Virtual Base Station Research Articles

An accelerated Benders decomposition approach for virtual base station formation in stochastic Cloud-RANs

In Cloud-RANs, the baseband unit (BBU) is separated from the remote radio head (RRH) at the cell site and migrated to a server cloud to decrease deployment and maintenance costs. A base station (BS) can be formed as a network slice in which the virtual network functions (VNFs) of its BBU can be placed on any server by network virtualization (NFV) technology and logically connected by software-defined networking (SDN). However, the uncertain demands of users make it very difficult to determine a network slice with appropriate VNF placement and flow routing between VNFs. We aim to minimize the cost of providing BS services while meeting their time-varying demands. We consider the network slice scheme and fronthaul establishment jointly and formulate this as an integer linear programming (ILP) problem. We propose scenario classification Benders decomposition (CBD) based on Benders decomposition (BD) but with fewer Benders cuts, accelerating the solution convergence. Moreover, we propose an efficient heuristic approach to obtain good solutions for the ILP problem based on the solutions of its relaxed problems obtained by BD or CBD. The numerical experiments show that when combining our heuristic approach with CBD, the results are close to the optimal solutions of the ILP problem, while the computation time is significantly shorter than those of previous approaches.

Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning

Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning

Joint Multi-Objective Optimization for Radio Access Network Slicing Using Multi-Agent Deep Reinforcement Learning

Radio access network (RAN) slices can provide various customized services for next-generation wireless networks. Thus, multiple performance metrics of different types of RAN slices need to be jointly optimized. However, existing efforts in multi-objective optimization problem (MOOP) for RAN slicing are only in the scalar form, which is difficult to achieve simultaneous optimization. In this paper, we consider a non-scalar MOOP for RAN slicing with three types of slices, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the high-bandwidth slice, the low-delay slice, and the wide-coverage slice over the same underlying physical network. We jointly optimize the throughput, the transmission delay, and the coverage area by user-oriented dynamic virtual base stations (vBSs)' deployment, and sub-channel and power allocation. An improved multi-agent deep deterministic policy gradient (IMADDPG) algorithm, having the characteristics of centralized training and distributed execution, is proposed to solve the above non-deterministic polynomial-time hard (NP-hard) problem. The rank voting method is introduced in the inference process to obtain near-Pareto optimal solutions. Simulation results verify that the proposed scheme can ensure better performance than the traditional scalar utility method and other benchmark algorithms. The proposed scheme has the advantage of flexibly approaching any point of the Pareto boundary, while the traditional scalar method only subjectively approaches one of the Pareto optimal solutions. Furthermore, our proposal strikes a compelling tradeoff among three types of RAN slices due to the non-dominance between Pareto optimal solutions.

Open Radio Access Networks (O-RAN) Experimentation Platform: Design and Datasets

The Open Radio Access Network (O-RAN) Alliance is driving the latest evolution of RAN deployments, moving from traditionally closed and dedicated hardware implementations towards virtualized instances running over shared platforms characterized by open interfaces. Such progressive decoupling of radio software components from the hardware paves the road for future efficient and cost-effective RAN deployments. Nevertheless, there are many open aspects towards the successful implementation of O-RAN networks, such as the real-time configuration of the network parameters to maximize performance, how to reliably share processing units among multiple virtualized base station (vBS) instances, how to palliate their energy consumption, or how to deal with the couplings between vRANs and other services co-located at the edge. Intending to shed light on these aspects, in this article, we showcase the design principles of an ORAN compliant testbed, and present different datasets collected over a wide set of experiments, which are made public to foster research in this field.

Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results

Virtualized base stations (vBS) can be implemented in diverse commodity platforms and are expected to bring unprecedented operational flexibility and cost efficiency to the next generation of cellular networks. However, their widespread adoption is hampered by their complex configuration options that affect in a non-traditional fashion both their performance and their power consumption requirements. Following an in-depth experimental analysis in a bespoke testbed, we characterize the vBS power cost profile and reveal previously unknown couplings between their various control knobs. Motivated by these findings, we develop a Bayesian learning framework for the orchestration of vBSs and design two novel algorithms: (i) BP-vRAN, which employs online learning to balance the vBS performance and energy consumption, and (ii) SBP-vRAN, which augments our optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, i.e., converge an order of magnitude faster than state-of-the-art Deep Reinforcement Learning methods, and achieve optimal performance. We demonstrate the efficacy of these solutions in an experimental prototype using real traffic traces.

A Single-Site Vehicle Positioning Method in the Rectangular Tunnel Environment

Due to the satellite signals are blocked, it is difficult to obtain the vehicle position in the tunnels. We propose a single-site vehicle localization scheme for the rectangular tunnel environment, where most satellite-based positioning methods can not provide the required localization accuracy. In the non-line-of-sight (NLOS) scenarios, we make use of the reflection paths as assistants for vehicle positioning. Specifically, first, the virtual stations are established based on the actual geometrical structure of the tunnel. Second, we use the direction-of-arrival (DOA) and time-of-arrival (TOA) information of reflection paths from two tunnel walls to achieve vehicle positioning. Especially, the Cramer-Rao lower bound (CRLB) of the joint TOA and DOA localization for NLOS propagations in a two-dimensional (2D) space is derived. In addition, based on the localization algorithms with and without filters, we assess the localization performance. In the line-of-sight (LOS) scenarios, we use the LOS path and two reflection paths from the tunnel walls to estimate the vehicle location. First, virtual base stations are established. Second, based on the obtained TOA information, different positioning algorithms are used to estimate the vehicle location. Simulation results illustrate that the proposed positioning approach can provide a small root mean square error. The localization algorithms using filters improve the localization accuracy, compared with the positioning algorithm without using filters, namely, the two-stage weighted least squares (TSWLS) algorithm. Moreover, the Unscented Particle Filter (UPF) algorithm achieves better positioning accuracy than other methods (i.e., Unscented Kalman Filter (UKF), Extended Kalman Filter (EKF), TSWLS algorithms).

Design of Elastic Call Admission Control in Tidal Load Scenario for Cloud-based Cellular Networks

Cellular networks have witnessed an unprecedented expansion represented by the number of subscribers in addition to the unlimited number of applications. In the era of IoT, connections may involve machines working side by side with humans on the same network. Tidal load is one of the key challenges in mobile networks, which represents a temporary phenomenon resulting from increasing traffic volumes at certain times of the day in particular places. This leads to the problem of overutilization of some base stations, particularly in fixed resources allocation systems such as 4G LTE architecture, which contains physically separated base stations. This results in increasing the dropping and blocking probability for the incoming calls in an overloaded area, while others suffer from underutilization or might be almost idle for a particular time. In this paper, an Elastic Call Admission Control (ECAC) approach is proposed to reduce the probability of blocking and dropping to improve the network performance in the tidal load scenario. This is achieved using the concept of gain multiplexing technique and cloud-based cellular architecture for utilizing the shared available recourses of the co-located virtual base stations in the cloud. A Fuzzy Type 2 (FT2) system is used to maintain the switching decision for resource sharing mode, the network load is predicted in advance using Gaussian Process Regression (GPR) model. Performance evaluation is carried out using the cloud-based Fog-Radio Access Network (F-RAN) platform. Compared to legacy LTE architecture, the results illustrate a noticeable enhancement in terms of minimizing blocking probability, enhancing overall network performance represented by network data throughput, and network utilization.

Deep Reinforcement Learning-Based Joint Satellite Scheduling and Resource Allocation in Satellite-Terrestrial Integrated Networks

Satellite-terrestrial integrated networks (STINs) are considered to be a new paradigm for the next generation of global communication because of its distinctive merits, such as wide coverage, high reliability, and flexibility. When the satellite associates with different base stations (BSs) and adopts different channels for communication, the utility of offloading data to BSs is different. In our work, we study how to jointly associate satellites with appropriate BSs and allocate channels to satellites. Our purpose is to maximize the utility of the data offloaded from satellites to BSs while considering the load balance of BSs. However, some satellites are often unable to connect to BSs because of their periodic flight characteristic, which makes the joint satellite-BS association and channel allocation more challenging. To solve the problem that satellites sometimes cannot connect to BSs, we abstract the communication model between satellites and BSs into a bipartite graph and add a virtual BS to ensure that all satellites can connect to at least one BS. Then, in the constructed joint optimization problem, we solve the assignment of satellites and channels simultaneously. Considering that the joint optimization problem is nonconvex, we use double deep Q-Network (DDQN) for achieving the optimal strategy of satellite association and channel allocation. Furthermore, the reward value in most state transition information generated by satellites is 0, which leads to the low learning efficiency of DDQN. Aiming at enhancing the learning efficiency of DDQN, the priority sampling-based DDQN (PSDDQN) algorithm is proposed. Experimental results demonstrate that PSDDQN gets better utility and achieves the load balance of BSs compared with other algorithms.

Проблемные вопросы использования спутниковой навигации при оценке состояния факторов «дорога» и «среда» в системе ВАДС

The main indicator characterizing the features and specifics of road traffic is the dynamic system «Driver - Car - Road - Environment» (DCRE), in which there is a continuous exchange of information between its components. The level of road traffic safety directly depends on the reliability of the system elements, as well as the specifics of their internal interaction with each other. The article provides an assessment of the factors affecting the reliability of the DCRE system. The authors substantiate the necessity of improving the quality of assessing the key factors of the subsystems «Road» and «Environment» in order to ensure road traffic safety of the motor transport complex. There has been carried out an analysis of the methodology of conducting field surveys in determining the transport and operational qualities of the elements of the «Road» subsystem to ensure the road traffic safety, taking into account the integral components of the DCRE system. The main problems of obtaining high-precision planned coordinates during the operation of road equipment are determined. The possibilities of using RTK (real-time kinematics) and VBS (virtual base station) technologies have been studied in order to improve the methodological apparatus for implementing road safety examinations. The results of the study allow increasing the potential for further work in the field of improving road traffic safety and reducing road traffic accidents` rate, taking into account the road environment features.

Feedback Control for QoS-Aware Radio Resource Allocation in Adaptive RAN

In order to meet the quality requirements of various communication services in the advanced 5G era around 2025, the authors have proposed an Adaptive radio access network (RAN) that changes the placement of virtualized base station functions according to the services. In Adaptive RAN, each base station function has its own scheduler, and multiple schedulers share common radio resources. Therefore, in order to guarantee communication quality for more users, it is necessary to control the allocation of the radio resources required for scheduling to each base station function with neither excess nor deficiency. This allocation control is performed based on the information collected from the base station functions by the RAN intelligent controller (RIC). If a large amount of information is used for control, allocation can be performed with higher accuracy, but the collection of information overuses the limited bandwidth of the network. Therefore, it is necessary to reduce the information volume required for control while at the same time guaranteeing communication quality. In this paper, we propose a method that guarantees communication quality while reducing the information volume required for control by combining a required resource estimation control in a long cycle and a resource allocation modification control based on feedback information related to communication quality in a short cycle. We evaluated the impact of the proposed method on communication quality compared to conventional methods through simulation, and verified that the proposed method can guarantee better communication quality than conventional methods while suppressing the increase in information volume.

Beach Topography Surveying Using Unmanned Aerial Vehicle Photogrammetry Technology

The mechanisms of in situ topography changes must be identified for spatial planning in coastal areas. In this study, the authors used in situ direct measurement methods and indirect remote-sensing technologies to develop a method for beach topography surveillance. Unmanned aerial vehicles (UAVs) have advantages of high portability and mobility and low operating altitude. Therefore, a UAV was equipped with a lightweight camera and a positioning system that comprised a global positioning system and an inertial measurement unit. Aerial photogrammetry and aerial triangu-lation methods were adopted for matching image feature points to obtain the corresponding topographic points in the sand. A virtual base station with real-time kinematic positioning functionality was used to measure the coordinates of the ground control points for correcting the actual coordinates to obtain actual topographic points of the beach sand. The results obtained using image-matching point clouds and direct measurements were compared. The ground sample distance of a UAV at the operational altitude of 70 m was 3.26 cm. Moreover, the average elevation error was 3.20 cm, and the root mean square error was 0.169 m. The measurement error was ±25 cm, which was within the acceptance criteria of±50 cm set by Water Resources Agency, Ministry of Economic Affairs, Taiwan. UAV imaging technology can increase the efficiency of conventional manual sampling and reduce the cost of indirect observations, thus minimizing the measurement errors and field measurement costs.

Study of DRX Sleep Mode Performance on Virtual Base Station Energy Saving in 5G Networks

No doubt that power consumption is one of the most important challenges which face the modern mobile networks. These modern networks do a nice job in multimedia services. Then, the physical downlink data transmission rate will change the Base Station (BS) power consumption. The Conventional BS (CBS) of the cellular legacy networks was consuming much energy due to complicated hardware connections and system levels. Now, the fifth generation of mobile networks (5G) is introducing the Virtual Base Station (VBS) based on the Cloud infrastructure. Therefore, it can reduce the traditional complexity and save more energy consumption. In this paper, the Discontinuous Reception (DRX) sleep mode is applied on the corresponding User Equipment (UE). This has an effective impact on the VBS power consumption. A series of MATLAB simulation experiments is conducted to assess the performance. Appropriate path loss models are considered in the experimental study for mmWave broadband spectrum of 5G.

Using UE-VBS for dynamic virtual small cells deployment and backhauling in 5G Ultra-Dense networks

This paper builds on the UE-based Virtual Base Station (UE-VBS) concept, which enables the Smartphones (i.e., UEs) of the general population to be enhanced with Base Station and Relay Node functionalities and be embedded as an integral part of the Mobile Network Infrastructure, offering relief in stressful or overloaded situations. This paper focuses on the selection process of eligible-UEs that will be activated on-the-fly into UE-VBSs to complement and support the Mobile Network infrastructure, either as virtual small cells (VSCs) or virtual relay nodes (VRNs). We adopt a Modified Affinity Propagation Clustering (MAPC) algorithm to select UE-VBSs to act as VSCs for capacity/data rate expansion in areas where the infrastructure is weak and a more effective and agile network operation is needed. Additionally, to provide for a more spectrum efficient backhauling of mobile data traffic from the VSCs towards the BS, the Multi-hop Load balanced Geographical Path selection (MLGP) algorithm is proposed. The MAPC and MLGP algorithms are implemented and evaluated in MATLAB and also validated using the NS3 network simulator, with mobile data traffic modeled as a non-uniform distribution with respect to the time and space domain. The results demonstrated that the proposed approaches improve the network performance in terms of throughput, delay and jitter.

Cоздание высокоточных цифровых моделей высот с использованием ГНСС-БПЛА

Unmanned Aerial Vehicles (UAVs) are increasingly involved in surveying work, becoming a reliable basis for information on three-dimensional terrain features. Until now, ground reference points have been used to provide reliable planimetric evidence for measurements from drones. Their placement and coordinates measurement takes quite a long time, which increases proportionally to the area under study. In addition, the use of these marks produces model distortions (especially over large areas of 1 sq. km or more), which then lead to the appearance of areas of local depressions and uplifts where they are not present in the terrain. These distortions arise due to camera position optimization error minimization algorithms and cannot be corrected. Refusing to use ground control points leads to other geometric distortions associated with characteristics of survey system lenses mounted on UAVs (so-called “saddle-shaped” models). This paper presents the results of high-precision digital elevation models creation using built-in UAV Global Navigation Satellite System (GNSS) receivers. The methodology has been tested at ten sites in the Zakamye region of the Republic of Tatarstan (Russia) with an area ranging from 4 to 58 hectares. Correction of GNSS rover position was performed in post-processing from a virtual base station network located at a 6 to 70 km range from the surveyed sites. For all objects, DEM errors did not exceed 0.05 m on axes X, Y, and Z. At the same time, the dependence of the error value on the study area size was not revealed. The received results of the analysis of errors can indicate the prospect of the use of low-cost GNSS-UAVs without the necessity of organizing a network of ground reference points, being limited only to the necessity of installing control points will repeatedly reduce the time of the field works, in particular on hardly accessible objects.

Licensed shared access field trial and a testbed for satellite‐terrestrial communication including research directions for 5G and beyond

SummaryThis paper describes a licensed shared access (LSA) testbed and field trials using a live network. The testbed includes real 4G base stations and up to 1000 virtual base stations, in the spectrum sharing scenario between satellite and cellular systems. The trials focus on 5G pioneer bands 3.4–3.8 GHz and 24.25–27.5 GHz where a satellite system is operating in the downlink direction and a cellular system is accessing the same band. The designed testbed supports both frequency bands. The performance evaluation concerns evacuation and frequency change times using different types of base stations in 3.6 GHz, that is, how fast the system relinquishes the shared band to the primary user and continues transmission using another band. We show that our LSA system is scalable and able to support large number of base stations. In addition, we investigate how satellite systems could reuse International Mobile Telecommunication (IMT) bands to offer enhanced satellite communication services for land, maritime, and aeronautical applications. Preliminary simulations and analysis confirm the possibility to reuse IMT spectrum for satellite systems without causing harmful interference to the terrestrial system.

Elastic Resource Provisioning for Increased Energy Efficiency and Resource Utilization in Cloud-RANs

Distributed Radio Access Networks (D-RANs), characterized by static deployment of Base Stations (BSs), are facing critical difficulties in handling the temporal and geographical fluctuations of capacity demands. Recently, Cloud RAN (C-RAN) has been introduced as a centralized cloud computing based paradigm for wireless cellular networks in which the BSs are physically decoupled into Virtual Base Stations (VBSs) and Remote Radio Heads (RRHs). In this article, a novel framework is proposed for C-RAN to adapt to the fluctuation in capacity demand while at the same time maximizing the energy efficiency and resource utilization. The RRHs and their corresponding VBSs are divided into clusters within which the active RRH density, transmission power, and size of Virtual Machines (VMs) are adjusted dynamically. To characterize the computational requirements of a VBS, a programmable C-RAN testbed is implemented using the OpenAirInterface (OAI) software platform and USRP boards. Testbed experiment results are presented to describe the consumption and utilization of computing resources in the cloud; simulation results are also presented to illustrate the performance gains of this elastic solution against the current static deployment.

A TDOA and PDR Fusion Method for 5G Indoor Localization Based on Virtual Base Stations in Unknown Areas

Indoor positioning and navigation is an essential field of location-based service. Non-line of sight (NLOS) error restricts the accuracy of indoor positioning. Many researchers have studied the localization problem in indoor NLOS environments, but there is still a problem that NLOS error cannot be mitigated in unknown areas. To solve the above problems, this paper proposes a method of constructing virtual base stations in unknown areas (UA-VBS), and presents the corresponding positioning algorithm to calculate the location of the user equipment. Firstly, the base stations are selected and the initial positioning is carried out. Then, multiple virtual base stations are constructed according to the user equipment positions in the first three steps. The LOS base stations and virtual base stations participate in the TDOA calculation together, and calculate the base stations’ combination with the minimum residual and the corresponding positioning result. Finally, the pedestrian dead reckoning fusion weight is updated by the residual value, and the accurate positioning result in NLOS environment is obtained. Simulation and experimental results show that the proposed algorithm has high positioning accuracy and stability in NLOS environment.

User-Centric Base-Station Wireless Access Virtualization for Future 5G Networks

User-centric wireless access virtualization (WAV) allows each user to be served by a set of carefully selected transmission points (TPs) forming a user-specific virtual base station (uVBS) adapted to its environment and quality-of-service (QoS) requirement. In this way, this new concept breaks away from the conventional cell-centric architecture to provide boundaryless communications in future fifth-generation (5G) networks. This fundamental structural 5G evolution and the ultra-dense multi-tier heterogeneous context foreseen in such networks require an inevitable rethinking of efficient scalable TP clustering. As such, this paper proposes three innovative low-cost clustering approaches that enable the user-centric WAV and provide dynamic, adaptive, and overlapping TP clusters while requiring not only negligible overhead cost but also minimum signaling changes at both network and user sides. Contrary to existing clustering techniques, the new ones we propose better leverage the 5G features such as extreme densification and massive connectivity as well as new concepts such as millimeter wave (mmWave) spectrum and massive multiple-input-multiple-output (MIMO). The simulations show that they may achieve until 154% and 282% of throughput and coverage gains, respectively. Furthermore, these approaches are flexible enough to be adapted to different network dimensions (i.e., space and time), thereby paving the way for achieving the dramatic performance improvements required by the 5G networks to cope with the upcoming mobile data deluge.

Local Traffic Aware Unicast Routing Scheme for Connected Car System

Connected cars are equipped with a rich set of sensors, such as GPS, accelerometer, video cameras, and pollution detectors. The information generated by these sensors can be used to offer a wide range of on-demand services, such as congestion notification, parking lots, and video surveillance. These services need a reliable and low latency unicast communication scheme in order to efficiently deliver the information requested by drivers. In this paper, a local traffic aware unicast routing scheme is proposed. To overcome network fragmentation, the proposed scheme relies on base stations and virtual base stations to transmit information from source car to the destination car using backhaul link. In this model, as base stations are sparsely deployed in the junction areas, a car moving in the junction area acts as a virtual base station node to support the routing process in the absence of a base station. Moreover, it avoids the impact of unreliable channel on information delivery. In the proposed scheme, each base station and virtual base station uses the short status messages (beacons) exchanged by the cars to form a local database of car locations. The stored information is used to find a base station or virtual base station that offers a minimum delay path to the destination car. The simulation results show that the proposed scheme outperforms the existing routing schemes in terms of end-to-end delay and packet delivery ratio. The proposed scheme is also validated by a connected car prototype built in an indoor laboratory environment.

Asymptotic Analysis of Sum Rate of CL-DAS Based on Expected Large Scale Fading

Under the assumption of large scale antenna, we analyze the sum rate of circular layout distributed antenna systems (CL-DAS). First, we propose a concept of virtual base station and an expected large scale fading (E-LSF) for an arbitrarily located user as a function of the locations of users and distributed antennas and a path loss exponent. Based on E-LSF, we derive an approximated sum rate of CL-DAS whose form is identical to the sum rate of centralized multiple-input multiple-output (C-MIMO) systems. These results make it possible that the sum rate of CL-DAS with arbitrary beamforming schemes or power allocation algorithms can be evaluated using the existing approaches for C-MIMO systems. Simulation results verify that our analyses with E-LSF are valid for practical systems.