Qualcomm is showing off the type of drones that could wind up being built on its dedicated Flight RB5 5G platform. The chip-maker has released a new reference design that contains all the latest connectivity and processing tech it has been talking up since last summer. That's the exoskeleton above, which is equipped with a Qualcomm Spectra 480 Image Signal Processor that can capture 200 megapixel photos and 8K video at 30 FPS. In addition, the drone can record in 4K at 120 FPS with support for HDR. At its core, the Flight RB5 5G platform uses the QRB5165 processor and Kryo 585 CPU and Adreno 650 GPU, based on the Snapdragon 865 CPU.

In this paper, we propose a new framework, exploiting the multi-agent deep deterministic policy gradient (MADDPG) algorithm, to enable a base station (BS) and user equipment (UE) to come up with a medium access control (MAC) protocol in a multiple access scenario. In this framework, the BS and UEs are reinforcement learning (RL) agents that need to learn to cooperate in order to deliver data. The network nodes can exchange control messages to collaborate and deliver data across the network, but without any prior agreement on the meaning of the control messages. In such a framework, the agents have to learn not only the channel access policy, but also the signaling policy. The collaboration between agents is shown to be important, by comparing the proposed algorithm to ablated versions where either the communication between agents or the central critic is removed. The comparison with a contention-free baseline shows that our framework achieves a superior performance in terms of goodput and can effectively be used to learn a new protocol.

With the continuous trend of data explosion, delivering packets from data servers to end users causes increased stress on both the fronthaul and backhaul traffic of mobile networks. To mitigate this problem, caching popular content closer to the end-users has emerged as an effective method for reducing network congestion and improving user experience. To find the optimal locations for content caching, many conventional approaches construct various mixed integer linear programming (MILP) models. However, such methods may fail to support online decision making due to the inherent curse of dimensionality. In this paper, a novel framework for proactive caching is proposed. This framework merges model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. For parallel training and simple design purposes, the proposed MILP model is first decomposed into a number of sub-problems and, then, convolutional neural networks (CNNs) are trained to predict content caching locations of these sub-problems. Furthermore, since the MILP model decomposition neglects the internal effects among sub-problems, the CNNs' outputs have the risk to be infeasible solutions. Therefore, two algorithms are provided: the first uses predictions from CNNs as an extra constraint to reduce the number of decision variables; the second employs CNNs' outputs to accelerate local search. Numerical results show that the proposed scheme can reduce 71.6% computation time with only 0.8% additional performance cost compared to the MILP solution, which provides high quality decision making in real-time.

Deep in the heart of Appalachia, modern science and America's bucolic past meet at a unique crossroad of scientific discovery and luddite lifestyles. The Quiet Zone, by journalist Stephen Kurczy, is the story of a sleepy small town that hosts the Green Bank radio telescope. But the presence of this installation comes at a price: due to the telescope's exceeding sensitivity, virtually every device and appliance that emits radio waves, Wi-Fi signals, or microwave radiation is banned for square miles around. That means that Green Bank, West Virginia has about as much tech today as it did in the 1950's (maybe even a little less) -- and some people very much like it that way. In the excerpt below, Pocahontas County attorney, Robert Martin, recounts the challenges of attempting to modernize the region without loosing a horde of gentrifiers upon it as well.

Tim has been selected for his proposal: 'A Fully Probabilistic Theory of Autonomous Decision Making'. Tim's proposal is about developing a fully probabilistic framework for reinforcement learning to provide reliable and mathematically rigorous uncertainty quantification. In contrast to previous approaches, he proposes to treat both the learning process as well as the model components, such an agent's policy, probabilistically. The approach will combine advances in probabilistic inference and modelling with probabilistic reinforcement learning. This will enable autonomous vehicles and machines to'know what they know' as well as what they don't know, and therefore to operate more safely and reliably.

The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customized production modes. For that, Artificial Intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This paper focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.

The core network architecture of telecommunication systems has undergone a paradigm shift in the fifth-generation (5G)networks. 5G networks have transitioned to software-defined infrastructures, thereby reducing their dependence on hardware-based network functions. New technologies, like network function virtualization and software-defined networking, have been incorporated in the 5G core network (5GCN) architecture to enable this transition. This has resulted in significant improvements in efficiency, performance, and robustness of the networks. However, this has also made the core network more vulnerable, as software systems are generally easier to compromise than hardware systems. In this article, we present a comprehensive security analysis framework for the 5GCN. The novelty of this approach lies in the creation and analysis of attack graphs of the software-defined and virtualized 5GCN through machine learning. This analysis points to 119 novel possible exploits in the 5GCN. We demonstrate that these possible exploits of 5GCN vulnerabilities generate five novel attacks on the 5G Authentication and Key Agreement protocol. We combine the attacks at the network, protocol, and the application layers to generate complex attack vectors. In a case study, we use these attack vectors to find four novel security loopholes in WhatsApp running on a 5G network.

Maximizing coverage with the least number of transmitters is a priority, said Shankar Arumugavelu, senior vice president and global chief information officer of Verizon. "When we build out these networks, these are very capital-intensive," he said. "We have to make sure that we are being very judicious in terms of how we are investing our capital." The models, designed by in-house data scientists and other employees, factor in a number of variables that can alter the strength of 5G signals, like buildings, bridges, terrain, the position of the transmitter, as well as other transmitters nearby. Verizon, along with rivals AT&T Inc. and T-Mobile US Inc., is racing to build out nationwide 5G service, a yearslong effort slowed by the lack of available airwaves for fast transmission and long signal ranges, and by the deployment of new network equipment, analysts have said.

Qualcomm can live without Google, but life without Apple would be more difficult. The market keeps pricing the chip maker for the latter eventuality. Alphabet's Google announced Monday that its coming line of new Pixel smartphones will be powered by its own in-house processor. The chip, called Tensor, has been specially designed for artificial intelligence uses, though Google didn't say much about its capabilities in its announcement. But the new chip does take over the slot that has been occupied by Qualcomm's Snapdragon processor for the past five generations of Pixel, since Google first launched the smartphone in 2016.

In this paper, a novel framework is proposed for channel charting (CC)-aided localization in millimeter wave networks. In particular, a convolutional autoencoder model is proposed to estimate the three-dimensional location of wireless user equipment (UE), based on multipath channel state information (CSI), received by different base stations. In order to learn the radio-geometry map and capture the relative position of each UE, an autoencoder-based channel chart is constructed in an unsupervised manner, such that neighboring UEs in the physical space will remain close in the channel chart. Next, the channel charting model is extended to a semi-supervised framework, where the autoencoder is divided into two components: an encoder and a decoder, and each component is optimized individually, using the labeled CSI dataset with associated location information, to further improve positioning accuracy. Simulation results show that the proposed CC-aided semi-supervised localization yields a higher accuracy, compared with existing supervised positioning and conventional unsupervised CC approaches.