Visitors look at a US company Qualcomm stand announcing '5G' technology at the Mobile World Congress (MWC) in Barcelona, Spain, 26 February 2018 (Photo: EPA-EFE/ALBERTO ESTEVEZ) Consumers will have a lot of new tech to digest in the new year ahead, in what is shaping up to be a year of transition. Here's what to look forward to in consumer tech in 2019. We've been hearing about the wicked fast next-generation of wireless for a few years now, and tests and early deployments of a relatively very small scale have long begun in earnest. But 2019 is when the commercial rollout of 5G networks starts to become more real in a lot more places, in the U.S. and overseas. In this country, the first available 5G-capable smartphones are expected to arrive by the spring, and you'll also see 5G hotspots and modems by then, if not sooner.

A monochromatic bird's-eye view of a city with multiple connected wireless points (Photo: GETTY IMAGES) MAUI, Hawaii--After a relatively ho-hum 2018 for mobile phones, 2019 is looking to be gangbuster year for smartphones and the whole wireless industry. In addition to the launch of foldable phones, next year also promises to see the debut of the world's first devices that can connect to next generation 5G wireless networks. Current smartphones use 4G LTE networks to send and receive all the photos, videos, texts, social media updates, emails, phone calls and other types of information that we consume on our trusty devices. While 4G networks have gotten faster and more reliable since their debut in 2009, technology innovations continue to move forward and the entire industry is on the verge of a once-a-decade generational shift to the next "G"--hence 5G. And 5G promises to bring with it not just faster connections to the mobile telecom networks offered by AT&T, Verizon, T-Mobile and Sprint--although that's certainly an important part of it--but the ability to create new types of experiences as well.

So what does it look like? There's been a lot of talk about 5G. Compared to 4G LTE, 5G is 20 times faster. And until 5G hits your home, you can dramatically speed up your current connection with a little-known secret setting. You don't need to be a techno wizard to do it, either.

Millimeter Wave (MmWave) communication is one of the key technology of the fifth generation (5G) wireless systems to achieve the expected 1000x data rate. With large bandwidth at mmWave band, the link capacity between users and base stations (BS) can be much higher compared to sub-6GHz wireless systems. Meanwhile, due to the high cost of infrastructure upgrade, it would be difficult for operators to drastically enhance the capacity of backhaul links between mmWave BSs and the core network. As a result, the data rate provided by backhaul may not be sufficient to support all mmWave links, the backhaul connection becomes the new bottleneck that limits the system performance. On the other hand, as mmWave channels are subject to random blockage, the data rates of mmWave users significantly vary over time. With limited backhaul capacity and highly dynamic data rates of users, how to allocate backhaul resource to each user remains a challenge for mmWave systems. In this article, we present a deep reinforcement learning (DRL) approach to address this challenge. By learning the blockage pattern, the system dynamics can be captured and predicted, resulting in efficient utilization of backhaul resource. We begin with a discussion on DRL and its application in wireless systems. We then investigate the problem backhaul resource allocation and present the DRL based solution. Finally, we discuss open problems for future research and conclude this article.

A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from datasets distributed across multiple local machines. The local machines can communicate with the central machine through a wireless multiple access channel. In this paper, we are interested in designing effective strategies where reliable learning is feasible under power and bandwidth limitations. Two approaches are proposed: Signs and Uncoded methods. In Signs method, the local machines quantize their data into binary vectors and an optimal channel coding scheme is used to reliably send the vectors to the central machine where the structure is learned from the received data. In Uncoded method, data symbols are scaled and transmitted through the channel. The central machine uses the received noisy symbols to recover the structure. Theoretical results show that both methods can recover the structure with high probability for large enough sample size. Experimental results indicate the superiority of Signs method over Uncoded method under several circumstances.

Recent networking research has identified that data-driven congestion control (CC) can be more efficient than traditional CC in TCP. Deep reinforcement learning (RL), in particular, has the potential to learn optimal network policies. However, RL suffers from instability and over-fitting, deficiencies which so far render it unacceptable for use in datacenter networks. In this paper, we analyze the requirements for RL to succeed in the datacenter context. We present a new emulator, Iroko, which we developed to support different network topologies, congestion control algorithms, and deployment scenarios. Iroko interfaces with the OpenAI gym toolkit, which allows for fast and fair evaluation of different RL and traditional CC algorithms under the same conditions. We present initial benchmarks on three deep RL algorithms compared to TCP New Vegas and DCTCP. Our results show that these algorithms are able to learn a CC policy which exceeds the performance of TCP New Vegas on a dumbbell and fat-tree topology. We make our emulator open-source and publicly available: https://github.com/dcgym/iroko

Supporting ultra-reliable low-latency communications (URLLC) is a major challenge of 5G wireless networks. Stringent delay and reliability requirements need to be satisfied for both scheduled and non-scheduled URLLC traffic to enable a diverse set of 5G applications. Although physical and media access control layer solutions have been investigated to satisfy only scheduled URLLC traffic, there is a lack of study on enabling transmission of non-scheduled URLLC traffic, especially in coexistence with the scheduled URLLC traffic. Machine learning (ML) is an important enabler for such a co-existence scenario due to its ability to exploit spatial/temporal correlation in user behaviors and use of radio resources. Hence, in this paper, we first study the coexistence design challenges, especially the radio resource management (RRM) problem and propose a distributed risk-aware ML solution for RRM. The proposed solution benefits from hybrid orthogonal/non-orthogonal radio resource slicing, and proactively regulates the spectrum needed for satisfying delay/reliability requirement of each URLLC traffic type. A case study is introduced to investigate the potential of the proposed RRM in serving coexisting URLLC traffic types. The results further provide insights on the benefits of leveraging intelligent RRM, e.g. a 75% increase in data rate with respect to the conservative design approach for the scheduled traffic is achieved, while the 99.99% reliability of both scheduled and nonscheduled traffic types is satisfied.

With two months to go until Samsung unveils its next flagship phone – presumably called the Galaxy S10 – we already know almost everything there is to know. A slew of leaks and rumours mean little has been left to the imagination about the iPhone rival, with the latest leak revealing the lengths the South Korean electronic giant has gone to avoid the "notch" design. For several years, smartphone manufacturers have been getting closer and closer to making an all-screen device, though necessary front-facing technologies like cameras and sensors have proved a major obstacle to achieving this goal. Apple's answer was to include a notch at the top of the screen, which it unveiled pm the iPhone X in 2017 to widespread acclaim. Not everyone was impressed and Samsung took the opportunity to mock its chief rival.

When T-Mobile's chief executive went before Senate lawmakers this year to make the case for his company's merger with Sprint, he argued that the deal could help preserve U.S. dominance in high-tech wireless networks for smartphones and other devices. "We'll make sure America wins the global 5G race," John Legere vowed. "5G will unlock capabilities that will fuel job creation and innovation well beyond what we have seen so far." The entire industry has spent much of the year marketing a dazzling future to consumers, one in which the successor to 4G LTE enables entirely new technologies, such as self-driving cars and remote medicine. But despite the hype, 5G is still a long way from becoming a reality for the majority of everyday Americans.

Small base stations (SBs) of fifth-generation (5G) cellular networks are envisioned to have storage devices to locally serve requests for reusable and popular contents by \emph{caching} them at the edge of the network, close to the end users. The ultimate goal is to shift part of the predictable load on the back-haul links, from on-peak to off-peak periods, contributing to a better overall network performance and service experience. To enable the SBs with efficient \textit{fetch-cache} decision-making schemes operating in dynamic settings, this paper introduces simple but flexible generic time-varying fetching and caching costs, which are then used to formulate a constrained minimization of the aggregate cost across files and time. Since caching decisions per time slot influence the content availability in future slots, the novel formulation for optimal fetch-cache decisions falls into the class of dynamic programming. Under this generic formulation, first by considering stationary distributions for the costs and file popularities, an efficient reinforcement learning-based solver known as value iteration algorithm can be used to solve the emerging optimization problem. Later, it is shown that practical limitations on cache capacity can be handled using a particular instance of the generic dynamic pricing formulation. Under this setting, to provide a light-weight online solver for the corresponding optimization, the well-known reinforcement learning algorithm, $Q$-learning, is employed to find optimal fetch-cache decisions. Numerical tests corroborating the merits of the proposed approach wrap up the paper.