Driven by the demand to accommodate today’s growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements. Considering the everincreasing complexity of the network, and the emergence of novel use cases such as autonomous cars, industrial automation, virtual reality, e-health, and several intelligent applications, machine learning (ML) is expected to be essential to assist in making the 5G vision conceivable. This paper focuses on the potential solutions for 5G from an ML-perspective. First, we establish the fundamental concepts of supervised, unsupervised, and reinforcement learning, taking a look at what has been done so far in the adoption of ML in the context of mobile and wireless communication, organizing the literature in terms of the types of learning.We then discuss the promising approaches for how ML can contribute to supporting each target 5G network requirement, emphasizing its specific use cases and evaluating the impact and limitations they have on the operation of the network. Lastly, this paper investigates the potential features of Beyond 5G (B5G), providing future research directions for how ML can contribute to realizing B5G. This article is intended to stimulate discussion on the role that ML can play to overcome the limitations for a wide deployment of autonomous 5G/B5G mobile and wireless communications.
The ongoing development of mobile communication networks to support a wide range of superfast broadband services has led to massive capacity demand. This problem is expected to be a significant concern during the deployment of the 5G wireless networks. The demand for additional spectrum to accommodate mobile services supporting higher data rates and having lower latency requirements, as well as the need to provide ubiquitous connectivity with the advent of the Internet of Things (IoT) sector, is likely to considerably exceed the supply, based on the current policy of exclusive spectrum allocation to mobile cellular systems. Hence, the imminent spectrum shortage has introduced a new impetus to identify practical solutions to make the most efficient use of the scarce licensed bands in a shared manner. Recently, the concept of dynamic spectrum sharing has received considerable attention from regulatory bodies and governments globally, as it could potentially open new opportunities for mobile operators to exploit spectrum bands whenever they are underutilised by their owners, subject to service level agreements. Although various sharing paradigms have been proposed and discussed, the impact and performance gains of different schemes can be scenario-specific and vary depending on the nature of the sharing parties, the level of sharing and spectrum access scheme. In this survey, we describe the main concepts of dynamic spectrum sharing, different sharing scenarios, as well as the major challenges associated with sharing licensed bands. Finally, we conclude this survey paper with open research challenges and suggest some future research directions.
The 5G technology has tapped into millimeter wave (mmWave) spectrum to create additional bandwidth for improved network capacity. The use of mmWave for specific applications including vehicular networks has widely discussed. However, applying mmWave to vehicular networks faces challenges of high mobility nodes and narrow coverage along the mmWave beams. In this paper, we focus on a mmWave small cell base station deployed in a city area to support vehicular network application. We propose profiling vehicle mobility for a machine learning agent to learn the performance of serving vehicles with different mobility profiles and utilize the past experiences to select appropriate mmWave beam to service a vehicle. Our machine learning agent is based on multi-armed bandit learning model, where classical multi-armed bandit and contextual multi-armed bandit are used. Particularly for the contextual multi-armed bandit, the contexts are vehicle mobility information. We show that the local street layout has naturally constrained vehicle movement creating distinct mobility information for vehicles, and the vehicle mobility information is highly related to communication performance. By using vehicle mobility information, the machine learning agent is able to identify vehicles that can remain within a beam for longer time period to avoid frequent handovers.
Multi-hop relay selection is a critical issue in vehicle to everything networks. In previous works, the optimal hopping strategy is assumed to be based on the shortest distance. This study proposes a hopping strategy based on the lowest propagation loss, considering the effect of the environment. We use a twostep machine learning routine: improved deep encoder-decoder architecture to generate environmental maps and Q-learning to search for the multi-hopping path with the lowest propagation loss. Simulation results show that our proposed method can improve environmental recognition and extend the reachability of multi-hop communications by up to 66.7%, compared with a shortest-distance selection.
Localization is crucial for various applications, this includes resource coordination in small and ultra-small cells, as well as the whole range of Location Based Service (LBS). Multilateration is a localization technique that is based on distance measurements between multiple reference nodes and a target node. This paper introduces a multilateration localization approach that uses Singular Value Decomposition (SVD) for 3D indoor positioning. It also provides a mathematical multilateration formulation which considers the coordinates of the reference nodes and the relative distance between transmitting nodes. In practical deployments, the relative distance can be estimated using RSSI; we apply Kalman filtering to the RSSI measurements aiming to get a more accurate RSSI value. The approach is complemented by using two selection methods which help chosing the best nodes for multilateration computation. The paper concludes with a discussion of the experimental evaluation results obtained.
In order to satisfy the requirements of future IMT-Advanced mobile systems, the concept of spectrum aggregation is introduced by 3GPP in its new LTE-Advanced (LTE Rel. 10) standards. While spectrum aggregation allows aggregation of carrier components (CCs) dispersed within and across different bands (intra/inter-band) as well as combination of CCs having different bandwidths, spectrum aggregation is expected to provide a powerful boost to the user throughput in LTE-Advanced (LTE-A). However, introduction of spectrum aggregation or carrier aggregation (CA) as referred to in LTE Rel. 10, has required some changes from the baseline LTE Rel. 8 although each CC in LTE-A remains backward compatible with LTE Rel. 8. This article provides a review of spectrum aggregation techniques, followed by requirements on radio resource management (RRM) functionality in support of CA. On-going research on the different RRM aspects and algorithms to support CA in LTE-Advanced are surveyed. Technical challenges for future research on aggregation in LTE-Advanced systems are also outlined. © 2014 IEEE.
5G New Radio (NR) is touted as a pivotal enabling technology for the genuine realization of connected and cooperative autonomous driving. Despite numerous research efforts in recent years, a systematic overview on the role of 5G NR in future connected autonomous communication networks is missing. To fill this gap and to spark more future research, this paper introduces the technology components of 5G NR and discusses the evolution from existing cellular vehicle-to-everything (V2X) technology towards NR-V2X. We primarily focus on the key features and functionalities of physical layer, Sidelink communication and its resource allocation, architecture flexibility, security and privacy mechanisms, and precise positioning techniques. Moreover, we envisage and highlight the potential of machine learning for further performance enhancement in NR-V2X services. Lastly, we show how 5G NR can be configured to support advanced V2X use cases.
This paper presents a framework for including cognitive management functionalities in the spectrum selection process for Opportunistic Networks (ONs).The framework is based on a decision making functionality interacting with a knowledge management block that stores and processes information about the spectrum use. Different approaches for spectrum selection are discussed covering specific cases including the capability to aggregate different bands and the possibility to jointly select the spectrum and the network interface. Illustrative results of the proposed framework are presented. © 2012 IIMC Ltd.
—In the fifth and beyond (5G/B5G) communication, wireless networks are evolved towards offering various services of different use cases and, therefore, need to span a wide range of requirements. While different services will be supported at the same time, radio resource management needs to consider the different requirements. In addition, as wireless systems are capable to support multi-connectivity, radio resource allocation becomes more challenging. In this context, we introduce a many-to-many matching game, and develop a distributed radio resource allocation algorithm supporting multi-connectivity. Simulation results demonstrate that the proposed approach improves the QoS levels of UEs by up to 14.9% considering their service requirements.
While ultra-reliable and low latency communication (uRLLC) is expected to cater to emerging services requiring real-time control, such as factory automation and autonomous driving, the design of uRLLC of stringent requirements would be very challenging. Among novel solutions to satisfy uRLLC's requirements, interface diversity is widely regarded as an efficient enabler of ultra-reliable connectivity. When mobile de- vices are connected to multiple base stations (BSs) of different radio access technologies (RATs) and same data is transmitted via multiple links simultaneously, the transmission reliability can be improved. How- ever, duplicate transmission of same data causes an increase in the traffic loads, leading to radio resource shortage. Considering it, efficient config- uration of multi-connectivity (MC) for mobile devices is important. In this paper, the RAT selection scheme including efficient MC configura- tion is proposed. By adopting distributed reinforcement learning (RL), each device could learn the policy for efficient MC configuration and select appropriate RATs. Simulation results show that 20.8% reliabil- ity improvements over the single connectivity scheme is observed. Com- paring to the method to configure MC for devices all the time, 37.6% improvement is achieved at high traffic loads.
Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory quality of service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as hostile wireless channels (e.g., high mobility or asynchronous transmissions), increasingly fragmented and congested spectrum, hardware imperfections, and explosive growth of vehicular communication devices. Therefore, it is highly desirable to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, we present a comprehensive survey on resource allocation (RA) schemes for a range of vehicular network technologies including dedicated short range communications (DSRC) and cellular based vehicular networks. We discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.
In the fifth and beyond (5G/B5G) communication, wireless networks are evolved towards offering various services of different use cases and, therefore, need to span a wide range of requirements. While different services will be supported at the same time, radio resource management needs to consider the different requirements. In addition, as wireless systems are capable to support multi-connectivity, radio resource allocation becomes more challenging. In this context, we introduce a manyto- many matching game, and develop a distributed radio resource allocation algorithm supporting multi-connectivity. Simulation results demonstrate that the proposed approach improves the QoS levels of UEs by up to 14.9% considering their service requirements.
In 5G network, dense deployment and millimetre wave (mmWave) are some of the key approaches to boost network capacity. Dense deployment of mmWave small cells using narrow directional beams will escalate the cell and beam related handovers for high mobility of vehicles, which may in turn limits the performance gain promised by 5G. One of the research issues in mmWave handover is to minimise the handover needs by identifying long lasting connections. In this paper, we first develop an analytical model to derive the vehicle sojourn time within a beam coverage. When multiple connections offered by nearby all mmWave small cells are available when upon a handover event, we further derive the longest sojourn time among all potential connections which represents the theoretical upperbound limit of the sojourn time performance. We then design a Fuzzy Logic (FL) based distributed beam-centric handover decision algorithm to maximise vehicle sojourn time. Simulation experiments are conducted to validate our analytical model and show the performance advantage of our proposed FLbased solution when compared with commonly used approach of connecting to the strongest connection.
With development of 5G and Beyond communication technologies and the recent achievements in autonomous driving, technical solutions to improve road safety have attracted great attention. In this paper, we present a collision avoidance system implemented using a 1/10 scale vehicle, as a research platform for autonomous driving connected a vehicular network. While the collision avoidance system exploits data fusion to make decisions relevant to predicting potential collision events, the effectiveness of the fusion of data obtained from in-vehicle sensors and vehicular communication is evaluated within a testbed environment.
It is envisaged that 5G can enable many vehicular use cases that require high capacity, ultra-low latency and high reliability. To support this, 5G proposes the use of dense small cells technology as well as and highly directional mmWave systems deployment, among many other new advanced communication technologies, to boost the network capacity, reduce latency and provide high reliability. In such systems, enabling vehicular communication, where the nodes are highly mobile, requires robust mobility management techniques to minimise signalling cost and interruptions during frequent handovers. This presents a major challenge that communication system engineers need to address to realise the promise of 5G systems for V2X and similar applications. In this paper, we provide an overview of recent progresses in the development of handover and beam management techniques in 5G communication systems. We conduct a critical appraisal of current research on beam level and cell level mobility management in 5G mmWave networks considering the ultra-reliable and low-latency communication requirements within the context of V2X applications. We also provide an insight into the open challenges and the emerging trends as well as the possible evolution beyond the horizon of 5G.
We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.
We investigate a collision-sensitive secondary network that intends to opportunistically aggregate and utilize spectrum of a primary network to achieve higher data rates. In opportunistic spectrum access with imperfect sensing of idle primary spectrum, secondary transmission can collide with primary transmission. When the secondary network aggregates more channels in the presence of the imperfect sensing, collisions could occur more often, limiting the performance obtained by spectrum aggregation. In this context, we aim to address a fundamental query, that is, how much spectrum aggregation is worthy with imperfect sensing. For collision occurrence, we focus on two different types of collision: one is imposed by asynchronous transmission; and the other by imperfect spectrum sensing. The collision probability expression has been derived in closed-form with various secondary network parameters: primary traffic load, secondary user transmission parameters, spectrum sensing errors, and the number of aggregated sub-channels. In addition, the impact of spectrum aggregation on data rate is analysed under the constraint of collision probability. Then, we solve an optimal spectrum aggregation problem and propose the dynamic spectrum aggregation approach to increase the data rate subject to practical collision constraints. Our simulation results show clearly that the proposed approach outperforms the benchmark that passively aggregates sub-channels with lack of collision awareness.
We consider resource allocation with aggregation for different types of traffic in heterogeneous networks, including WLANs. While mobile data traffic is expected to increase, efficient management of multiple bands including unlicensed band becomes increasingly important. In this context, we formulate a resource allocation problem using utility functions for heterogeneous traffic and propose a novel algorithm that considers the estimated UE speed, traffic types and channel quality. Simulation results illustrate performance of the proposed algorithm in terms of higher utility value and fairness, even at high traffic loads. Additional improvements in resource utilization through estimating UE speed and allocating low-mobility UEs to Wi-Fi are shown.
We consider the resource allocation with aggregation of multiple bands including unlicensed band for heterogeneous traffic. While the mobile data traffic including high volume of video traffic is expected to increase significantly, an efficient management of radio resources from multiple bands is required to guarantee the quality of service (QoS) of different traffic types. In this context, we formulate an optimal resource allocation by using different utility functions for heterogeneous traffic and the two-step resource allocation algorithm including resource grouping has been proposed. Simulation results demonstrate that the proposed algorithm enhances the connection robustness and shows good performance in terms of higher utility value of inelastic traffic even at high traffic loads by steering elastic traffic to unlicensed band.