Abstract--Cellular wireless systems are witnessing the proliferation of frequency bands over a wide spectrum, particularly with the expansion of new bands in FR3. These bands must be supported in user equipment (UE) handsets with multiple antennas in a constrained form factor . Rapid variations in channel quality across the bands from motion and hand blockage, limited field-of-view of antennas, and hardware and power-constrained measurement sparsity pose significant challenges to reliable multi-band channel tracking. This paper formulates the problem of predicting achievable rates across multiple antenna arrays and bands with sparse historical measurements. We propose a transformer-based neural architecture that takes asynchronous rate histories as input and outputs per-array rate predictions. Evaluated on ray-traced simulations in a dense urban micro-cellular setting with FR1 and FR3 arrays, our method demonstrates superior performance over baseline predictors, enabling more informed band selection under realistic mobility and hardware constraints.

Attentional Neural Network is a new framework that integrates top-down cognitive bias and bottom-up feature extraction in one coherent architecture. The top-down influence is especially effective when dealing with high noise or difficult segmentation problems. Our system is modular and extensible. It is also easy to train and cheap to run, and yet can accommodate complex behaviors. We obtain classification accuracy better than or competitive with state of art results on the MNIST variation dataset, and successfully disentangle overlaid digits with high success rates. We view such a general purpose framework as an essential foundation for a larger system emulating the cognitive abilities of the whole brain.

The deployment of unmanned aerial vehicle (UAV) swarm-assisted communication networks has become an increasingly vital approach for remediating coverage limitations in infrastructure-deficient environments, with especially pressing applications in temporary scenarios, such as emergency rescue, military and security operations, and remote area coverage. However, complex geographic environments lead to unpredictable and highly dynamic wireless channel conditions, resulting in frequent interruptions of air-to-ground (A2G) links that severely constrain the reliability and quality of service in UAV swarm-assisted mobile communications. To improve the quality of UAV swarm-assisted communications in complex geographic environments, we propose an integrated communication and control co-design mechanism. Given the stringent energy constraints inherent in UAV swarms, our proposed mechanism is designed to optimize energy efficiency while maintaining an equilibrium between equitable communication rates for mobile ground users (GUs) and UAV energy expenditure. We formulate the joint resource allocation and 3D trajectory control problem as a Markov decision process (MDP), and develop a multi-agent reinforcement learning (MARL) framework to enable real-time coordinated actions across the UAV swarm. To optimize the action policy of UAV swarms, we propose a novel multi-agent hybrid proximal policy optimization with action masking (MAHPPO-AM) algorithm, specifically designed to handle complex hybrid action spaces. The algorithm incorporates action masking to enforce hard constraints in high-dimensional action spaces. Experimental results demonstrate that our approach achieves a fairness index of 0.99 while reducing energy consumption by up to 25% compared to baseline methods.

Deploying Large Language Models (LLMs) on mobile devices faces the challenge of insufficient performance in smaller models and excessive resource consumption in larger ones. This paper highlights that mobile Neural Processing Units (NPUs) have underutilized computational resources, particularly their matrix multiplication units, during typical LLM inference. To leverage this wasted compute capacity, we propose applying parallel test-time scaling techniques on mobile NPUs to enhance the performance of smaller LLMs. However, this approach confronts inherent NPU challenges, including inadequate hardware support for fine-grained quantization and low efficiency in general-purpose computations. To overcome these, we introduce two key techniques: a hardware-aware tile quantization scheme that aligns group quantization with NPU memory access patterns, and efficient LUT-based replacements for complex operations such as Softmax and dequantization. We design and implement an end-to-end inference system that leverages the NPU's compute capability to support test-time scaling on Qualcomm Snapdragon platforms. Experiments show our approach brings significant speedups: up to 19.0 for mixed-precision GEMM and 2.2 for Softmax. More importantly, we demonstrate that smaller models using test-time scaling can match or exceed the accuracy of larger models, achieving a new performance-cost Pareto frontier.

In WSN/IoT, node localization is essential to long-running applications for accurate environment monitoring and event detection, often covering a large area in the field. Due to the lower time resolution of typical WSN/IoT platforms (e.g., 1 microsecond on ESP32 platforms) and the jitters in timestamping, packet-level localization techniques cannot provide meter-level resolution. For high-precision localization as well as world-wide interoperability via 2.4-GHz ISM band, a new variant of LoRa, called LoRa 2.4 GHz, was proposed by semtech, which provides a radio frequency (RF) time of flight (ToF) ranging method for meter-level localization. However, the existing datasets reported in the literature are limited in their coverages and do not take into account varying environmental factors such as temperature and humidity. To address these issues, LoRa 2.4 GHz RF ToF ranging data was collected on a sports field at the XJTLU south campus, where three LoRa nodes logged samples of ranging with a LoRa base station, together with temperature and humidity, at reference points arranged as a 3x3 grid covering 400 square meter over three weeks and uploaded all measurement records to the base station equipped with an ESP32-based transceiver for machine and user communications. The results of a preliminary investigation based on a simple deep neural network (DNN) model demonstrate that the environmental factors, including the temperature and humidity, significantly affect the accuracy of ranging, which calls for advanced methods of compensating for the effects of environmental factors on LoRa RF ToF ranging outdoors.

Abstract--The Sixth-Generation (6G) network envisions pervasive artificial intelligence (AI) as a core goal, enabled by edge intelligence through on-device data utilization. T o realize this vision, federated learning (FL) has emerged as a key paradigm for collaborative training across edge devices. However, the sensitivity and heterogeneity of edge data pose key challenges to FL: parameter sharing risks data reconstruction, and a unified global model struggles to adapt to diverse local distributions. In this paper, we propose a novel federated learning framework that integrates personalized differential privacy (DP) and adaptive model design. T o protect training data, we leverage sample-level representations for knowledge sharing and apply a personalized DP strategy to resist reconstruction attacks. T o ensure distribution-aware adaptation under privacy constraints, we develop a privacy-aware neural architecture search (NAS) algorithm that generates locally customized architectures and hyperparameters. T o the best of our knowledge, this is the first personalized DP solution tailored for representation-based FL with theoretical convergence guarantees. Our scheme achieves strong privacy guarantees for training data while significantly outperforming state-of-the-art methods in model performance. Experiments on benchmark datasets such as CIF AR-10 and CIF AR-100 demonstrate that our scheme improves accuracy by 6.82% over the federated NAS method PerFedRLNAS, while reducing model size to 1/10 and communication cost to 1/20. CCORDING to the International Telecommunication Union (ITU), the Sixth-Generation (6G) mobile communication network are expected to fundamentally reshape current network architectures [1]. This transformation will be driven by an unprecedented degree of connectivity. These edge devices--such as smartphones, wearables, and sensors--will continuously generate vast volumes of local data. These data, rich in contextual information and latent intelligence, are key enablers for delivering efficient and responsive artificial intelligent (AI) services. Nowadays, the utilization of data generated at the edge is still significantly limited in the Fifth-Generation mobile communication system (5GS). Y ang Lv is with the School of Cyber Engineering, Xidian University, Xi'an, China (e-mail: lyuyang@stu.xidian.edu.cn).

Abstract--Intent-Based Networking (IBN) aims to simplify network management by enabling users to specify high-level goals that drive automated network design and configuration. However, translating informal natural-language intents into formally correct optical network topologies remains challenging due to inherent ambiguity and lack of rigor in Large Language Models (LLMs). T o address this, we propose a novel hybrid pipeline that integrates LLM-based intent parsing, formal methods, and Optical Retrieval-Augmented Generation (RAG). By enriching design decisions with domain-specific optical standards and systematically incorporating symbolic reasoning and verification techniques, our pipeline generates explainable, verifiable, and trustworthy optical network designs. Intent-Based Networking (IBN) simplifies network management by allowing users to express high-level objectives--such as connectivity, performance, or security--without specifying implementation details [1], [2]. Standardization bodies like TM Forum and the Internet Engineering Task Force define intent as a declarative statement of desired outcomes, delegating the detailed configuration and implementation tasks to automated systems. By abstracting away low-level complexities, IBN significantly reduces operational overhead, human error, and management complexity [2]. Existing research predominantly explores intent translation into configurations or incremental topology adjustments [3], [4], but largely overlooks the initial phase of comprehensive network design, particularly for optical networks. Poor initial design decisions can lead to significant performance degradation or expensive reconfigurations throughout the operational lifecycle [5], [6].

Cloud computing is an established technology allowing users to share resources on a large scale, never before seen in IT history. A cloud system connects multiple individual servers in order to process related tasks in several environments at the same time. Clouds are typically more cost-effective than single computers of comparable computing performance. The sheer physical size of the system itself means that thousands of machines may be involved. The focus of this research was to design a strategy to dynamically allocate tasks without overloading Cloud nodes which would result in system stability being maintained at minimum cost. This research has added the following new contributions to the state of knowledge: (i) a novel taxonomy and categorisation of three classes of schedulers, namely OS-level, Cluster and Big Data, which highlight their unique evolution and underline their different objectives; (ii) an abstract model of cloud resources utilisation is specified, including multiple types of resources and consideration of task migration costs; (iii) a virtual machine live migration was experimented with in order to create a formula which estimates the network traffic generated by this process; (iv) a high-fidelity Cloud workload simulator, based on a month-long workload traces from Google's computing cells, was created; (v) two possible approaches to resource management were proposed and examined in the practical part of the manuscript: the centralised metaheuristic load balancer and the decentralised agent-based system. The project involved extensive experiments run on the University of Westminster HPC cluster, and the promising results are presented together with detailed discussions and a conclusion.

--Customer churn prediction in the telecommunications sector represents a critical business intelligence task that has evolved from subjective human assessment to sophisticated algorithmic approaches. In this work, we present a comprehensive framework for telecommunications churn prediction leveraging deep neural networks. Through systematic problem formulation, rigorous dataset analysis, and careful feature engineering, we develop a model that captures complex patterns in customer behavior indicative of potential churn. We conduct extensive empirical evaluations across multiple performance metrics, demonstrating that our proposed neural architecture achieves significant improvements over existing baseline methods. Our approach not only advances the state-of-the-art in churn prediction accuracy but also provides interpretable insights into the key factors driving customer attrition in telecommunications services.

Abstract--Recent progress in robotics has underscored the demand for real-time control in applications such as manufacturing and healthcare systems, where the timely delivery of mission-critical commands under heterogeneous robotic traffic is paramount for operational efficacy and safety. In these scenarios, mission-critical traffic follows a strict deadline-constrained communication pattern: commands must arrive within defined deadlines, otherwise late arrivals can degrade performance or destabilize control loops. In this work, we demonstrate on a real-time software-defined radio (SDR) platform that CSMA, widely adopted in robotic communications, suffers severe degradation with contention-induced collisions and delays disrupting the on-time arrival of mission-critical packets. This degradation arises under a common robotic traffic pattern where non-critical traffic dominates the channel, while lightweight mission-critical commands must be delivered frequently with strict deadlines over the shared medium. T o address this, we propose an IEEE 802.11-compatible hybrid TDMA/CSMA protocol that combines TDMA's deterministic slot scheduling with CSMA's adaptability for heterogeneous robot traffic. The protocol achieves collision-free, low-latency mission-critical command delivery and IEEE 802.11 compatibility through the synergistic integration of sub-microsecond PTP-based slot synchronization, a three-section superframe with dynamic TDMA allocation for structured and adaptable traffic management, and beacon-NA V protection to preemptively secure critical communication applications from interference. Emulation experiments on a real-time SDR testbed show that the proposed protocol reduces missed-deadline errors by 93% compared to the CSMA baseline under a robotic traffic setup at an overall aggregate channel load of 77.1%, wherein 99.9% of the traffic is from non time-critical applications and 0.1% of the traffic is from deadline-constraint applications. In a high-speed robot path-tracking Robot Operating System (ROS) simulation, the protocol lowers root mean square trajectory error by up to 90% compared with the CSMA baseline, while maintaining throughput for non-critical traffic within 2%. Robotics has undergone remarkable advancements in recent years, playing critical roles in domains such as manufacturing [1], healthcare [2]-[4], and autonomous systems [5]. Multi-robot cooperation has emerged as a key enabler for complex robotic applications that require seamless coordination among multiple devices, such as collaborative assembly [6], warehouse automation [7], and search-and-rescue missions [8]. The work was partially supported by the Shen Zhen-Hong Kong-Macao technical program (Type C) under Grant No. SGDX20230821094359004. As the number of robots grows rapidly in a multi-robot system, communications between robots are becoming increasingly data-intensive.