The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy. However, the ever exploding data volume and model complexity will limit centralized solutions to learn and respond within a reasonable time. Therefore, scalability becomes a critical issue to be solved. In this article, we aim to provide a systematic discussion on the building blocks of scalable data-driven wireless networks. On one hand, we discuss the forward-looking architecture and computing framework of scalable data-driven systems from a global perspective. On the other hand, we discuss the learning algorithms and model training strategies performed at each individual node from a local perspective. We also highlight several promising research directions in the context of scalable data-driven wireless communications to inspire future research.

Intel's sale of its consumer 5G modem unit signaled its exit from the smartphone business last year, but the company remains heavily committed to participating in the growing 5G marketplace -- primarily on the carrier and enterprise sides. Today, the company announced three chips built for various types of 5G computers, plus a 5G-optimized network adapter for PCs. Up first is an updated second-generation Xeon Scalable processor, now at a top speed of 3.9GHz and bolstered by additional AI capabilities to aid with inference applications. The new chip promises up to 36% more performance than the first-generation version, with up to 42% more performance per dollar, though early second-generation chips were introduced in April 2019. Intel says the Xeon Scalable is the "only CPU with AI built in" -- a pitch that's not exactly accurate, given the range of existing laptop and mobile CPUs with AI features, but one Intel further explains means "the only CPU on the market that features integrated deep learning acceleration."

In the summer of 2014, Ahti Heinla, one of the software engineers who helped develop Skype, started taking photos of his house. There is nothing particularly unusual about this, of course. Only he kept on doing it. Month after month, as summer turned to fall and fall gave way to winter, Heinla went out to the same exact spot on the sidewalk and snapped new, seemingly identical pictures of his home. Was the man who had played a crucial role in building a multibillion dollar telecommunications app losing his mind?

We introduce a new routing algorithm for capsule networks, in which a child capsule is routed to a parent based only on agreement between the parent's state and the child's vote. The new mechanism 1) designs routing via inverted dot-product attention; 2) imposes Layer Normalization as normalization; and 3) replaces sequential iterative routing with concurrent iterative routing. When compared to previously proposed routing algorithms, our method improves performance on benchmark datasets such as CIFAR-10 and CIFAR-100, and it performs at-par with a powerful CNN (ResNet-18) with 4 fewer parameters. On a different task of recognizing digits from overlayed digit images, the proposed capsule model performs favorably against CNNs given the same number of layers and neurons per layer. We believe that our work raises the possibility of applying capsule networks to complex real-world tasks. Our code is publicly available at: https://github.

Atlas 900 stood out with its world-leading AI computing power, ultimate heat dissipation system, and best-in-class cluster network. Atlas 900 accelerates global basic AI research and quickly brings AI applications to industries to advance the AI era with unparalleled AI computing power. Innovative technology has propelled the mobile industry far beyond the wildest expectations of early tech pioneers. GSMA awards the GLOMO Award – Tech of the Future Award to recognize technology that is ahead of its time and reshapes the world. Atlas 900 is the world's fastest AI training cluster.

Wild Sumatran rhinos were once a common sight in northern Borneo, gamboling through the rainforests, but that ended last month when the last of their number died in a cage. Their habitat was eroded and Malaysia's rhinos are officially extinct. The TECH4ALLL programme exists to see how tech and AI can save the homes of creatures like the Sumatran rhinos, and build opportunities for the humans who live alongside them. "We want to protect vulnerable groups and make ordinary people extraordinary," said Ken Hu, Huawei's Deputy Chairman, at Huawei Connect 2019. Huawei created this programme to tie into the United Nations Sustainable Development Goals (SDGs), and is working with partners to protect tropical rainforests, keep food sources sustainable and diagnose visual disorders in children early.

The future depends on connectivity. From artificial intelligence and self-driving cars to telemedicine and mixed reality to as yet undreamt technologies, all the things we hope will make our lives easier, safer, and healthier will require high-speed, always-on internet connections. To keep up with the explosion of new connected gadgets and vehicles, not to mention the deluge of streaming video, the mobile industry is working on something called 5G--so named because it's the fifth generation of wireless networking technology. The promise is that 5G will bring speeds of around 10 gigabits per second to your phone. US carriers promise that 5G will be available nationwide by 2020, but the first 5G networks won't be nearly so fast. Carriers have launched demos and pilot programs that demonstrate big leaps in wireless performance, but mobile networks based on the "millimeter-wave" technology that may deliver the fastest speeds probably won't be widely available for years.

This paper studies the trajectory optimization problem for an aerial vehicle with the mission of flying between a pair of given initial and final locations. The objective is to minimize the travel time of the aerial vehicle ensuring that the communication connectivity constraint required for the safe operation of the aerial vehicle is satisfied. We consider two different criteria for the connectivity constraint of the aerial vehicle which leads to two different scenarios. In the first scenario, we assume that the maximum continuous time duration that the aerial vehicle is out of the coverage of the ground base stations (GBSs) is limited to a given threshold. In the second scenario, however, we assume that the total time periods that the aerial vehicle is not covered by the GBSs is restricted. Based on these two constraints, we formulate two trajectory optimization problems. To solve these non-convex problems, we use an approach based on the double Q-learning method which is a model-free reinforcement learning technique and unlike the existing algorithms does not need perfect knowledge of the environment. Moreover, in contrast to the well-known Q-learning technique, our double Q-learning algorithm does not suffer from the over-estimation issue. Simulation results show that although our algorithm does not require prior information of the environment, it works well and shows near optimal performance.

In future cell-free (or cell-less) wireless networks, a large number of devices in a geographical area will be served simultaneously in non-orthogonal multiple access scenarios by a large number of distributed access points (APs), which coordinate with a centralized processing pool. For such a centralized cell-free network with static predefined beamforming design, we first derive a closed-form expression of the uplink per-user probability of outage. To significantly reduce the complexity of joint processing of users' signals in presence of a large number of devices and APs, we propose a novel dynamic cell-free network architecture. In this architecture, the distributed APs are partitioned (i.e. clustered) among a set of subgroups with each subgroup acting as a virtual AP equipped with a distributed antenna system (DAS). The conventional static cell-free network is a special case of this dynamic cell-free network when the cluster size is one. For this dynamic cell-free network, we propose a successive interference cancellation (SIC)-enabled signal detection method and an inter-user-interference (IUI)-aware DAS's receive diversity combining scheme. We then formulate the general problem of clustering APs and designing the beamforming vectors with an objective to maximizing the sum rate or maximizing the minimum rate. To this end, we propose a hybrid deep reinforcement learning (DRL) model, namely, a deep deterministic policy gradient (DDPG)-deep double Q-network (DDQN) model, to solve the optimization problem for online implementation with low complexity. The DRL model for sum-rate optimization significantly outperforms that for maximizing the minimum rate in terms of average per-user rate performance. Also, in our system setting, the proposed DDPG-DDQN scheme is found to achieve around $78\%$ of the rate achievable through an exhaustive search-based design.

In the future 6th generation networks, ultra-reliable and low-latency communications (URLLC) will lay the foundation for emerging mission-critical applications that have stringent requirements on end-to-end delay and reliability. Existing works on URLLC are mainly based on theoretical models and assumptions. The model-based solutions provide useful insights, but cannot be directly implemented in practice. In this article, we first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC, and discuss some open problems of these methods. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC. The basic idea is to merge theoretical models and real-world data in analyzing the latency and reliability and training deep neural networks (DNNs). Deep transfer learning is adopted in the architecture to fine-tune the pre-trained DNNs in non-stationary networks. Further considering that the computing capacity at each user and each mobile edge computing server is limited, federated learning is applied to improve the learning efficiency. Finally, we provide some experimental and simulation results and discuss some future directions.