Some pairings create an exquisite experience that's simply not otherwise imaginable. The same is true of artificial intelligence (AI) and 5G networks. As all types of 5G-capable devices become more widely available, 5G networks roll out globally delivering greater than 10 times the speed at a fraction of the latency of 4G. Meanwhile, AI use cases for consumers and enterprises also continue to mature quickly. These two trends are significantly interrelated, and together have huge implications for consumers, enterprises and communication service providers (CSPs) alike.

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No one could have predicted where 2020 would take us: The last six months alone have produced more digital transformation than the last decade, with every transformation effort already underway finding itself accelerated, and at scale. While many of my digital transformation predictions from a year ago benefited from this shift, others were displaced by more urgent needs, like 24/7 secure and reliable connectivity. What does this mean for 2021? Will core technologies like AI and data analytics still dominate headlines, or will we see newer, previously emerging technologies take the lead? Only time will tell, but here are my top ten digital transformation predictions for 2021.

Qualcomm has unveiled the Snapdragon 750G, a 5G-capable processor designed for mid-range smartphones rather than higher-tier devices that might use the Snapdragon 765G. The key feature is an updated 4 TOPS (trillion operations per second) AI system that can do echo cancellation and background vocal noise suppression for gaming, chat and voice assistants. Qualcomm says it can filter noise like "construction, children, or a barking dog" so that listeners only hear your voice. The new chip offers a 20 percent CPU and 10 percent GPU boost over the Snapdragon 730G, thanks to new Adreno 619 GPU and Kryo 750 CPU. And just like the Snapdragon 765G, it supports both mmWave and sub-6 GHz 5G, along with TDD, FDD, dynamic spectrum sharing and multi-SIM support.

No one could have predicted where 2020 would take us: The last six months alone have produced more digital transformation than the last decade, with every transformation effort already underway finding itself accelerated, and at scale. While many of my digital transformation predictions from a year ago benefited from this shift, others were displaced by more urgent needs, like 24/7 secure and reliable connectivity. What does this mean for 2021? Will core technologies like AI and data analytics still dominate headlines, or will we see newer, previously emerging technologies take the lead? Only time will tell, but here are my top ten digital transformation predictions for 2021.

A cellular-connected unmanned aerial vehicle (UAV)faces several key challenges concerning connectivity and energy efficiency. Through a learning-based strategy, we propose a general novel multi-armed bandit (MAB) algorithm to reduce disconnectivity time, handover rate, and energy consumption of UAV by taking into account its time of task completion. By formulating the problem as a function of UAV's velocity, we show how each of these performance indicators (PIs) is improved by adopting a proper range of corresponding learning parameter, e.g. 50% reduction in HO rate as compared to a blind strategy. However, results reveal that the optimal combination of the learning parameters depends critically on any specific application and the weights of PIs on the final objective function.

The paper focuses on some versions of connected dominating set problems: basic problems and multicriteria problems. A literature survey on basic problem formulations and solving approaches is presented. The basic connected dominating set problems are illustrated by simplifyed numerical examples. New integer programming formulations of dominating set problems (with multiset estimates) are suggested.

Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. With integrated solutions across four key domains – telecom networks, IT, smart devices, and cloud services – we are committed to bringing digital to every person, home and organization for a fully connected, intelligent world. At Huawei, innovation focuses on customer needs. We invest heavily in basic research, concentrating on technological breakthroughs that drive the world forward. We have more than 180,000 employees, and we operate in more than 170 countries and regions.

Telecommunication networks play a critical role in modern society. With the arrival of 5G networks, these systems are becoming even more diversified, integrated, and intelligent. Traffic forecasting is one of the key components in such a system, however, it is particularly challenging due to the complex spatial-temporal dependency. In this work, we consider this problem from the aspect of a cellular network and the interactions among its base stations. We thoroughly investigate the characteristics of cellular network traffic and shed light on the dependency complexities based on data collected from a densely populated metropolis area. Specifically, we observe that the traffic shows both dynamic and static spatial dependencies as well as diverse cyclic temporal patterns. To address these complexities, we propose an effective deep-learning-based approach, namely, Spatio-Temporal Hybrid Graph Convolutional Network (STHGCN). It employs GRUs to model the temporal dependency, while capturing the complex spatial dependency through a hybrid-GCN from three perspectives: spatial proximity, functional similarity, and recent trend similarity. We conduct extensive experiments on real-world traffic datasets collected from telecommunication networks. Our experimental results demonstrate the superiority of the proposed model in that it consistently outperforms both classical methods and state-of-the-art deep learning models, while being more robust and stable.

According to Cisco's forecast, there will be 850 ZB of data generated by mobile users and IoT devices by 2021. With a surge in data, challenges like latency will emerge. And, if one has to derive intelligence from algorithms in real-time, the traditional systems cannot be trusted for long. Thanks to the efforts of top companies to place supercomputers in the pockets, edge computing systems have garnered significant attention. Over the past couple of years chipmakers have been bullish on developing integrated solutions for edge cases.