A long-standing question for urban and regional planners pertains to the ability to describe urban patterns quantitatively. Cities' transport infrastructure, particularly street networks, provides an invaluable source of information about the urban patterns generated by peoples' movements and their interactions. With the increasing availability of street network datasets and the advancements in deep learning methods, we are presented with an unprecedented opportunity to push the frontiers of urban modelling towards more data-driven and accurate models of urban forms. In this study, we present our initial work on applying deep generative models to urban street network data to create spatially explicit urban models. We based our work on Variational Autoencoders (VAEs) which are deep generative models that have recently gained their popularity due to the ability to generate realistic images. Initial results show that VAEs are capable of capturing key high-level urban network metrics using low-dimensional vectors and generating new urban forms of complexity matching the cities captured in the street network data.

Despite the robust structure of the Internet, it is still susceptible to disruptive routing updates that prevent network traffic from reaching its destination. In this work, we propose a method for early detection of large-scale disruptions based on the analysis of bursty BGP announcements. We hypothesize that the occurrence of large-scale disruptions is preceded by bursty announcements. Our method is grounded in analysis of changes in the inter-arrival times of announcements. BGP announcements that are associated with disruptive updates tend to occur in groups of relatively high frequency, followed by periods of infrequent activity. To test our hypothesis, we quantify the burstiness of inter-arrival times around the date and times of three large-scale incidents: the Indosat hijacking event in April 2014, the Telecom Malaysia leak in June 2015, and the Bharti Airtel Ltd. hijack in November 2015. We show that we can detect these events several hours prior to when they were originally detected. We propose an algorithm that leverages the burstiness of disruptive updates to provide early detection of large-scale malicious incidents using local collector data. We describe limitations, open challenges, and how this method can be used for large-scale routing anomaly detection.

Network slicing is a key technology in 5G communications system, which aims to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in one base station on sharing the same bandwidth. Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying demands and the allocated bandwidth as the environment \emph{state} and \emph{action}, respectively. In order to obtain better quality of experience (QoE) satisfaction ratio and spectrum efficiency (SE), we propose generative adversarial network (GAN) based deep distributional Q network (GAN-DDQN) to learn the distribution of state-action values. Furthermore, we estimate the distributions by approximating a full quantile function, which can make the training error more controllable. In order to protect the stability of GAN-DDQN's training process from the widely-spanning utility values, we also put forward a reward-clipping mechanism. Finally, we verify the performance of the proposed GAN-DDQN algorithm through extensive simulations.

Traffic prediction plays a vital role in efficient planning and usage of network resources in wireless networks. While traffic prediction in wired networks is an established field, there is a lack of research on the analysis of traffic in cellular networks, especially in a content-blind manner at the user level. Here, we shed light into this problem by designing traffic prediction tools that employ either statistical, rule-based, or deep machine learning methods. First, we present an extensive experimental evaluation of the designed tools over a real traffic dataset. Within this analysis, the impact of different parameters, such as length of prediction, feature set used in analyses, and granularity of data, on accuracy of prediction are investigated. Second, regarding the coupling observed between behavior of traffic and its generating application, we extend our analysis to the blind classification of applications generating the traffic based on the statistics of traffic arrival/departure. The results demonstrate presence of a threshold number of previous observations, beyond which, deep machine learning can outperform linear statistical learning, and before which, statistical learning outperforms deep learning approaches. Further analysis of this threshold value represents a strong coupling between this threshold, the length of future prediction, and the feature set in use. Finally, through a case study, we present how the experienced delay could be decreased by traffic arrival prediction.

Packet routing is one of the fundamental problems in computer networks in which a router determines the next-hop of each packet in the queue to get it as quickly as possible to its destination. Reinforcement learning has been introduced to design the autonomous packet routing policy namely Q-routing only using local information available to each router. However, the curse of dimensionality of Q-routing prohibits the more comprehensive representation of dynamic network states, thus limiting the potential benefit of reinforcement learning. Inspired by recent success of deep reinforcement learning (DRL), we embed deep neural networks in multi-agent Q-routing. Each router possesses an independent neural network that is trained without communicating with its neighbors and makes decision locally. Two multi-agent DRL-enabled routing algorithms are proposed: one simply replaces Q-table of vanilla Q-routing by a deep neural network, and the other further employs extra information including the past actions and the destinations of non-head of line packets. Our simulation manifests that the direct substitution of Q-table by a deep neural network may not yield minimal delivery delays because the neural network does not learn more from the same input. When more information is utilized, adaptive routing policy can converge and significantly reduce the packet delivery time.

Optimal resource allocation is a fundamental challenge for dense and heterogeneous wireless networks with massive wireless connections. Because of the non-convex nature of the optimization problem, it is computationally demanding to obtain the optimal resource allocation. Recently, deep reinforcement learning (DRL) has emerged as a promising technique in solving non-convex optimization problems. Unlike deep learning (DL), DRL does not require any optimal/ near-optimal training dataset which is either unavailable or computationally expensive in generating synthetic data. In this paper, we propose a novel centralized DRL based downlink power allocation scheme for a multi-cell system intending to maximize the total network throughput. Specifically, we apply a deep Q-learning (DQL) approach to achieve near-optimal power allocation policy. For benchmarking the proposed approach, we use a Genetic Algorithm (GA) to obtain near-optimal power allocation solution. Simulation results show that the proposed DRL-based power allocation scheme performs better compared to the conventional power allocation schemes in a multi-cell scenario.

As the world races to deploy speedy 5G mobile networks on the ground, some companies remain focused on floating cell towers in the sky. During the final session of the sixth annual Brooklyn 5G Summit on Thursday, Silicon Valley and telecom leaders discussed whether aerial drones and balloons could finally begin providing commercial mobile phone and Internet service from the air. That same day, Alphabet subsidiary Loon, a balloon-focused graduate of the Google X research lab, unveiled a strategic partnership with Softbank's HAPSMobile to leverage both solar-powered balloons and drones to expand mobile Internet coverage and aid in deploying 5G networks. No high-altitude network connectivity services have taken off commercially so far, but some Brooklyn 5G Summit speakers were optimistic that it would happen soon. "The opportunity is in our hands in terms of truly leveraging 5G in conjunction with the massive paradigm shift when it comes to UAS--drones--and also satellites," said Volker Ziegler, CTO at Nokia Bell Labs.

The fifth-generation cellular networks (5G) has boosted the unprecedented convergence between the information world and physical world. On the other hand, empowered with the enormous amount of data and information, artificial intelligence (AI) has been universally applied and pervasive AI is believed to be an integral part of the future cellular networks (e.g., beyond 5G, B5G). Consequently, benefiting from the advancement in communication technology and AI, we boldly argue that the conditions for collective intelligence (CI) will be mature in the B5G era and CI will emerge among the widely connected beings and things. Afterwards, we introduce a regular language (i.e., the information economy metalanguage) supporting the future communications among agents and augment human intelligence. Meanwhile, we demonstrate the achievement of agents in a simulated scenario where the agents collectively work together to form a pattern through simple indirect communications. Finally, we discuss an anytime universal intelligence test model to evaluate the intelligence level of collective agents.

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.