$\texttt{gCastle}$ is an end-to-end Python toolbox for causal structure learning. It provides functionalities of generating data from either simulator or real-world dataset, learning causal structure from the data, and evaluating the learned graph, together with useful practices such as prior knowledge insertion, preliminary neighborhood selection, and post-processing to remove false discoveries. Compared with related packages, $\texttt{gCastle}$ includes many recently developed gradient-based causal discovery methods with optional GPU acceleration. $\texttt{gCastle}$ brings convenience to researchers who may directly experiment with the code as well as practitioners with graphical user interference. Three real-world datasets in telecommunications are also provided in the current version. $\texttt{gCastle}$ is available under Apache License 2.0 at \url{https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle}.

Highly dynamic mobile ad-hoc networks (MANETs) remain as one of the most challenging environments to develop and deploy robust, efficient, and scalable routing protocols. In this paper, we present DeepCQ+ routing protocol which, in a novel manner integrates emerging multi-agent deep reinforcement learning (MADRL) techniques into existing Q-learning-based routing protocols and their variants and achieves persistently higher performance across a wide range of topology and mobility configurations. While keeping the overall protocol structure of the Q-learning-based routing protocols, DeepCQ+ replaces statically configured parameterized thresholds and hand-written rules with carefully designed MADRL agents such that no configuration of such parameters is required a priori. Extensive simulation shows that DeepCQ+ yields significantly increased end-to-end throughput with lower overhead and no apparent degradation of end-to-end delays (hop counts) compared to its Q-learning based counterparts. Qualitatively, and perhaps more significantly, DeepCQ+ maintains remarkably similar performance gains under many scenarios that it was not trained for in terms of network sizes, mobility conditions, and traffic dynamics. To the best of our knowledge, this is the first successful application of the MADRL framework for the MANET routing problem that demonstrates a high degree of scalability and robustness even under environments that are outside the trained range of scenarios. This implies that our MARL-based DeepCQ+ design solution significantly improves the performance of Q-learning based CQ+ baseline approach for comparison and increases its practicality and explainability because the real-world MANET environment will likely vary outside the trained range of MANET scenarios. Additional techniques to further increase the gains in performance and scalability are discussed.

If there is to be a "6G Wireless," its proponents will need to learn some significant lessons from the era of 5G. Already, 5G Wireless as a market strategy is four years old. The R&D divisions of telecommunications firms whose 5G rollouts are well under way, are now looking ahead to whatever the next version of wireless may be. . . So far, what they're seeing may be a bit far out. It's a capital improvement project the size of the entire planet, replacing one wireless architecture created this century with another one that aims to lower energy consumption and maintenance costs. "6G must deliver an outcome that is aligned with real needs," remarked David Lister, Head of 6G Research and Development Technology at Europe's Vodafone Group, "and deliver outcomes that are sustainable and commercially driven." Lister was speaking at an annual conference called the 6G Symposium. Yes, there is already an annual 6G Symposium. Back in 1998, the leading stakeholders in global telecommunications formed the 3GPP consortium, to officially designate which technologies belong to a "G" and which don't.

The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from "connected things" to "connected intelligence". However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems.

Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach to enable future intelligent and autonomous systems that rely on real-time decision-making in complex dynamic environments. Nonetheless, in practical scenarios, CDRL faces many challenges due to the heterogeneity of agents and their learning tasks, different environments, time constraints of the learning, and resource limitations of wireless networks. To address these challenges, in this paper, a novel semantic-aware CDRL method is proposed to enable a group of heterogeneous untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network. To this end, a new heterogeneous federated DRL (HFDRL) algorithm is proposed to select the best subset of semantically relevant DRL agents for collaboration. The proposed approach then jointly optimizes the training loss and wireless bandwidth allocation for the cooperating selected agents in order to train each agent within the time limit of its real-time task. Simulation results show the superior performance of the proposed algorithm compared to state-of-the-art baselines.

Liu Leqi is a Ph.D. student in the Machine Learning Department at Carnegie Mellon University, Pittsburgh, PA, USA. Her research interests include AI and human-centered problems in machine learning. Dylan Hadfield-Menell is an assistant professor of artificial intelligence and decision-making at the Massachusetts Institute of Technology, Cambridge, MA, USA. His recent work focuses on the risks of (over-) optimizing proxy metrics in AI systems. Zachary C. Lipton is the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University, Pittsburgh, PA, USA, and a Visiting Scientist at Amazon AI. He directs the Approximately Correct Machine Intelligence (ACMI) lab, whose research spans core machine learning methods, applications to clinical medicine and NLP, and the impact of automation on social systems. He can be found on Twitter (@zacharylipton), GitHub (@zackchase), or his lab's website (acmilab.org).

Nov 16 (Reuters) - Qualcomm Inc (QCOM.O) on Tuesday said that German automaker BMW (BMWG.DE) will use its chips in its next generation of driver-assistance and self-driving systems. San Diego-based Qualcomm is the world's biggest supplier of chips for mobile phones but has been diversifying its business, with more than a third of its chip sales coming from sources other than makers of handsets. Qualcomm's announcement of the BMW win came before an investor presentation where it detailed how the company is working with firms like Meta Platforms Inc (FB.O)on virtual reality hardware and with Microsoft Corp (MSFT.O)on Windows laptops that use Qualcomm chips. Qualcomm Chief Executive Cristiano Amon said he believes the addressable market for the firm's technologies is now $700 billion, seven times higher than the phone chip industry alone. "We've never had so many end-market opportunities for Qualcomm as we have today," Amon said.

Qualcomm processors for PCs enhanced by the company's Nuvia design team will sample in 2022 for devices shipping in 2023, Qualcomm executives said Tuesday. The company also boldly pledged to offer Adreno graphics that could compete with desktop PCs. At the company's 2021 investor day in New York, Dr. James Thompson, chief technology officer at Qualcomm, offered an overview of the company's technology roadmap in several areas. A key focus, naturally, will be how and when Qualcomm's Snapdragon processors will integrate the Nuvia design team, an Arm CPU developer that Qualcomm acquired in January. Processor development takes time, however, and that integration won't happen immediately.

Shankar Arumugavelu is what you might call a Verizon lifer. He was a director at telecom GTE when Bell Atlantic acquired it in 2000 to form Verizon. Today he's SVP and global CIO of Verizon, where he's helping to drive the company's adoption of emerging technologies like AI and machine learning in service of creating competitive advantage and improving customer experience. "As we look at emerging technologies, AI is a big area of focus," Arumugavelu says. "You have disciplines within AI as well, whether it's NLP or computer vision, robotic process automation, cognitive decisioning, etc. We have work going on across every single one of those disciplines to see how we can leverage that to drive a competitive advantage."

The main challenge to deploy deep neural network (DNN) over a mobile edge network is how to split the DNN model so as to match the network architecture as well as all the nodes' computation and communication capacity. This essentially involves two highly coupled procedures: model generating and model splitting. In this paper, a joint model split and neural architecture search (JMSNAS) framework is proposed to automatically generate and deploy a DNN model over a mobile edge network. Considering both the computing and communication resource constraints, a computational graph search problem is formulated to find the multi-split points of the DNN model, and then the model is trained to meet some accuracy requirements. Moreover, the trade-off between model accuracy and completion latency is achieved through the proper design of the objective function. The experiment results confirm the superiority of the proposed framework over the state-of-the-art split machine learning design methods.