Cloud-based Radio Access Network Research Articles

Latency optimized C-RAN in optical backhaul and RF fronthaul architecture for beyond 5G network: A comprehensive survey

The need for high-speed data connection has significantly strained Mobile Network Operators (MNOs) due to an exponential increase in traffic. Numerous innovative technologies have been used to fulfill consumer service needs in capacity, high-speed data, network coverage, and Quality of Service (QoS), but they still require more thought. Because of several advantageous features in terms of resource utilization, service deployment, Capital Expenditure (CAPEX), Operating Expense (OPEX), and network management, the Cloud-based Radio Access Network (C-RAN) has played a significant role in mitigating the issues faced by mobile network operators. However, the C-RAN technology places high demands on the mobile fronthaul network due to the risk of connection interruptions. This article thoroughly analyzes the essential technologies, problems, design, needs, and solutions for effective fronthaul and backhaul communication a fifth-generation (5G) and beyond systems. There have been reviews of optical-based fronthaul and backhaul technologies. This article makes suggestions for ways to make fronthaul and backhaul communication systems more effective by lowering the costs, bandwidth, latency, and power requirements.

Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources.

Cloud-based Radio Access Network (Cloud-RAN) leverages virtualization to enable the coexistence of multiple virtual Base Band Units (vBBUs) with collocated workloads on a single edge computer, aiming for economic and operational efficiency. However, this coexistence can cause performance degradation in vBBUs due to resource contention. In this paper, we conduct an empirical analysis of vBBU performance on a Linux RT-Kernel, highlighting the impact of resource sharing with user-space tasks and Kernel threads. Furthermore, we evaluate CPU management strategies such as CPU affinity and CPU isolation as potential solutions to these performance challenges. Our results highlight that the implementation of CPU affinity can significantly reduce throughput variability by up to 40%, decrease vBBU's NACK ratios, and reduce vBBU scheduling latency within the Linux RT-Kernel. Collectively, these findings underscore the potential of CPU management strategies to enhance vBBU performance in Cloud-RAN environments, enabling more efficient and stable network operations. The paper concludes with a discussion on the efficient realization of Cloud-RAN, elucidating the benefits of implementing proposed CPU affinity allocations. The demonstrated enhancements, including reduced scheduling latency and improved end-to-end throughput, affirm the practicality and efficacy of the proposed strategies for optimizing Cloud-RAN deployments.

Resource allocation in 5G cloud‐RAN using deep reinforcement learning algorithms: A review

AbstractThis paper reviews recent research on resource allocation in 5G cloud‐based radio access networks (C‐RAN) using deep reinforcement learning (DRL) algorithms. It explores the potential of DRL for learning complex decision‐making policies without human intervention. The paper first introduces the C‐RAN architecture and resource allocation concepts, followed by an overview of DRL algorithms applied to C‐RAN. It discusses the challenges and potential solutions in applying DRL to C‐RAN resource allocation, including scalability, convergence, and fairness. The review concludes by highlighting open research directions for future investigation. By providing insights into the state‐of‐the‐art techniques for resource allocation in 5G C‐RAN using DRL, this paper emphasizes their potential impact on advancing 5G network technology.

Analysis of Area Spectral & Energy Efficiency in a CoMP-Enabled User-Centric Cloud RAN

In this paper, we consider coordinated multipoint transmission (CoMP) by using distributed data base stations (DBSs) in a User-centric Cloud-based Radio Access Network (UCRAN). The creation of non-overlapping virtual cells (or service zones) centered around a user, contrary to traditional cellular architecture, shifts the mode of operation from base station centric “always ON” cells to user-centric “on-demand cells.” In contrast to existing literature, we show that CoMP is only beneficial in network architectures where cell-edge users are present. In a UCRAN architecture, where the cell-edge users are virtually removed due to non-overlapping user-centric cells, the performance gain provided by enabling CoMP is insignificant. We analyze this by performing a comparative performance analysis of UCRAN with different joint transmission schemes of CoMP. We also provide analytical expressions for network-wide coverage probability, area spectral efficiency, and energy efficiency by taking advantage of the stochastic geometry tools. Additionally, we investigate the impact of new degrees of freedom such as the size of the service zones and density of data base station on the spectral and energy efficiencies of CoMP-enabled UCRAN. Extensive Monte Carlo simulations validate the proposed analytical framework as well as provide useful insights into the design of next-generation cellular architectures.

Delay-Aware Energy-Saving Strategies for BBU Pool in C-RAN: Modeling and Optimization

The surging mobile traffic poses serious challenges for mobile operators and the advancement of information and communication technologies (ICT). The cost and energy consumption cannot support affordable mobile services and sustainable growth. As a new architecture, cloud-based radio access network (C-RAN) is proposed to confront these challenges. C-RAN is a deployment paradigm that seeks to isolate baseband unit (BBU) from its remote radio unit (RRU) in base station (BS), consolidating the BBUs into a common place (i.e. BBU pool). In the BBU pool, the computing resources provided by the BBUs can be dynamically assigned to RRUs on demand by the BBU controller. Thus, with the fluctuation of data traffic from RRUs, a part of BBUs can be dynamically turned on or off. As a result, the energy consumption of the BBU pool can be reduced correspondingly. In this paper, we design a Markov chain-based calculation model to formulate the energy-saving operation of the BBU pool with an active/sleep mode under dynamic RRU traffic load. On this basis, we propose two different BBU state transition strategies for the BBU pool, i.e., “Cautious ON, Bold OFF (CO-BF)” strategy and “Bold ON, Cautious OFF (BO-CF)” strategy. We formulate the key performance indicators in terms of the energy saving efficiency (ESE), the consequent packet queuing delay (PQD) and the normalized tradeoff cost as well. In extensive simulations, we investigate the effect of these key parameter indicators on the system performances of the BBU pool. Achieving the minimum normalized cost, an optimal operating strategy for the BBU pool can be determined by adjusting the parameter values of the ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> ). The simulation results show when RRU traffic load is light (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda =0.1$ </tex-math></inline-formula> ), the proposed BO-CF strategy can lead to the minimum normalized cost, which corresponds to ESE of up to 75.4% and PQD of 0.0097ms. For heavier load (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda =0.4$ </tex-math></inline-formula> ), the minimum normalized cost can be obtained along with ESE of up to 74.1% and PQD of 0.1241ms, when the CO-BF strategy is adopted. Hence, we observe that the BO-CF strategy is slightly better than the CO-BF for the light traffic load of the BBU pool, while the CO-BF strategy would be more suitable for the BBU pool with the heavier traffic load. It proves the efficacy of our proposed Markov chain-based calculation model to pursuit an optimal operating strategy for the delay-aware and energy-efficient BBU pool.

Linear Interference Cancellation for the Cell-Free C-RAN Uplink

Drawing on the notion of parallel interference cancellation, this paper formulates a one-shot linear receiver for the uplink of centralized, possibly cloud-based, radio access networks (C-RANs) operating in a cell-free fashion. This receiver exhibits substantial interference rejection abilities, yet it does not involve any matrix inversions; rather, its structure hinges on the pairwise projections of the users' channel vectors. Its performance is markedly superior to that of matched-filter beamforming, while the computational cost is decidedly inferior to that of an MMSE filter, altogether constituting an attractive alternative in terms of performance vs cost. Furthermore, with a proper sparsification of the channel matrix that it estimates and processes, the proposed receiver can be rendered scalable in the sense of its computational cost per access point not growing with size of the network. Uplink power control is also readily accommodated.

Intelligent multi-agent based C-RAN architecture for 5G radio resource management

5G commercialized mobile networks are expected to arrive in 2020. Key research of 5G is to support massive applications with spectral efficiency (SE) and low energy consumption. However, how to manage limited spectrum for such a massive services demand is a challenge in 5G. To cope with the challenge, new network architectures with dynamic spectrum sharing - are needed. This paper proposes a new intelligent cloud-based radio access network (ICB-RAN) architecture for dynamic 5G radio resource management. This study also develops a new multi-agent learning cloud assisted double auction (MLCADA) mechanism for dynamic spectrum reusability. Finally, experiment results verify the performance of the proposed approach.

Efficiency ratio optimization on uplink transmission power for Cloud-Based Radio Access Network

With the adoption of fifth generation (5G) wireless network systems into the fourth Industrial Revolution, the data transfer efficiency and speed will see a significantly improvement, which will lead to systems having the ability to provide a real-time information, and a solution to the capacity problem. Cloud-Based Radio Access Network (C-RAN) is the groundwork on which future 5G networks are based on, and it can increase the spectral efficiency and lower the transmission latency. Moreover, the used of C-RAN in future is expected to decrease the capital, operational and maintenance expenses of mobile operators. This study aims to investigate and validate the joint measures of spectral efficiency and latency optimization by using a quantization process of uplink signal at the remote radio head (RRH) of the C-RAN network. The quantization process is used to convert the received radio frequency signal from mobile user equipment into in-phase and quadrature baseband samples. The uplink transmission power and the number of quantization bits are computed in a way that the performance of spectral efficiency and latency of the RRHs are simultaneously optimized. Results shows that the proposed scheme is capable working with the different types of RRH.

Face-to-Face EML Transceiver Tandem for Full-Duplex Analogue Radio-over-Air

An analogue coherent-optical free-space link for local cloud-based radio access networks is proposed and experimentally demonstrated. The adoption of a single externally modulated laser as optical transmitter and coherent homodyne receiver at either link end guarantees conceptual simplicity for the opto-electronic sub-systems of this optical fronthaul. The realization of full-duplex signal transmission over a single, laser-based transceiver device further allows to off-load the directional split in the optically fronthauled radio signal chain to the radio-frequency domain. We prove this concept for an in-door link and show that bidirectional transmission of orthogonally frequency division multiplexed radio signals with 64-point quadrature amplitude modulated formats is possible over an optical budget of 21.3 dB, or over an estimated reach of ∼100 m in case of bad atmospheric conditions. A small penalty of less than 1% in terms of error vector magnitude compared to a direct-detection receiver confirms the correct operation of the low-complexity coherent homodyne detector, even though no digital signal processing functions are applied for the purpose of signal recovery. Continuous long-term measurements including Ethernet payloads confirm the stability of the free-space optical link architecture, for which a small fraction of less than 1% has shown an excursion in reception penalty.

On efficient radio resource calendaring in cloud radio access network

We propose in this paper a radio resource allocation scheme in the context of Cloud-based Radio Access Networks (C-RAN) run by a single operator. We specifically leverage bandwidth calendaring, a technique that allows shifting bulk data transfers, typically of large size with less stringent real-time constraints, to future epochs when the network is less congested. In particular, we propose an auction-based framework for bandwidth calendaring, where the C-RAN operator, as the spectrum auctioneer, runs an auction with its users, with the aim of maximizing its revenue. The auction-based mechanism takes as input the set of the users’ bids and outputs the calendaring and pricing decisions. We first formulate the calendaring problem using Integer Linear Programming (ILP) and the pricing problem using the Vickrey Clarke Groves (VCG) pricing scheme. We further make use of the Bayesian settings to compute the optimal revenue. Due to the exponential time induced by the NP-hardness of the ILP formulation, we propose an effective approach that satisfies desired auction properties such as individual rationality and truthfulness while achieving a sub-optimal revenue, in polynomial time. We explicitly evaluate the impact of mobile systems features such as spatial frequency re-use and interference among mobile users, and study their impact on the overall system’s performance. Extensive simulations, conducted in representative network scenarios, demonstrate the effectiveness of our proposal in improving the performance of C-RAN scheduling.

5G Wireless Communication Network Architecture and Its Key Enabling Technologies

The wireless mobile communication systems have developed from the second generation (2G) through to the current fourth generation (4G) wireless system, transforming from simply telephony system to a network transporting rich multimedia contents including video conferencing, 3-D gaming and in-flight broadband connectivity (IFBC) where airline crew use augmented reality headsets to address passengers personally. However, there are still many challenges that are beyond the capabilities of the 4G as the demand for higher data rate, lower latency, and mobility requirement by new wireless applications sores leading to mixed content-centric communication service. The fifth generation (5G) wireless system has thus been suggested, and research is ongoing for its deployment beyond 2020. In this article, we investigate the various challenges of 4G and propose an indoor, outdoor segregated cellular architecture with cloud-based Radio Access Network (C-RAN) for 5G, we review some of its key emerging wireless technologies needed in meeting the new demands of users including massive multiple input multiple output (mMIMO) system, Device-to-Device (D2D), Visible Light Communication (VLC), Ultra-dense network, Spatial Modulation and Millimeter wave technology. It is also shown how the benefits of the emerging technologies can be optimized using the Software Defined Networks/Network Functions Virtualization (SDN/NFV) as a tool in C-RAN. We conclude that the new 5G wireless architecture will derive its strength from leveraging on the benefits of the emerging hardware technologies been managed by reconfigurable SDN/NFV via the C-RAN. This work will be of immense help to those who will engage in further research expedition and network operators in the search for a smooth evolution of the current state of the art networks toward 5G networks.

Profitable and Energy-Efficient Resource Optimization for Heterogeneous Cloud-Based Radio Access Networks

As network operators invest more and more in infrastructure to keep up with the ever-increasing traffic demand, it has become important for them to operate networks in a profitable manner. The resulting expansion of network infrastructure also increases power consumption, which has a negative impact on both the environment and revenue. In this regard, the cloud-based radio access network (C-RAN), which is a promising next-generation network architecture, has gained much attention as a solution. In addition, by utilizing the macro base stations for coverage, the resulting heterogeneous C-RAN (H-CRAN) can further help optimize the network while increasing the complexity of resource optimization. In this paper, we study an optimal resource allocation for C-RANs to maximize profit while minimizing power consumption considering the inherent network uncertainties. Moreover, by allowing network operators to share networking resources among themselves, we show that the service outage can be minimized without installing additional bandwidth or base stations. The proposed multi-stage stochastic programming model makes a robust optimal decision that effectively responds to uncertainties from users’ mobility and service demand while maximizing both profit and energy efficiency. The extensive evaluation and comparison results show that the proposed solution can maximize profit and energy efficiency while minimizing the service outage under network uncertainties.

Analysis of energy efficiency in cloud based heterogeneous RAN with large-scale antenna systems

To address the capacity requirements resulting from huge growth in mobile data traffic, the mobile network operators (MNOs) are densifying their networks with more base stations, and with more spectrum layers. The addition of base stations and spectrum layers increases the energy consumption of the access network. Hence the network needs to be optimized to lower the power consumption (e.g.) through cloud, or deployment of spectral and power efficient radio units like Large-Scale Antenna Systems (LSAS) or massive Multiple Input Multiple Output (MIMO) radio units.In this paper, we analyse the network evolution towards Cloud based Radio Access Network (C-RAN) for a mix of base stations with Macro and LSAS Remote Radio Units (RRU). We derive the computational complexity of the base station components using a flexible power model. We evaluate the need for base station architecture split, when C-RAN is used with LSAS radio units and large antenna configurations. We use a combination of simulators viz.NS3 (for LTE radio network simulation) and CloudSIM Plus (for Cloud based base station simulation). For each base station type, we compare the computational complexity of different sub-components. Further, based on the selected power model, we study the effect of base station's scheduling algorithm (viz.) Proportionate-Fair or Round-Robin, on the computational complexity of the various base station sub-components. We consider Time and Wavelength Division Multiplexed Passive Optical Network (TWDM-PON) for the high capacity low latency fronthaul network. Using the overall radio cluster throughput and power consumption information from Cloud base station, Radio site, and fronthaul network, we assess the energy efficiency metrics for the Cloud RAN and LSAS (with different antenna configurations) with Macro RRUs as reference.Through the simulations, we observe that, the computation complexity was relatively higher for RR scheduling (compared to PF scheduling). But the variations of energy efficiency metric were similar for both the scheduling schemes for different base station configurations.We observe that compared to Macro2TR, LSAS variants are highly energy efficient. This gain is mainly associated with higher throughput due to MU-MIMO (where multiple UEs are accommodated in the same set of radio resources) and lower power consuming components in LSAS. Between LSAS32TR, LSAS64TR and LSAS128TR, we find that LSAS32TR had lower energy efficiency. Further, we observe that the energy efficiency of LSAS64TR is almost same as LSAS128TR, though LSAS64TR supports just half the number of MU-MIMO layers. This implies that the additional number of layers that LSAS128TR system supports, is compensated by power consumed to support the higher antenna chains and spatially multiplexed layers.Thus, MNOs interested in deploying high spectral efficiency solution along with high energy efficiency, may prefer to deploy LSAS64 systems for tower-top deployments of Radio unit deployment, since it offers higher energy efficiency with lower wind-load effect due to smaller antenna size.

A two-level auction for resource allocation in multi-tenant C-RAN

We consider in this paper a dynamic resource allocation scheme between several Mobile Virtual Network Operators (MVNOs), sharing common radio resources at a Cloud-based Radio Access Network (C-RAN) run by a central operator. We specifically propose a two-level coupled auction so as to enhance resource utilization and maximize the revenues both for the central operator and the MVNOs: at the lower level, end users belonging to a given MVNO bid for resources and, at the higher-level, MVNOs compete for resources at the central operator based on the output of the lower-level auction. We show fundamental economic properties of our proposal: truthfulness and individual rationality, and propose a greedy algorithm to enhance its computational efficiency. We prove the existence of Nash equilibrium for the global auction and its uniqueness in a typical duopoly scenario. Further numerical results illustrate the performance of our proposal in various network settings.

Caching at the Mobile Edge: A Practical Implementation

Thanks to recent advances in mobile networks, it is becoming increasingly popular to access heterogeneous content from mobile terminals. There are, however, unique challenges in mobile networks that affect the perceived quality of experience (QoE) at the user end. One such challenge is the higher latency that users typically experience in mobile networks compared with wired ones. Cloud-based radio access networks with content caches at the base stations (BSs) are seen as a key contributor in reducing the latency required to access content and thus improve the QoE at the mobile user terminal. In this paper, a prototype implementation of a mobile edge cache system is presented. The proposal focuses on compliance with the existing long-term evolution deployment and content-location solutions. The prototype is designed to perform assessment tests and evaluation of caching solutions. Results are then shown for the QoE improvements for the mobile user obtained by caching content at the BSs. This is quantified with a comparison to noncached content by means of ping tests (10%–11% shorter times), a higher response rate for Web traffic (1.73–3.6 times higher), and an improvement in the jitter (6% reduction).

Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users

In this paper, the problem of proactive caching is studied for cloud radio access networks (CRANs). In the studied model, the baseband units (BBUs) can predict the content request distribution and mobility pattern of each user, determine which content to cache at remote radio heads and BBUs. This problem is formulated as an optimization problem which jointly incorporates backhaul and fronthaul loads and content caching. To solve this problem, an algorithm that combines the machine learning framework of echo state networks with sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs can predict each user's content request distribution and mobility pattern while having only limited information on the network's and user's state. In order to predict each user's periodic mobility pattern with minimal complexity, the memory capacity of the corresponding ESN is derived for a periodic input. This memory capacity is shown to be able to record the maximum amount of user information for the proposed ESN model. Then, a sublinear algorithm is proposed to determine which content to cache while using limited content request distribution samples. Simulation results using real data from Youku and the Beijing University of Posts and Telecommunications show that the proposed approach yields significant gains, in terms of sum effective capacity, that reach up to 27.8% and 30.7%, respectively, compared to random caching with clustering and random caching without clustering algorithm.

Joint Uplink Radio Access and Fronthaul Reception Using MMSE Estimation

In cloud-based radio access networks, remote radio units and central baseband units are connected by fronthaul links, which are commonly assumed to be error-free. However, especially for wireless millimeter wave fronthaul links, this might be challenging to achieve, as they face a more unreliable environment than the conventionally used fiber links. In this paper, we hence aim to mitigate the impact of imperfect fronthaul links. For this, we propose the concept of joint radio access and fronthaul reception, which considers to recover the transmitted messages correctly at the centralized baseband unit, rather than to ensure a nearly perfect fronthaul transmission in between. Based on the Bayesian minimum mean square error criterion, we develop a joint access and fronthaul estimation scheme that can be utilized for various signals transported over the fronthaul, including in-phase/quadrature phase (I/Q) samples, soft-bits, synchronization, and reference signals. In addition, we develop an approximated variant of the scheme to reduced complexity, and an iterative extension to further improve the performance. We demonstrate that our scheme can operate under less reliable fronthaul than conventional approaches by numerical simulation for different signals, and show that our method can be implemented in a parallel architecture to achieve a reasonable computational complexity.

CaaS: Caching as a Service for 5G Networks

In recent years, demands for rich multimedia services over mobile networks have been soaring at a tremendous pace. Traditional dedicated networking equipment may not be able to efficiently support the phenomenal growth of the traffic load and user demand dynamics while consuming an unnecessarily large amount of energy resources. Recently, mobile content caching, whereby popular contents are cached inside the mobile front-haul and back-haul networks so that demands for these contents from users in proximity can be easily accommodated without redundant transmissions from the remote sources, has emerged as an efficient technique for multimedia content delivery. Mobile content caching is particularly suitable for fifth generation (5G) mobile systems that are being designed to incorporate advanced cloud computing technologies and network function virtualization techniques. Therefore, in this paper, we first propose the concept of “Caching-as-a-Service” (CaaS) based on cloud-based radio access networks, and virtualized evolved packet core, which provides the capability to cache anything at anytime, anywhere in the cloud-based 5G mobile systems to satisfy user demands from any service location with high elasticity and adaptivity, and to empower third-party service providers with flexible controllability and programmability. Then, we study the potential techniques related to the virtualization of caching, and discuss the technical details of virtualization and optimization of CaaS in 5G mobile networks. Some novel schemes for CaaS are proposed to target different mobile applications and services. We also explore new opportunities and challenges for further research.

Fronthaul Constrained Coordinated Transmission in Cloud-Based 5G Radio Access Network: Energy Efficiency Perspective

Fronthaul Constrained Coordinated Transmission in Cloud-Based 5G Radio Access Network: Energy Efficiency Perspective

A frameless network architecture for the way forward of C-RAN

The key technologies involved in the evolution of the Cloud-based Radio Access Network (C-RAN) are discussed in this paper. Taking the Frameless Network Architecture (FNA) as a starting point, a cell-less-based network topology for a multi-tier Heterogeneous Network (HetNet) and ultra-dense network is proposed. The FNA network topology modeling is researched with centralized processing and distributed antenna deployments. The Antenna Element (AE) is released as a new dimensional radio resource that is included in the centralized Radio Resource Management (RRM) processes. This contributes to the on-demand user-centric serving-set associations with cell-edge effect elimination. The Control Plane (CP) and User Plane (UP) separation and adaptation are introduced for energy efficiency improvements. The centralized RRM and different optimization goals are discussed for fully exploring the merits from the centralized computing of C-RAN. Considering the complexity, near-optimal approaches for specific users' Quality-of-Service (QoS) requirements are addressed. Finally, based on the research highlighted above, the way forward of C-RAN evolution is discussed.