The proliferation of autonomous Unmanned Aerial Vehicles (UAVs) in Beyond Visual Line of Sight (BVLOS) applications is critically dependent on resilient, high-bandwidth, and low-latency communication links. Existing solutions face critical limitations: TCP's head-of-line blocking stalls time-sensitive data, UDP lacks reliability and congestion control, and cellular networks designed for terrestrial users degrade severely for aerial platforms. This paper introduces AQUILA, a cross-layer communication architecture built on QUIC to address these challenges. AQUILA contributes three key innovations: (1) a unified transport layer using QUIC's reliable streams for MAVLink Command and Control (C2) and unreliable datagrams for video, eliminating head-of-line blocking under unified congestion control; (2) a priority scheduling mechanism that structurally ensures C2 latency remains bounded and independent of video traffic intensity; (3) a UAV-adapted congestion control algorithm extending SCReAM with altitude-adaptive delay targeting and telemetry headroom reservation. AQUILA further implements 0-RTT connection resumption to minimize handover blackouts with application-layer replay protection, deployed over an IP-native architecture enabling global operation. Experimental validation demonstrates that AQUILA significantly outperforms TCP- and UDP-based approaches in C2 latency, video quality, and link resilience under realistic conditions, providing a robust foundation for autonomous BVLOS missions.

In this paper, a measurement-driven framework is proposed for early radio link failure (RLF) prediction in 5G non-standalone (NSA) railway environments. Using 10 Hz metro-train traces with serving and neighbor-cell indicators, we benchmark six models, namely CNN, LSTM, XGBoost, Anomaly Transformer, PatchTST, and TimesNet, under varied observation windows and prediction horizons. When the observation window is three seconds, TimesNet attains the highest F1 score with a three-second prediction horizon, while CNN provides a favorable accuracy-latency tradeoff with a two-second horizon, enabling proactive actions such as redundancy and adaptive handovers. The results indicate that deep temporal models can anticipate reliability degradations several seconds in advance using lightweight features available on commercial devices, offering a practical path to early-warning control in 5G-based railway systems.

Next-generation wireless communication systems must support ultra-reliable low-latency communication (URLLC) service for mission-critical applications. Meeting stringent URLLC requirements is challenging, especially for two-hop cooperative communication. In this paper, we develop an adaptive transmission design for a two-hop relaying communication system. Each hop transmission adaptively configures its transmission parameters separately, including numerology, mini-slot size, and modulation and coding scheme, for reliable packet transmission within a strict latency constraint. We formulate the hop-specific transceiver configuration as a Markov decision process (MDP) and propose a dual-agent reinforcement learning-based cooperative latency-aware transmission (DRL-CoLA) algorithm to learn latency-aware transmission policies in a distributed manner. Simulation results verify that the proposed algorithm achieves the near-optimal reliability while satisfying strict latency requirements.

Unmanned Aerial Vehicles (UAVs) are increasingly being utilized in various private and commercial applications, e.g., traffic control, parcel delivery, and Search and Rescue (SAR) missions. Machine Learning (ML) methods used in UAV-Assisted Sensor Networks (UASNETs) and, especially, in Deep Reinforcement Learning (DRL) face challenges such as complex and lengthy model training, gaps between simulation and reality, and low sampling efficiency, which conflict with the urgency of emergencies, such as SAR missions. In this paper, an In-Context Learning (ICL)-Data Collection Scheduling (ICLDC) system is proposed as an alternative to DRL in emergencies. The UAV collects sensory data and transmits it to a Large Language Model (LLM), which creates a task description in natural language. From this description, the UAV receives a data collection schedule that must be executed. A verifier ensures safe UAV operations by evaluating the schedules generated by the LLM and overriding unsafe schedules based on predefined rules. The system continuously adapts by incorporating feedback into the task descriptions and using this for future decisions. This method is tested against jailbreaking attacks, where the task description is manipulated to undermine network performance, highlighting the vulnerability of LLMs to such attacks. The proposed ICLDC significantly reduces cumulative packet loss compared to both the DQN and Maximum Channel Gain baselines. ICLDC presents a promising direction for intelligent scheduling and control in UASNETs.

In this work, we address the problem of training Reinforcement Learning (RL) agents over communication networks. The RL paradigm requires the agent to instantaneously perceive the state evolution to infer the effects of its actions on the environment. This is impossible if the agent receives state updates over lossy or delayed wireless systems and thus operates with partial and intermittent information. In recent years, numerous frameworks have been proposed to manage RL with imperfect feedback; however, they often offer specific solutions with a substantial computational burden. To address these limits, we propose a novel architecture, named Homomorphic Robust Remote Reinforcement Learning (HR3L), that enables the training of remote RL agents exchanging observations across a non-ideal wireless channel. HR3L considers two units: the transmitter, which encodes meaningful representations of the environment, and the receiver, which decodes these messages and performs actions to maximize a reward signal. Importantly, HR3L does not require the exchange of gradient information across the wireless channel, allowing for quicker training and a lower communication overhead than state-of-the-art solutions. Experimental results demonstrate that HR3L significantly outperforms baseline methods in terms of sample efficiency and adapts to different communication scenarios, including packet losses, delayed transmissions, and capacity limitations.

--Service robots are increasingly deployed in diverse and dynamic environments, where both physical layouts and social contexts change over time and across locations. In these unstructured settings, conventional navigation systems that rely on fixed parameters often fail to generalize across scenarios, resulting in degraded performance and reduced social acceptance. Although recent approaches have leveraged reinforcement learning to enhance traditional planners, these methods often fail in real-world deployments due to poor generalization and limited simulation diversity, which hampers effective sim-to-real transfer . T o tackle these issues, we present LE-Nav, an interpretable and scene-aware navigation framework that leverages multi-modal large language model reasoning and conditional variational autoencoders to adaptively tune planner hyperpa-rameters. T o achieve zero-shot scene understanding, we utilize one-shot exemplars and chain-of-thought prompting strategies. Experiments show that LE-Nav can generate hyperparameters achieving human-level tuning across diverse planners and scenarios. Real-world navigation trials and a user study on a smart wheelchair platform demonstrate that it outperforms state-of-the-art methods on quantitative metrics such as success rate, efficiency, safety, and comfort, while receiving higher subjective scores for perceived safety and social acceptance. Note to Practitioners--Service robots often experience degraded performance of traditional local planners due to changing and dynamic environmental conditions during navigation. This work investigates automatic hyperparameter tuning for planners such as DW A and TEB, and our framework LE-Nav can be used to adjust hyperparameters of any optimization-based planner . Existing navigation frameworks are typically either end-to-end, lacking safety guarantees, or rely on reinforcement learning-based tuning with limited generalization. By designing two prompting strategies, we enable the MLLM to generate stable and accurate scene descriptions. We use a conditional variational autoencoder to learn human expert tuning strategies, enhanced with data augmentation and attention masking to address inevitable MLLM packet loss in real applications. The decoupling of the MLLM and action modules improves decision transparency, allowing clear insight into how scene analysis informs navigation behavior . Experiments demonstrate that our method adaptively generates hyperparameters comparable to human experts, while being robust to packet loss and compatible with various MLLMs. Future work includes enhancing real-time scene understanding with advanced MLLMs, expanding support to more planners with personalized tuning, and extending the framework to collaborative multi-robot systems.

State-of-the-art language and vision models are routinely trained across thousands of GPUs, often spanning multiple data-centers, yet today's distributed frameworks still assume reliable connections (e.g., InfiniBand or RoCE). The resulting acknowledgment traffic and retransmissions inflate tail latencies and limit scalability. Leveraging unreliable connections will reduce latency but may sacrifice model accuracy and convergence once packets are dropped. A principled, end-to-end solution that preserves accuracy and convergence guarantees under genuine packet loss has previously been missing. We address this critical gap by introducing a novel distributed training framework capable of operating over unreliable connections, offering unbiased gradient aggregation and bounded parameter drift without modifying model code or optimizers. The key insight is a two-stage defense against missing messages: (i) Unbiased gradient aggregation: each worker reconstructs a consistent gradient estimate from whatever packets arrive, guaranteeing expectation-level correctness; and (ii) Bounded-drift parameter broadcasts: we prove the inter-worker model discrepancy remains O(1) even after arbitrarily many iterations, preventing the unbounded divergence typical of asynchronous setups. Analytical bounds are matched by experiments on the LLAMA2 7B model with 64 GPUs: tolerating 10% random packet loss yields at most 0.8% perplexity change. This work bridges the gap between communication-efficient datacenter protocols and the accuracy and generalization guarantees demanded by modern large-model training, enabling robust, high-throughput learning on commodity or wide-area networks.

Tokens are fundamental processing units of generative AI (GenAI) and large language models (LLMs), and token communication (TC) is essential for enabling remote AI-generate content (AIGC) and wireless LLM applications. Unlike traditional bits, each of which is independently treated, the semantics of each token depends on its surrounding context tokens. This inter-token dependency makes TC vulnerable to outage channels, where the loss of a single token can significantly distort the original message semantics. Motivated by this, this paper focuses on optimizing token packetization to maximize the average token similarity (ATS) between the original and received token messages under outage channels. Due to inter-token dependency, this token grouping problem is combinatorial, with complexity growing exponentially with message length. To address this, we propose a novel framework of semantic packet aggregation with lookahead search (SemPA-Look), built on two core ideas. First, it introduces the residual semantic score (RSS) as a token-level surrogate for the message-level ATS, allowing robust semantic preservation even when a certain token packet is lost. Second, instead of full search, SemPA-Look applies a lookahead search-inspired algorithm that samples intra-packet token candidates without replacement (fixed depth), conditioned on inter-packet token candidates sampled with replacement (fixed width), thereby achieving linear complexity. Experiments on a remote AIGC task with the MS-COCO dataset (text captioned images) demonstrate that SemPA-Look achieves high ATS and LPIPS scores comparable to exhaustive search, while reducing computational complexity by up to 40$\times$. Compared to other linear-complexity algorithms such as the genetic algorithm (GA), SemPA-Look achieves 10$\times$ lower complexity, demonstrating its practicality for remote AIGC and other TC applications.

--We propose SAL T (Split-Adaptive Lightweight T un-ing), a lightweight model adaptation framework for Split Computing under closed constraints, where the head and tail networks are proprietary and inaccessible to users. In such closed environments, conventional adaptation methods are infeasible since they require access to model parameters or architectures. SAL T addresses this challenge by introducing a compact, trainable adapter on the client side to refine latent features from the head network, enabling user-specific adaptation without modifying the original models or increasing communication overhead. We evaluate SAL T on user-specific classification tasks with CIF AR-10 and CIF AR-100, demonstrating improved accuracy with lower training latency compared to fine-tuning methods. With minimal deployment overhead, SAL T offers a practical solution for personalized inference in edge AI systems under strict system constraints. The increasing scale of deep learning models deployed in cloud-based AI services has raised concerns regarding server-side computational load and inference latency. To address these challenges, Split Computing has emerged as a promising paradigm that offloads part of a large cloud-based model to the client device [1], [2]. In this architecture, the neural network model is partitioned into a head network executed on the client and a tail network retained on the cloud.

As load and delta load increase, the policy puts more flows on the Internet link. Increasing Internet delay puts the flows on MPLS. The contribution of Internet loss seems counter intuitive as it seems to put more load on Internet Link. However, even if its coefficient is near to 1.0, the overall contribution of the term is negligible as compared to load because loss in our scenario varies from 0 to around 0.15. This applies to delay too. For minimising loss, we extract the following: a 1. 9 1 .1( 2 λ 3 + 1) 2 2λ i 5 + 10 d i 3 + u i 10 (4) This policy can be interpreted as follows, and we may refer to Figure 1 as well. The ratio starts near 0.8 and increasing load, with increasing delta, puts more traffic on Internet link. Increasing Internet delay and Internet link utilisation slightly shifts the balance towards putting more traffic on MPLS link. Distillation of symbolic equations of PPO policy: In this method, we train policy using PPO, generate trajectory data and then generate the symbolic equations using auto-regressive models [22].