparty communication (SMC). Unlike differential privacy under the central model, a more popular framework at the moment for making it difficult to distinguish the presence or absence of individuals in training data for a model, this protocol aims to ensure that a pretrained classifier may be used on new text data without leaking that data to the classifier's owner. Though the underlying classifier is not a SOTA solution to the test classification problem, hate speech detection, it is a nontrivial classifier of text and can classify a single example in a matter of seconds, substantially improving over the performance of approaches using homomorphic encryption. The authors test their approach on a collection of 10,000 tweets with binary labels describing whether they are hate speech, demonstrating the effectiveness of this tool in aiding automatic moderation of sensitive content. I want to be open that I am not an expert on SMC, and my primary knowledge of privacy-preserving ML is through differential privacy and natural language processing.

By synergistically integrating mobile networks and embodied artificial intelligence (AI), Mobile Embodied AI Networks (MEANETs) represent an advanced paradigm that facilitates autonomous, context-aware, and interactive behaviors within dynamic environments. Nevertheless, the rapid development of MEANETs is accompanied by challenges in trustworthiness and operational efficiency. Fortunately, blockchain technology, with its decentralized and immutable characteristics, offers promising solutions for MEANETs. However, existing block propagation mechanisms suffer from challenges such as low propagation efficiency and weak security for block propagation, which results in delayed transmission of vehicular messages or vulnerability to malicious tampering, potentially causing severe traffic accidents in blockchain-enabled MEANETs. Moreover, current block propagation strategies cannot effectively adapt to real-time changes of dynamic topology in MEANETs. Therefore, in this paper, we propose a graph Resfusion model-based trustworthy block propagation optimization framework for consortium blockchain-enabled MEANETs. Specifically, we propose an innovative trust calculation mechanism based on the trust cloud model, which comprehensively accounts for randomness and fuzziness in the miner trust evaluation. Furthermore, by leveraging the strengths of graph neural networks and diffusion models, we develop a graph Resfusion model to effectively and adaptively generate the optimal block propagation trajectory. Simulation results demonstrate that the proposed model outperforms other routing mechanisms in terms of block propagation efficiency and trustworthiness. Additionally, the results highlight its strong adaptability to dynamic environments, making it particularly suitable for rapidly changing MEANETs.

We propose a novel multi-agent reinforcement learning (RL) approach for inter-cell interference mitigation, in which agents selectively share their experiences with other agents. Each base station is equipped with an agent, which receives signal-to-interference-plus-noise ratio from its own associated users. This information is used to evaluate and selectively share experiences with neighboring agents. The idea is that even a few pertinent experiences from other agents can lead to effective learning. This approach enables fully decentralized training and execution, minimizes information sharing between agents and significantly reduces communication overhead, which is typically the burden of interference management. The proposed method outperforms state-of-the-art multi-agent RL techniques where training is done in a decentralized manner. Furthermore, with a 75% reduction in experience sharing, the proposed algorithm achieves 98% of the spectral efficiency obtained by algorithms sharing all experiences.

The design of wireless communication receivers to enhance signal processing in complex and dynamic environments is going through a transformation by leveraging deep neural networks (DNNs). Traditional wireless receivers depend on mathematical models and algorithms, which do not have the ability to adapt or learn from data. In contrast, deep learning-based receivers are more suitable for modern wireless communication systems because they can learn from data and adapt accordingly. This survey explores various deep learning architectures such as multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and autoencoders, focusing on their application in the design of wireless receivers. Key modules of a receiver such as synchronization, channel estimation, equalization, space-time decoding, demodulation, decoding, interference cancellation, and modulation classification are discussed in the context of advanced wireless technologies like orthogonal frequency division multiplexing (OFDM), multiple input multiple output (MIMO), semantic communication, task-oriented communication, and next-generation (Next-G) networks. The survey not only emphasizes the potential of deep learning-based receivers in future wireless communication but also investigates different challenges of deep learning-based receivers, such as data availability, security and privacy concerns, model interpretability, computational complexity, and integration with legacy systems.

Wireless positioning technologies hold significant value for applications in autonomous driving, extended reality (XR), unmanned aerial vehicles (UAVs), and more. With the advancement of artificial intelligence (AI), leveraging AI to enhance positioning accuracy and robustness has emerged as a field full of potential. Driven by the requirements and functionalities defined in the 3rd Generation Partnership Project (3GPP) standards, AI/machine learning (ML)-based positioning is becoming a key technology to overcome the limitations of traditional methods. This paper begins with an introduction to the fundamentals of AI and wireless positioning, covering AI models, algorithms, positioning applications, emerging wireless technologies, and the basics of positioning techniques. Subsequently, focusing on standardization progress, we provide a comprehensive review of the evolution of 3GPP positioning standards, with an emphasis on the integration of AI/ML technologies in recent and upcoming releases. Based on the AI/ML-assisted positioning and direct AI/ML positioning schemes outlined in the standards, we conduct an in-depth investigation of related research. we focus on state-of-the-art (SOTA) research in AI-based line-of-sight (LOS)/non-line-of-sight (NLOS) detection, time of arrival (TOA)/time difference of arrival (TDOA) estimation, and angle estimation techniques. For Direct AI/ML Positioning, we explore SOTA advancements in fingerprint-based positioning, knowledge-assisted AI positioning, and channel charting-based positioning. Furthermore, we introduce publicly available datasets for wireless positioning and conclude by summarizing the challenges and opportunities of AI-driven wireless positioning.

Federated learning faces significant challenges in scenarios with heterogeneous data distributions and adverse network conditions, such as delays, packet loss, and data poisoning attacks. This paper proposes a novel method based on the SCAFFOLD algorithm to improve the quality of local updates and enhance the robustness of the global model. The key idea is to form intermediary nodes by merging local models with high similarity, using the Pearson correlation coefficient as a similarity measure. The proposed merging algorithm reduces the number of local nodes while maintaining the accuracy of the global model, effectively addressing communication overhead and bandwidth consumption. Experimental results on the MNIST dataset under simulated federated learning scenarios demonstrate the method's effectiveness. After 10 rounds of training using a CNN model, the proposed approach achieved accuracies of 0.82, 0.73, and 0.66 under normal conditions, packet loss and data poisoning attacks, respectively, outperforming the baseline SCAFFOLD algorithm. These results highlight the potential of the proposed method to improve efficiency and resilience in federated learning systems.

The widespread use of Multi-layer perceptrons (MLPs) often relies on a fixed activation function (e.g., ReLU, Sigmoid, Tanh) for all nodes within the hidden layers. While effective in many scenarios, this uniformity may limit the networks ability to capture complex data patterns. We argue that employing the same activation function at every node is suboptimal and propose leveraging different activation functions at each node to increase flexibility and adaptability. To achieve this, we introduce Local Control Networks (LCNs), which leverage B-spline functions to enable distinct activation curves at each node. Our mathematical analysis demonstrates the properties and benefits of LCNs over conventional MLPs. In addition, we demonstrate that more complex architectures, such as Kolmogorov-Arnold Networks (KANs), are unnecessary in certain scenarios, and LCNs can be a more efficient alternative. Empirical experiments on various benchmarks and datasets validate our theoretical findings. In computer vision tasks, LCNs achieve marginal improvements over MLPs and outperform KANs by approximately 5\%, while also being more computationally efficient than KANs. In basic machine learning tasks, LCNs show a 1\% improvement over MLPs and a 0.6\% improvement over KANs. For symbolic formula representation tasks, LCNs perform on par with KANs, with both architectures outperforming MLPs. Our findings suggest that diverse activations at the node level can lead to improved performance and efficiency.