The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: 1) RAN; 2) core network; and 3) caching. We also present a general overview of major 5G cellular network elements such as software defined network, network function virtualization, caching, and mobile edge computing capable of meeting latency and other 5G requirements.

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

The Internet of Things (IoT)-centric concepts like augmented reality, high-resolution video streaming, self-driven cars, smart environment, e-health care, etc. have a ubiquitous presence now. These applications require higher data-rates, large bandwidth, increased capacity, low latency and high throughput. In light of these emerging concepts, IoT has revolutionized the world by providing seamless connectivity between heterogeneous networks (HetNets). The eventual aim of IoT is to introduce the plug and play technology providing the end-user, ease of operation, remotely access control and configurability. This paper presents the IoT technology from a bird’s eye view covering its statistical/architectural trends, use cases, challenges and future prospects. The paper also presents a detailed and extensive overview of the emerging 5G-IoT scenario. Fifth Generation (5G) cellular networks provide key enabling technologies for ubiquitous deployment of the IoT technology. These include carrier aggregation, multiple-input multiple-output (MIMO), massive-MIMO (M-MIMO), coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV) and cognitive radios (CRs). This paper presents an exhaustive review for these key enabling technologies and also discusses the new emerging use cases of 5G-IoT driven by the advances in artificial intelligence, machine and deep learning, ongoing 5G initiatives, quality of service (QoS) requirements in 5G and its standardization issues. Finally, the paper discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.

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Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios

The demand for wireless connectivity has grown exponentially over the last few decades. Fifth-generation (5G) communications, with far more features than fourth-generation communications, will soon be deployed worldwide. A new paradigm of wireless communication, the sixth-generation (6G) system, with the full support of artificial intelligence, is expected to be implemented between 2027 and 2030. Beyond 5G, some fundamental issues that need to be addressed are higher system capacity, higher data rate, lower latency, higher security, and improved quality of service (QoS) compared to the 5G system. This paper presents the vision of future 6G wireless communication and its network architecture. This article describes emerging technologies such as artificial intelligence, terahertz communications, wireless optical technology, free-space optical network, blockchain, three-dimensional networking, quantum communications, unmanned aerial vehicles, cell-free communications, integration of wireless information and energy transfer, integrated sensing and communication, integrated access-backhaul networks, dynamic network slicing, holographic beamforming, backscatter communication, intelligent reflecting surface, proactive caching, and big data analytics that can assist the 6G architecture development in guaranteeing the QoS. Besides, expected applications with 6G communication requirements and possible technologies are presented. We also describe potential challenges and research directions for achieving this goal.

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6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions

Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues

The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers’ structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain.

https://biblio.ugent.be/publication/8585223/file/8585225.pdf

A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives

5G is the next cellular generation and is expected to quench the growing thirst for taxing data rates and to enable the Internet of Things. Focused research and standardization work have been addressing the corresponding challenges from the radio perspective while employing advanced features, such as network densification, massive multiple-input-multiple-output antennae, coordinated multi-point processing, inter-cell interference mitigation techniques, carrier aggregation, and new spectrum exploration. Nevertheless, a new bottleneck has emerged: the backhaul. The ultra-dense and heavy traffic cells should be connected to the core network through the backhaul, often with extreme requirements in terms of capacity, latency, availability, energy, and cost efficiency. This pioneering survey explains the 5G backhaul paradigm, presents a critical analysis of legacy, cutting-edge solutions, and new trends in backhauling, and proposes a novel consolidated 5G backhaul framework. A new joint radio access and backhaul perspective is proposed for the evaluation of backhaul technologies which reinforces the belief that no single solution can solve the holistic 5G backhaul problem. This paper also reveals hidden advantages and shortcomings of backhaul solutions, which are not evident when backhaul technologies are inspected as an independent part of the 5G network. This survey is key in identifying essential catalysts that are believed to jointly pave the way to solving the beyond-2020 backhauling challenge. Lessons learned, unsolved challenges, and a new consolidated 5G backhaul vision are thus presented.

5G Backhaul Challenges and Emerging Research Directions: A Survey

Wireless technology is the strongest contender for catering for the 5G backhaul (BH) stipulated performance, where optical fiber is unavailable. In the presence of ultra-dense networks, such occurrences are exponentially increasing, and different wireless technologies are investigated for this application. We present the first BH-specific wireless link performance modeling that considers its inherent line-of-sight nature, together with an appropriate representation of the network topology using stochastic geometry. To this end, novel tractable models are obtained to capture the performance of wireless BH links. These are integrated into a multi-hop hybrid BH performance modeling framework and are applied in the analysis of a BH-aware user association optimization problem.

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Wireless Backhaul: Performance Modeling and Impact on User Association for 5G

5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, self-optimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users’ requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users’ achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users’ QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes.

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

Heterogeneous networks are considered a promising solution to address the explosive increase in wireless networks capacity demand. Due to their limited coverage, small cells offer better area spectral efficiency than macro-cells, and advanced features, such as cell range extension, aim at biasing the choice of users towards small cells. Whereas these features are designed to maximise the air interface capacity while respecting interference limitations and signal quality degradation, they do not address the backhaul constraints. The backhaul network is expected to match the number of small cells and their corresponding capacity while maintaining a minimum latency. Thus, with the advent of small cells and the help of enabling features to control inter-layer interference, the bottleneck of the wireless network is shifting from the traditional air interface towards the backhaul. In this paper, we propose an adaptive cell range extension approach that is optimised in view of the backhaul capacity and resilience as well as air interface constraints, thus, gears the traffic towards the cell that is capable of insuring an end-to-end service from the user to the core network. We use a reinforcement learning technique, whereby each small cell dynamically sets its bias value in view of the air interface and backhaul varying conditions.

An adaptive backhaul-aware cell range extension approach

Selection of relays is central to efficient utilization of cooperative diversity gains when multiple relays are available in the network. Such selection is generally based on some form of channel state information (CSI), which is always imperfect in practice. The effects of using imperfect CSI in a relay selection process have been generally considered in existing literature without the account for spatial distribution of relays, while current works on relay selection in random networks mainly assume perfect CSI. In this paper, we analyze the outage performance of a single source-destination pair communicating through a decode-and-forward relay, chosen from a Poisson point process (PPP) of candidate relays using perfect and imperfect CSI. We derive exact outage probability expressions for the selection cooperation strategy. Closed-form expressions are provided for special cases, and asymptotic analysis is conducted to highlight the highSNR system behavior.

Anvar Tukmanov

Papers

5G Backhaul Challenges and Emerging Research Directions: A Survey

Wireless Backhaul: Performance Modeling and Impact on User Association for 5G

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

An adaptive backhaul-aware cell range extension approach

Outage Performance Analysis of Imperfect-CSI-Based Selection Cooperation in Random Networks