Co-operative automated driving (CAD) use cases involve group-based vehicle-to-vehicle (V2V) communications with a wide range of quality-of-service (QoS) requirements. This work introduces and exploits fifth generation mobile networks (5G) functional architecture support for vehicle-to-everything (V2X) applications to address V2V sidelink radio resource management (RRM) for CAD use cases. A QoS requirement-aware sidelink resource allocation optimization problem is formulated for multicast group V2V communications with reliability constraints and half-duplex limitation. Furthermore, the problem is analyzed for cloud-based sidelink RRM and a dynamic vehicular environment. Accounting for the challenges in acquiring channel state information (CSI), a low-complexity scheduling scheme is presented that makes use of slowly varying large-scale channel parameters (e.g. path loss). Simulation results show significant gains in terms of packet delay performance while meeting the reliability requirements on V2V links.

Co-operative automated driving (CAD) is a key fifth generation mobile networks (5G) use case in which autonomous vehicles communicate over vehicle-to-vehicle (V2V) links requiring a wide range of rate-reliability-delay performance. One key 5G enabler for CAD sidelink radio resource management (RRM) in a multi-operator environment is the virtualization of RRM at the cloud server. This, however, is challenging due to an increase in control plane delay, signaling overhead and complexity. This paper introduces an edge cloud-enabled end-to-end vehicle-to-everything (V2X) architecture to support sidelink RRM in CAD scenarios. Analyzing the problem of a cloud-based sidelink resource allocation for CAD, a utility-based multi-objective optimization problem is described and is translated to three tasks: 1) a vehicle cluster formation as a solution to the clique partitioning problem ensuring vehicle reachability on the control plane, 2) an inter-cluster resource block pool (RB-pool) allocation as a solution to a max-min fairness problem and 3) an intra-cluster resource allocation. The proposed solution framework aims to achieve high modularity, low complexity and decouples cluster formation and RB-pool assignment from the intra-cluster optimum resource allocation, which may be performed on different time scales at different edge cloud entities. Simulation results in a realistic vehicular deployment show significant gains in terms of sidelink throughput and delay while maintaining high link quality.

This paper introduces a novel Vehicular-to- Everything (V2X) service localization concept, which enhances reliability and latency requirements through indirect communication via 5G Radio Access Network (RAN) nodes. The indirect communication over New Radio (NR) is expected to benefit the overall reliability especially for vehicles, which are not in close vicinity and widen the service coverage that is essential for highly automated, safety- related, cooperative driving scenarios. However, this may come at the cost of high complexity and increasing end-to-end (e2e) latency compared to direct Vehicular-to-Vehicular (V2V) communication. Our proposal aims to exploit the benefits of both indirect and direct communication, by employing localized e2e paths. A key challenge is how to optimize the uplink, downlink and wireless backhaul (BH) constituents of a local path in order to ensure e2e Quality of Service (QoS) guarantees for V2X services in multi-cell environments. To this end, we propose a framework, which decouples a dynamic path- tailored Time Division Duplex (TDD) configuration scheme from a sub-gradient utility optimization method for balancing the resources between BH and access links, based on partial dual decomposition. Numerical results based on a realistic system level simulation setup show significant gains in both e2e reliability, latency as well as V2V path rate, over the benchmark solutions.

Network Slicing has become one of the fundamental pillars of the fifth generation (5G) mobile and wireless communications systems, which promises integration of a plethora of new vertical industries with tailored network design. To this end, 3rd generation partnership project (3GPP) Release 15 has provided the basis of the network slicing support including radio access network (RAN). Nevertheless, for the realization of native end-to-end (E2E) network slicing, further features and optimizations shall still be introduced. This paper places the focus on enabling network slicing operation in small cell deployments. In particular, the flexible network deployment capitalizing on unplanned dynamic small cells (DSCs) is analyzed, and the associated slice-aware operation is highlighted. Under the light of evaluations, key design recommendations are provided. In addition, architectural enhancements are discussed along with the required signaling procedures to support such slice-aware DSC operation.

Co-operative automated driving (CAD) is a key fifth generation mobile networks (5G) use case having a wide range of rate-reliability-delay requirements on vehicle-to-vehicle (V2V) communications. One key 5G enabler for the management and optimization of sidelink resources in a multi-operator environment is the virtualization of radio resource management (RRM) at the cloud server. This, however, is challenging due to an increase in control plane delay, signaling overhead and complexity limiting the possibility to exploit the benefits of the virtualization of sidelink RRM. This paper analyzes the problem of cloud-based sidelink resource allocation for multicast group transmissions and describes the multi-objective optimization problem being translated to three tasks: 1) a vehicle cluster formation for ensuring optimal vehicle reachability in the control plane, 2) an inter-cluster resource block pool (RB-pool) allocation and 3) an intra-cluster resource allocation. A graph-based solution framework is proposed aiming at achieving high modularity, low complexity and decoupling of cluster formation and RB-pool assignment from the intra-cluster optimum resource allocation, which may be performed on different time scales at different entities. Simulation results in a realistic vehicular deployment show significant gains in terms of sidelink throughput and delay while maintaining high link quality.

In this paper, we define a flexible, adaptable, and programmable architecture for 5G mobile networks, taking into consideration the requirements, KPIs, and the current gaps in the literature, based on three design fundamentals: (i) split of user and control plane, (ii) service-based architecture within the core network (in line with recent industry and standard consensus), and (iii) fully flexible support of E2E slicing via per-domain and cross-domain optimisation, devising inter-slice control and management functions, and refining the behavioural models via experiment-driven optimisation. The proposed architecture model further facilitates the realisation of slices providing specific functionality, such as network resilience, security functions, and network elasticity. The proposed architecture consists of four different layers identified as network layer, controller layer, management and orchestration layer, and service layer. A key contribution of this paper is the definition of the role of each layer, the relationship between layers, and the identification of the required internal modules within each of the layers. In particular, the proposed architecture extends the reference architectures proposed in the Standards Developing Organisations like 3GPP and ETSI, by building on these while addressing several gaps identified within the corresponding baseline models. We additionally present findings, the design guidelines, and evaluation studies on a selected set of key concepts identified to enable flexible cloudification of the protocol stack, adaptive network slicing, and inter-slice control and management.

This paper studies the joint path and resource optimization problem for 5G Radio Access Networks (RANs) based on traffic load and user mobility prediction information, assuming an underlying densely deployed small cell multi-hop mm-Wave environment. The optimization problem introduced aims to benefit enhanced Mobile Broadband (eMBB) traffic and is decoupled into two sub-problems. A user-oriented path selection sub-problem based on a predictive traffic and mobility model; and subsequently a resource allocation sub-problem considering end-to-end Key Performance Indicators (KPIs) assuming different traffic types. The latter sub-problem is also solved in conjunction with pro-active caching of video segments at different radio access elements to relax the backhaul capacity and reduce the latency over the multi-hop densely deployed network. Numerical evaluations show significant improvements in signaling cost, spectrum utilization and throughput.

This paper studies the notion of network slicing in the emerging 5G Time Division Duplex (TDD) networks, considering different applications with diverse service requirements. The proposed solution relies on flexible slicing considering the entire spectrum band, wherein service oriented slices can independently adopt and adjust an UL/DL ratio reflecting traffic conditions. To this end, this paper initially describes and analyses the inter-slice resource allocation problem taking into account different TDD patterns, the slice traffic load and the inter-node interference, which originates by multiple and diverse sources. In this context, a graph-based framework is proposed to reduce the complexity of the problem by decoupling it in two sub-problems: the selection of TDD patterns to find which links should be activated at particular time instances and accordingly perform graph-based resource allocation for all slice traffic collectively. Our performance evaluations show significant enhancements of the UL and DL throughput respectively.

This paper presents an adaptive zone configuration scheme for vehicle-to-vehicle (V2V) communications in terms of resource block pool (RB-pool) and power allocation. A cloud-assisted graph-based dynamic zone configuration framework is proposed for efficient resource utilization which can be well applicable to distributed V2V scenarios. Heuristic solutions are presented by means of vertex clique covering (VCC) and vertex coloring for RB-pool allocation considering the resource demands of each zone to avoid the hidden node problem. The presented scheme is evaluated for LTE-V Mode-4 using system level simulations for an intersection movement assist (IMA) use case. Results show that the dynamic zone configuration approach significantly outperforms both existing zone-less and static zone configurations in terms of packet reception rate (PRR) for high vehicular densities.

While video streaming has dominated the Internet traffic, video service providers (VSPs) compete on how to assure the best quality of experience (QoE) to their customers. HTTP Adaptive Streaming (HAS) has become the de facto way that helps VSPs work-around potential network bottlenecks that inevitably cause stallings. However, HAS-alone cannot guarantee a seamless viewing experience, since this highly relies on the mobile network operators' (MNOs) infrastructure and evolving network conditions. Software-defined networking (SDN) has brought new perspectives to this traditional paradigm where VSPs and MNOs are isolated, allowing the latter to open their network for more flexible, service-oriented programmability. This paper takes advantage of recent standardization trends in SDN and proposes a programmable QoE-SDN App, enabling network exposure feedback from MNOs to VSPs towards network-aware video segment selection and caching, in the context of HAS. The video selection problem is formulated using Knapsack optimization and relaxed to partial sub-problems that provide segment encodings that can mitigate stallings. Furthermore, a mobility prediction mechanism based on the Self-similar Least-Action Walk model is introduced, toward proactive segment caching. A number of use cases, enabled by the QoE-SDN App, are designed to evaluate the proposed scheme, revealing QoE benefits for VSPs and bandwidth savings for MNOs.

Network slicing allows the support of logical autonomous networks on top of a common infrastructure, offering a customized networking experience addressing distinct business demands. 5G networks are expected to support a plethora of applications and multi-tenant services, often with conflicting performance requirements. By enabling network slicing, 5G networks can assure the desired performance, but the limitation of radio spectrum brings new challenges in resource allocation and scheduling, especially for achieving the desired flexibility in resource sharing. In this context, this paper analyzes the joint path selection and backhaul link scheduling problem in a dense small cell network, assuming mm-Wave multi-hop backhaul. For supporting efficiently multiple slices with diverse Key Performance Indicators (KPIs), a slice-tailored resource allocation, scheduling, and selection of backhaul links and redundant paths is proposed. Such resource allocation process is complemented with adaptive routing and other flexible small cell related operations with particular focus on delay critical and throughput oriented slices.

Millimeter wave (mm-wave) frequencies provide orders of magnitude larger spectrum than current cellular allocations and allow usage of high dimensional antenna arrays for exploiting beamforming and spatial multiplexing. This paper addresses the problem of joint scheduling and radio resource allocation optimization in mm-wave heterogeneous networks where mm-wave small cells are densely deployed underlying the conventional homogeneous macro cells. Furthermore, mm-wave small cells operate in time division duplexing mode and share the same spectrum and air-interface for backhaul and access links. The scheme proposed in this paper can significantly enhance network throughput by exploiting space-division multiple access, i.e., allowing non-conflicting flows to be transmitted simultaneously. The optimization problem of maximizing network throughput is formulated as a mixed integer nonlinear programming problem. To find a practical solution, this is decomposed into three steps: concurrent transmission scheduling, time resource allocation, and power allocation. A maximum independent set based algorithm is developed for concurrent transmission scheduling to improve resource utilization efficiency with low computational complexity. Through extensive simulations, we demonstrate that the proposed algorithm achieves significant gain over benchmark schemes in terms of user throughput.

This paper discusses a resource management abstraction framework which involves the clustering of densely deployed access points and the selection of access nodes to act as cluster schedulers based on the RAN characteristics, the resource situation and the network slice requirements. To this end, the adaptive placement of Radio Resource Management (RRM) functionalities to the RAN nodes and the interactions among functionalities are determined on per slice basis. By taking into account the slice requirements, the backhaul#x002F;access channel conditions and the traffic load, a central management entity assigns RRM functionalities to the controllers with different levels of centralization in order to meet the per slice SLAs (in terms of throughput, reliability, latency). The controller, cluster and RRM split configuration problems are formulated and interpreted as three dependent graph-based sub- problems, where heuristic approaches with low complexity and signalling cost are proposed within this framework.

This paper quantifies the impact of co-tier interference in femtocells (i.e. inter-cell interference arising from other neighbouring femtocells) through a semi-analytical approach concluding that without interference mitigation techniques, QoS in the form of acceptable outage probability is not achievable in scenarios representing medium to worst cases of interference. Results obtained from semi-analytical approach are also compared against Monte Carlo simulations for evaluation purposes. Subsequently, some important radio access parameters and deployment configurations, such as path loss model, shadowing, wall penetration loss, location of femtocells and user distribution are further examined as key elements that can potentially affect, positively or negatively, the femtocell-to-femtocell interference in a multi-femtocell deployment. The results can be used as guidelines in the deployments of femtocells.