Functional Split Research Articles

Joint Optimization of Functional Split, Base Station Sleeping, and User Association in Crosshaul-Based V-RAN

Joint Optimization of Functional Split, Base Station Sleeping, and User Association in Crosshaul-Based V-RAN

Delay and Fairness Analysis of C-RAN for Single and Multi Scheduling Domain Strategies

Background: Centralized Radio Access Network (C-RAN) is the most promising network architecture for next-generation communication networks. It meets the need for flexibility on fronthaul as well as large bandwidth on backhaul of the network. All along, scheduling is very important for the transmission of information in an organized manner. C-RAN has not been studied with the scheduling domain strategies yet in the literature. Objective: In this work, packet transmission duration, overall transmission time, wait time, and fairness index parameters have been calculated and analysed for C-RAN architecture for two different scheduling domains. The total transmission cycle time parameter is calculated for the three upper functional split options of C-RAN. The overall transmission time is a parameter calculated for the entire uplink channel. Methods: To implement the network scenario, extensive scripting is done on MATLAB Editor for single scheduling domain (SSD) and multi-scheduling domain (MSD) for three higher functional split options of C-RAN. The data traffic generated in the network is considered random. Results: A closer examination of results reveals the advantages and disadvantages of both algorithms, as well as trade-offs between them. Conclusion: For quicker data transmission, SSD should be preferred whereas MSD should be preferred if multiple users want to access resources simultaneously. Lower functional split options of C-RAN require less transmission cycle time. The MSD technique is fairer than SSD.

Phylogenetic Trends in the Dissymmetrisation of Genitalia in Hadenini (Lepidoptera, Noctuidae)

The external genital appendages of Noctuidae represent correlated–coevolved elements of a complex structure. The pathways of changes are delimited by some constraints, and they are parallelly evolved in different phyletic lines. Asymmetrical male external genitalia were found in Hadenini, in Polia and its closely related genera, but also in Anarta (s. l.). In Poliina, the asymmetry of the genital capsule is mostly expressed in the Holarctic Polia nebulosa species group, forming the basal split within Polia (s. str.). Higher species diversity, as a consequence of asymmetry connected with the split of functions between the right and left sides, was observed in Polia (s. str.) and in Anarta (s. l.). The highest level of expansivity was observed in the Holarctic Anarta, where some species are migrating and becoming invasive ones, while a bulk of species populates the steppic and mountainous habitats of North America and Central Asia. Parallelly, while some genera/subgenera of the subtribe Poliina are oligotypic and/or consist of strictly localised species, the sensu stricto Polia species with asymmetrical genital capsules are widely distributed. The diversification of the Hadulina phylogenetic line should have been initiated by the Messinian aridisation crisis. The core area of diversification of Poliina was connected with the Sino-Himalayan region, followed by expansion into the Holarctic boreo-nemoral zone.

Organic 6G networks: ultra-flexibility through extensive stateless functional split

AbstractWith the increase in hardware performance, the 5G mobile network architecture shifted from physical components to software-only micro-services. The very modular network functions can be deployed flexibly on commodity hardware. However, the extensive modularity of these network functions is increasing the number of managed entities, and the core network request latency. Also, it requires extensive procedures to be able to re-select the components for specific devices, a fundamental condition for a potential system scale-down. In this paper, we propose a new organic 6G network architecture that handles these challenges through a new functionality split based on the experience of IT software services. Furthermore, we provide an analysis based on main 5G procedures, showing that the newly proposed architecture is handling the re-selection of functionality significantly better, which is a cornerstone of high-speed scaling (especially scaling-out), as well as migration of functionality and users.

A Study on the Implementation of a Network Function for Real-time False Base Station Detection for the Next Generation Mobile Communication Environment

The threat posed by false base stations remains pertinent across the 4G, 5G, and forthcoming 6G generations of mobile communication. In response, this paper introduces a real-time detection method for false base stations employing two approaches: machine learning and specification-based. Utilizing the UERANSIM open 5G RAN (Radio-Access Network) test platform, we assess the feasibility and practicality of applying these techniques within a 5G network environment. Emulating real-world 5G conditions, we implement a functional split in the 5G base station and deploy the False Base Station Detection Function (FDF) as a 5G NF (Network Function) within the CU (Central Unit). This setup enables real-time detection seamlessly integrated into the network. Experimental results indicate that specification-based detection outperforms machine learning, achieving a detection accuracy of 99.6% compared to 96.75% for the highest-performing machine learning model XGBoost. Furthermore, specification-based detection demonstrates superior efficiency, with CPU usage of 1.2% and memory usage of 16.12MB, compared to 1.6% CPU usage and 182.4MB memory usage for machine learning on average. Consequently, the implementation of specification-based detection using the proposed method emerges as the most effective technique in the 5G environment.

Context-Based e2e Autonomous Operation in B5G Networks.

The research and innovation related to fifth-generation (5G) networks that has been carried out in recent years has decided on the fundamentals of the smart slice in radio access networks (RANs), as well as the autonomous fixed network operation. One of the most challenging objectives of beyond 5G (B5G) and sixth-generation (6G) networks is the deployment of mechanisms that enable smart end-to-end (e2e) network operation, which is required for the achievement of the stringent service requirements of the envisioned use cases to be supported in the short term. Therefore, smart actions, such as dynamic capacity allocation, flexible functional split, and dynamic slice management need to be performed in tight coordination with the autonomous capacity management of the fixed transport network infrastructure. Otherwise, the benefits of smart slice operation (i.e., cost and energy savings while ensuring per-slice service requirements) might be cancelled due to uncoordinated autonomous fixed network operation. Notably, the transport network in charge of supporting slices from the user equipment (UE) to the core expands across access and metro fixed networks. The required coordination needs to be performed while keeping the privacy of the radio and fixed network domains, which is important in multi-tenant scenarios where both network segments are managed by different operators. In this paper, we propose a novel approach that explores the concept of context-aware network operation, where the slice control anticipates the aggregated and anonymized information of the expected slice operation that is sent to the fixed network orchestrator in an asynchronous way. The context is then used as the input for the artificial intelligence (AI)-based models used by the fixed network control for the predictive capacity management of optical connections in support of RAN slices. This context-aware network operation aims at enabling accurate and reliable autonomous fixed network operation under extremely dynamic traffic originated by smart RAN operation. The exhaustive numerical results show that slice context availability improves the benchmarking fixed network predictive methods (90% reduction in prediction maximum error) remarkably in the foreseen B5G scenarios, for both access and metro segments and in heterogeneous service demand scenarios. Moreover, context-aware network operation enables robust and efficient operation of optical networks in support of dense RAN cells (>32 base stations per cell), while the benchmarking methods fail to guarantee different operational objectives.

Slicing Enabled Flexible Functional Split and Multi-Dimensional Resource Provisioning in 5G-and-Beyond RAN

5G/B5G networks are expected to deliver huge traffic and support various use cases with diverse requirements. With the increasing demand for network capacity, a cost-effective and flexible RAN is urgently needed to provide customized services for users. On this basis, advanced flexible RAN architectures with functional splits are introduced. In this paper, we study the slice-centric fine-grained functional split and resource allocation problem in flexible RAN. We first formulate a multi-objective problem to jointly optimize the functional split selection, processing, and transmission resource allocation for slices, aiming at maximizing the functional split gain while satisfying slices’ requirements. Since a multi-objective problem may have multiple Pareto optimal solutions and is difficult to solve, we mathematically analyze and transform the problem into an equivalent parametric convex problem. Then, we propose an upper bound algorithm and a dual based resource allocation algorithm to find the solution for the optimization problem. Theoretical analysis and simulation results show that the proposed algorithms can effectively solve the functional split gain maximization problem and obtain a trade-off between processing and transmission resource gain. In addition, the proposed algorithms also outperform other benchmark approaches in terms of resource saving and flexibility.

Fixed Point Iterations for Functional Equations and Split Feasibility Problems in CAT(0) Spaces

Among other things, finding solutions for functional as well as other types of problems (including differential and integral) by suggesting fixed-point procedures is a difficult task, especially when we study the approximation techniques in the absence of linearity in the domain of definition. In this paper, an effective iterative approximation procedure is successfully applied to find fixed points of a general class of operators in the nonlinear setting of CAT(0) spaces. The results are illustrated with the help of some examples. Some numerical computations are also provided. Eventually, we prove that our new results are applicable to solving split feasibility problems. Our results are new and complement some recently published results from the literature.

Optical access networks for fixed and mobile applications [Invited Tutorial

Optical access networks are playing a more critical role in providing diverse emerging applications for human- and machine-type communications. With the ever-growing demand for higher capacity, higher fidelity, and massive connections, numerous innovative transmission schemes and digital signal processing algorithms have been proposed in a cost-effective way. Here, we focus on two main scenarios, namely, fixed and mobile access. For the fixed access network, the solutions are categorized into direct detection, simplified coherent detection, and self-coherent detection. For the mobile fronthaul, we classify the schemes according to the functional split, including the analog radio-over-fiber, common public radio interface (CPRI), enhanced CPRI (eCPRI), and integrated and hybrid digital/analog-over-fiber interfaces. This tutorial provides an overview of the enabling technologies for fixed and mobile applications in the fifth-generation (5G) era and beyond.

Enabling realistic experimentation of disaggregated RAN: Design, implementation, and validation of a modular multi-split eNodeB

Software-defined networking and network function virtualization are the key enablers in the division of base station functionalities and the deployment of some of these in central nodes. In this way, eNodeB tasks, traditionally deployed at dedicated nodes, are distributed between Central Units (CU) and Distributed Units (DU), which are closer to the Radio Units (RU). The 3GPP has specified eight possible functional splits between the CU and the DU. Despite their importance, to date, there is a lack of practical approaches that enable the real separation of multiple functional splits into different nodes, which would in turn enable the adaptation of the functional split configuration, according to network conditions and traffic load. This paper presents the design and implementation of an experimental architecture that allows the actual separation of protocol layers into different nodes, enabling the experimentation with up to four functional split configurations. The validation results demonstrate the feasibility of the proposed implementation, which copes with the delay requirements established by the Small Cell Forum in all configurations.

Anderson–Fano resonances in Fock space: Adding electrons to two-dimensional materials in strong magnetic fields

The structure of the Hilbert–Fock space of electronic configurations determines the properties of atoms, molecules, and solids. A nontrivial example of such a structure at the single-particle level is described by Anderson–Fano resonances of localized states coupled to a continuum. Here we describe Anderson–Fano resonances in the Fock space of electronic configurations which can be realized by adding electrons to two-dimensional materials in a strong magnetic field. The resonances manifest themselves as a splitting of spectral functions in two maxima separated by the continuum of spin excitations.

CoMP-Enhanced Flexible Functional Split for Mixed Services in Beyond 5G Wireless Networks

With explosively escalating service demands, beyond fifth generation (B5G) aims to realize various requirements for multi-service networks, i.e., higher performance of mixed enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services than 5G. To flexibly serve diverse traffic, various functional split options (FSOs) are specified by 5G protocols enabling different network functions. In order to improve signal qualities for edge users, we consider FSO-based coordinated multi-point (CoMP) transmission as a prominent technique capable of supporting high traffic demands. However, due to conventional confined hardware processing capability, a processor sharing (PS) model is introduced to deal with high latency for multi-service FSO-based networks. Therefore, it becomes essential to assign CoMP-enhanced functional split modes under PS model. A more tractable FSO-based network in terms of ergodic rate and reliability is derived by stochastic geometry approach. Moreover, we have proposed CoMP-enhanced functional split mode allocation (CFSMA) scheme to adaptively assign FSOs to provide enhanced mixed throughput and latency-aware services. The simulation results have validated analytical derivation and demonstrated that the proposed CFSMA scheme optimizes system spectrum efficiency while guaranteeing stringent latency requirement. The proposed CFSMA scheme with the designed PS FFS-CoMP system outperforms the benchmarks of conventional FCFS scheduling, non-FSO network, fixed FSOs, and limited available FSO selections in open literature.

Fronthaul latency and capacity constrained cost-effective and energy-efficient 5G C-RAN deployment

The third-generation partnership project (3GPP) has identified different new functional split points, defined as options 1–7, between the baseband unit (BBU) and the remote unit (RU) for the 5G centralized radio access networks (C-RANs). The functional splits facilitate lower fronthaul line rate and latency requirements while imposing technical complexity and cost-efficiency trade-offs. Among all the functional split options, the standardization groups such as 3GPP, Open RAN (O-RAN), and small cell forum have selected option-7 as the most suitable choice for the 5G C-RAN deployment that can meet the operator’s requirements. One of the biggest challenges for designing option-7 functional split-based C-RAN is the capacity and latency requirement fulfillment as specified by 3GPP, while minimizing the deployment cost. In this regard, we propose an optimization model for cost-effective and energy-efficient option-7 functional split-based C-RAN architecture using wavelength division multiplexed passive optical network (WDM-PON) in the fronthaul. For the given locations of the next-generation core (NGC) and the set of RUs, the proposed optimization model explores the most suitable locations for the placement of BBU pools and arrayed waveguide gratings (AWGs). It provides complete interconnection details among NGC, BBU pools, AWGs, and RUs. It also provides the optimal number of digital units in each selected BBU pool, the splitting ratio of each AWG, and maximum fronthaul reach, satisfying the fronthaul latency, capacity, and power budget constraints. To the best of our knowledge, for the first time, we estimate the worst-case queuing delay for the upstream traffic flow using the G/G/1 queuing model and investigate its impact on fronthaul latency and deployment cost for different RU configurations. We compare the performance of the proposed option-7 functional split-based C-RAN with the traditionally used option-8 functional split-based C-RAN in terms of the deployment cost and fronthaul latency improvement.

Coordinated Slicing and Admission Control Using Multi-Agent Deep Reinforcement Learning

5G Cloud Radio Access Networks (C-RANs) facilitate new forms of flexible resource management as dynamic RAN function splitting and placement. Virtualized RAN functions can be placed at different sites in the substrate network based on resource availability and slice constraints. Due to limited resources in the substrate network and variability in revenue of slices, the Infrastructure Provider (InP) must perform network slicing in a strategic manner, and accept or reject slice-requests to maximize long-term revenue. In this paper, we propose to use multi-agent Deep Reinforcement Learning (DRL) to jointly solve the problems of network slicing and slice Admission Control (AC). Multi-agent DRL along with reward shaping is a promising choice, which is well-suited to problems where multiple distinct tasks have to be performed optimally. The proposed DRL approach can learn the dynamics of slice-request traffic and effectively address these joint problems. We compare multi-agent DRL to approaches that use: (i) simple heuristics to address the problems, and (ii) DRL to address either slicing or AC. Our results show that the proposed approach achieves up to 30% and 5.18% gain in long-term InP revenue when compared to approaches (i) and (ii), respectively. Additionally, we show that multi-agent DRL is preferable to a single-agent DRL approach for the joint problems in terms of convergence time and InP revenue. Finally, we evaluate the robustness of the trained agents in scenarios that differ from training, such as different arrival rates and real dynamic traffic patterns.

An Analytical Study on Functional Split in Martian 3-D Networks

As space agencies are planning manned missions to reach Mars, researchers need to pave the way for supporting astronauts during their sojourn. This will also be achieved by providing broadband and low-latency connectivity through wireless network infrastructures. In such a framework, we propose a Martian deployment of a 3-D network acting as cloud radio access network (C-RAN). The scenario consists of unmanned aerial vehicles (UAVs) and small satellite platforms. Thanks to the thin Martian atmosphere, CubeSats can stably orbit at very-low-altitude. This allows to meet strict delay requirements to split baseband processing functions between drones and CubeSats. The detailed analytical study, presented in this article, confirmed the viability of the proposed 3-D architecture, under some constraints and tradeoff concerning the involved network infrastructures, that will be discussed in detail.

Anisotropic structure of the Australian continent

The Australian continent preserves some of the oldest lithosphere on Earth in the Yilgarn, Pilbara, and Gawler Cratons. In this study we present shear wave splitting and Ps receiver function results at long running stations across the continent. We use these results to constrain the seismic anisotropic structure of Australia’s cratons and younger Phanerozoic Orogens. For shear wave splitting analysis, we utilize SKS and SKKS phases at 35 broadband stations. For Ps receiver function analysis, which we use to image horizontal boundaries in anisotropy, we utilize 14 stations. Shear wave splitting results at most stations show strong variations in both orientation of the fast direction and delay time as a function of backazimuth, an indication that multiple layers of anisotropy are present. In general, observed fast directions do not appear to be the result of plate motion alone, nor do they typically follow the strike of major tectonic/geologic features at the surface, although we do point out several possible exceptions. Our Ps receiver function results show significant variations in the amplitude and polarity of receiver functions with backazimuth at most stations across Australia. In general, our results do not show evidence for distinctive boundaries in seismic anisotropy, but instead suggest heterogenous anisotropic structure potentially related to previously imaged mid-lithospheric discontinuities. Comparison of Ps receiver function and shear wave splitting results indicates the presence of laterally variable and vertically layered anisotropy within both the thicker cratonic lithosphere to the west, as well as the Phanerozoic east. Such complex seismic anisotropy and seismic layering within the lithosphere suggests that anisotropic fabrics may be preserved for billions of years and record ancient events linked to the formation, stabilization, and evolution of cratonic lithosphere in deep time.

Slice Aware Baseband Function Placement in 5G RAN Using Functional and Traffic Split

5G and Beyond 5G (B5G) are undergoing numerous architectural changes to enable higher flexibility and efficiency in mobile networks. Unlike traditional mobile networks, baseband functions in 5G are disaggregated into multiple components - Radio Unit (RU), Distributed Unit (DU), and Centralized Unit (CU). These components can be placed in different geographical locations based on the latency sensitivity and available capacity in the network. Processing baseband functions in a centralized location offer various advantages (known as centralization benefit in RAN) to mobile network operators, which have been a point of interest for several research works. However, achieving maximum centralization is challenging due to various factors such as limited capacity in the midhaul network, delay requirement of different functional splits and network slices, etc. In this work, we aim to address these challenges by proposing a slice-aware baseband function placement strategy. Our primary objective is to maximize the degree of centralization in the network by appropriate selection of functional split. To achieve this objective, we jointly consider functional split, traffic split, different placement options for baseband functions, and network slice-specific requirements. We also consider the minimization of active processing nodes in cloud infrastructure of different levels (edge and regional) to provide additional resource efficiency. To this end, we formulate an optimization model using Mixed Integer Linear Programming (MILP) and compare its performance with different baseline techniques. We show that the proposed model achieves 6.5% more degree of centralization than the state-of-the-art while placing baseband functions in the network. To tackle the high computational complexity of the MILP model, we also present a polynomial-time heuristic algorithm for solving the problem in large-scale scenarios. We show that although the optimization model achieves around 4% more degree of centralization than the heuristic, the heuristic solves the problem in a reasonable amount of time, making it suitable for real deployment scenarios.

A Discussion of “A Note on Universal Inference” by Tse and Davison

A Discussion of “A Note on Universal Inference” by Tse and Davison

A Tutorial on the Characterisation and Modelling of Low Layer Functional Splits for Flexible Radio Access Networks in 5G and Beyond

A Tutorial on the Characterisation and Modelling of Low Layer Functional Splits for Flexible Radio Access Networks in 5G and Beyond

Dynamic Joint Functional Split and Resource Allocation Optimization in Elastic Optical Fronthaul

Dynamic reconfigurability in optical and mobile networks can facilitate heterogeneous service provisioning while utilizing minimal resources. This allows cost-efficient service delivery resulting in high revenues for network operators. Deployment of elastic mobile and optical networks is a key driver for enabling reconfigurability in modern networks. Elastic optical networks can be exploited as the fronthaul portion of new generation of mobile networks. Such elastic optical fronthaul networks facilitate joint reconfiguration of flexible radio and optical elements and provide considerable performance improvements. In this paper, we focus on the joint dynamic selection of functional splits and configuration of optical transponders and illustrate that designing a converged network with optical and radio elements improves network power efficiency. A time-averaged stochastic optimization problem is formulated and its solution is derived using a modified version of the Lyapunov drift technique. Simulation results demonstrate that the proposed scheme can reduce the average power consumption by up to 70% compared to a cloud radio access network with a traditional optical fronthaul. Further, the results show that the modified Lyapunov technique can afford stringent fronthaul delays below 250 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> . We also discuss how future technology upgrades such as increasing the number of radio antenna ports and decreasing the granularity of fiber spectrum grid may influence the results.