Beginning today, AT&T customers can link their phone numbers to Alexa devices. Once they're connected, customers will be able to make and answer calls through Alexa as they would on their phone. As The Verge notes, Echo devices have been able to call mobile numbers and landlines for free since 2017, but this integration with AT&T goes a little deeper, essentially turning your Alexa device into an extension of your phone. Amazon already rolled this feature out abroad. In the UK and Germany, Vodafone OneNumber customers can link their mobile accounts, and EE customers in the UK can do the same.

Alexa has long been able to call friends and loved ones on their home and cell phones, but now you can ask Alexa to perform a new trick: placing and receiving calls using your AT&T mobile number. The feature, which allows AT&T subscribers to link their mobile numbers with Alexa, comes courtesy of a just-announced partnership between Amazon and AT&T, according to The Verge. Once you have your AT&T account linked, you can ask Alexa to call a contact with your AT&T number, answer incoming calls, or even dial a number by saying the digits. Alexa's "carrier calling" feature is new in the U.S. but not in other territories, with The Verge noting that users in the UK and Germany can link (respectively) their Vodafone OneNumber and EE mobile numbers with Alexa. You can ask Alexa to make and receive calls with your AT&T number once you link your AT&T account in the Alexa app.

As several new spectrum bands are opening up for shared use, a new paradigm of \textit{Diverse Band-aware Dynamic Spectrum Access} (d-DSA) has emerged. d-DSA equips a secondary device with software defined radios (SDRs) and utilize whitespaces (or idle channels) in \textit{multiple bands}, including but not limited to TV, LTE, Citizen Broadband Radio Service (CBRS), unlicensed ISM. In this paper, we propose a decentralized, online multi-agent reinforcement learning based cross-layer BAnd selection and Routing Design (BARD) for such d-DSA networks. BARD not only harnesses whitespaces in multiple spectrum bands, but also accounts for unique electro-magnetic characteristics of those bands to maximize the desired quality of service (QoS) requirements of heterogeneous message packets; while also ensuring no harmful interference to the primary users in the utilized band. Our extensive experiments demonstrate that BARD outperforms the baseline dDSAaR algorithm in terms of message delivery ratio, however, at a relatively higher network latency, for varying number of primary and secondary users. Furthermore, BARD greatly outperforms its single-band DSA variants in terms of both the metrics in all considered scenarios.

Earlier this year a new initiative to create standards for artificial intelligence (AI) and machine learning (ML) in the cable telecommunications industry was launched. The working group, which draws members from both inside and outside of cable including giants like IBM, is exploring how AI and ML can be leveraged to make the network more efficient. Using machine learning to solve this challenge, an algorithm considers multiple variables including service load and cost to provide an actionable and prioritized report for the cable operator to act on. By applying ML to automate node splits, the network will run more efficiently, and customers will continue to receive their high-speed services without interruption as the network grows. The working group is also looking at creating standards to control video piracy by applying artificial intelligence on the network that detects signatures of bad actors.

Network Slicing is expected to become a game changer in the upcoming 5G networks and beyond, enlarging the telecom business ecosystem through still-unexplored vertical industry profits. This implies that heterogeneous service level agreements (SLAs) must be guaranteed per slice given the multitude of predefined requirements. In this paper, we pioneer a novel radio slicing orchestration solution that simultaneously provides latency and throughput guarantees in a multi-tenancy environment. Leveraging on a solid mathematical framework, we exploit the exploration-vs-exploitation paradigm by means of a multi-armed-bandit-based (MAB) orchestrator, LACO, that makes adaptive resource slicing decisions with no prior knowledge on the traffic demand or channel quality statistics. As opposed to traditional MAB methods that are blind to the underlying system, LACO relies on system structure information to expedite decisions. After a preliminary simulations campaign empirically proving the validness of our solution, we provide a robust implementation of LACO using off-the-shelf equipment to fully emulate realistic network conditions: near-optimal results within affordable computational time are measured when LACO is in place. L. Zanzi, V. Sciancalepore, A. Garcia-Saavedra and X. Costa-Pérez are with NEC Laboratories Europe GmbH., 69115 Heidelberg, Germany. The quest for new sources of revenue that revitalizes the mobile industry has spawned an unprecedented hype around the fifth-generation of mobile networks (5G) and, in particular, the network slicing concept. A high-level view of the system considered in this paper is described in Figure 1. The figure represents a series of sliceable base stations as a pool of radio resources (coloured cubes in the figure). The resource allocation process is considered hierarchical: while bundles of radio resources are assigned to different tenants (namely radio slices), each tenant autonomously schedules its bundle of radio resources to each individual user following classic radio scheduling policies. The difference between such operations is subtle but of paramount importance: a slice controller operates at a larger timescale and thus over a coarser granularity [2], [3]. While most prior work on network slicing focuses on average bit-rate guarantees [3], [4], latency considerations have received little attention.

In this paper we show that the data plane of commodity programmable (Network Interface Cards) NICs can run neural network inference tasks required by packet monitoring applications, with low overhead. This is particularly important as the data transfer costs to the host system and dedicated machine learning accelerators, e.g., GPUs, can be more expensive than the processing task itself. We design and implement our system -- N3IC -- on two different NICs and we show that it can greatly benefit three different network monitoring use cases that require machine learning inference as first-class-primitive. N3IC can perform inference for millions of network flows per second, while forwarding traffic at 40Gb/s. Compared to an equivalent solution implemented on a general purpose CPU, N3IC can provide 100x lower processing latency, with 1.5x increase in throughput.

Here's a sign that the troubled Windows on Snapdragon platform isn't going away anytime soon: Qualcomm is announcing today its new made-for-PC processor based on ARM design. The Snapdragon 8cx Gen 2 follows up 2018's Snapdragon 8cx, and back then the company said the "x" in the name stood for "extreme" power. This year's model offers better AI performance and support for newer standards of WiFi and Bluetooth, but doesn't appear to run any faster than before. Acer and HP both also announced today that they'll be offering laptops with the new chipset, with Acer's Spin 7 being the first to use it. HP's product will be a business-centric notebook, and the company said more information will be shared later this year.

Midrange phones are on the rise, and Qualcomm is updating its portfolio to power more of them. The company today announced the Snapdragon 732G, which follows up last year's Snapdragon 730G. The newest chipset offers better on-device AI performance as well as the expected improvements in CPU and GPU speeds. Xiaomi affiliate Poco also announced today that its new smartphone will be powered by the Snapdragon 732G, though other details like price and availability remain unclear. Poco's global head of products Sam Jiang said in a statement, "We believe the device will set a new benchmark in the mid-range category, completely redefining the relationship between a phone's price and its capabilities."

Wide area networking infrastructures (WANs), particularly science and research WANs, are the backbone for moving large volumes of scientific data between experimental facilities and data centers. With demands growing at exponential rates, these networks are struggling to cope with large data volumes, real-time responses, and overall network performance. Network operators are increasingly looking for innovative ways to manage the limited underlying network resources. Forecasting network traffic is a critical capability for proactive resource management, congestion mitigation, and dedicated transfer provisioning. To this end, we propose a nonautoregressive graph-based neural network for multistep network traffic forecasting. Specifically, we develop a dynamic variant of diffusion convolutional recurrent neural networks to forecast traffic in research WANs. We evaluate the efficacy of our approach on real traffic from ESnet, the U.S. Department of Energy's dedicated science network. Our results show that compared to classical forecasting methods, our approach explicitly learns the dynamic nature of spatiotemporal traffic patterns, showing significant improvements in forecasting accuracy. Our technique can surpass existing statistical and deep learning approaches by achieving approximately 20% mean absolute percentage error for multiple hours of forecasts despite dynamic network traffic settings.

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