This paper considers the problem of cost-aware downlink sum-rate maximization via joint optimal radio access technologies (RATs) assignment and power allocation in next-generation heterogeneous wireless networks (HetNets). We consider a future HetNet comprised of multi-RATs and serving multi-connectivity edge devices (EDs), and we formulate the problem as mixed-integer non-linear programming (MINP) problem. Due to the high complexity and combinatorial nature of this problem and the difficulty to solve it using conventional methods, we propose a hierarchical multi-agent deep reinforcement learning (DRL)-based framework, called DeepRAT, to solve it efficiently and learn system dynamics. In particular, the DeepRAT framework decomposes the problem into two main stages; the RATs-EDs assignment stage, which implements a single-agent Deep Q Network (DQN) algorithm, and the power allocation stage, which utilizes a multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm. Using simulations, we demonstrate how the various DRL agents efficiently interact to learn system dynamics and derive the global optimal policy. Furthermore, our simulation results show that the proposed DeepRAT algorithm outperforms existing state-of-the-art heuristic approaches in terms of network utility. Finally, we quantitatively show the ability of the DeepRAT model to quickly and dynamically adapt to abrupt changes in network dynamics, such as EDs mobility.

The issue of data-driven neural network model construction is one of the core problems in the domain of Artificial Intelligence. A standard approach assumes a fixed architecture with trainable weights. A conceptually more advanced assumption is that we not only train the weights, but also find out the optimal model architecture. We present a new method that realizes just that. This article is an extended version of our conference paper titled "Dynamic Growing and Shrinking of Neural Networks with Monte Carlo Tree Search [26]". In the paper, we show in detail how to create a neural network with a procedure that allows dynamic shrinking and growing of the model while it is being trained. The decision-making mechanism for the architectural design is governed by a Monte Carlo tree search procedure which simulates network behavior and allows to compare several candidate architecture changes to choose the best one. The proposed method was validated using both visual and time series datasets, demonstrating its particular effectiveness in multivariate time series classification. This is attributed to the architecture's ability to adapt dynamically, allowing independent modifications for each time series. The approach is supplemented by Python source code for reproducibility. Experimental evaluations in visual pattern and multivariate time series classification tasks revealed highly promising performance, underscoring the method's robustness and adaptability.

The Edge computing paradigm has gained prominence in both academic and industry circles in recent years. By implementing edge computing facilities and services in robotics, it becomes a key enabler in the deployment of artificial intelligence applications to robots. Time-sensitive robotics applications benefit from the reduced latency, mobility, and location awareness provided by the edge computing paradigm, which enables real-time data processing and intelligence at the network's edge. While the advantages of integrating edge computing into robotics are numerous, there has been no recent survey that comprehensively examines these benefits. This paper aims to bridge that gap by highlighting important work in the domain of edge robotics, examining recent advancements, and offering deeper insight into the challenges and motivations behind both current and emerging solutions. In particular, this article provides a comprehensive evaluation of recent developments in edge robotics, with an emphasis on fundamental applications, providing in-depth analysis of the key motivations, challenges, and future directions in this rapidly evolving domain. It also explores the importance of edge computing in real-world robotics scenarios where rapid response times are critical. Finally, the paper outlines various open research challenges in the field of edge robotics.

Graphs are a powerful data structure for representing relational data and are widely used to describe complex real-world systems. Probabilistic Graphical Models (PGMs) and Graph Neural Networks (GNNs) can both leverage graph-structured data, but their inherent functioning is different. The question is how do they compare in capturing the information contained in networked datasets? We address this objective by solving a link prediction task and we conduct three main experiments, on both synthetic and real networks: one focuses on how PGMs and GNNs handle input features, while the other two investigate their robustness to noisy features and increasing heterophily of the graph. PGMs do not necessarily require features on nodes, while GNNs cannot exploit the network edges alone, and the choice of input features matters. We find that GNNs are outperformed by PGMs when input features are low-dimensional or noisy, mimicking many real scenarios where node attributes might be scalar or noisy. Then, we find that PGMs are more robust than GNNs when the heterophily of the graph is increased. Finally, to assess performance beyond prediction tasks, we also compare the two frameworks in terms of their computational complexity and interpretability.

Detecting structural change in dynamic network data has wide-ranging applications. Existing approaches typically divide the data into time bins, extract network features within each bin, and then compare these features over time. This introduces an inherent tradeoff between temporal resolution and the statistical stability of the extracted features. Despite this tradeoff, reminiscent of time-frequency tradeoffs in signal processing, most methods rely on a fixed temporal resolution. Choosing an appropriate resolution parameter is typically difficult and can be especially problematic in domains like cybersecurity, where anomalous behavior may emerge at multiple time scales. We address this challenge by proposing ANIE (Adaptive Network Intensity Estimation), a multi-resolution framework designed to automatically identify the time scales at which network structure evolves, enabling the joint detection of both rapid and gradual changes. Modeling interactions as Poisson processes, our method proceeds in two steps: (1) estimating a low-dimensional subspace of node behavior, and (2) deriving a set of novel empirical affinity coefficients that quantify change in interaction intensity between latent factors and support statistical testing for structural change across time scales. We provide theoretical guarantees for subspace estimation and the asymptotic behavior of the affinity coefficients, enabling model-based change detection. Experiments on synthetic networks show that ANIE adapts to the appropriate time resolution and is able to capture sharp structural changes while remaining robust to noise. Furthermore, applications to real-world data showcase the practical benefits of ANIE's multiresolution approach to detecting structural change over fixed resolution methods.

Existing learning models often exhibit poor generalization when deployed across diverse scenarios. It is primarily due to that the underlying reference frame of the data varies with the deployment environment and settings. However, despite that data of each scenario has a distinct reference frame, its generation generally follows common underlying physical rules. Based on this understanding, this article proposes a deep learning framework named analogical learning (AL), which implicitly retrieves the reference frame information associated with a scenario and then to make accurate prediction by relative analogy with other scenarios. Specifically, we design a bipartite neural network called Mateformer. Its first part captures the relativity within multiple latent feature spaces between the input data and a small amount of embedded data from the studied scenario, while its second part uses this relativity to guide the nonlinear analogy. We apply AL to the typical multi-scenario learning problem of intelligent wireless localization in cellular networks. Extensive experiments validate AL's superiority across three key dimensions. First, it achieves state-of-the-art accuracy in single-scenario benchmarks. Second, it demonstrates stable transferability between different scenarios, avoiding catastrophic forgetting. Finally, and most importantly, it robustly adapts to new, unseen scenarios--including dynamic weather and traffic conditions--without any tuning. All data and code are available at https://github.com/ziruichen-research/ALLoc.

--Direct-satellite-to-device (DS2D) communication is emerging as a promising solution for global mobile service extension, leveraging the deployment of satellite constellations. However, the challenge of managing DS2D connectivity for multi-constellations becomes outstanding, including high interference and frequent handovers caused by multi-coverage overlap and rapid satellite movement. Moreover, existing approaches primarily operate within single-constellation shell, which inherently limits the ability to exploit the vast potential of multi-constellation connectivity provision, resulting in suboptimal DS2D service performances. T o address these challenges, this article proposes a Constellation as a Service (CaaS) framework, which treats the entire multi-constellation infrastructure as a shared resource pool and dynamically forms optimal sub-constellations (SCs) for each DS2D service region. The formation of each SC integrates satellites from various orbits to provide tailored connectivity based on user demands, guided by two innovative strategies: predictive satellite beamforming using generative artificial intelligence (GenAI) and pre-configured handover path for efficient satellite access and mobility management. Simulation results demonstrate that CaaS significantly improves satellite service rates while reducing handover overhead, making it an efficient and continuable solution for managing DS2D connectivity in multi-constellation environments. A defining feature of 6G wireless communications is the extension of traditional terrestrial network (TN) architectures to include non-terrestrial networks (NTNs) [1]. This advancement aims to eliminate TN coverage gaps, ensuring ubiquitous and reliable connectivity, particularly in remote and underserved areas [2].

A key unit of Japanese conglomerate SoftBank Group is offering 1 billion of dollar bonds in the unit's first US high-grade deal. SoftBank Corp.'s five- and 10-year notes are set to respectively price 0.9 and 1.10 percentage points above Treasuries, according to a person with knowledge of the deal who asked not to be identified disclosing private details. Those levels are a quarter-point less than revised price talk unveiled early Monday in New York. Each tranche will be 500 million in size, in line with company expectations last week. The money raised would go toward general corporate purposes, the person said, adding that the notes are expected to be rated BBB by S&P Global Ratings and BBB by Fitch Ratings.

As users in small cell networks increasingly rely on computation-intensive services, cloud-based access often results in high latency. Multi-access edge computing (MEC) mitigates this by bringing computational resources closer to end users, with small base stations (SBSs) serving as edge servers to enable low-latency service delivery. However, limited edge capacity makes it challenging to decide which services to deploy locally versus in the cloud, especially under unknown service demand and dynamic network conditions. To tackle this problem, we model service demand as a linear function of service attributes and formulate the service placement task as a linear bandit problem, where SBSs act as agents and services as arms. The goal is to identify the service that, when placed at the edge, offers the greatest reduction in total user delay compared to cloud deployment. We propose a distributed and adaptive multi-agent best-arm identification (BAI) algorithm under a fixed-confidence setting, where SBSs collaborate to accelerate learning. Simulations show that our algorithm identifies the optimal service with the desired confidence and achieves near-optimal speedup, as the number of learning rounds decreases proportionally with the number of SBSs. We also provide theoretical analysis of the algorithm's sample complexity and communication overhead.

--In this work we revisit the Mobility Robustness Optimisation (MRO) algorithm and study the possibility of learning the optimal Cell Individual Offset tuning using offline Reinforcement Learning. Such methods make use of collected offline datasets to learn the optimal policy, without further exploration. We adapt and apply a sequence-based method called Decision Transformers as well as a value-based method called Conservative Q-Learning to learn the optimal policy for the same target reward as the vanilla rule-based MRO. The same input features related to failures, ping-pongs, and other handover issues are used. Evaluation for realistic New Radio networks with 3500 MHz carrier frequency on a traffic mix including diverse user service types and a specific tunable cell-pair shows that offline-RL methods outperform rule-based MRO, offering up to 7% improvement. Furthermore, offline-RL can be trained for diverse objective functions using the same available dataset, thus offering operational flexibility compared to rule-based methods. Self-Organizing Network (SON) functionalities have become key aspects of modern cellular networks for automation. They make use of collected data to allow the network to self-configure, self-optimize, and self-heal. Mobility Robustness Optimization (MRO) is the related SON feature whose aim is to optimize the configuration of relevant mobility parameters and allow users to experience seamless connectivity.