5G Cloud Radio Access Network Research Articles

Towards an Optimal Cloud-Based Resource Management Framework for Next-Generation Internet with Multi-Slice Capabilities

With the advent of 5G networks, the demand for improved mobile broadband, massive machine-type communication, and ultra-reliable, low-latency communication has surged, enabling a wide array of new applications. A key enabling technology in 5G networks is network slicing, which allows the creation of multiple virtual networks to support various use cases on a unified physical network. However, the limited availability of radio resources in the 5G cloud-Radio Access Network (C-RAN) and the ever-increasing data traffic volume necessitate efficient resource allocation algorithms to ensure quality of service (QoS) for each network slice. This paper proposes an Adaptive Slice Allocation (ASA) mechanism for the 5G C-RAN, designed to dynamically allocate resources and adapt to changing network conditions and traffic delay tolerances. The ASA system incorporates slice admission control and dynamic resource allocation to maximize network resource efficiency while meeting the QoS requirements of each slice. Through extensive simulations, we evaluate the ASA system’s performance in terms of resource consumption, average waiting time, and total blocking probability. Comparative analysis with a popular static slice allocation (SSA) approach demonstrates the superiority of the ASA system in achieving a balanced utilization of system resources, maintaining slice isolation, and provisioning QoS. The results highlight the effectiveness of the proposed ASA mechanism in optimizing future internet connectivity within the context of 5G C-RAN, paving the way for enhanced network performance and improved user experiences.

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.

Functional Split-Aware Optimal BBU Placement for 5G Cloud-RAN Over WDM Access/Aggregation Network

Fifth generation (5G) cloud-radio access network (C-RAN) aims at providing better performance and support to various applications with stringent data rate and latency requirements. In C-RAN, the processing units, known as the baseband units (BBUs), are segregated from the individual remote radio heads (RRHs) and moved to a convenient location to serve more than one RRH. This migration leads to an efficient resource allocation and cost-effective solution at the expense of a huge fronthaul traffic between the RRH and BBU hotel. The required fronthaul data rate largely depends on the employed functional split options. In this article, we propose a novel optimal BBU placement with a mixed functional split scheme to combat the fronthaul latency challenge while providing the network with greater flexibility and cost reduction. We introduce an integer linear programming (ILP)-based BBU placement problem with a mixed functional split selection approach to simultaneously minimize the number of BBU hotels and fibers, thereby reducing the network cost. Furthermore, a heuristic algorithm is proposed to solve the proposed model for large network scenarios. The obtained results show that an improvement of 25% and 50% is realized with the proposed scheme over the conventional fixed split option scheme for small and large network scenarios, respectively.

Enhanced Metaheuristic Algorithm-Based Load Balancing in a 5G Cloud Radio Access Network

Mobile operators must increase investments in network infrastructures due to the emergent growth of the internet and technological advancements. Mobile operators consider cloud-RAN and software defined networking to be developing technologies that can reduce costs and increase scalability for fifth-generation mobile communication networks (5G). A base station consists of two important components, namely baseband (BBU) and remote radio head (RRH) units. Unbalanced data traffic can arise, leading to call dropping and call blocking. When network traffic conditions start to vary, the performance of the system becomes suboptimal. Self-optimization of the network is necessary to reduce the load of overloaded eNode’s with more call blocking, that increase the load of underloaded eNode’s with less utilization of resources. The main objective of a self-organizing network is to reduce call blocking and optimize an unbalanced network. The proposed algorithm is an enhanced version of the cat swarm optimization algorithm performed by the host manager entity to select the best BBU-RRH combination after analyzing the quality-of-service (QoS) information from the remaining BBU-RRH configurations. Optimization is carried out on each user after a QoS analysis for every new BBU-RRH combination. The proposed algorithm is implemented in Matlab R2020a and evaluation is conducted in terms of blocking probability, response time, and throughput. The simulation results show that the proposed ECSO optimization algorithm reduces blocking probability by 10%, throughput is increased by 8%, and response time is reduced by 7% as compared with the existing PSO and CSO algorithms.

Cooperative evolution of support vector machine empowered knowledge-based radio resource management for 5G C-RAN

Cloud-radio access network (C-RAN) is an innovative approach in 5th Generation communication networks, where baseband processing units (BBUs) are dissociated from the remote radio heads (RRHs), and a remote cloud-based centralized pool of BBUs is formed. This paper provides an efficient multi-class classification radio resources management (RRM) scheme based on the cooperative evolution of support vector machine (SVM) to assign the spectrum resources of macrocellular users (MUE) i.e. sub-channel of variable bandwidth, to remote-head users (RUEs) and D2D pairs, such that sub-channels can be reused without compromising the quality of service (QoS). First, the C-RAN resource allocation problem is modeled as a NILP problem, with CUs and D2D nodes sharing the same channel. Following that, the proposed strategy is used to allocate resources. A realistic dataset is built for assessing machine learning-based techniques using 5G experimental prototype based on Open Air Interface (OAI). Finally, experimental results demonstrate the effectiveness of the proposed cooperative evolution of SVM-based, multi-class classification RRM over alternative schemes in terms of network throughput, prediction performance, and system utilization.

Cooperative evolution of SVM-based resource allocation for 5G cloud-radio access network system with D2D communication

In fifth generation (5G) communication networks, the cloud-radio access network (C-RAN) is a new technique, where baseband processing units are decoupled from remote radio heads and a remote cloud-based centralised pool of baseband processing units is established. However, as the system's capacity grows, managing interference between cellular users (CUs) and device-to-device (D2D) users becomes a critical issue. This paper proposes a multi-class classification resources allocation scheme based on the cooperative evolution of support vector machine (SVM) to assign macrocellular users (MUs) spectrum resources to remote-head users (RUs) and D2D pairs, allowing sub-channels to be reused without compromising quality of service (QoS). First, the key resource allocation sets are determined, and a C-RAN resource allocation model is created. Because CUs and D2D nodes are allowed to access the same sub-channel, the ultimate challenge is described as a many-to-one matching sub-game. The 5G C-RAN system allocates resources via a cooperative evolution of an SVM-based multi-class classification algorithm based on user position estimates, with intercell and intracell interference utilised to build the training dataset. The training dataset is prepared based on intercell and intracell interference. Finally, the results show that the proposed cooperative evolution of SVM-based resource allocation approach outperforms standard resource allocation methods.

Cooperative evolution of SVM-based resource allocation for 5G cloud-radio access network system with D2D communication

Cooperative evolution of SVM-based resource allocation for 5G cloud-radio access network system with D2D communication

Call Admission Control Decision Maker Based on Optimized Fuzzy Inference System for 5G Cloud Radio Access Networks

Call Admission Control Decision Maker Based on Optimized Fuzzy Inference System for 5G Cloud Radio Access Networks

Task Allocation Framework For Software-Defined Fog v-RAN

The fifth-generation wireless technology (5G) has been developed with an aim to provide ubiquitous and scalable connectivity for Internet-of-Things (IoT) nodes. Likewise, the cloud radio access network (C-RAN) architecture can be exploited to enable efficient network access to IoT nodes. Nevertheless, the 5G C-RAN architecture is based on large data centers geographically located far apart, which introduces an inevitable overhead. Therefore, to supply real-time data services near by the data terminals, fog computing emerges as a promising solution. However, constrained physical fog resources and delay-sensitive services hinder the application of new virtualization technologies in the baseband unit (BBU) task allocation management of the fog network. To tackle these challenges, a task allocation framework for hierarchical software-defined fog virtual radio access networks (v-RANs) is proposed in this article. Precisely, we apply an enhanced ant colony optimization (ACO) in combination with a max-min algorithm to efficiently determine the optimal path for BBU task allocation management, while minimizing the transmission time for parallel task execution scheduling. Experimental results demonstrate that the queue delay in our approach is 98.38% and 98.82% lower than the round-robin (RR) algorithm and least connection technique (LCT), respectively.

Performance and evaluation of Volterra Nonlinear Equalizer for coherent RoF mobile front-haul network: a case study of QOSTBC-OFDM and QOSTBC-GFDM waveforms

In this paper, we propose and investigate a multi-Gigabits-per-second baseband signal over Standard Single-Mode Fiber (SSMF) link using a 16-Quadrature Amplitude Modulation (16-QAM). This architecture can be deployed within the context of fifth-generation (5G) Cloud Radio Access Network (C-RAN). It consists of 4 × 4 Quasi-Orthogonal Space–Time Block Code Generalized Frequency Division Multiplexing (QOSTBC-GFDM) and Orthogonal Frequency Division Multiplexing (QOSTBC-OFDM) waveforms. It is based on the implementation of a third order Volterra Nonlinear Equalizer (VNLE) in the Remote Radio Unit (RRU). The VNLE has been efficiently employed to reduce the impact of the harmful effect of the photonic devices. A Zero Forcing (ZF) equalizer has been used at the User Equipment (UE) to overcome the wireless channel impairments. Our research shows that VNLE can be a promised solution to compensate signal distortions and non-linearity effects. Furthermore, this study shows that QOSTBC-GFDM is more efficient than QOSTBC-OFDM in terms of Out-Of-Band (OOB) emission and Spectral Efficiency (SE). Moreover, a comparable performance between the two waveforms in terms of Bit Error Rate (BER) and Error Vector Magnitude (EVM) is attained.

Reducing Power Consumption in C-RAN Using Switch On/Off of MC-RRH Sectors and Small Cells

The evolution toward 5G wireless networks was the result of an exponential growth in data traffic demands and a significant increase in the number of connected devices with stringent quality of services (QoS) requirements. This evolution leads to an increase in the total power consumption and indirectly produces pollution intensive carbon footprints. To overcome 5G challenges in traffic demands and power consumption, Cloud Radio Access Network (C-RAN) architecture is introduced. This paper investigates the energy consumption in 5G C-RAN using switch on/off cell sectors and densification by exploiting Small Cell Remote Radio Heads (SC-RRHs). We develop sector switch on/off algorithms based on reducing the number of underutilized sectors per Macro Cell RRH (MC-RRH). Then, we apply a selective SC-RRHs distribution allowing SC-RRHs to serve users of switched off sectors. Simulations results show that our proposed approaches based on the switch on/off by sector can achieve more than 55% of power saving which makes them more efficient than those based on the switch on/off by cell in terms of power saving.

QoS assurance for PON-based fronthaul and backhaul systems of 5G cloud radio access networks

The newly proposed fronthaul and backhaul (FB) segments in the cloud-radio access networks (C-RANs) could be leveraging solutions to satisfy the 5G demands, but the rigorous one-way delay and packet delay variations (PDVs) requirements of fronthaul networks are the bottleneck. Time division multiplexing (TDM)-based passive optical networks (PONs) can be a promising candidate to carry the fronthaul traffic, but the TDM-PON systems have limitations to meet the fronthaul QoS requirements due to unavoidable delays at the switching nodes. Moreover, the unification of FB traffics over the same segment is vital to fully exploit the cost benefits, but due to the poor performance of the PON systems for the fronthaul traffic, the backhaul traffic could not be forwarded through the same fronthaul segments. Therefore, in this paper, we propose and implemented the strict priority and frame pre-emption-based buffering schemes to transport the FB packet flows simultaneously at tolerable delays and PDVs.

QoS assurance for PON-based fronthaul and backhaul systems of 5G cloud radio access networks

The newly proposed fronthaul and backhaul (FB) segments in the cloud-radio access networks (C-RANs) could be leveraging solutions to satisfy the 5G demands, but the rigorous one-way delay and packet delay variations (PDVs) requirements of fronthaul networks are the bottleneck. Time division multiplexing (TDM)-based passive optical networks (PONs) can be a promising candidate to carry the fronthaul traffic, but the TDM-PON systems have limitations to meet the fronthaul QoS requirements due to unavoidable delays at the switching nodes. Moreover, the unification of FB traffics over the same segment is vital to fully exploit the cost benefits, but due to the poor performance of the PON systems for the fronthaul traffic, the backhaul traffic could not be forwarded through the same fronthaul segments. Therefore, in this paper, we propose and implemented the strict priority and frame pre-emption-based buffering schemes to transport the FB packet flows simultaneously at tolerable delays and PDVs.

Resource Allocation for CoMP Enabled URLLC in 5G C-RAN Architecture

Ultrareliable low-latency communication (URLLC) is one of the important service categories of 5G, where high reliability and low latency are to be met simultaneously. Coordinated multipoint (CoMP), where a user equipment (UE) is connected to multiple base stations simultaneously, can provide high reliability for interference limited cell-edge UEs. Short-packet communication and 5G physical layer enablers combined with CoMP can be a potential solution to meet the quality of service requirements of the URLLC traffic. However, there exist some challenges in using CoMP for URLLC in a cloud radio access network (C-RAN) architecture; mainly fronthaul capacity and remote radio head resource availability constraints. These resource constraints are addressed in this article; and a resource allocation scheme based on the effective bandwidth concept and 5G enablers is proposed, which can meet the reliability and delay requirements of the URLLC traffic. End-to-end error and latency components are identified and the interplay existing among the error components is utilized for bandwidth minimization. A novel packet delivery mechanism, queuing strategy, and time-frequency resource allocation for CoMP enabled URLLC in C-RAN architecture are proposed. The proposed scheme shows superior performance, in terms of the resource utilization and UE satisfaction, when compared with the existing techniques.

Utilization-aware VB migration strategy for inter-BBU load balancing in 5G cloud radio access networks

The battle between traffic demand and resource availability limits the efficiency of the existing cellular network. To overcome these limitations, the 5G needs to be scalable and flexible to allow multiple radio access technology. The next-generation base station architecture also aims to maximize physical resource utilization and minimize its associated cost. In this paper, we proposed a framework for inter-BBU load balancing based on VB’s live migration in a C-RAN architecture. The migration is performed in-between two C-BBU, which resides at a single BBU-Pool. The load balancing across BBU is performed based on two consecutive steps, i.e. (a) an expected completion time-based resources allocation and (b) a utilization factor-based VB migration. We validate our proposed approach using CloudSim simulator. The result shows that the adaptation of cloud-computing technology along with suitable resources allocation and load balancing strategy increases BBU utilization by overcoming the imbalance load conditions. Further, the use of the live migration technique improves the QoS of C-RAN by reducing the server downtime and migration time.

Protecting Physical Communications in 5G C-RAN Architectures through Resonant Mechanisms in Optical Media.

Future 5G networks are characterized by three basic ideas: enhanced mobile broadband communications, massive machine-type communications, and ultra-low-latency communications. Any of these requirements needs, to be fulfilled, the implementation of high-efficiency technologies at all levels. This includes some of the costliest mechanisms in terms of computational time and bitrate: information protection solutions. Typical techniques in this area employ complex algorithms and large protocol headers, which strongly reduces the effective baud rate and latency of future 5G networks and communications. This is especially relevant in the access network, which in 5G networks will follow a cloud-based architecture, where thousands of different devices must communicate, before aggregating all those streams to be sent to the backbone. Then, new and more efficient mechanisms are needed in the cloud radio access networks (C-RAN) for future 5G systems. Therefore, in this paper it is proposed a novel information protection scheme for C-RAN architectures based on resonant phenomena in optical fibers communicating the fronthaul and backhaul in 5G networks. Resonant structures and physical nonlinearities generate a chaotic signal which may encrypt and hide at physical level every communication stream in a very efficient manner. To evaluate the proposed mechanism, an experimental validation based on simulation techniques is also described and results discussed.

Deep Reinforcement Learning Aided Cell Outage Compensation Framework in 5G Cloud Radio Access Networks

As one of the key technologies of 5G, Cloud Radio Access Networks (C-RAN) with cloud BBUs (Base Band Units) pool architecture and distributed RRHs (Remote Radio Heads) can provide the ubiquitous services. When failure occurs at RRH, it can’t be alleviated in time and will lead to a significant drop in network performance. Therefore, the cell outage compensation (COC) problem for RRH in 5G C-RAN is very important. Although deep reinforcement learning (DRL) has been applied to many scenarios related to the self-organizing network (SON), there are fewer applications for cell outage compensation. And most intelligent algorithms are hard to obtain globally optimized solutions. In this paper, aiming at the cell outage scenario in C-RAN with the goal of maximizing the energy efficiency, connectivity of RRH while meeting service quality demands of each compensation user, a framework based on DRL is presented to solve it. Firstly, compensation users are allocated to adjacent RRHs by using the K-means clustering algorithm. Secondly, DQN is used to find the antenna downtilt and the power allocated to compensation users. Comparing to different genetic algorithms, simulation result shows that the proposed framework converges quickly and tends to be stable, and reaches 95% of the maximum target value. It verifies the efficiency of the DRL-based framework and its effectiveness in meeting user requirements and handling cell outage compensation.

Machine Learning Adaptive Computational Capacity Prediction for Dynamic Resource Management in C-RAN

Efficient computational resource management in 5G Cloud Radio Access Network (C-RAN) environments is a challenging problem because it has to account simultaneously for throughput, latency, power efficiency, and optimization tradeoffs. The assumption of a fixed computational capacity at the baseband unit (BBU) pools may result in underutilized or oversubscribed resources, thus affecting the overall Quality of Service (QoS). As resources are virtualized at the BBU pools, they could be dynamically instantiated according to the required computational capacity (RCC). In this paper, a new strategy for Dynamic Resource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML) techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: support vector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higher than the predicted computational capacity (PCC). To further improve, two new strategies are proposed and tested in a realistic scenario: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting (DRM-AC-ES), reducing the average of unsatisfied resources by 98 % and 99.9 % compared to the DRM-AC, respectively.

An efficient resource allocation to improve QoS of 5G slicing networks using general processor sharing‐based scheduling algorithm

SummaryFifth generation (5G) slicing is an emerging technology for software‐defined networking/network function virtualization–enabled mobile networks. Improving the utilization and throughput to meet the quality of service (QoS) requirements of 5G slicing is very important for the operators of mobile networks. With growing data traffic from different applications of numerous smart mobile devices having several QoS requirements, we expect networks to face problems of congestion and overload that prevent the effective functioning of a radio access network (RAN). This paper proposes a more effective packet‐based scheduling scheme for data traffic by 5G slicing with two operation modes for improving the resource utilization of 5G cloud RAN and providing an efficient isolation of the 5G slices. These two operation modes are referred to as static sharing resource (SSR) scheme and dynamic sharing resources (DSR) scheme. The SSR scheme is a modified version of an existing method. The goal of this approach is to reallocate the shared available resources of 5G network fairly and maximize the utilization of bandwidth while protecting a 5G slice from overwhelming other 5G slices. Throughput and delays of the system model are also discussed to show its performance limits. On the basis of the simulation outcomes, we observed that the proposed DSR scheme outperforms the SSR scheme in terms of provided delay and throughput. In addition, the token bucket parameters together with the assigned capacity weight for each slice can be selected and configured based on the required QoS. Finally, a good estimate for the maximum delay bounds of the slices is provided by the derived theoretical delay bound.

A Novel Real-Time, Lightweight Chaotic-Encryption Scheme for Next-Generation Audio-Visual Hearing Aids

Next-generation audio-visual (AV) hearing aids stand as a major enabler to realize more intelligible audio. However, high data rate, low latency, low computational complexity, and privacy are some of the major bottlenecks to the successful deployment of such advanced hearing aids. To address these challenges, we propose an integration of 5G Cloud-Radio Access Network (C-RAN), Internet of Things (IoT), and strong privacy algorithms to fully benefit from the possibilities these technologies have to offer. Existing audio-only hearing aids are known to perform poorly in noisy situations where overwhelming noise is present. Current devices make the signal more audible but remain deficient in restoring intelligibility. Thus, there is a need for hearing aids that can selectively amplify the attended talker or filter out acoustic clutter. The proposed 5G IoT-enabled AV hearing-aid framework transmits the encrypted compressed AV information and receives encrypted enhanced reconstructed speech in real time to address cybersecurity attacks such as location privacy and eavesdropping. For security implementation, a real-time lightweight AV encryption is proposed, based on a piece-wise linear chaotic map (PWLSM), Chebyshev map, and a secure hash and S-Box algorithm. For speech enhancement, the received secure AV (including lip-reading) information in the cloud is used to filter noisy audio using both deep learning and analytical acoustic modelling. To offload the computational complexity and real-time optimization issues, the framework runs deep learning and big data optimization processes in the background, on the cloud. The effectiveness and security of the proposed 5G-IoT-enabled AV hearing-aid framework are extensively evaluated using widely known security metrics. Our newly reported, deep learning-driven lip-reading approach for speech enhancement is evaluated under four different dynamic real-world scenarios (cafe, street, public transport, pedestrian area) using benchmark Grid and ChiME3 corpora. Comparative critical analysis in terms of both speech enhancement and AV encryption demonstrates the potential of the envisioned technology to deliver high-quality speech reconstruction and secure mobile AV hearing aid communication. We believe our proposed 5G IoT enabled AV hearing aid framework is an effective and feasible solution and represents a step change in the development of next-generation multimodal digital hearing aids. The ongoing and future work includes more extensive evaluation and comparison with benchmark lightweight encryption algorithms and hardware prototype implementation.