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

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 countries 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 continuously a hot buzzword in the news,attracting a huge focus from the whole society.

A Machine Learning Method for Prediction of Multipath Channels

A machine learning method for predicting the evolution of a mobile communication channel based on a specific type of convolutional neural network is developed and evaluated in a simulated multipath transmission scenario.The simulation and channel estimation are designed to replicate real-world scenarios and common measurements supported by reference signals in modern cellular networks.The capability of the predictor meets the requirements that a deployment of the developed method in a radio resource scheduler of a base station pos es.Possible applications of the method are discussed.

Device-to-Device Based Cooperative Relaying for 5G Network:A Comparative Review

Due to the proliferation of mobile internet access, the cellu-lar traffic is envisaged to experience a 1000-fold growth inthe second decade of the 21 st century. To meet such a hugetraffic demand, the Fifth Generation(5G) network have toadopt new techniques to substantially increase spectral effi-ciency and reliability. At the base station side, available re-sources(power supply, equipment size, processing capability,etc.) are far more sufficient than that of the terminal side,which imposes a high challenge on the uplink transmission.The concept of cooperative communications opens a possibili-ty of using multiple terminals to cooperatively achieve spa-tial diversity that is typically obtained by means of multipleantennas in the base station. The application of Device-to-Device(D2D) communications in the 3GPP LTE system fur-ther pushes the collaboration of terminals from the theory tothe practice. The utilization of D2D-based cooperative relay-ing is promising in the era of 5G. In this paper, we compara-tively study several cooperative multi-relay schemes, includ-ing the proposed opportunistic space-time coding, in thepresence of imperfect channel state information. The numeri-cal results reveal that the proposed scheme is the best coop-erative solution until now from the perspective of multiplex-ing-diversity tradeoff.

Machine Learning for Network Slicing Resource Management:A Comprehensive Survey

The emerging technology of multi-tenancy network slicing is considered as an es sential feature of 5G cellular networks.It provides network slices as a new type of public cloud services and therewith increases the service flexibility and enhances the network re source efficiency.Meanwhile,it raises new challenges of network resource management.A number of various methods have been proposed over the recent past years,in which machine learning and artificial intelligence techniques are widely deployed.In this article,we provide a survey to existing approaches of network slicing resource management,with a highlight on the roles played by machine learning in them.

The Digital Agricultural Knowledge and Information System(DAKIS):Employing digitalisation to encourage diversified and multifunctional agricultural systems

Multifunctional and diversified agriculture can address diverging pressures and demands by simultaneously enhancing productivity,biodiversity,and the provision of ecosystem services.The use of digital technologies can support this by designing and managing resource-efficient and context-specific agricultural systems.We present the Digital Agricultural Knowledge and Information System(DAKIS)to demonstrate an approach that employs digital technologies to enable decision-making towards diversified and sustainable agriculture.To develop the DAKIS,we specified,together with stakeholders,requirements for a knowledge-based decision-support tool and reviewed the literature to identify limitations in the current generation of tools.The results of the review point towards recurring challenges regarding the consideration of ecosystem services and biodiversity,the capacity to foster communication and cooperation between farmers and other actors,and the ability to link multiple spatiotemporal scales and sustainability levels.To overcome these challenges,the DAKIS provides a digital platform to support farmers'decision-making on land use and management via an integrative spatiotemporally explicit approach that analyses a wide range of data from various sources.The approach integrates remote and in situ sensors,artificial intelligence,modelling,stakeholder-stated demand for biodiversity and ecosystem services,and participatory sustainability impact assessment to address the diverse drivers affecting agricultural land use and management design,including natural and agronomic factors,economic and policy considerations,and socio-cultural preferences and settings.Ultimately,the DAKIS embeds the consideration of ecosystem services,biodiversity,and sustainability into farmers'decision-making and enables learning and progress towards site-adapted small-scale multifunctional and diversified agriculture while simultaneously supporting farmers'objectives and societal demands.

Part-based methods for handwritten digit recognition

In this paper, we intensively study the behavior of three part-based methods for handwritten digit recognition. The principle of the proposed methods is to represent a handwritten digit image as a set of parts and recognize the image by aggregating the recognition results of individual parts. Since part-based methods do not rely on the global structure of a character, they are expected to be more robust against various delormations which may damage the global structure. The proposed three methods are based on the same principle but different in their details, for example, the way of aggregating the individual results. Thus, those methods have different performances. Experimental results show that even the simplest part-based method can achieve recognition rate as high as 98.42% while the improved one achieved 99.15%, which is comparable or even higher than some state-of-the-art method. This result is important because it reveals that characters can be recognized without their global structure. The results also show that the part-based method has robustness against deformations which usually appear in handwriting.