Touch is currently seen as the modality that will complement audition and vision as a third media stream over the Internet in a variety of future haptic applications which will allow full immersion and that will, in many ways, impact society. Nevertheless, the high requirements of these applications demand networks which allow ultra-reliable and low-latency communication (URLLC) for the challenging task of applying the required Quality of Service (QoS) for maintaining the user’s Quality of Experience (QoE) at optimum levels. In this survey, we enlist, discuss and evaluate methodologies and technologies of the necessary infrastructure for haptic communication. Furthermore, we focus on how the fifth generation (5G) of mobile networks will allow haptic applications to take life, in combination with the haptic data communication protocols, bilateral teleoperation control schemes and haptic data processing needed. Finally, we state the lessons learned throughout the surveyed research material along with the future challenges and infer our conclusions.

A large number of new consumer and industrial applications are likely to change the classic operator’s business models and provide a wide range of new markets to enter. This paper analyzes the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them, operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analyzed. Conclusions show that the operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.

Cellular-connected unmanned aerial vehicle (UAV) with flexible deployment is foreseen to be a major part of the sixth generation (6G) networks. The UAVs connected to the base station (BS), as aerial users (UEs), could exploit machine learning (ML) algorithms to provide a wide range of advanced applications, like object detection and video tracking. Conventionally, the ML model training is performed at the BS, known as centralized learning (CL), which causes high communication overhead due to the transmission of large datasets, and potential concerns about UE privacy. To address this, distributed learning algorithms, including federated learning (FL) and split learning (SL), were proposed to train the ML models in a distributed manner via only sharing model parameters. FL requires higher computational resource on the UE side than SL, while SL has larger communication overhead when the local dataset is large. To effectively train an ML model considering the diversity of UEs with different computational capabilities and channel conditions, we first propose a novel distributed learning architecture, a hybrid split and federated learning (HSFL) algorithm by reaping the parallel model training mechanism of FL and the model splitting structure of SL. We then provide its convergence analysis under non-independent and identically distributed (non-IID) data with random UE selection scheme. By conducting experiments on training two ML models, Net and AlexNet, in wireless UAV networks, our results demonstrate that the HSFL algorithm achieves higher learning accuracy than FL and less communication overhead than SL under IID and non-IID data, and the learning accuracy of HSFL algorithm increases with the increasing number of the split training UEs. We further propose a Multi-Arm Bandit (MAB) based best channel (BC) and best 2-norm (BN2) (MAB-BC-BN2) UE selection scheme to select the UEs with better wireless channel quality and larger local model updates for model training in each round. Numerical results demonstrate it achieves higher learning accuracy than BC, MAB-BC and MAB-BN2 UE selection scheme under non-IID, Dirichlet-nonIID and Dirichlet-Imbalanced data.

The Tactile Internet will enable users to physically explore remote environments and to make their skills available across distances. An important technological aspect in this context is the acquisition, compression, transmission, and display of haptic information. In this paper, we present the fundamentals and state of the art in haptic codec design for the Tactile Internet. The discussion covers both kinesthetic data reduction and tactile signal compression approaches. We put a special focus on how limitations of the human haptic perception system can be exploited for efficient perceptual coding of kinesthetic and tactile information. Further aspects addressed in this paper are the multiplexing of audio and video with haptic information and the quality evaluation of haptic communication solutions. Finally, we describe the current status of the ongoing IEEE standardization activity P1918.1.1 which has the ambition to standardize the first set of codecs for kinesthetic and tactile information exchange across communication networks.

The 5G mobile network is expected to meet the diverse demands from multiple types of business services. At the same time, some of the 5G use cases come with hard, and often expensive to meet, requirements in terms of latency and bandwidth. It is a common understanding that one system can not fit all and there is a need for customizing network according to the requirements of specific business use cases. Network slicing is introduced to partition the physical network to different slices to be configured for providing different quality of service as requested by the slice' operator and required by the slice' users. Since these slices will be used by the businesses, e.g. verticals, allocating physical resources to the network slices, is not anymore only a matter of performance but also a matter of revenue and business model. In this paper, we address a joint resource and revenue optimization a novel auction based model. Through extensive simulation study, we demonstrate our proposed auction model can allocate network resources to network slices for providing (i) higher satisfaction of requirements per network slice, and (ii) increased network revenue¹.