Millimeter wave (mmWave) and terahertz (THz) radio access technologies (RAT) are expected to become a critical part of the future cellular ecosystem providing an abundant amount of bandwidth in areas with high traffic demands. However, extremely directional antenna radiation patterns that need to be utilized at both transmit and receive sides of a link to overcome severe path losses, dynamic blockage of propagation paths by large static and small dynamic objects, macroand micromobility of user equipment (UE) makes provisioning of reliable service over THz/mmWave RATs an extremely complex task. This challenge is further complicated by the type of applications envisioned for these systems inherently requiring guaranteed bitrates at the air interface. This tutorial aims to introduce a versatile mathematical methodology for assessing performance reliability improvement algorithms for mmWave and THz systems. Our methodology accounts for both radio interface specifics as well as service process of sessions at mmWave/THz base stations (BS) and is capable of evaluating the performance of systems with multiconnectivity operation, resource reservation mechanisms, priorities between multiple traffic types having different service requirements. The framework is logically separated into two parts: (i) parameterization part that abstracts the specifics of deployment and radio mechanisms, and (ii) queuing part, accounting for details of the service process at mmWave/THz BSs. The modular decoupled structure of the framework allows for further extensions to advanced service mechanisms in prospective mmWave/THz cellular deployments while keeping the complexity manageable and thus making it attractive for system analysts.

The support of multicast communications in the fifth-generation (5G) New Radio (NR) system poses unique challenges to system designers. Particularly, the highly directional antennas do not allow to serve all the user equipment devices (UEs) that belong to the same multicast session in a single transmission. However, the capability of modern antenna arrays to utilize multiple beams simultaneously, with potentially varying half-power beamwidth, adds a new degree of freedom to the UE scheduling. This work addresses the challenge of optimal multicasting in 5G millimeter wave (mmWave) systems by presenting a globally optimal solution for multi-beam antenna operation. The optimization problem is formulated as a special case of multiperiod variable cost and size bin packing problem that allows to not impose any constraints on the number of the beams and their configurations. We also propose heuristic solutions having polynomial time complexity. Our results show that for small cell radii of up to 100 meters, a single beam is always utilized. For higher cell coverage and practical ranges of the number of users (5-50), the optimal number of beams is upper bounded by 3.

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IoT is a new communication paradigm that gains a very high importance in the past few 18 years. This communication paradigm supports various heterogeneous applications in many fields 19 and with the dramatic increase of the number of sensor devices, it becomes a demand. Designing 20IoT networks faces many challenges that include security, massive traffic, high availability, high 21 reliability and energy constraints. Thus, new communication technologies and paradigms should 22 be deployed for IoT networks to overcome these challenges and achieve high system performance. 23Distributed computing techniques (e.g. fog and MEC), software defined networking (SDN), 24 network virtualization and blockchain are common recent paradigms that should be deployed for 25IoT networks, either combined or individually, to achieve the main requirements of the IoT 26 networks at a high system performance. Fog computing is a form of edge computing that has been 27 developed to provide the computing capabilities (e.g. storage and processing) at the edge of the 28 access network. Employing Fog computing in IoT networks, as an intermediate layer between IoT 29 devices and the remote cloud, becomes a demand to make use of the edge computing benefits. In 30 this work, we provide a framework for the IoT system structure that employs an edge computing 31 layer of Fog nodes controlled and managed by SDN network with the blockchain technology to 32 achieve a high level of security for latency sensitive IoT applications. The proposed system 33 employs SDN network with distributed controllers and distributed OpenFlow switches; these 34 switches are enabled with limited computing and processing capabilities. Furthermore, a data 35 offloading algorithm is developed to allocate different processing and computing tasks to the 36 distributed OpenFlow switches with available resources. Moreover, a traffic model is proposed to 37 model and analyze the traffic among different parts of the network. The proposed work achieves 38 various benefits to the IoT network, such as the latency reduction, security improvement and high 39 efficiency of resources utilization. The proposed algorithm is simulated and also the proposed 40 system is experimentally tested over a developed testbed to validate the proposed structure. 41Experimental results show that the proposed system achieves higher efficiency in terms of latency, 42 security and resource utilization. 43network that enables the communication and interaction between physical objects; this transforms 49 these objects from being blind to be smart [2]. Recently, IoT gains a very high significance because of 50 the great impact of all life fields [3]. IoT is expected to completely change our life by introducing 51 wide range of applications in various fields [4]. These applications include smart home, smart 52 cities, health care, smart vehicle and remote monitoring [5,6]. The IoT technology has a high market 53 impact as it comes with big market opportunities for various sectors such as hardware 54 manufactu...

Recently standardized New Radio (NR) technology supports both ultra-reliable low-latency (URLLC) service and conventional enhanced mobile broadband (eMBB) service. Owing to extreme latency and reliability requirements an explicit prioritization needs to be provided to URLLC service when these traffic types are mixed up at the air interface. In this work, we consider simultaneous support of these two services in an industrial environment, where production line equipment utilizes URLLC service for reorganization and synchronous operation while eMBB service is used for remote monitoring. By utilizing the tools of stochastic geometry and queuing theory, we formalize the model with pre-emptive priority service at NR base station (BS) with and without direct device-to-device (D2D) communications. Our numerical results indicate that the priority-based implementation of URLLC and eMBB coexistence allows us to isolate the former traffic efficiently and requires no external control. D2D-aware strategy, where the BS explicitly reserves some resources for direct communications, drastically outperforms those, where no explicit reservation is utilized, as well as the baseline strategy where all the traffic goes through the BS. This strategy can achieve 10 −5 of URLLC drop probability when the baseline strategy produces just 5 × 10 −3 , leading to three orders of magnitude reduction in drop probability and without significant impact produced on eMBB session drop probability. The developed model can be utilized to estimate the NR BS density required to support prescribed performance guarantees for all the considered strategies.

This paper is concerned with blocking probabilities in loss networks where both multicast and unicast connections are present. Each connection specifies a route and a bandwidth requirement. Multicast connections are point-tomultipont and allow transmission from one source to several receivers, any of which can join the connection at any time. Users that subscribe to the same data stream share one multicast connection so that no additional bandwidth is required. We present a Markov process model of a single network link. We also develop a convolution algorithm that finds the blocking probabilities on a single link and a reduced load approximation for estimating end-to-end blocking probabilities in a network. Keywords-Blocking probability, loss network, multicast, reduced load approximation I.INTRODUCTION Multicast is an efficient communication paradigm for transmitting the same data to multiple receivers. Instead of sending an identical data stream, or message, to each receiver, the source sends only a single stream, regardless of how many receivers have requested it. This stream is then replicated by multicast-enabled network equipment, but only to branches of the tree where receivers that requested the stream are located. Allowing bandwidth savings, this technique is useful for distributing software updates, live audio or video streams or for applications such as nearreal-time stock tickers.Since multicast has become an integral part of many networks (e.g., ATM, IP) during the last few years, the problem of evaluating performance of multicast networks and networks with both multicast and unicast traffic is certainly worth attention. Chan and Geraniotis [1] have considered a multicast network with several sources that stream layered video, they have given explicit expressions for end-to-end blocking probabilities and have developed a reduced load approximation to estimate those probabilities. Rykov and Samouylov [2] have proposed a model of a multicast network as a reversible Markov process. Gaidamaka and Samouylov in [3] have developed a Markov process model of a single-link multicast network and a computational algorithm that finds blocking probabilities. An exact algorithm that computes blocking probabilities in a single-source multicast network (tree topologies) was proposed by Virtamo et al. in [4]. They have extended their model of multicast network to include unicast background traffic (see also [5]). Boussetta and Belyot [6] have generalized the Kaufman-Roberts recursion [7], [8] to handle a single link network with unicast and multicast connections.

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