Abstract--The evolution toward Level 4 (L4) Autonomous Networks (AN) represents a strategic inflection point in telecommunications, where networks must transcend reactive automation to achieve genuine cognitive capabilities--fulfilling AN's vision of self-configuring, self-healing, and self-optimizing systems that deliver zero-wait, zero-touch, and zero-fault services. This work bridges the gap between architectural theory and operational reality by implementing Joseph Sifakis's AN Agent reference architecture in a functional cognitive system, deploying coordinated proactive-reactive runtimes driven by hybrid knowledge representation. Specifically, the system demonstrates sub-10 ms real-time control in 5G NR sub-6 GHz environments. Empirical results show a 4% increase in downlink throughput over Outer Loop Link Adaptation (OLLA) algorithms for enhanced mobile broadband (eMBB). Furthermore, for the ultra-reliable low-latency communication (URLLC) scenario, the agent achieves an 85% reduction in Block Error Rate (BLER). These improvements confirm the architecture's viability in overcoming traditional autonomy barriers and advancing critical L4-enabling capabilities toward next-generation objectives. UTONOMOUS Networks (AN), a purpose-specific telecommunications technology pioneered by the TM Forum (TMF) in 2019, target networks with intrinsic self-configuration, self-healing, and self-optimization capabilities--collectively termed the Three-Self Capabilities [1]. These fundamental properties enable the realization of zero-wait, zero-touch, and zero-fault network services, known as the Three-Zero Objectives, which collectively deliver optimal user experiences while maximizing resource utilization throughout the entire network lifecycle. By strategically integrating emerging general-purpose technologies including artificial intelligence (AI), digital twins, and big data analytics, AN not only transforms conventional network operations but fundamentally reorients value creation paradigms from traditional device-centric and management-centric models toward customer-oriented, service-driven, and business-focused frameworks.

Abstract--The complexity of large-scale 6G-and-beyond networks demands innovative approaches for multi-objective optimization over vast search spaces, a task often intractable. Quantum computing (QC) emerges as a promising technology for efficient large-scale optimization. We present our vision of leveraging QC to tackle key classes of problems in future mobile networks. By analyzing and identifying common features, particularly their graph-centric representation, we propose a unified strategy involving QC algorithms. Specifically, we outline a methodology for optimization using quantum annealing as well as quantum reinforcement learning. Additionally, we discuss the main challenges that QC algorithms and hardware must overcome to effectively optimize future networks. Quantum computing (QC) has rapidly emerged as a promising field, with its unparalleled potential to tackle problems typically intractable for classical computers. Quantum bits (qubits) leverage the principles of superposition, interference and entanglement to accelerate computations and open the door to previously unimaginable algorithms. This fundamental characteristic allows quantum computers to perform complex calculations at speeds exponentially faster than their classical counterparts in certain domains, enabling breakthroughs in fields such as cryptography, materials science, and artificial intelligence (AI). Developments in QC pave the way for novel solutions to intractable optimization problems and are expected to play a disruptive role in multiple industries.

Traffic demand prediction plays a critical role in intelligent transportation systems. Existing traffic prediction models primarily rely on temporal traffic data, with limited efforts incorporating human knowledge and experience for urban traffic demand forecasting. However, in real-world scenarios, traffic knowledge and experience derived from human daily life significantly influence precise traffic prediction. Such knowledge and experiences can guide the model in uncovering latent patterns within traffic data, thereby enhancing the accuracy and robustness of predictions. To this end, this paper proposes integrating structured temporal traffic data with textual data representing human knowledge and experience, resulting in a novel knowledge-guided cross-modal feature representation learning (KGCM) model for traffic demand prediction. Based on regional transportation characteristics, we construct a prior knowledge dataset using a large language model combined with manual authoring and revision, covering both regional and global knowledge and experiences. The KGCM model then learns multimodal data features through designed local and global adaptive graph networks, as well as a cross-modal feature fusion mechanism. A proposed reasoning-based dynamic update strategy enables dynamic optimization of the graph model's parameters, achieving optimal performance. Experiments on multiple traffic datasets demonstrate that our model accurately predicts future traffic demand and outperforms existing state-of-the-art (SOTA) models.

This article comprehensively reviews recent developments and research on deep learning-based (DL-based) techniques for integrated sensing and communication (ISAC) systems. ISAC, which combines sensing and communication functionalities, is regarded as a key enabler for 6G and beyond networks, as many emerging applications, such as vehicular networks and industrial robotics, necessitate both sensing and communication capabilities for effective operation. A unified platform that provides both functions can reduce hardware complexity, alleviate frequency spectrum congestion, and improve energy efficiency. However, integrating these functionalities on the same hardware requires highly optimized signal processing and system design, introducing significant computational complexity when relying on conventional iterative or optimization-based techniques. As an alternative to conventional techniques, DL-based techniques offer efficient and near-optimal solutions with reduced computational complexity. Hence, such techniques are well-suited for operating under limited computational resources and low latency requirements in real-time systems. DL-based techniques can swiftly and effectively yield near-optimal solutions for a wide range of sophisticated ISAC-related tasks, including waveform design, channel estimation, sensing signal processing, data demodulation, and interference mitigation. Therefore, motivated by these advantages, recent studies have proposed various DL-based approaches for ISAC system design. After briefly introducing DL architectures and ISAC fundamentals, this survey presents a comprehensive and categorized review of state-of-the-art DL-based techniques for ISAC, highlights their key advantages and major challenges, and outlines potential directions for future research.

Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a Graph Convolutional Network (GCN) integrated with a Deep Q-Network (DQN) reinforcement learning framework for improved interference coordination in multi-cell, multi-channel networks. Unlike LDP's static priorities, our approach dynamically learns link priorities based on real-time traffic demand, network topology, remaining transmission opportunities, and interference patterns. The GCN captures spatial dependencies, while the DQN enables adaptive scheduling decisions through reward-guided exploration. Simulation results show that our GCN-DQN model achieves mean SINR improvements of 179.6\%, 197.4\%, and 175.2\% over LDP across three network configurations. Additionally, the GCN-DQN model demonstrates mean SINR improvements of 31.5\%, 53.0\%, and 84.7\% over our previous CNN-based approach across the same configurations. These results underscore the effectiveness of our GCN-DQN model in addressing complex URLLC requirements with minimal overhead and superior network performance.

Abstract--V ehicular ad hoc networks (V ANETs) are a crucial component of intelligent transportation systems; however, routing remains challenging due to dynamic topologies, incomplete observations, and the limited resources of edge devices. Existing reinforcement learning (RL) approaches often assume fixed graph structures and require retraining when network conditions change, making them unsuitable for deployment on constrained hardware. We present TrajA ware, an RL-based framework designed for edge AI deployment in V ANETs. TrajA ware integrates three components: (i) action space pruning, which reduces redundant neighbour options while preserving two-hop reachability, alleviating the curse of dimensionality; (ii) graph cross-attention, which maps pruned neighbours to the global graph context, producing features that generalise across diverse network sizes; and (iii) trajectory-aware prediction, which uses historical routes and junction information to estimate real-time positions under partial observations. We evaluate TrajA ware in the open-source SUMO simulator using real-world city maps with a leave-one-city-out setup. Results show that TrajA ware achieves near-shortest paths and high delivery ratios while maintaining efficiency suitable for constrained edge devices, outperforming state-of-the-art baselines in both full and partial observation scenarios. OMMUNICA TION and routing are challenging in a vehicular ad hoc network (V ANET) [1], as vehicles can observe only part of the network, and the network's structure shifts rapidly; a previously obtained observation may soon become obsolete (as shown by Figure 1). Although compared to classical software algorithms, RL routing algorithms can potentially deal with more complex objectives (e.g., optimising delay while minimising the bandwidth overhead) [2], the problems of partial observation and network dynamics put a strain on the RL routing models. Several studies have shown that graph neural networks (GNNs) generalise better on routing tasks compared to other neural networks like multilayer perceptrons (MLPs) [3]-[7]. This work will be submitted to the IEEE for possible publication. Xiaolu Fu is an AI research engineer at Unicom Data Intelligence, China Unicom, Hangzhou, China (fuxl67@chinaunicom.cn), and a former student of the Computing Department, Imperial College London, London, UK (email: andy.fu23@alumni.imperial.ac.uk). Ziyuan Bao is an independent researcher and a former MSc student of the Computing Department, Imperial College London, London, UK (email: ziyuan.bao23@alumni.imperial.ac.uk).

We propose a simple algorithm that needs only a few data samples from a single graph for learning local routing policies that generalize across a rich class of geometric random graphs in Euclidean metric spaces. We thus solve the all-pairs near-shortest path problem by training deep neural networks (DNNs) that let each graph node efficiently and scalably route (i.e., forward) packets by considering only the node's state and the state of the neighboring nodes. Our algorithm design exploits network domain knowledge in the selection of input features and design of the policy function for learning an approximately optimal policy. Domain knowledge also provides theoretical assurance that the choice of a ``seed graph'' and its node data sampling suffices for generalizable learning. Remarkably, one of these DNNs we train -- using distance-to-destination as the only input feature -- learns a policy that exactly matches the well-known Greedy Forwarding policy, which forwards packets to the neighbor with the shortest distance to the destination. We also learn a new policy, which we call GreedyTensile routing -- using both distance-to-destination and node stretch as the input features -- that almost always outperforms greedy forwarding. We demonstrate the explainability and ultra-low latency run-time operation of Greedy Tensile routing by symbolically interpreting its DNN in low-complexity terms of two linear actions.

In various scenarios such as multi-drone surveillance and search-and-rescue operations, deploying multiple robots is essential to accomplish multiple tasks at once. Due to the limited communication range of these vehicles, a decentralised task allocation algorithm is crucial for effective task distribution among robots. The consensus-based bundle algorithm (CBBA) has been promising for multi-robot operation, offering theoretical guarantees. However, CBBA demands continuous communication, leading to potential congestion and packet loss that can hinder performance. In this study, we introduce an event-driven communication mechanism designed to address these communication challenges while maintaining the convergence and performance bounds of CBBA. We demonstrate theoretically that the solution quality matches that of CBBA and validate the approach with Monte-Carlo simulations across varying targets, agents, and bundles. Results indicate that the proposed algorithm (ED-CBBA) can reduce message transmissions by up to 52%.

Meeting minimum data rate constraints is a significant challenge in wireless communication systems, particularly as network complexity grows. Traditional deep learning approaches often address these constraints by incorporating penalty terms into the loss function and tuning hyperparameters empirically. However, this heuristic treatment offers no theoretical convergence guarantees and frequently fails to satisfy QoS requirements in practical scenarios. Building upon the structure of the WMMSE algorithm, we first extend it to a multi-channel setting with QoS constraints, resulting in the enhanced WMMSE (eWMMSE) algorithm, which is provably convergent to a locally optimal solution when the problem is feasible. To further reduce computational complexity and improve scalability, we develop a GNN-based algorithm, JCPGNN-M, capable of supporting simultaneous multi-channel allocation per user. To overcome the limitations of traditional deep learning methods, we propose a principled framework that integrates GNN with a Lagrangian-based primal-dual optimization method. By training the GNN within the Lagrangian framework, we ensure satisfaction of QoS constraints and convergence to a stationary point. Extensive simulations demonstrate that JCPGNN-M matches the performance of eWMMSE while offering significant gains in inference speed, generalization to larger networks, and robustness under imperfect channel state information. This work presents a scalable and theoretically grounded solution for constrained resource allocation in future wireless networks.

Sixth generation (6G) systems require environment-aware communication, driven by native artificial intelligence (AI) and integrated sensing and communication (ISAC). Radio maps (RMs), providing spatially continuous channel information, are key enablers. However, generating high-fidelity RM ground truth via electromagnetic (EM) simulations is computationally intensive, motivating machine learning (ML)-based RM construction. The effectiveness of these data-driven methods depends on large-scale, high-quality training data. Current public datasets often focus on single-input single-output (SISO) and limited information, such as path loss, which is insufficient for advanced multi-input multi-output (MIMO) systems requiring detailed channel state information (CSI). To address this gap, this paper presents UrbanMIMOMap, a novel large-scale urban MIMO CSI dataset generated using high-precision ray tracing. UrbanMIMOMap offers comprehensive complex CSI matrices across a dense spatial grid, going beyond traditional path loss data. This rich CSI is vital for constructing high-fidelity RMs and serves as a fundamental resource for data-driven RM generation, including deep learning. We demonstrate the dataset's utility through baseline performance evaluations of representative ML methods for RM construction. This work provides a crucial dataset and reference for research in high-precision RM generation, MIMO spatial performance, and ML for 6G environment awareness. The code and data for this work are available at: https://github.com/UNIC-Lab/UrbanMIMOMap.