Cloud Radio Access Network Architecture Research Articles

Formal dependability analysis of fault tolerant Virtual Machine allocation strategies in Cloud Radio Access Network

Cloud Radio Access Network (C-RAN) has been proposed as a fifth generation (5G) cellular network that is designed to physically separate the Baseband Units (BBUs) from the Remote Radio Heads (RRHs). The BBUs are placed in the BBU pool/hotel, while the RRHs are located in the cells. This separation enables sharing baseband processing resources among RRHs by generating Virtual Machines (VMs) in the BBU pool, leading to radio coordination and energy saving opportunities. Nevertheless, the failure of a BBU can affect several RRHs, causing outages in the radio network. Therefore, designing a reliable and resilient C-RAN is essential to provide high availability and service continuity in case of BBU failure. This paper deals with using formal verification techniques to analyze the performance and the dependability of C-RAN architecture. We propose fault tolerant VM allocation strategies that can handle BBU failures, thus improving the system’s overall reliability. Based on redundancy techniques, these strategies ensure call recovery through migration to functional BBUs in the case of a BBU failure. In order to evaluate the mutual impact of performance measures on the system’s dependability, we develop performability (combination of performance and dependability) Markov Reward Models (MRMs) for the proposed strategies. We use Probabilistic Model Checking (PMC) to check the performability requirements written with Continuous Stochastic Reward Logic (CSRL). The proposed models are implemented and checked using the PRISM model checker, which supports solving MRMs and checking CSRL properties.

A Task Offloading Method Based on User Satisfaction in C-RAN With Mobile Edge Computing

With the continuous development of the communication service industry, users pay more attention to the quality of network service. Previous studies on offloading problems, especially in the Cloud Radio Access Network (C-RAN) architecture with Mobile Edge Computing (MEC), are primarily focused on the economic perspective, with little consideration given to user-oriented satisfaction problems. To fill this gap, this paper proposes a mathematical model for maximizing user satisfaction in the C-RAN architecture with multi-layer MEC. The problem is divided into two stages for solution. The first stage addresses the optimal connection problem between users and Remote Radio Heads (RRHs). The second stage then schedules user tasks reasonably based on the solution obtained in the first stage. The two-stage problems are all proved to be NP-Hard. Two efficient approximation algorithms, namely User-to-RRH Association Algorithm (URAA) and Maximum Satisfaction Algorithm (MSA), are proposed to solve the problems in different stages. This paper proves and analyzes the theoretical performance of the two algorithms. Finally, the performance of the proposed algorithms is verified by simulation experiments. The experimental results demonstrate that the two proposed algorithms can achieve reasonable solutions to the problems, and the user satisfaction level can be maintained at a high level.

On-Demand Coordinated Spectrum and Resource Provisioning Under an Open C-RAN Architecture for Dense Small Cell Networks

Mobile networks have seen more than a thousand times traffic increases in the past decade. Network operators face an ever increasing demand and cost to deploy denser networks in order to maintain the quality of services. In view of this paradigm shift, an open cloud radio access network (C-RAN) service model is studied in this work. Integrating the cross-layer functions of spectrum resource sharing, channel and power allocation, and interference management in a C-RAN architecture, we explore the feasibility of providing a scalable yet efficient broadband wireless service with dense small cell networks (DSN). The proposed methods and architecture can be applied to DSN that support the functions of (further enhanced) inter-cell interference cancelation (feICIC/ICIC) techniques of 3GPP standard, and have the potential to provide a cost-effective solution for high-quality DSN. Simulation results across an area of 100 km <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> show that the proposed scheme can offer an aggregate downlink throughput of 21 Gbps over a maximum channel bandwidth of 20 MHz, which is 5 times the throughput with a typical ICIC method. Moreover, the service satisfaction degree can reach 85% under the proposed C-RAN architecture, making it a promising and cost-effective solution for future broadband wireless services.

An Enhanced Deep Reinforcement Learning-based Slice Acceptance Control System (EDRL-SACS) for Cloud–Radio Access Network

In 5G networks, Network Slicing (NS) allows service providers (SPs) to create customized standalone networks on shared platforms provided by infrastructure providers (InPs). However, the challenge arises when prioritizing slice requests sent by SPs to InPs due to limited and shared resources. The Cloud Radio Access Network (C-RAN) architecture is a novel approach that supports various services in 5G networks, but efficient resource utilization and increased revenue across the three layers of C-RAN are necessary. To address this issue, we propose an Enhanced Deep Reinforcement Learning-based Slice Acceptance Control System (EDRL-SACS) technique to maximize network utility by accepting suitable requests. EDRL-SACS considers time-varying resource situations, various service requirements, and the frequency of requests from SPs to endorse model-free solutions for Slice Acceptance Control (SAC). By adding effective parameters to the deep reinforcement learning algorithm, the proposed methodology improves its efficiency and performance resulting in better resource utilization, coverage, revenue, and rejection of users’ request terms. The system’s evaluation shows that EDRL-SACS performs well and effectively maximizes the utility of the network by providing slices to SPs in response to their slice requests.

Minimizing energy consumption by joint radio and computing resource allocation in Cloud-RAN

Cloud-RAN is a key 5G enabler; it centralizes the baseband processing of several base stations by executing the baseband functions in a centralized, virtualized, and shared entity known as the Base Band Unit (BBU)-Pool. Cloud-RAN paves the way for joint management of the radio and computing resources of multiple base stations. In fact, centralization and virtualization allow for decreasing energy consumption which decreases Capital Expenditure (CAPEX) and Operational Expenditure (OPEX). Cloud-RAN architecture permits jointly allocating the radio and computing resources of multiple base stations. The radio resources include the Resource Blocks (RBs), the transmission power, and the Modulation Coding Scheme (MCS), whereas the computing resources include the CPUs resources. This paper investigates the potential benefits that could be scored thanks to the joint allocation of these two types of resources, with respect to energy consumption and overall throughput, when radio resources are finite and computing resources are not. The latter is an effect of the C-RAN architecture, which allows scalability and fast computing resource provisioning. Due to the unconstrained availability of computing resources, the joint allocation of radio and computing resources has a negligible impact when the objective is throughput maximization. However, it is highly beneficial when the target is energy consumption minimization in comparison to the sequential allocation that consists of allocating radio resources first, and then computing resources are allocated. For that, we formulate a Mixed Integer Linear Programming (MILP) problem having the objective of minimizing energy consumption. When the goal is to minimize energy consumption, the joint allocation of radio and computing resources reduces the total energy consumption by up to 21.3% when compared to the case where radio and computing resources in the BBU pool are allocated sequentially. Furthermore, given the NP-hardness of solving a MILP problem, we propose a two-step low-complexity matching game-based algorithm with a transmission power adjustment mechanism that aims at performing close to the MILP solver. The results show that our proposed matching game algorithm is a good alternative for solving the joint-allocation MILP problem, producing results that are very close to the MILP optimal solutions.

D-RAN: A DRL-Based Demand-Driven Elastic User-Centric RAN Optimization for 6G &amp; Beyond

With highly heterogeneous application requirements, 6G and beyond cellular networks are expected to be demand-driven, elastic, user-centric, and capable of supporting multiple services. A redesign of the one-size-fits-all cellular architecture is needed to support heterogeneous application needs. While several recent works have proposed user-centric cloud radio access network (UCRAN) architectures, these works do not consider the heterogeneity of application requirements or the mobility of users. Even though significant gains in performance have been reported, the inherent rigidity of these methods limits their ability to meet the quality of service (QoS) expected from future cellular networks. This paper addresses this need by proposing an intelligent, demand-driven, elastic UCRAN architecture capable of providing services to a diverse set of use cases including augmented/virtual reality, high-speed rails, industrial robots, E-health, and more applications. The proposed framework leverages deep reinforcement learning to adjust the size of a user-centered virtual cell based on each application’s heterogeneous requirements. Furthermore, the proposed architecture is adaptable to varying user demands and mobility while performing multi-objective optimization of key network performance indicators (KPIs). Finally, numerical results are presented to validate the convergence, adaptability, and performance of the proposed approach against meta-heuristics and brute-force methods.

Pricing‐based power allocation in cloud radio access network with multiple access technology selection

AbstractIn this article, we consider an uplink economy‐efficient resource allocation framework in a multicellular cloud radio access network (C‐RAN) architecture with network virtualization, where a mobile network operator (MNO) interacts with a number of mobile virtual network operators (MVNOs) with a predetermined business model. OFDMA and Massive MIMO multiple access technologies have been assumed to be available for each MVNO at two different prices. In this setup, we propose a multi‐access technology selection approach (MATSA) with the objective of maximizing delivered rate to end users, reducing operation costs and maximizing MVNOs' profit subject to a set of constraints, which leads to a nonconvex resource allocation problem with very high computational complexity. The utility function is defined as the difference between the total throughput and the utilization cost for each technology. To tackle this problem, we apply complementary geometric programming (CGP) and successive convex approximation (SCA), that results in a two‐step iterative solution. Simulation results demonstrate superiority of the proposed approach compared to a similar scenario with predetermined multi‐access technology, especially for large numbers of users.

Analysis of an active and passive optical fronthaul with dense wavelength division multiplexing for coexisting 4G/5G cloud radio access network architectures based on software defined radio

We experimentally investigate the performance of an active and passive optical fronthaul to be employed in a cloud/centralized radio access network (C-RAN) architecture based on software-defined radio (SDR) for 4G systems, and we discuss the viability to integrate these fronthaul configurations in coexisting 4G/5G systems based on SDR by considering the reconfigurable characteristics of SDRs and the capacity and latency characteristics of the optical fronthaul. The active optical fronthaul consists of two transponders employing dense wavelength division multiplexing (DWDM), whereas the passive optical fronthaul consists of a point-to-point scheme based on DWDM only. To perform a comparative study between the active and passive optical fronthaul in the C-RAN architecture, we have measured the throughput (bit rate) generated via iPerf® for uplink and downlink transmissions at different DWDM channels, and we analyze the losses and latency presented in both optical fronthaul schemes. We found that for different optical fronthaul lengths up to 21.76 km, the bit rate is practically the same for the passive and active fronthaul despite the higher optical losses present in the passive fronthaul in comparison to the active fronthaul. In addition, the latency among active and passive fronthaul are almost similar with an estimated increment of 20 μs in the active fronthaul. These results are independently of the number of remote radio heads (RRHs) and user equipment (UEs) considered in the C-RAN architecture. Our findings put forward the proposed DWDM passive fronthaul as a viable, less complex, and cost-sensitive solution for C-RAN systems with fronthaul lengths up to 21.76 km. These results and all the experiences reported on the C-RAN implementation provide valuable information to design and develop 4G and 5G C-RAN architectures based on SDR with the capability to operate in a DWDM optical fronthaul infrastructure.

Analysis of Compressing PAPR-Reduced OFDM IQ Samples for Cloud Radio Access Network

A common problem of the virtualized cloud radio access network architecture (C-RAN) is the compression of the time-domain IQ samples before transmission over the fronthaul link. Considering a multicarrier waveform such as OFDM, whose IQ samples follow a quasi-Gaussian distribution, the conventional Gaussian quantizer may be used as the optimal solution to the compression problem. However, since the high peak-to-average power ratio (PAPR) of OFDM signals remains a serious problem, various techniques may be employed to reduce the time-domain fluctuations of the IQ samples in the OFDM, resulting in a change in its distribution. The latter fact makes the Gaussian quantizer suboptimal. The literature lacks a performance analysis of the conventional OFDM-based compression techniques when the PAPR of the OFDM signal is reduced. Therefore, in this paper, we study for the first time the impact of reducing the PAPR of the OFDM signal before compression in the C-RAN architecture through rate-distortion analysis. We consider clipping and tone reservation PAPR reduction algorithms. The former is the simplest PAPR reduction approach, while the latter is one of the most effective algorithms used in broadcasting standards such as DVB-T2 and ATSC 3.0. We first derive the distribution of the PAPR-reduced OFDM IQ samples. This is used to optimize the thresholds and codebook levels of a non-uniform scalar quantizer and the number of quantization bits allocated for each quantized level in the entropy coding stage, along with the MER performance analysis. The simulation results show that the conventional Gaussian-based compression techniques applied to a PAPR-reduced signal is not very robust to the statistical changes in the signal unless the signal distribution at the input of the Gaussian quantizer is not significantly affected. However, a significant gain is obtained when the quantizer is optimized with respect to the true distribution of the PAPR-reduced IQ samples.

A C-RAN Feasibility Study

The number of mobile phone users today is growing dramatically, which will cause network jamming and require a substantial amount of bandwidth. Cloud radio access network (C-RAN) is thus one of the interesting technology alternatives to solve this issue. C-RAN communicates with users using dispersed Remote Radio Heads (RRHs), each of which has one baseband unit (BBU). RRH performs soft relaying and is connected to the BBU via high capability, low latency front-haul lines. Due to the exchange of raw I/Q samples between the RRHs and the BBU, the C-RAN architecture places a restriction on front-haulfront-haul bandwidth. In BBU, many methods, including joint decompression and decoding (JDD) and wyner-ziv coding, are utilised to increase capacity.

Gated recurrent unit based prediction model for front haul with XG-PON in CRAN architecture

10 Gigabit Passive Optical Network (XG-PON) is a well-advanced and cost-effective front-haul solution for Cloud Radio Access Network architecture (CRAN). Whose foremost requirement is latency; the use of a database administrator (DBA) mechanism for upstream transmission is required for meeting it. The number of packets in DBA that arrive at the ONU buffer could be calculated using the prediction method. This would eliminate the Round Trip Time (RTT) in traditional DBA Algorithms and keep meeting the latency requirement for the front haul solution. This paper presents the GRU-RNN prediction-based DBA algorithm which provides better performance than the LSTN-RNN based DBA algorithm and FNN based DBA algorithm in terms of latency, packet loss, and Jitter. In terms of upstream delay, the GRU-DBA outperforms the FNN-DBA and LSTM-DBA by 55% and 8%, respectively. This deployment of the model in a real-world environment would be more cost-effective due to its use of fewer resources, making it a lighter architecture model for the CRAN network in 5 G. It also outperforms packet delivery ratio by 15%, 28%, and jitter by 1%, respectively.

Energy and Delay Guaranteed Joint Beam and User Scheduling Policy in 5G CoMP Networks

Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads’ (RRHs’) energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namely BEANS. We prove that the proposed BEANS ensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possibly negative submodular objective function and non-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations.

A Software-Defined Programmable Testbed for Beyond 5G Optical-Wireless Experimentation at City-Scale

Wireless traffic will significantly increase in next-generation networks. Therefore, there is a need for novel optical network front-/mid-/backhaul (x-haul) architectures that can deliver ultra-high bandwidth and ultra-low-latency services to wireless and edge cloud systems, as well as support various cloud radio access network (C-RAN) architectures. Designing and evaluating such architectures requires large-scale experimental platforms. In this article, we present the design and implementation of a unique, remotely accessible, disaggregated, and programmable optical network as the key component of the city-scale PAWR COSMOS advanced wireless testbed. We discuss the design and implementation of a dedicated multi-functional Ryu software-defined networking (SDN) controller for the testbed's optical network wavelength channel assignment and topology reconfiguration. We present an example use of the SDN controller for monitoring and automated measurement of the wavelength-dependent amplifier gain in deployed reconfigurable optical add/drop mul-tiplexer (ROADM) units. We also demonstrate a pilot experiment focusing on a wireless handover scheme for C-RAN architectures in the COSMOS testbed, where high traffic volume can be supported by dynamic wavelength allocation via optical switching in the fronthaul. The field-deployed testbed and SDN controller can be used by the research community to push the envelope in the area of optical networks for beyond 5G and 6G.

Ultra-Reliable Communication for Critical Machine Type Communication via CRAN-Enabled Multi-Connectivity Diversity Schemes.

This paper focuses on edge-enabled cloud radio access network architecture to achieve ultra-reliable communication, a crucial enabler for supporting mission-critical machine-type communication networks. We propose coordinated multi-point transmission schemes taking advantage of diversity mechanisms in interference-limited downlink cellular networks. The network scenario comprises spatially distributed multiple remote radio heads (RRHs) that may cooperate through silencing, or by using more elaborated diversity strategies such as maximum ratio transmission or transmit antenna selection to serve user equipment in the ultra-reliable operation regime. We derive an exact closed-form expression for the outage probabilities and expected values of signal-to-interference ratio for silencing, transmit antenna selection and maximum ratio transmission schemes. We formulate rate control and energy efficiency under reliability constraints to test the performance and resource usage of the proposed schemes. Furthermore, we study the impact on average system sum throughput with throughput-reliability trade-off under cooperative communication. Extensive numerical analysis shows the feasibility of ultra-reliable communication by implementing diversity schemes with RRHs cooperation.

Signalling load reduction in 5G network based on cloud radio access network architecture

The rapid growth of both mobile users and application numbers has caused a huge load on the core network (CN). This is attributed to the large numbers of control messages circulating between CN entities for each communication or service request, however, making it imperative to develop innovative designs to handle this load. Consequently, a variety of proposed architectures, including a software defined network (SDN) paradigm focused on the separation of control and data plans, have been implemented to make networks more flexible. Cloud radio access network (C-RAN) architecture has been suggested for this purpose, which is based on separating base band units (BBU) from several base stations and assembling these in one place. In this work, a novel approach to realize this process is based on SDN and C-RAN, which also distributes the control elements of the CN and locates them alongside the BBU to obtain the lowest possible load. The performance of this proposed architecture was evaluated against traditional architecture using MATLAB simulation, and. the results of this assessment indicated a major reduction in signalling load as compared to that seen in the traditional architecture. Overall, the number of signalling messages exchanged between control entities was decreased by 53.19 percent as compared to that seen in the existing architecture.

Model-Based Learning Network for 3-D Localization in mmWave Communications

Millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate cooperative localization, in which large bandwidths and antenna arrays and increased densities of base stations enhance the delay and angular resolution. This study considers the joint location and velocity estimation of user equipment (UE) and scatterers in a three-dimensional mmWave CRAN architecture. Several existing works have achieved satisfactory results by using neural networks (NNs) for localization. However, the black box NN localization method has limited robustness and accuracy and relies on a prohibitive amount of training data to increase localization accuracy. Thus, we propose a model-based learning network for localization to address these problems. In comparison with the black box NN, we combine NNs with geometric models. Specifically, we first develop an unbiased weighted least squares (WLS) estimator by utilizing hybrid delay and angular measurements, which determine the location and velocity of the UE in only one estimator, and can obtain the location and velocity of scatterers further. The proposed estimator can achieve the Cramér-Rao lower bound under small measurement noise and outperforms other state-of-the-art methods. Second, we establish a NN-assisted localization method called NN-WLS by replacing the linear approximations in the proposed WLS localization model with NNs to learn the higher-order error components, thereby enhancing the performance of the estimator, especially in a large noise environment. The solution possesses the powerful learning ability of the NN and the robustness of the proposed geometric model. Moreover, the ensemble learning is applied to improve the localization accuracy further. Comprehensive simulations show that the proposed NN-WLS is superior to the benchmark methods in terms of localization accuracy, robustness, and required time resources.

A Literature Survey on Cloud-RAN Architecture toward 5G Green Wireless Communication Systems

Cell network architectures confront enormous challenges due to the unusual rise in mobile data traffic usage, thus reducing spectrum accessibility and heavy power consumption. Research communities are looking for breakthroughs in the development of advanced infrastructure networks to meet increased consumer demand and, at the same time, to reduce network operators’ capital and operational costs. Cloud Radio Access Network (Cloud-RAN) architecture is a recently proposed solution for wireless networks and is considered a strong candidate for potential 5G cellular coverage worldwide. This paper presents a literature survey on Cloud-RAN architectures toward Green 5G wireless communication systems and highlights Cloud-RAN benefits and limitations. Furthermore, this paper discusses Cloud-RAN’s potential enhancement techniques, including robust beamforming, multiple-input-multiple-output (MIMO) with non-orthogonal multiple access (NOMA) cooperative scheme, massive MIMO, mmWave communication, clustering techniques, coalition formation, energy harvesting and reinforcement learning output.

Unsupervised Deep Learning Approach for Near Optimal Power Allocation in CRAN

The cloud radio access network (CRAN) is a promising architecture that can face the hard challenges imposed on next cellular generations. However, the high computational complexity of the optimization techniques currently proposed in the literature prevents from totally reaping the benefits of the CRAN architecture. Learning based techniques are expected to replace the conventional optimization techniques due to their high performance and very low online computational complexity. In this paper, we consider tackling the power allocation in CRAN via an unsupervised deep learning based approach. Different from the previous works, user association is considered in our optimization problem to reflect a real cellular scenario. Additionally, we propose a novel scheme that can enhance the deep learning based power allocation approaches, significantly. We provide intensive analysis to discuss the trade-offs faced when employing our deep learning based approach for power allocation. Simulation results prove that the proposed technique can obtain a very close to optimal performance with negligible computational complexity.

Joint service placement and request routing in mobile edge computing

Mobile edge computing (MEC) is envisioned as a prospective technology that supports latency-critical and computation-intensive applications by using storage and computation resources in network edges. The advantages of this technology are trapped in limited edge cloud resources, and one of the prime challenges is how to allocate available edge cloud resources to satisfy user requests. However, previous works usually optimize service (data&code) placement and request routing simultaneously within the same timescale, ignoring the fact that frequent service replacement will incur expensive operating expenses. In this paper, we jointly optimize service placement and request routing in the MEC network for data analysis applications, under the constraints of computation and storage resource. In particular, the Cloud Radio Access Network (C-RAN) architecture is applied to pool available resources and realize load balancing among edge clouds. In addition, we adopt a two timescale framework to reduce high operating expenses caused by frequent cross-cloud service replication and replica deletion. Then, we develop a greedy-based approximation algorithm for service placement subproblem and a linear programming (LP) relaxation-based heuristic algorithm for request routing subproblem, respectively. Finally, the numerical results demonstrate that our proposed solution reaches 90% of the optimal performance in services homogeneous case and 76% in services heterogeneous case.

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems With Minimal End-to-End Latency

Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Network (D-RAN) that relies on non-cooperative ENs equipped with one-way uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finite-capacity two-way fronthaul links. Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.