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Reinforcement Learning Based Dynamic FunctionSplitting in Disaggregated Green Open RANsTurgay Pamuklu †, Melike Erol-Kantarci ‡, Senior Member, IEEE, Cem Ersoy †, Senior Member, IEEE

NETLAB, Department of Computer Engineering, Bogazici University, Istanbul, Turkey{turgay.pamuklu, ersoy}@boun.edu.tr

School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canadamelike.erolkantarci@uottawa.ca

Abstract—With the growing momentum around Open RAN(O-RAN) initiatives, performing dynamic Function Splitting(FS) in disaggregated and virtualized Radio Access Networks(vRANs), in an efficient way, is becoming highly important. Anequally important efficiency demand is emerging from the energyconsumption dimension of the RAN hardware and software. Sup-plying the RAN with Renewable Energy Sources (RESs) promisesto boost the energy-efficiency. Yet, FS in such a dynamic setting,calls for intelligent mechanisms that can adapt to the varyingconditions of the RES supply and the traffic load on the mobilenetwork. In this paper, we propose a reinforcement learning (RL)-based dynamic function splitting (RLDFS) technique that decideson the function splits in an O-RAN to make the best use ofRES supply and minimize operator costs. We also formulate anoperational expenditure minimization problem. We evaluate theperformance of the proposed approach on a real data set of solarirradiation and traffic rate variations. Our results show that theproposed RLDFS method makes effective use of RES and reducesthe cost of an MNO. We also investigate the impact of the sizeof solar panels and batteries which may guide MNOs to decideon proper RES and battery sizing for their networks.

Index Terms—Energy Efficiency, Open Radio Access Networks(O-RAN), virtualized RAN (vRAN), Function Splitting, Rein-forcement Learning.

I. INTRODUCTION

The ideas of virtualized RANs (vRANs) and functionalsplits date back to Small Cell Forum’s studies on small cellvirtualization [1]. Accordingly, vRAN mainly aims to disag-gregate Baseband Units (BBU) and remote radio head (RRU)functionalities and allow softwarized network functions ofBBUs to be hosted on common-of-the-shelf (COST) hardware.The new Open Radio Access Network (O-RAN) architectureaims to define open interfaces in disaggregated vRANs suchthat equipment from multiple vendors can be interoperable [2].Within the O-RAN architecture, horizontal and vertical splitsallow network functions to be handled by either the hardwareat the edge cloud, known as Distributed Unit (DU), or thehardware at the central cloud, i.e. the centralized unit (CU).

In O-RAN, function splitting (FS) can be either betweenphysical and virtualized resources, or between DU and CU,yielding many possibilities. Yet, the full potential of FS arisesfrom dynamic splits where network functions are placed basedon varying feedback from the network. The traditional user

This work is supported by the Turkish Directorate of Strategy and Budgetunder the TAM Project number 2007K12-873.

Quality of Service (QoS) is certainly central to decisionmaking in dynamic FS. For instance, the traffic type, whether itis enhanced mobile broadband (eMBB) or ultra-reliable low-latency communication (URLLC), impacts the FS decision.In addition, energy efficiency plays a key role in a mobilenetwork operator’s (MNO) decision-making process due tothe tremendous growth in their carbon footprint and cost inrelation to densification and data demand [3]. Using renewableenergy sources (RESs) in telco clouds as an alternative to on-grid energy is a promising approach to reduce the excessiveenergy consumption from the grid. On the other hand, renew-able energy has two critical drawbacks. First, MNOs shouldstore this energy in a battery that has limited capacity. Dueto the increasing CapEx of RES and battery, MNOs need tooptimize energy usage [4]. Second, RES is intermittent and thesupply has some level of uncertainty [5]. Therefore, under thevariability of the RES and the variability of the wireless envi-ronment, optimal functional splitting in disaggregated greenvirtualized RANs introduces a great degree of complexitywhich can be best addressed by machine learning techniques,in particular reinforcement learning-based methods [6].

This paper focuses on a novel system model in which thefunctions are split between a CU and several DUs while usingthe RES as an alternative energy source for the telco cloud.Figure 1 presents this architecture. We propose a reinforcementlearning-based (RL) dynamic function splitting (RLDFS) ap-proach and implement Q-learning and SARSA algorithms. Ourmotivation to choose these algorithms originates from theirlight-weight implementation and their dynamically learningproperties. Thus, our studied network can conveniently adaptto the continuous traffic and solar radiation changes. Weexperiment with various densities of traffic loads and real solarenergy data collected at different seasons to see the impact ofseasonal changes in four globally separate geographical areas,which have significantly diverse solar radiation distributions.Our results show that the proposed solution reduces the OpExof an MNO significantly under various solar radiation andtraffic load.

The remainder of this paper is organized as follows. Section2 summarizes the related work. We define the system modeland the cost optimization problem in Section 3. In Section 4,we explain the RLDFS technique and present its performancein Section 5. Section 6 concludes the paper.

Accepted Paper. 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, includingreprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any

copyrighted component of this work in other works.

arX

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.NI]

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DUs

CU

Energy SourceDecisions

Network Model Function SplittingDecisions

RUs

FEC&QAMPRECODDING

RLC&MAC

PDCP

CRFs

FEC&QAMPRECODDING

RLC&MAC

PDCP

CRFs

Umbrella Analog Unit

SmallCells

SmallCells

SmallCells

LocalDigitalUnits

CentralizedMore

EfficientDigitalUnits

UserRelated

Functions

Fig. 1. Dynamic function splitting between energy harvesting telco clouds. A Mobile network operator has to deal with two cost-related decisions (CU:central unit, DU: distributed unit, RU: radio unit, PDCP: packet data convergence protocol, RLC: radio link control, MAC: medium access control, FEC:forward error correction, QAM: quadrature amplitude modulation, CRF: cell-related functions).

II. RELATED WORK

Temesgene et al. have proposed the first studies that mergethe FS approach and RESs usage in a RAN. The authorsdetail the energy consumption in virtual small cells (vSCs) andimplement reinforcement learning (RL) methods to optimizethe FS decisions between the vSCs and a macro base station[6], [7]. Meanwhile, Wang et al. propose a heuristic solutionfor an architecture that the functions that are split betweenthe RRHs and BBUs [8]. In addition, Ko et al. focus ona similar architecture and formulate a constrained Markovdecision process model [9]. Nevertheless, all of these studiesaim to split the functions between the RRHs and a BBU center.

Larsen et al. provide a broad survey for both static anddynamic FS methods. Also, it is among the first researchworks that consider O-RAN architecture [10]. Furthermore,Alabbasi et al. introduce a study called Hybrid Cloud RANwhere they aim to jointly minimize the energy and bandwidthconsumption in their RAN with a constrained programmingsolver [11]. Their results highlight the importance of FSdecisions for delay and energy consumption minimization.However, their study does not consider using RESs usage inthe RAN.

In our previous study, we provide mixed-integer program-ming solver and heuristic solutions for this joint problem [3].Although the new study is based on this paper and aimed tominimize the OpEx, the previous research considers a hybridC-RAN architecture and includes a bin-packing problem dueto function-micro data center assignment decisions. Differentthan prior work, in this paper, we decide the function splittingsbased on the user/traffic types (URLLC/eMBB) tuples, andwe focus on a novel O-RAN architecture. Also, we choose

two RL-techniques, Q-Learning and SARSA, as solution tech-niques.

III. SYSTEM MODEL

We consider a virtualized RAN environment where networkfunctions can be split dynamically between CU and DU, basedon the network conditions. This allows for more flexibility thanthe fixed functional split options provided by 3GPP. Figure 1illustrates a three-tier vRAN model that employs radio unit(RU), DU and CU, in addition to being green by having solarpower generation capability at DUs and CUs.

We may classify the network functions into two groups: Theuser-related functions (URFs) dedicated to specific user dataand cell-related functions (CRFs) that process the multiplexedURFs in the physical layer [12]. The top-tier, CU, has energyefficient digital units to execute only URFs. This CU hasdirected fiber optic links to the middle-level DUs (r ∈ R).DUs are responsible for both URFs and CRFs. There aretwo reasons to prevent handling the CRFs at the CU level.First, processing the CRFs at this level need huge bandwidthallocation at the fiber optic links [13]. Second, operating thesefunctions at the DUs provides relaxation for the stringentdelay requirements [3]. Finally, a set of RUs are connectedto their corresponding DU with a point-to-point millimeter-wave or dedicated fiber link [11]. In this architecture, theMNO can deploy the RUs near to their corresponding DUto hold their capital expenditure (CapEx) at more economicalrates. Furthermore, as DUs are located closer to the users, theyprovide latency advantage over CUs for certain types of traffic.

Deciding the optimum splits for network functions (f ∈ F)is the fundamental goal in this system. The split decisionsshould be dynamically made for a set of time intervals in a

day (t ∈ T ) for each traffic type (i ∈ I), such as eMBB orURLCC. The other significant decision problem is related tousing two different energy sources at the DU and CU. The firstenergy source, a solar panel, reduces the OpEx of the MNOwith renewable energy; the second one, on-grid energy, acts asreliable energy source in the case of insufficient green energy.Before formulating the relations between these two types ofenergy sources, we will describe the total energy consumptionin the DU (EDUrt ) and the CU (ECUt ) determined by eqs. ( 1)and (2), respectively:

EDUrt =

EDUS +∑i∈I

∑f∈F

Urit ∗ aritf ∗ EDUD

(1)

ECUt =

ECUS +∑r∈R

∑i∈I

∑f∈F

Urit ∗ (1− aritf ) ∗ ECUD

(2)

EDUS is the static energy consumption of a DU due to thecooling system and the other idle-mode energy consumptionwhich do not change by the function split decisions. A CU alsohas a static energy consumption (ECUS ) due to its idle-modeenergy consumption. Besides, dynamic energy consumptionhas three main components. The first component Urit is thetraffic load of traffic type i at the DU r in time slot t. Thesecond component, aritf , is a binary decision variable thatequals to one if the URF f of traffic type i is executed atthe DU r in time slot t. The last one is the dynamic energycoefficient, represented as EDUD and ECUD for the DU and CU,respectively. Lastly, the energy consumption of CU (eq. (2))depends on the number of functions that are not processedin DU (1 − aritf ) and the traffic loads of these functions(Urit). Thus, it is crucial to optimize the FS decisions to reduceoverall energy consumption.

The relation between the two types of energy sources canbe formulated with the following OpEx minimization problemas in eq. (3). The OpEx of the system is the overall on-gridelectricity bills of the CU and the DUs. Note that the energyconsumption of the RU and the cost of solar generation, interms of investment, are not included in OpEx calculation.The reason is that these costs do not impact the dynamic FSproblem.

In this model, we consider three fundamental problems toreduce the energy bill of an MNO. First, we have to reducethe total energy consumption with the efficient splitting of theURFs. Second, we have to promote the usage of solar energy(pCUt and pDUrt ) instead of on-grid energy. Hence, the amountof surplus solar energy needs to be minimized, and in relation,the capacity for solar generation and batteries needs to beselected such that the cost of MNO is minimized. Lastly, wehave to consider the variation of the electricity prices in a dayperiod (cEt ) and use renewable energy during peak electricityrates.

Minimize:

O =∑t∈T

[ECUt − pCUt +

∑r∈R

(EDUrt − pDUrt )

]∗ cEt (3)

Subject to:

(1− aritfx) ∗fy∈F∑fy≥fx

aritfy = 0,∀fx ∈ F ,∀i ∈ I,∀r ∈ R

(4)

bCUt = bCU(t−1) − pCUt + ωCUGCUt (5)

bDUrt = bDUr(t−1) − pDUrt + ωDUr GDUrt , ∀r ∈ R (6)

bCUt ≤ βCU (7)

bDUrt ≤ βDUr , ∀r ∈ R (8)

pCUt ≤ ECUt (9)

pDUrt ≤ EDUrt , ∀r ∈ R (10)

Here, eq. (4) guarantees that the URFs chain is broken onlyonce between the DUs and the CU. Note that each constraintshould be satisfied for all time intervals (∀t ∈ T ). Assume thatthe function fx is executed at the CU, then aritfx equals tozero. After that, the remaining upper layer functions fy shouldalso be completed at the CU (aritfy = 0); otherwise, themultiplication on the left side of the equation will be differentfrom zero.

Equations 5 to 10 regulate the energy usage by the servers inCU and DU. The first two equations determine the renewableenergy in the batteries of the solar generators that sit eitherbeside DU or CU. For the CU side (eq.(5)), the current batteryenergy (bCUt ) depends on the battery’s remaining energyfrom the previous time interval (bCU(t−1)). This value increaseswith the size of the solar panel at the CU (ωCU ), and thegenerated renewable energy of a panel’s unit size (GCUt ). Onthe other hand, the energy in the CU battery reduces withthe consumed green energy (pCUt ). The calculation is similarfor the DU side, which is provided by eq. (6). Meanwhile,the batteries’ capacities (βCU and βDUr ) limit the maximumstored renewable energy presented by eqs. (7) and (8) forthe CU and DUs, respectively. Lastly, it is clear that theconsumed green energy (pCUt and pDUt ) should be lower thanthe total energy consumption in a specific time interval, andthis constraint is guaranteed by eqs. (9) and (10). Solving thisoptimization model for each arriving user traffic, and undervarying solar conditions is not practical. Instead, we proposeto use reinforcement learning to allow the network to adapt todynamic conditions.

IV. REINFORCEMENT LEARNING BASED DYNAMICFUNCTION SPLITTING (RLDFS)

In our model, we use a multi-agent system where DUsand CU act as independent agents. The states for a DU arecalculated with the tuple, SDUt = {bDUrt , Urit, t

D}, whichincludes the remaining energy in the DU’s battery, the trafficload on this DU and the time of the day (tD = t (mod TD)),

URLLC 1

DU 1

eMBB 1

URLLC 1

CRFsURF 1URF 2URF 3

eMBB 1

CRFsURF 1

CUeMBB 1

URLLC 2

DU 2

eMBB 2

URLLC 2

CRFsURF 1URF 2URF 3

eMBB 2

CRFsURF 1

URF 2URF 3

CRFsURF 1

URF 2

URF 3eMBB 2

URF 2URF 3

eMBB 3

URF 2URF 3

URF 1

eMBB 4

URLLC 3

DU 3

eMBB 3

URLLC 3

CRFsURF 1URF 2URF 3

eMBB 3

CRFsURF 1

URLLC 4

DU 4

eMBB 4

URLLC 4

CRFsURF 1URF 2URF 3

eMBB 4

CRFs

Fig. 2. Traffic type (eMBB or uRLLC) based function splitting in thevirtualized open RAN.

respectively. Meanwhile, CU states are calculated with thetuple, SCUt = {bCUrt ,

∑r∈R

Urit, tD}, which includes the re-

maining energy in the CU’s battery, the total traffic load onthe network, and the time of the day, respectively. There aretwo main benefits to include the time of the day as a stateparameter. First, we want to combine the impact of solarirradiance and the electricity price tariffs together. Second, wewant to reduce the state space to improve the convergence.Otherwise, we would have to specify individual states fordifferent solar data and electricity prices. Combining thisapproach with the multi-agent system, we limit the state spaceto a reasonable size, which yields fast converging rates for theoptimal functional split decisions that need to be determinedin hours timescale.

The OpEx minimization problem has two important deci-sion variables. The actions of the DU agents render themwith the tuple ADUt = {aritf , pDUrt }. Meanwhile, the CUagent needs to decide only its renewable energy consumption(ACUt = {pCUt }). In a real-world scenario, the CU automat-ically processes the URFs not chosen to be processed at theDUs. Finally, the reward is calculated by eq. ( 11), in whichOt′ is the OpEx in time slot t

′, ψ is the normalization factor,

and τ is the window size.

0 50 100 150 200 250 300 350Days of a Year

0.0

0.2

0.4

0.6

0.8

1.0

Norm

alize

d Tr

affic

Loa

d

Fig. 3. eMBB traffic load in a year period. Each legend represents an hourof a day.

Rt+1 = −ψt∑

t′=t−τ

Ot′ (11)

Q(St, At)← Q(St, At) + α

[Rt+1

+ γmaxA

Q(St+1, A)−Q(St, At)

](12)

Q(St, At)← Q(St, At) + α

[Rt+1

+ γQ(St+1, At+1)−Q(St, At)

](13)

We solve the OpEx minimization problem with two RLapproaches. The first one, called Q Learning, is represented byeq. (12). The second one, called Sarsa, is represented by eq.(13). The main difference between these two methods relieson the calculation of the next q-table (St, At). The first onepromotes the action that provides the maximum q-value; thesecond one applies the next action (At+1) directly into accountto calculate the next q-table [14]. Otherwise, the essentialdetail to implement an RL solution is to decide the states St,the actions At and the reward calculation Rt+1.

V. PERFORMANCE EVALUATION

The evaluation setting of our primary use case is shown inFigure 2. We have 1 CU and 20 DUs that each one has onesolar panel and a battery to store harvested renewable energy.

TABLE IENERGY PARAMETERS.

Instance Unit CU Side DU SideES kWh 10 5ED kWh 0.9 1ω kWh 500 100β kWh 500 100cE $ [0.03, 0.07, 0.11] [0.03, 0.07, 0.11]

Low Medium High60

70

80

90

100(a) Stockholm

Low Medium High60

70

80

90

100(b) Cairo

Low Medium High60

70

80

90

100(c) Jakarta

Low Medium High60

70

80

90

100(d) Istanbul

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Nom

inal

OpE

x

D-RAN C-RAN RLDFS-QL RLDFS-Sarsa

Fig. 4. Comparison of RLDFS-QL and RLDFS-Sarsa methods with centralized RAN and distributed RAN approaches for different cities and traffic rates.

TABLE IIRL PARAMETERS.

Explanation Not. ValueNumber of Episodes NEP 4000

Learning Rate α 0.05Discount γ 0.90

Exploration Starting Value ε 0.5Exploration Decay Value εD 5E-5

Reward Window Size τ 48

There are 10000 users serviced by each DU. These usersgenerate two types of traffic, i.e. URLLC and eMBB. TheURLLC traffic demands low latency; thus, they are processeddirectly on DUs [15]. On the other hand, the functions ofeMBB traffic, which creates a ten times larger load than theURLLC, are split between the DUs and the CU.

λit =1

2ν[1 + sin(πt/12 + ϕ)]ν + n(t), i ∈ I (14)

The users have a daily sinusoidal shape traffic load de-scribed by eq. (14) in which ϕ is a random value betweenthe 3π/4 and 7π/4 which defines the peak hour of the trafficprofile, ν = 7 determines the slope of the traffic profile andn(t) is a random value which produces a fluctuation in thistraffic profile [16]. We also provide a yearly load variation tounderstand the impact of the distinction between the seasons(Figure 3). In addition, we generate different peak hours foreach DU to affect distinctive zones in a city such as residential,industrial, or shopping areas [4], [17]. Thus, we simulate bothtemporal and spatial variations of a traffic load in the regionof a city.

The pvWatts application calculates generated green energy(Gyrt) from a solar panel [18]. We use the solar radiation dataof four different cities (Stockholm, Istanbul, Cairo, Jakarta)

02:00 06:00 10:00 14:00 18:00 22:000

200

400

600

800

Ener

gy (k

Wh)

(a) Renewable Energy Consumption

02:00 06:00 10:00 14:00 18:00 22:00Hours of a Day

0100200300400500600700

Ener

gy (k

Wh)

(b) Amount of Unstored EnergyD-RANC-RANRLDFS-QLRLDFS-Sarsa

Fig. 5. Daily distribution of renewable energy consumption and unstoredenergy in Jakarta-Medium Traffic.

with a distinct distribution in a year period. Thus, we caninvestigate the effect of seasonal change in our model. Theother energy consumption parameters are given in Table I. Theelectricity price values are from the Republic of Turkey EnergyMarket Regulatory Authorities (EPDK) variable electricitytariff regulation with different price policies according to thetime of the day and calculated based on exchange rates as ofSeptember 2020 [19].

Table II details the reinforcement learning parameters. Theidea to choose a 48 hours window for cost minimization relieson making the decisions to consider the daily variance oftraffic loads, harvested energy and electricity prices. Also, we

50k 100k 150k 200kBattery Size

340

350

360

370

380

390(a) Solar Panel:50k

50k 100k 150k 200kBattery Size

260

280

300

320

(b) Solar Panel:100k

50k 100k 150k 200kBattery Size

220

240

260

280

(c) Solar Panel:150k

50k 100k 150k 200kBattery Size

180

200

220

240

260

(d) Solar Panel:200k

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

OpEx

(k$)

D-RAN C-RAN RLDFS-QL RLDFS-Sarsa

Fig. 6. Comparison of methods for different sizes of solar panels andbatteries.

want to extend the decisions between the consecutive days. Weevaluate our RL methods with comparison to two approaches.The first one, called distributed-RAN (D-RAN), processesboth URLLC and eMBB packets at DUs. The second one,centralized-RAN (C-RAN), handle URLLC packets at DUs tonot violate delay requirements and transfer eMBB packets tothe CU for cost-efficiency. Figure 4 shows the performance ofRLDFS-QL and RLDFS-Sarsa methods according to differentcities (solar radiation distributions) and traffic rates. Thesemethods outperform D-RAN and C-RAN approaches in allfour cities and under varying traffic intensities (low, medium,high). Besides, our methods perform better with increasingsolar radiation rates.

Figure 5a provides more insights to the results in Figure4 by considering the city of Jakarta and medium traffic load.The proposed RLDFS-Sarsa algorithm can consume a higheramount of renewable energy in the noontime; thus, it’s themethod that has the lowest unstored energy in Figure 5b.Meanwhile, RLDFS-QL algorithm can shift renewable energyusage according to traffic load and the electricity tariffs. C-RAN fails to efficiently use renewable energy owing to notmigrating the eMBB packets to the DUs, in the case oflower URLLC traffic loads on DUs. We further investigate theimpact of RES and battery sizing, which is shown in Figure 6.As observed, RL-based methods have lower costs than theother techniques and their performance improves with largersolar panels and batteries. Adaptation to a larger amount ofrenewable energy is the main reason for this outcome.

VI. CONCLUSION

In this paper, we introduce a novel RAN concept thatcombines energy-efficiency with virtualization that will beapplicable to future O-RAN deployments. We propose areinforcement learning based technique and solve it with twodifferent approaches (Q-learning and Sarsa) to make dynamic

function split decisions among DUs and the CU. We alsoformulate an OpEx minimization problem. Our results showthat RL-based solutions make the best use of renewable energyand are cost-efficient. Finally, we present the findings of theimpact of different solar panel sizes and the batteries on thisnetwork model, which helps to evaluate the feasibility of usingRES for an MNO. As a future work, we plan to investigatethe optimal RES and battery sizing for an MNO.

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[18] National Renewable Energy Laboratory, “PVWatts,” 2020. Available athttp://pvwatts.nrel.gov/.

[19] EPDK, “Fourth Quarter Tariffs 2020,” 2020. Available atwww.epdk.gov.tr.