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.

SEP 07 2020: Growing complexities in the communication networks today calls for an intelligent approach to network planning and optimization. With the rise of Artificial Intelligence (AI) techniques, new technology paradigms such as network virtualization, self-organizing networks (SONs), intelligent antennas, AI-powered radio-frequency (RF) front end and intelligent chipsets can be easily embedded into the communication networks. Telecom companies are therefore leveraging AI solutions to achieve hyper-automation of telecom networks and usher in an era of self-healing and self-configuring networks. Inclusion of network intelligence allows mobile network operators (MNOs) to achieve efficient network management and cross spectrum protection. This report includes a comprehensive analysis of the adoption of AI in telecom, highlighting the major technology trends and opportunities available across the ecosystem.

Edge caching is a new paradigm that has been exploited over the past several years to reduce the load for the core network and to enhance the content delivery performance. Many existing caching solutions only consider homogeneous caching placement due to the immense complexity associated with the heterogeneous caching models. Unlike these legacy modeling paradigms, this paper considers heterogeneous content preference of the users with heterogeneous caching models at the edge nodes. Besides, aiming to maximize the cache hit ratio (CHR) in a two-tier heterogeneous network, we let the edge nodes collaborate. However, due to complex combinatorial decision variables, the formulated problem is hard to solve in the polynomial time. Moreover, there does not even exist a ready-to-use tool or software to solve the problem. We propose a modified particle swarm optimization (M-PSO) algorithm that efficiently solves the complex constraint problem in a reasonable time. Using numerical analysis and simulation, we validate that the proposed algorithm significantly enhances the CHR performance when comparing to that of the existing baseline caching schemes.

Deep reinforcement learning offers a model-free alternative to supervised deep learning and classical optimization for solving the transmit power control problem in wireless networks. The multi-agent deep reinforcement learning approach considers each transmitter as an individual learning agent that determines its transmit power level by observing the local wireless environment. Following a certain policy, these agents learn to collaboratively maximize a global objective, e.g., a sum-rate utility function. This multi-agent scheme is easily scalable and practically applicable to large-scale cellular networks. In this work, we present a distributively executed continuous power control algorithm with the help of deep actor-critic learning, and more specifically, by adapting deep deterministic policy gradient. Furthermore, we integrate the proposed power control algorithm to a time-slotted system where devices are mobile and channel conditions change rapidly. We demonstrate the functionality of the proposed algorithm using simulation results.

Communicating effectively with customers is a challenge for many marketers, but especially in a context that is both pivotal to individual long-term financial well-being and difficult to understand: pensions. Around the world, participants are reluctant to consider their pension in advance, it leads to a lack of preparation of their pension retirement [1], [2]. In order to engage participants to obtain information on their expected pension benefits, personalizing the pension providers' email communication is a first and crucial step. We describe a machine learning approach to model email newsletters to fit participants' interests. The data for the modeling and analysis is collected from newsletters sent by a large Dutch pension provider of the Netherlands and is divided into two parts. The first part comprises 2,228,000 customers whereas the second part comprises the data of a pilot study, which took place in July 2018 with 465,711 participants. In both cases, our algorithm extracts features from continuous and categorical data using random forests, and then calculates node embeddings of the decision boundaries of the random forest. We illustrate the algorithm's effectiveness for the classification task, and how it can be used to perform data mining tasks. In order to confirm that the result is valid for more than one data set, we also illustrate the properties of our algorithm in benchmark data sets concerning churning. In the data sets considered, the proposed modeling demonstrates competitive performance with respect to other state of the art approaches based on random forests, achieving the best Area Under the Curve (AUC) in the pension data set (0.948). For the descriptive part, the algorithm can identify customer segmentations that can be used by marketing departments to better target their communication towards their customers.

In this paper, we investigate the suitability of state-of-the-art representation learning methods to the analysis of behavioral similarity of moving individuals, based on CDR trajectories. The core of the contribution is a novel methodological framework, mob2vec, centered on the combined use of a recent symbolic trajectory segmentation method for the removal of noise, a novel trajectory generalization method incorporating behavioral information, and an unsupervised technique for the learning of vector representations from sequential data. Mob2vec is the result of an empirical study conducted on real CDR data through an extensive experimentation. As a result, it is shown that mob2vec generates vector representations of CDR trajectories in low dimensional spaces which preserve the similarity of the mobility behavior of individuals.

Beyond asking for the latest temperature, calendar appointments and recipes, Amazon Echo and Google Nest Hub devices can be used for phone calls. Amazon announced on Wednesday a new alliance with wireless carrier AT&T to enable AT&T customers (on "eligible rate plans") to link their mobile numbers and turn their speaker into a two-way phone. This will enable them to make calls and answer their phone from contacts at home by saying "Alexa answer" without having to search for the phone, or answer on a dead battery. You can also have a choice of where you want to answer, via the phone, on your device, or Echo speaker. The alliance is exclusive with AT&T.

But the potential for 5G in business leaves plenty of room for excitement, too, and organizations should also start thinking about how 5G could improve processes and production. The time to dream is now.

In the months before its launch, Motorola's Razr generated ungodly levels of hype -- our quick hands-on, for instance, has the most views of any non-sex robot video we've ever made. Even a functionally perfect foldable would've had a hard time living up to expectations, and in case you missed it, we most certainly did not get a perfect foldable. That left Motorola will little choice but to buckle down, make some changes, and try again. That's where the brand's new Razr comes in -- it sports a modified design, 5G, and fixes for at least some of the issues the first model was notorious for. Mind you, it's still not a flagship phone, and at $1400 we're not sure it's a great deal either. But for people who want an extremely pocket-friendly foldable that's also usable while closed, Motorola just might be on the right track.