Flying Ad Hoc Networks (FANETs) are self-organizing wireless networks composed of Unmanned Aerial Vehicles (UAVs), designed to enable collaborative tasks without relying on fixed infrastructure. Characterized by high mobility, dynamic topology, and three-dimensional spatial movement, FANETs pose critical challenges to routing protocols, particularly to the broadcast storm problem caused by uncontrolled flooding in large-scale deployments. To address the challenges of broadcast storms in FANETs, this paper proposes RHARP, a role-based hierarchical routing protocol for FANETs. RHARP innovatively integrates bio-inspired swarm communication principles with role-based network clustering, establishing functional correspondence between decentralized coordination in biological swarms and differentiated node roles in FANETs. Through functional decoupling, RHARP delegates intra-cluster routing decisions to gateway nodes to alleviate cluster head (CH) workload. It achieves dynamic inter-cluster communication optimization through hierarchical routing strategies and on-demand path discovery mechanisms. By systematically integrating role-based clustering with adaptive routing, RHARP provides a scalable and cost-effective communication framework for large-scale FANET deployments while maintaining protocol agility. The simulation results demonstrate the effectiveness of RHARP across various dynamic scenarios.

In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents. To organize this knowledge from experts uniformly, we propose to create a Tele-KG (tele-knowledge graph). Using this valuable data, we further propose a tele-domain language pre-training model TeleBERT and its knowledge-enhanced version, a tele-knowledge re-training model KTeleBERT. which includes effective prompt hints, adaptive numerical data encoding, and two knowledge injection paradigms. Concretely, our proposal includes two stages: first, pre-training TeleBERT on 20 million tele-related corpora, and then re-training it on 1 million causal and machine-related corpora to obtain KTeleBERT. Our evaluation on multiple tasks related to fault analysis in tele-applications, including root-cause analysis, event association prediction, and fault chain tracing, shows that pretraining a language model with tele-domain data is beneficial for downstream tasks. Moreover, the KTeleBERT re-training further improves the performance of task models, highlighting the effectiveness of incorporating diverse tele-knowledge into the model.

The performance of existing maneuvering target tracking methods for highly maneuvering targets in cluttered environments is unsatisfactory. This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both data-driven and model-based algorithms. The time-varying constant velocity model is integrated into the Gaussian process (GP) of online learning to improve the performance of GP prediction. This integration is further combined with a generalized probabilistic data association algorithm to realize multi-target tracking. Through the simulations, it has been demonstrated that the hybrid-driven approach exhibits significant performance improvements in comparison with widely used algorithms such as the interactive multi-model method and the data-driven GP motion tracker.

A novel algorithm that combines the generalized labeled multi-Bernoulli (GLMB) filter with signal features of the unknown emitter is proposed in this paper. In complex electromagnetic environments, emitter features (EFs) are often unknown and time-varying. Aiming at the unknown feature problem, we propose a method for identifying EFs based on dynamic clustering of data fields. Because EFs are time-varying and the probability distribution is unknown, an improved fuzzy C-means algorithm is proposed to calculate the correlation coefficients between the target and measurements, to approximate the EF likelihood function. On this basis, the EF likelihood function is integrated into the recursive GLMB filter process to obtain the new prediction and update equations. Simulation results show that the proposed method can improve the tracking performance of multiple targets, especially in heavy clutter environments.

In order to cope with the energy challenge emerging with the process of parallelism in embedded storing device, this paper focuses on the static code restructuring of nested loop array accessing related to storing device. Departing the running time of different devices can extend the duration time when it is in low- power state. Experiment shows that power-aware code restructuring can effectively reduce state switching times and extra power wasted. Static method described in this paper can achieve up to 12% power reduction if it cooperates with proper dynamic method.