Editorial:Special Topic on Computational Radio Intelligence:One Key for 6G Wireless

The year of 2019 is the first deployment year of the fifth generation (5G) mobile communications.As we are writing the editorial for this special issue,a list of coun?tries such as South Korea,the United States,China,Switzerland,the United Kingdom,and Spain have launched commercial 5G services for the general public,while this list is growing quickly and is envisioned to become much longer in the near future.For the past months,5G has been continuous?ly a hot buzzword in the news,attracting a huge focus from the whole society.It even goes beyond the technical and commer?cial scopes,becoming the frontline of geopolitical contention and conflict.As a revolutionary technology,5G will penetrate into all aspects of society―not only human daily life but also manufacturing,education,health care,and scientific activities―generating tremendous economic and societal benefits.From the perspective of technology research,however,it is al?ready the time to start considering what future beyond-5G or the sixth generation (6G) mobile networks should be,in order to satisfy the demand on communications and networking in 2030.Although a discussion is ongoing within the wireless community about whether counting should be stop at 5,adopt?ing the Microsoft Windows’approach where Windows 10 is the ultimate version,several pioneering works on the next-gen?eration wireless networks have been initiated.The Internation?al Telecommunication Union Telecommunication Standardiza?tion Sector (ITU-T) Focus Group Technologies for Network 2030(FG NET-2030)was established in July 2018.The Focus Group intends to study the capabilities of networks for the year 2030 and beyond,when it is expected to support novel forwardlooking scenarios,such as holographic type communications,extremely fast response in critical situations,and high-preci?sion communication demands of emerging market verticals.The European Commission initiated to sponsor beyond-5G re?search activities,such as its recent Horizon 2020 call―5G Long Term Evolution―where a number of pioneer projects will be kicked off at the early beginning of 2020.In Finland,the University of Oulu has begun ground-breaking 6G research as part of Academy of Finland’s flagship program,6G-Enabled Wireless Smart Society and Ecosystem (6Genesis),which fo?cuses on several challenging research areas including reliable near-instant unlimited wireless connectivity,distributed com?puting and intelligence,as well as materials and antennas to be utilized in future for circuits and devices.

Among the short list of 6G enabling technologies that can be envisioned currently,such as Terahertz communications,visi?ble light communications,photonics-defined radio,holographic radio,super massive multiple-input and multiple-output (MI?MO),quantum communications,and dense satellite constella?tion,artificial intelligence (AI) is the most recognized candi?date,which can provide computational radio and network intel?ligence from the fundamental physical layer to the upper net?work management layer.Due to its powerful nonlinear map?ping and distribution processing capability,deep neural net?works based machine learning technology is being considered as a very promising tool to attack the big challenge in wireless communications and networks imposed by the explosively in?creasing demands in terms of capacity,coverage,latency,effi?ciency (power,frequency spectrum and other resources),flexi?bility,compatibility,quality of experience,and silicon conver?gence.Mainly categorized into the supervised learning,the un?supervised learning,and the reinforcement learning,various machine learning algorithms can be used to provide a better channel modelling and estimation in millimeter and terahertz bands,to select a more adaptive modulation (waveform,coding rate,bandwidth,and filtering structure) in massive MIMO,to design a more efficient front-end and RF processing (pre-dis?tortion for power amplifier compensation,beam-forming and crest-factor reduction),to deliver a better compromise in selfinterference cancellation for full-duplex transmission and de?vice-to-device communications,and to offer a more practical solution for intelligent network optimization,orchestration and management,mobile edge and fog computing,networking slic?ing and radio resource management related to wireless big da?ta,mission critical communications,massive machine-type communications and tactile internet.

From practical application and research development per?spective,this special issue aims to be the first single form to provide a comprehensive and highly coherent treatment on all the technology aspects related to machine learning for wireless communications and networks by covering multipath fading channel,channel coding,physical-layer design,network slic?ing,resource management,mobile edge architecture,fog com?puting,and autonomous network management.The call-for-pa?pers of this special issue have brought excellent submissions in both quality and quantity.After rigorous reviews,six excellent articles have been selected for publication in this special issue which is organized into the following three category groups.

Consisting of two articles,the first group of this special issue focuses on the exploration of replacing conventional modelbased statistical methods with data-driven learning approaches in spatial-temporal-spectral radio signal processing,in order to simplify the physical layer implementation or boost the transmis?sion performance.As its title“To Learn or Not to Learn:Deep Learning Assisted Wireless Modem Design”exactly means,the first article by XUE Songyan et al.provides a fundamental re?think of the wireless modem design to answer a frequently-asked question:what additional values artificial intelligence could bring to the physical layer.Three case studies,i.e.,deep learn?ing assisted parallel decoding of convolutional codes for a sub?stantial reduction of decoding latency,deep learning aided multi-user frequency synchronization,and deep learning based coherent multi-user multi-antenna signal detection,are present?ed in this article,By adapting transmission parameters such as the constellation size,coding rate,and transmit power to instan?taneous fading channel conditions,adaptive wireless communi?cations can potentially achieve great performance.To realize this potential,accurate channel state information (CSI) is re?quired at the transmitter.However,unless the mobile speed is very low,the obtained CSI quickly becomes outdated due to the rapid channel variation caused by multi-path fading.The sec?ond article,“A Machine Learning Method for Prediction of Mul?tipath Channels”by Julian AHRENS et al.,investigates the fea?sibility of predicting fading channels by means of a convolution?al neural network.The numerical results verify the effectiveness of machine learning based channel prediction in the presence of outdated CSI.It is envisioned that the channel prediction is ap?plicable to a wide variety of adaptive transmission techniques,such as pre-coding and multi-user scheduling in MIMO systems,massive MIMO,beam-forming,interference alignment,closedloop transmit diversity,transmit antenna selection,opportunistic relaying,orthogonal frequency-division multiplexing (OFDM),coordinated multi-point transmission (CoMP),mobility manage?ment,and physical layer security.

Mobile networks’troubleshooting (systems failures,cyberattacks,performance optimization,etc.)still cannot avoid man?ual operations.A mobile operator has to keep an operational group with a large number of network administrators with high expertise,leading to a costly Operational Expenditure (OPEX)that is currently three times that of Capital Expenditure (CA?PEX) and keeps rising.The 5G and next-generation networks are more complicated and heterogeneous than previous sys?tems.It inevitably imposes a great challenge on manual and semi-automatic network management that is already costly,vul?nerable and time-consuming.Therefore,the second group of this special issue is about the application of machine learning approaches to realize an intelligent and autonomous network management that can keep OPEX under an affordable level,improve system Quality-of-Service(QoS)and end users’Quali?ty-of-Experience (QoE),and shorten time-to-market of new ser?vices.In the third article entitled“A Case Study on Intelligent Operation System for Wireless Networks”,LIU Jianwei et al.propose a comprehensive and flexible framework to achieve an intelligent operation system.Two use cases are studied to il?lustrate machine learning algorithms to automate the anomaly detection and fault diagnosis of key performance indicators in wireless networks.The effectiveness of the proposed machine learning algorithms is demonstrated by the real data experi?ments.Next,HAN Bin et al.provide a comprehensive over?view on the metrics of machine learning for network slicing re?source management in their article“Machine Learning for Net?work Slicing Resource Management:A Comprehensive Sur?vey”.Two problems of resource management in network slic?ing,namely the slice admission control and the cross-slice re?source management,are discussed,illustrating the benefits of machine learning techniques in the improvement of service flexibility and network resource efficiency.

The new demanding features for advanced networks,e.g.,mobile edge computing and fog computing,foster novel servic?es and applications that never emerged in previous networks,such as Unmanned Aerial Vehicle (UAV),the Internet of Things,connected and automated cars,and tactile internet.This will in turn impose new technical challenges on the cellular networks but can well be overcome in the 6G networks.Orga?nized into the third group,the fifth and sixth articles of this spe?cial issue focus on cross-layer optimization for the novel network architecture and new services by taking advantage of machine learning techniques.More specifically,the fifth article“Ma?chine Learning Based Unmanned Aerial Vehicle Enabled Fog-Radio Access Network and Edge Computing”by Mohammed SEID et al.presents the use of machine learning in the UAV en?abled fog-radio access network of edge computing architecture.Moreover,this article also addresses the future research direc?tion of machine learning roles in UAV connected cellular net?works.Last but not the least,the sixth article of this special is?sue“A Survey on Machine Learning Based Proactive Caching”by Stephen ANOKYE et al.provides an overview on smart and efficient mobile edge caching relying on machine learning ap?proaches.Issues affecting edge caching,such as caching enti?ties,policies,and algorithms,are discussed,followed by a sum?mary on challenges and future research directions.

As we conclude the introduction to this special issue and the content of six articles,we would like to thank all authors for their valuable contributions.We also express our sincere grati?tude to all the reviewers for their timely and insightful com?ments on all submitted articles.It is hoped that this special is?sue is informative and useful from various aspects related to the application of machine learning approaches for next-gener?ation wireless networks.