Contribution to the design and the implementationof a Cloud Radio Access Network

Veronica Quintuna Rodriguez∗, Fabrice Guillemin∗ and Philippe Robert†

∗Orange Labs, 2 Avenue Pierre Marzin, 22300 Lannion, France†Inria, 2 rue Simone Iff, 75589 Paris, France

{veronica.quintunarodriguez, fabrice.guillemin}@orange.com, philippe.robert@inria.fr

Abstract—This dissertation paper presents the maincontributions to the design and the implementationof a Cloud-RAN solution. We concretely address thetwo main challenges of Cloud-RAN systems: real-timeprocessing of radio signals and reduced fronthaul ca-pacity. We propose a multi-threading model to achievelatency reduction of critical RAN functions as well as anadapted functional split for optimizing the transmissionof radio signals. We model the performance of the pro-posed solution by means of stochastic service systemswhich reflect the behavior of high performance comput-ing architectures based on parallel processing and yielddimensioning rules for the required computing capacity.Finally, we validate the accuracy of the theoreticalproposals by a Cloud-RAN testbed implemented onthe basis of open source solutions, namely Open AirInterface (OAI).

Keywords: Cloud-RAN, queuing systems, NFV, ser-vice chaining, scheduling, resource pooling, parallel pro-gramming.

I. Introduction

The emergence of the virtualization technology plays acrucial role in the evolution of telecommunications networkarchitectures, notably by enabling Network Function Vir-tualization (NFV). This is clearly a groundbreaking evo-lution in the design of future networks and IT infrastruc-tures, which will eventually be completely merged. NFVmoreover promises significant economic savings as wellas more flexible and accurate management of resources.NFV precisely consists of decoupling network functionsfrom their hosting hardware. Network operators are thusable to instantiate on the fly Virtualized Network Func-tions (VNFs), which appear as software suites runningat various network locations in order to meet customerrequirements.

The main goal of this PhD thesis [1] is to investigate theperformance of VNFs by considering a driving use case.In this PhD, we have chosen Cloud-RAN, introduced inearlier papers (see for instance [2]). Cloud-RAN notablypresents ramifications in terms of network design and isemblematic in terms of performance. The design of Cloud-RAN includes (i) the analysis in terms of architecture, (ii)the identification of Key Performance Indicators (KPIs)that reflect the performance, (iii) the decomposition of

global network functions into elementary components, (iv)the dimensioning of cloud resources by taking into accountthe scheduling strategy and (v) the validation of thetheoretical models by means of a testbed implementation.

Cloud-RAN aims at virtualizing and centralizing higher-Radio Access Network (RAN) functions in the networkwhile keeping lower-RAN functions in distributed units(near to antennas). These two nodes, so-called respec-tively, Central Unit (CU) and Distributed Unit (DU)by the 3GPP enable flexible and scalable functionalsplits which can be adapted to the required networkperformance. In addition, the co-location of CUs withMobile/Multi-access Edge Computing facilities opens thedoor to the realization of low latency services, thus meetingthe strict requirements of Ultra Reliable Low LatencyCommunications (URLLC) [3].

The implementation of Cloud-RAN actually raises manyissues, in particular with regard to the network archi-tecture (CU/DU placement, required bandwidth for thefronthaul, etc.) as well as to resources allocation (requiredcomputing capacity for processing real-time radio signalsin the cloud). The collocation of various virtual BaseBand Units (BBUs) in the cloud however enables thepossibility of joined radio resource allocation for interfer-ence reduction and data rate improvements. Similarly, thecentralized base band processing of radio signals in thecloud allows applying resource pooling and statistical mul-tiplexing principles when allocating computing resources.These claims are in the core of this study.

Furthermore, we notably focus on improving the pro-cessing time of virtual RAN functions in the aim ofincreasing the front-haul time-budget (distance betweenDU and CU), and as a consequence to reach a higheraggregation of virtual radio cells in the Central Office(CO). In this work, we particularly propose a multi-threading model based on parallel processing to reducethe runtime of encoding/decoding functions on generalpurpose computers. Beyond low latency processing, weintroduce an adapted functional split in order to keepthe most resource consuming RAN functions (namely,the channel encoding/decoding function) in the CU whileoptimizing the required fronthaul capacity.

For dimensioning purposes, we specifically introduce

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a batch queuing model, namely the M [X]/M/C multi-service system, to assess the needed processing capacityin a data center while meeting the RAN latency re-quirements. The model particularly considers the execu-tion of base band functions under the principle of high-performance parallel programming on multi core systems,i.e., parallel runnable jobs are executed at the same phys-ical instant on separate cores. In addition, we studiedthe M [X]/M/1 PS for modeling the parallel processingin concurrent environments, i.e., when various jobs sharea single processing unit by interleaving execution steps ofeach process via time-sharing slices (also referred to astime-slots) [4], [5].

In order to confirm the accuracy of the theoreticalmodels, we perform numerical experiments using statis-tical parameters captured from the Cloud-RAN emulationwhile using Open Air Interface (OAI), an open-source solu-tion which implements the RAN functionality in software.Beyond simulation, as a proof of concept, we implementthe proposed multi-threading models in a OAI-based test-bed. Performance results show important gains in terms oflatency which make true the promises of fully centralizedCloud-RAN architectures and enable the cloudification ofcritical network functions.

This paper is organized as follows: In Section II, weintroduce a general analysis of Cloud-RAN and the designguidelines for implementing a Cloud RAN solution. In Sec-tion III, we model the behavior of Cloud-RAN by means ofstochastic service systems for dimensioning purposes. Thetheoretical models are validated in Section IV, we notablydescribe a proof of concept and the main performanceresults. Concluding remarks and research perspectives arepresented in Section V.

II. Cloud-RAN Analysis and ImplementationGuidelines

Cloud-RAN, also referred to as C-RAN, aims at imple-menting the whole base-band processing of radio signals insoftware while keeping distributed antennas and gatheringBBUs in a CO. A virtual BBU implements in softwareall network functions belonging to the three lower layersof the E-UTRAN protocol stack. These functions mainlyconcern PHY functions as signal generation, IFFT/FFT,modulation and demodulation, encoding and decoding;radio scheduling; concatenation/segmentation of RadioLink Control (RLC) protocol; and encryption/decryptionprocedures of Packet Data Convergence Protocol (PDCP),for the down-link and up-link directions [6], [7]. In Cloud-RAN systems, the whole base-band processing of radiosignals must meet strict latency requirements (namely, 1millisecond in the down-link direction and 2 millisecondsin the up-link).

Virtualizing network functions and running them ondistant servers raise many issues in terms of performanceand network control. The case of Cloud-RAN exemplifiesthe complexity of virtualizing real-time network functions,

however virtualizing and orchestrating an end-to-end mo-bile network involves additional intricate issues (see forinstance [8]).

A. Fronthaul analysis

One of the main issues of Cloud-RAN is the requiredbandwidth to transmit radio signals between the BBU-pool (namely, CUs) and each DU placed near to antennas.When considering current used transmission protocols asCommon Public Radio Interface (CPRI), the requiredfronthaul capacity strictly depends of the number of vir-tual radio cells hosted in the data center (CO) and not ofthe traffic in the cells.

The fronthaul capacity problem relies not only on theconstant bit rate used by CPRI [9] but on the highredundancy present in the transmitted I/Q signals. Manyefforts are currently being devoted to reduce optic-fiberresource consumption such as I/Q compression [10], non-linear quantization, sampling rate reduction, and even,packetization of CPRI. Several functional splits of thephysical layer are also being an object of study in orderto save fiber bandwidth [11], [9]. The required fronthaulcapacity for the various functional splits considered by3GPP is analyzed in [12]. It turns out that the requiredfronthaul capacity significantly decreases when the func-tional split is shifted after the PHY layer or even after theMAC layer [11], however, these architectures do not enableexploiting the advantages of the base band centralization.

In the following, we focus in a fully centralized RANarchitecture that processes the most resource consumingbase band functions in the cloud. In view of the analysiscarried out in [1], [12], we adopt a bidirectional intra-PHYsplit (referred to as Functional Slit VI). This split central-izes the channel encoding/decoding functions and keepsthe modulation/demodulation functions near to antennas.As shown in Figure 1, the split transmits hard bits in thedownlink and soft bits (real and not binary values) in theuplink. The soft bits represent the Log-Likelihood Ratio(LLR), i.e., the radio of the probability that a particularbit was 1 and the probability that the same bit was 0.(Log is used for better precision). The split notably enablesa significant gain when comparing it to the initial CPRIsolution (the required fronthaul capacity in the downlink isthen up to 100 Mbps. when using normal cyclic prefix andthe maximum modulation order supported in LTE) [12].

The hard/soft bits are encapsulated into Ethernetframes and transmitted by fiber links. Namely, Radio overEthernet (RoE) is considered by IEEE Next Generationfronthaul Interface (1914) Working Group as well as bythe xRAN fronthaul Working Group of the xRAN Forum.The proposed functional split is under development on thebasis of OAI code (see [13]).

B. Runtime analysis

Beyond the hard real-time constraints involved in thebase band processing of radio signals, the major per-

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formance problem of Cloud-RAN is due to the non-deterministic behavior of the channel coding function.Much of this variability is due to radio channel conditionsof User Equipments (UEs) attached to the base station,the data load per UE, as well as the amount of traffic in thecell. The above observations raise fundamental questionswith regard to conceiving virtual RAN functions anddimensioning the required computing capacity to executethem in the Cloud.

Fig. 2. Multi-threading model.

To achieve important gains in terms of latency, wehave introduced a multi-threading model for executing thechannel encoding/decoding function by means of massiveparallel processing in a multi-core system. Enabling par-allelism as much as possible and avoiding chaining whendesigning VNFs are fundamental principles to gain fromthe available computing resources. Chaining impacts areanalyzed in [14]. The proposed threading model uses thefact that radio sub-frames are composed of TransportBlock (TB) whose content belongs to a single UE forrunning UEs in parallel. In addition, when the size of aTB is too big, it is segmented into shorter data units,referred to as Code Blocks (CBs). A CB represents the

smallest processing unit, which can be executed in parallel.As shown in Figure 2, VNFs (e.g. vBBUs) appear as achain of sub-functions which are executed on the top of thevirtualization layer while sharing the available computingresources. A global scheduler is in charge of allocating thecapacity of servers.

III. Cloud-RAN modeling for dimensioningpurposes

A. Job scheduling

We model parallel runnable jobs of VNFs as batchesarriving at a processing facility composed of several cores.The objective then is to select the best scheduling strategyto meet latency requirements. In this PhD thesis, wehave considered several scheduling solutions (under greedy,round robin and dedicated principles) which can apply toa large range of virtualized network functions. Detailedresults are not presented in this paper but can be foundin [14]. For Cloud-RAN, we have specifically consideredtwo queuing models:

· M [X]/M/C as an illustration of greedy disciplines(batches are queued and the head of line job is servedas soon as a core is available);· M [X]/M/1 Processor Sharing (PS) as a round robin

strategy (all jobs are treated in parallel, there is nodelay before accessing servers, but jobs receive anequal share of the global capacity).

The two above strategies respectively illustrate paralleland concurrent computing. In parallel computing, eachjob runs on a single core and only one at any instant [4].Conversely, concurrent computing enables the simultane-ous execution of jobs on a single core by overlapping time-periods; this leads to Processor Sharing (PS) models [15],[16]. However, the drawback of processor sharing is in thatmultitasking on the same core requires context switchingand memory splitting, which may considerably increasethe latency.

In the framework of the present PhD thesis, we haveobtained new results for both models. The sojourn timeof a job in the M [X]/M/1-PS queue has been obtainedin [16]; we have acquired the probability survival functionof the sojourn time, which has enabled us to derive thetail of the probability distribution. This result extends aprevious result obtained by Kleinrock et al in the 70’s forthe mean value [17]. The derivation of the sojourn time ofa job is much more challenging as it involves correlationsbetween the sojourn time of jobs. This issue is still underinvestigation.

For the M [X]/M/C queuing system, we have obtainedthe Laplace transform of the sojourn time of a batch (onthe basis of [18] by Cromie et al) and derived the tail dis-tribution. We subsequently use this system to dimensionthe required amount of computing resources for the Cloud-RAN implementation, i.e., we use parallel processing in astrict sense so that jobs are simultaneously executed on

separate cores avoiding latency introduced by time-sharingprocessors [7], [6].

B. Cloud-RAN modeling

From a modeling point of view, each radio element,Radio Remote Head (RRH), belonging to a Cloud-RANsystem represents a source of jobs in the up-link direction;while for the down-link direction, jobs arrive from the corenetwork, which provides connection to external networks(e.g., Internet or other service platforms). There are thentwo queues of jobs for each radio element (antenna, DU),one in each direction. Since the time-budget for processingdown-link sub-frames is half of that for up-link ones,they might be executed separately on dedicated processingunits. However, dedicating processors to each queue is notan efficient way of using limited resources.

For dimensioning purposes, we assume that virtualBBUs (notably, virtual encoding/decoding functions) areinvoked according to a Poisson process, i.e., inter-arrivaltimes of runnable BBU functions are exponentially dis-tributed. This reasonably captures the fact that in Cloud-RAN systems there is a sufficiently great number of an-tennas, which are not synchronized. In fact, RRHs areat different distances of the BBU-pool, furthermore whenconsidering no dedicated links, the fronthaul delay (inter-arrival time) can strongly vary because of network traffic.The occurrence of jobs then results from the superpositionof independent point processes which justifies the Poissonassumption. The Poisson assumption is in some sensea worst case assumption with regard to fixed relativephases1.

The parallel execution of RAN functions on a multi-coresystem with C cores can then be modeled by bulk arrivalsystems, namely, an M [X]/G/C queuing system [19]. Asshown in Figure 3, we consider each task-arrival to bein reality the arrival of B parallel runnable sub-tasks orjobs, B being a random variable. Each sub-task requiresa single stage of service with a general time distribution.The runtime of each sub-task depends on the workload aswell as on the network sub-function that it implements.The number of parallel runnable sub-tasks belonging toa network sub-function is variable. Thus, we considera non fixed-size bulk to arrive at each request arrivalinstant. The inter-arrival time is exponential with rateλ. The batch size B is independent of the state of thesystem. When assuming that the computing platform hasa non-limited buffer, the stability of the system requiresρ = λE[B]/µC < 1.

We further assume that the processing time of a job isexponentially distributed with mean 1/µ. This assumptionis intended to capture the randomness in the runtime ofUEs due to the non-deterministic behavior of the channelcoding function. When considering that the number Bof UEs per subframe is geometrically distributed with

1In the same way as an M/D/1 queue is “worse” with regard towaiting time than an

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mean 1/(1 − q) (i.e., P(B = k) = (1 − q)qk−1 fork ≥ 1), the complete service time of a radio subframeis then exponentially distributed with mean 1/((1− q)µ).The geometric distribution as the discrete analog of theexponential distribution capture the variability of sched-uled UEs in a subframe. From the above analysis, theM [X]/M/C model enables the evaluation of the runtime ofa subframe in a Cloud-RAN architecture based on parallelprocessing in a multi-core platform. The batch model aswell as the analysis of the Markovian chain are presentedin [20].

Fig. 3. Stochastic service model for Cloud-RAN.

C. Cloud-RAN dimensioning

The final goal of Cloud-RAN modeling is to determinethe amount of computing resources needed in the cloud toguarantee the base-band processing of a given number ofcells within deadlines. For this purpose, we evaluate theM [X]/M/C model while increasing C, until an acceptableprobability of deadline exceedance (say, ε). The requirednumber of cores is then the first value that achieves P (T >δ) < ε, where T represents the sojourn time of a batch,δ is a prescribed deadline and ε is a tolerance for sojourntime exceedance and hence for the loss probability of anentire batch.

We validate by simulation the effectiveness of theM [X]/M/C model with the behavior of the real Cloud-RAN system hosting 100 Evolved NodeBs (eNBs) of 20MHz during the reception process. As illustrated in Fig-ure 4, we observe that for a given ε = 0.00615, the requirednumber of cores is Cr = 151, which is in accordancewith the real C-RAN performance, where the probabilityof deadline exceedance is barely 0.00018. When C takesvalues lower than a certain threshold Cs, the C-RANsystem is overloaded, i.e., the number of cores is notsufficient to process the vBBUs workload; the system isthen unstable.

We have additionally studied the impatience criterion(which reflects RAN time-budgets). Results showed thatimpatience is not incident when the probability of deadlineexceedance is low enough. See [20] for details. A worst-case

Fig. 4. Cloud-RAN dimensioning using the M [X]/M/C model.

analysis regarding the dimensioning problem was depictedin [21].

IV. Proof of Concept

A. Testbed description

As a proof of concept, we have implemented on the basisof various open-source solutions notably OAI, an end-to-end virtualized mobile network. This platform notably im-plements the proposed models and scheduling strategies.The parallel processing of both encoding (downlink) anddecoding (uplink) functions is carried out by using multi-threading in a multi-core server within a single process.Latency is considerably reduced since we avoid multi-tasking across different processes. The workload of threadsis managed by a global non-preemptive scheduler. Eachthread is assigned to a dedicated single core with real-time OS priority and is executed until completion withoutinterruption. The isolation of threads is provided by aspecific configuration performed in the OS which preventsfrom the use of channel coding computing resources forany other job.

As shown in Figure 5, the platform is based on acomplete separation of the user and control plane asrecommended by 3GPP for 5G networks, referred to asControl User Plane Separation (CUPS), and implementedin b<>com’s solution (Wireless Edge Factory, WEF).As virtualization engines we use KVM, OpenStack, andOpenDaylight. The radio element of the mobile networkis performed by an USRP B210 card. Commercial smart-phones can be then connected.

When a UE attaches to the network, the AAA (Au-thentication, Authorization, and Accounting) procedureis triggered by the MME; user profiles are validated bythe HSS. When access is granted to the UE, the DHCPcomponent provides it the IP-address. The end-to-endconnection is assured after the creation of the GTP-U andGTP-C tunnels. The NAT component provides addresstranslation and is deployed between the SGi interface andthe Internet network.

WEF-C

WEF-U

Fig. 5. Proof of Concept, multi-threading implementation.

B. Performance Results

The behavior of Cloud-RAN in terms of latency whenperforming parallel processing has been evaluated by bothsimulation and testbed. The simulation considers a Cloud-RAN system of 100 eNBs running in a data centerequipped with 151 cores (acquired with the M [X]/M/Cmodel). Results lead relevant conclusions in terms of scal-ability.

Figure 6 shows the CDF of the sojourn time of radiosub-frames when performing parallel programming. It isobserved that more than 99% of sub-frames are processedwithin 472 microseconds and 1490 microseconds whenperforming parallelism by CBs and UEs, respectively. Itrepresents a gain of 1130 microseconds (CB) and 100 mi-croseconds (UE) with respect to the original system (non-parallelism). These gains in the sojourn time enable net-work operators to increase the maximum distance betweenantennas and the central office. Hence, when consideringthe light-speed in the optic-fiber, i.e., 2.25 ∗ 108m/s, thedistance can be increased up to ≈ 250 km when runningCBs in parallel.

The testbed implementation considers a single virtual-ized BBU (namely, an eNB of 20 MHz) and three UEs. Asshown in Figure 7, the testbed confirmed the accuracyof the performance gains obtained by simulation [20].Decoding function shows a performance gain of 72, 6%when executing CBs in parallel. Results open the doorfor deploying fully centralized cloud-native RAN architec-tures.

V. Conclusion and Research perspectives

We have introduced in this PhD thesis a solution forimplementing and dimensioning Cloud-RAN systems. Thesolution is based on parallel processing of the channel

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Fig. 6. Cloud-RAN performance while considering 100 eNBs.

Fig. 7. Testbed performance results (Uplink).

coding function in multi-core platforms. This principlesignificantly reduces latency of RAN functions and thusenables the distance between DU and CU to be signif-icantly increased (achieving as a consequence a higheraggregation of eNBs in the cloud for taking advantage ofresource pooling and statistical multiplexing principles).The proposed solution has been implemented on a testbedon the basis of OAI open source RAN code.

Furthermore, we have investigated and modeled twoscheduling strategies which have led us to establish newresults for two queuing systems (namely, the M [X]/M/Cand M [X]/M/1-PS), both widely studied in the queuingliterature. The M [X]/M/C can then be used to dimensionthe multi-core platform supporting the Cloud-RAN.

Beyond increased distance between CU and DU, wehave designed an intra-PHY functional split (Figure 1)which enables the reduction of the required bandwidth inthe fronthaul network. This split has been implementedon OAI code and exhibits excellent performance resultsin terms of bandwidth [13]. Since the bit rate betweenDU and CU is no more constant (in contrary to classicalCPRI) it is envisaged to develop statistical multiplexingstrategies between the radio and the optical layers in orderto optimize the use of the bandwidth in the fronthaul [22].

One major outcome of this PhD thesis is to show thatcritical RAN functions can be virtualized and then orches-trated as any other network function. In [8], we have shownhow a complete mobile network can be orchestrated witha carrier grade automation platform, namely the OpenNetwork Automation Platform (ONAP), in the contextof network slicing.

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