Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

© 1998-2012 IEEE. Future 5th generation networks are expected to enable three key services-enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements

Intelligent Multi-Dimensional Resource Management in MEC-Assisted Vehicular Networks

Benefiting from advances in the automobile industry and wireless communication technologies, the vehicular network has been emerged as a key enabler of intelligent transportation services. Allowing real-time information exchanging between vehicle and everything, traffic safety and efficiency are significantly enhanced, and ubiquitous Internet access is enabled to support new data services and applications. However, with more and more services and applications, mobile data traffic generated by vehicles has been increasing and the issue on the overloaded computing task has been getting worse. Because of the limitation of spectrum and vehicles' on-board computing and caching resources, it is challenging to promote vehicular networking technologies to support the emerging services and applications, especially those requiring sensitive delay and diverse resources. To overcome these challenges, in this thesis, we propose a new vehicular network architecture and design efficient resource management schemes to support the emerging applications and services with different levels of quality-of-service (QoS) guarantee. Firstly, we propose a multi-access edge computing (MEC)-assisted vehicular network (MVNET) architecture that integrates the concepts of software-defined networking (SDN) and network function virtualization (NFV). With MEC, the interworking of multiple wireless access technologies can be realized to exploit the diversity gain over a wide range of radio spectrum, and at the same time, vehicle's computing/caching tasks can be offloaded to and processed by the MEC servers. By enabling NFV in MEC, different functions can be programmed on the server to support diversified vehicular applications, thus enhancing the server's flexibility. Moreover, by using SDN concepts in MEC, a unified control plane interface and global information can be provided, and by subsequently using this information, intelligent traffic steering and efficient resource management can be achieved. Secondly, under the proposed MVNET architecture, we propose a dynamic spectrum management framework to improve spectrum resource utilization while guaranteeing QoS requirements for different applications, in which, spectrum slicing, spectrum allocating, and transmit power controlling are jointly considered. Accordingly, three non-convex network utility maximization problems are formulated to slice spectrum among base stations (BSs), allocate spectrum among vehicles associated with the same BS, and control transmit powers of BSs, respectively. Via linear programming relaxation and first-order Taylor series approximation, these problems are transformed into tractable forms and then are jointly solved by a proposed alternate concave search algorithm. As a result, optimal spectrum slicing ratios among BSs, optimal BS-vehicle association patterns, optimal fractions of spectrum resources allocated to vehicles, and optimal transmit powers of BSs are obtained. Based on our simulation, a high aggregate network utility is achieved by the proposed spectrum management scheme compared with two existing schemes. Thirdly, we study the joint allocation of the spectrum, computing, and caching resources in MVNETs. To support different vehicular applications, we consider two typical MVNET architectures and formulate multi-dimensional resource optimization problems accordingly, which are usually with high computation complexity and overlong problem-solving time. Thus, we exploit reinforcement learning to transform the two formulated problems and solve them by leveraging the deep deterministic policy gradient (DDPG) and hierarchical learning architectures. Via off-line training, the network dynamics can be automatically learned and appropriate resource allocation decisions can be rapidly obtained to satisfy the QoS requirements of vehicular applications. From simulation results, the proposed resource management schemes can achieve high delay/QoS satisfaction ratios. Fourthly, we extend the proposed MVNET architecture to an unmanned aerial vehicle (UAV)-assisted MVNET and investigate multi-dimensional resource management for it. To efficiently provide on-demand resource access, the macro eNodeB and UAV, both mounted with MEC servers, cooperatively make association decisions and allocate proper amounts of resources to vehicles. Since there is no central controller, we formulate the resource allocation at the MEC servers as a distributive optimization problem to maximize the number of offloaded tasks while satisfying their heterogeneous QoS requirements, and then solve it with a multi-agent DDPG (MADDPG)-based method. Through centrally training the MADDPG model offline, the MEC servers, acting as learning agents, then can rapidly make vehicle association and resource allocation decisions during the online execution stage. From our simulation results, the MADDPG-based method can achieve a comparable convergence rate and higher delay/QoS satisfaction ratios than the benchmarks. In summary, we have proposed an MEC-assisted vehicular network architecture and investigated the spectrum slicing and allocation, and multi-dimensional resource allocation in the MEC- and/or UAV-assisted vehicular networks in this thesis. The proposed architecture and schemes should provide useful guidelines for future research in multi-dimensional resource management scheme designing and resource utilization enhancement in highly dynamic wireless networks with diversified data services and applications

Cooperative Communications for 5G Wireless Networks and Beyond.

Cooperative communication is an appealing technique stemming from the information-theoretic notion of cooperative diversity and having gradually evolved into a mainstream design paradigm in 4G LTE-Advanced. By exploiting cooperation among multiple transmission/reception nodes the technique reveals tremendous benefits, both in theoretical research and practical deployment, to enhance network performance in terms of throughput, reliability, latency, and network coverage. Due to these desirable attributes, it is expected that the technique will continue to be utilized in the coming generations of wireless networks. However, a major challenge to enable cooperative communication in the context of 5G and beyond is the advent of new wireless technologies and network architectures. The emergence of these technologies, on the one hand, enables a plethora of new applications. On the other hand, they entail new network operational constraints, which hinders the cooperation among network nodes. To address this challenge, it is crucial to study cooperative communication in specific network scenarios where these new technologies are employed to gain new insights their impact on in-network cooperation. Motivated by this, the thesis studies the role of cooperative communication in two futuristic network scenarios, encompassing two novel wireless technologies, namely non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAVs)

D3.2 First performance results for multi -node/multi -antenna transmission technologies

This deliverable describes the current results of the multi-node/multi-antenna technologies investigated within METIS and analyses the interactions within and outside Work Package 3. Furthermore, it identifies the most promising technologies based on the current state of obtained results. This document provides a brief overview of the results in its first part. The second part, namely the Appendix, further details the results, describes the simulation alignment efforts conducted in the Work Package and the interaction of the Test Cases. The results described here show that the investigations conducted in Work Package 3 are maturing resulting in valuable innovative solutions for future 5G systems.Fantini. R.; Santos, A.; De Carvalho, E.; Rajatheva, N.; Popovski, P.; Baracca, P.; Aziz, D.... (2014). D3.2 First performance results for multi -node/multi -antenna transmission technologies. http://hdl.handle.net/10251/7675

Resource Allocation and Service Management in Next Generation 5G Wireless Networks

The accelerated evolution towards next generation networks is expected to dramatically increase mobile data traffic, posing challenging requirements for future radio cellular communications. User connections are multiplying, whilst data hungry content is dominating wireless services putting significant pressure on network's available spectrum. Ensuring energy-efficient and low latency transmissions, while maintaining advanced Quality of Service (QoS) and high standards of user experience are of profound importance in order to address diversifying user prerequisites and ensure superior and sustainable network performance. At the same time, the rise of 5G networks and the Internet of Things (IoT) evolution is transforming wireless infrastructure towards enhanced heterogeneity, multi-tier architectures and standards, as well as new disruptive telecommunication technologies. The above developments require a rethinking of how wireless networks are designed and operate, in conjunction with the need to understand more holistically how users interact with the network and with each other. In this dissertation, we tackle the problem of efficient resource allocation and service management in various network topologies under a user-centric approach. In the direction of ad-hoc and self-organizing networks where the decision making process lies at the user level, we develop a novel and generic enough framework capable of solving a wide array of problems with regards to resource distribution in an adaptable and multi-disciplinary manner. Aiming at maximizing user satisfaction and also achieve high performance - low power resource utilization, the theory of network utility maximization is adopted, with the examined problems being formulated as non-cooperative games. The considered games are solved via the principles of Game Theory and Optimization, while iterative and low complexity algorithms establish their convergence to steady operational outcomes, i.e., Nash Equilibrium points. This thesis consists a meaningful contribution to the current state of the art research in the field of wireless network optimization, by allowing users to control multiple degrees of freedom with regards to their transmission, considering mobile customers and their strategies as the key elements for the amelioration of network's performance, while also adopting novel technologies in the resource management problems. First, multi-variable resource allocation problems are studied for multi-tier architectures with the use of femtocells, addressing the topic of efficient power and/or rate control, while also the topic is examined in Visible Light Communication (VLC) networks under various access technologies. Next, the problem of customized resource pricing is considered as a separate and bounded resource to be optimized under distinct scenarios, which expresses users' willingness to pay instead of being commonly implemented by a central administrator in the form of penalties. The investigation is further expanded by examining the case of service provider selection in competitive telecommunication markets which aim to increase their market share by applying different pricing policies, while the users model the selection process by behaving as learning automata under a Machine Learning framework. Additionally, the problem of resource allocation is examined for heterogeneous services where users are enabled to dynamically pick the modules needed for their transmission based on their preferences, via the concept of Service Bundling. Moreover, in this thesis we examine the correlation of users' energy requirements with their transmission needs, by allowing the adaptive energy harvesting to reflect the consumed power in the subsequent information transmission in Wireless Powered Communication Networks (WPCNs). Furthermore, in this thesis a fresh perspective with respect to resource allocation is provided assuming real life conditions, by modeling user behavior under Prospect Theory. Subjectivity in decisions of users is introduced in situations of high uncertainty in a more pragmatic manner compared to the literature, where they behave as blind utility maximizers. In addition, network spectrum is considered as a fragile resource which might collapse if over-exploited under the principles of the Tragedy of the Commons, allowing hence users to sense risk and redefine their strategies accordingly. The above framework is applied in different cases where users have to select between a safe and a common pool of resources (CPR) i.e., licensed and unlicensed bands, different access technologies, etc., while also the impact of pricing in protecting resource fragility is studied. Additionally, the above resource allocation problems are expanded in Public Safety Networks (PSNs) assisted by Unmanned Aerial Vehicles (UAVs), while also aspects related to network security against malign user behaviors are examined. Finally, all the above problems are thoroughly evaluated and tested via a series of arithmetic simulations with regards to the main characteristics of their operation, as well as against other approaches from the literature. In each case, important performance gains are identified with respect to the overall energy savings and increased spectrum utilization, while also the advantages of the proposed framework are mirrored in the improvement of the satisfaction and the superior Quality of Service of each user within the network. Lastly, the flexibility and scalability of this work allow for interesting applications in other domains related to resource allocation in wireless networks and beyond

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig