The UK government is happy to allow Huawei equipment to form part of the UK's 5G network – just not any of the crucial parts, according to leaked discussions from the National Security Council. The United States and Australia have taken much more hardline approach, with complete bans on using Huawei equipment to form any part of their 5G network. What is all the worry about? According to telecoms firm Qualcomm, 5G mobile internet gives a massive speed boost – at least 10 or 20 times greater – over our current 4G networks. As devices start sharing more and more data, from phones streaming data-rich video to self-driving cars, it's vital to have a speedy connection.

A great deal of attention has been recently given to Machine Learning (ML) techniques in many different application fields. This paper provides a vision of what ML can do in Power Line Communications (PLC). We firstly and briefly describe classical formulations of ML, and distinguish deterministic problems from statistical problems with relevance to communications. We then discuss ML applications in PLC for each layer, namely, for characterization and modeling, for physical layer algorithms, for media access control and networking algorithms. Finally, other applications of PLC that can benefit from the usage of ML, as grid diagnostics, are analyzed. Illustrative numerical examples are reported to serve the purpose of validating the ideas and motivate future research endeavors in this stimulating signal/data processing field.

The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, the current communication systems, which were designed on the basis of conventional communication theories, significantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learningbased communication methods are presented along with the research opportunities and challenges. H. Huang, G. Gui, Z. Yang, and H. Sari are with Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education, Nanjing 210003, China. S. Guo is with Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (Email: song.guo@polyu.edu.hk). J. Zhang is with Beijing University of Posts and Telecommunication (BUPT), Beijing 100876, China (Email: jhzhang@bupt.edu.cn). F. Adachi is with Wireless Signal Processing Research Group, Research Organization of Electrical Communication (ROEC), Tohoku University, Sendai 980-8577, Japan (Email: adachi@ecei.tohoku.ac.jp).

A pillar box red electric train connects Paris, Verona and Grenada via Budapest's Liberty Bridge and on to Heidelberg Castle in a 120-hectare fantasy business park dreamt up by the Chinese billionaire Ren Zhengfei. Ren, 74, a former Red Army engineer who founded the telecommunications company Huawei in 1987 and still owns a 1.14% stake, asked the Japanese architect Kengo Kuma to recreate some of Europe's most historic cities. He hoped to inspire an army of 25,000 research and development staff to challenge Apple, Google and Samsung. While its US competitors keep their research facilities on lockdown to prevent corporate espionage (oft allegedly by the Chinese), Huawei is inviting the world's media into its labs and factories in an attempt to dispel the US government's claims that the privately held company is an arm of the Chinese state and that its technology could be used to hack into western governments. US politicians allege that Huawei's forthcoming 5G mobile phone networks could be hacked by Chinese spies to eavesdrop on sensitive phone calls, gain access to counter-terrorist operations – and potentially even kill targets by crashing driverless cars.

By any criteria, 5G is a significant advance on the previous 4G standards. The first 5G networks already offer download speeds of up to 600 megabits per second and have the potential to get significantly faster. By contrast, 4G generally operates at up to 28 Mbits/s--and most mobile-phone users will have experienced that rate grinding to zero from time to time, for reasons that aren't always clear.

Apple has settled a major argument with chip maker Qualcomm that could help change the future of the iPhone. The two companies were locked in a bitter and worldwide international dispute about the technology that iPhones use to connect to the internet. The pair had been expected to try and resolve the dispute in legal hearings in San Diego, in a case that involved Apple's key iPhone suppliers. But just as that case began, the surprise truce was announced, with few details of the settlement being revealed. We'll tell you what's true.

Synthetic population generation is the process of combining multiple socioeonomic and demographic datasets from various sources and at different granularity, and downscaling them to an individual level. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this study, we propose a multi-stage framework called SynC (Synthetic Population via Gaussian Copula) to fill the gap. SynC first removes potential outliers in the data and then fits the filtered data with a Gaussian copula model to correctly capture dependencies and marginal distributions of sampled survey data. Finally, SynC leverages neural networks to merge datasets into one and then scales them accordingly to match the marginal constraints. We make four key contributions in this work: 1) propose a novel framework for generating individual level data from aggregated data sources by combining state-of-the-art machine learning and statistical techniques, 2) design a metric for validating the accuracy of generated data when the ground truth is hard to obtain, 3) demonstrate its effectiveness with the Canada National Census data and presenting two real-world use cases where datasets of this nature can be leveraged by businesses, and 4) release an easy-to-use framework implementation for reproducibility.

Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theoretical guarantees. The embedding can be used to estimate the community structure of the network, with strong consistency results in the stochastic blockmodel framework. One of the main practical limitations of standard algorithms for community detection from spectral embeddings is that the number of communities and the latent dimension of the embedding must be specified in advance. In this article, a novel Bayesian model for simultaneous and automatic selection of the appropriate dimension of the latent space and the number of blocks is proposed. Extensions to directed and bipartite graphs are discussed. The model is tested on simulated and real world network data, showing promising performance for recovering latent community structure.

In the emerging edge-computing paradigm, mobile devices offload the computational tasks to an edge server by routing the required data over the wireless network. The full potential of edge-computing becomes realized only if the devices select the most appropriate server in terms of the latency and energy consumption, among many available ones. This problem is, however, challenging due to the randomness of the environment and lack of prior information about the environment. Therefore, a smart device, which sequentially chooses a server under uncertainty, attempts to improve its decision based on the historical time- and energy consumption. The problem becomes more complicated in a dynamic environment, where key variables might undergo abrupt changes. To deal with the aforementioned problem, we first analyze the required time and energy to data transmission and processing. We then use the analysis to cast the problem as a budget-constrained multi-armed bandit problem, where each arm is associated with a reward and cost, with time-variant statistical characteristics. We propose a policy to solve the formulated bandit problem and prove a regret bound. The numerical results demonstrate the superiority of the proposed method compared to a number of existing solutions.

We show that compact fully connected (FC) deep learning networks trained to classify wireless protocols using a hierarchy of multiple denoising autoencoders (AEs) outperform reference FC networks trained in a typical way, i.e., with a stochastic gradient based optimization of a given FC architecture. Not only is the complexity of such FC network, measured in number of trainable parameters and scalar multiplications, much lower than the reference FC and residual models, its accuracy also outperforms both models for nearly all tested SNR values (0 dB to 50dB). Such AE-trained networks are suited for in-situ protocol inference performed by simple mobile devices based on noisy signal measurements. Training is based on the data transmitted by real devices, and collected in a controlled environment, and systematically augmented by a policy-based data synthesis process by adding to the signal any subset of impairments commonly seen in a wireless receiver.