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The Evolution of Radio Access Network Towards
Open-RAN: Challenges And Opportunities
Sameer Kumar Singh, Rohit Singh and Brijesh Kumbhani
Electrical Engineering Department
Indian Institute of Technology Ropar, India
{2018eey0007, 2017eez0007, brijesh}@iitrpr.ac.in
Abstract—The coexistence of massive Internet of Things (IoT)
network and modern technologies (e.g., high speed gaming and
self driving vehicles) requires a versatile network which can
provide support to all such applications. Since the Quality of
Service (QoS) requirement of each application is different from
one another, the existing Radio Access Network(RAN) is unable
to support such diverse applications. Consequently, Open Radio
Access Network(O-RAN) is being considered as the most viable
solution for next generation RAN. In this paper, we present
the evolution of RAN along with the possible architecture and
features of the most promising next generation RAN (i.e., O-
RAN). This work mainly discusses architectural and functional
advancement of the RAN in each generation. In addition, we dis-
cuss various challenges associated with O-RAN implementation
and possible opportunities created with the advent by O-RAN.
Index Terms—Open-RAN, IoT, Cloud-RAN
I. INTRODUCTION
In the past fifty years wireless communication technology
has gone through several transformations [1]- [7]. Specifically,
past few decades have witnessed a remarkable growth in wire-
less communication framework due to the advent of massive
IoT and modern real time applications such as high speed
video gaming, self driving vehicles, etc [8]- [9]. However, as
depicted in Fig. 1, the QoS requirement of each application
is different from the other. For instance, connected vehicles
demand high speed communication with high degree of relia-
bility [8]. On the other hand, some applications (e.g., IoT) seek
low throughput requirement but excellent coverage with low
power consumption [9]. In contrast, some applications require
low latency and real time data processing. Consequently, the
co-existence of such diverse variety of applications require a
versatile network which posses all features. Unfortunately, all
these targets cannot be achieved by existing/previous RAN
which creates the demand of network up-gradation. One way
to support such connectivity is to design separate network
for different set of applications (shown in Fig. 1 ). However,
it is not a feasible solution from economics and operators
respectives. As a result, both the academia and industries
are trying to make the mobile network more software driven,
virtualized, flexible, intelligent and energy efficient [10]- [11].
Moreover, the network has to be cost efficient and reliable.
Another possible way to fulfil all the given requirements is
to split the RAN into various parts based on the functionality.
Splitting can make the architecture smarter and versatile.
This new architecture is known as Open RAN (O-RAN).
Specifically, the advent of O-RAN is a step towards the
Fig. 1. QoS requirement of various set of applications.
software oriented infrastructure which enables the network
to behave differently according to the QoS requirement of
the processed application. From the market view point, O-
RAN creates a chance for the small vendors and operators to
start their own services and to increase their market revenue
[28]- [30]. Though the advent of O-RAN may provide several
benefits, there are a number of challenges associated with it.
Some of those challenges are as follow:
•Due to different QoS requirements, it is difficult to design
a stand alone service oriented architecture.
•The network should be flexible to support further upgra-
dations and must be compatible with the existing devices.
•The network should not exert extreme burden on the
backhaul and must posses low computational complexity.
In this paper, we present the evolution of RAN along with the
possible architecture and features of the most promising next
generation RAN (i.e., O-RAN). This work mainly discusses
architectural and functional advancement of the RAN in each
generation. In addition, we discuss the challenges associated
to O-RAN implementation and possible opportunities created
with the advent of O-RAN.
II. RAN OVERVIEW AND EVOLUTION
RAN is the major part of the wireless communication
system as it connects the user equipment(UE) to the core
network by radio connectivity [12]- [13] as shown in Fig.
2 . The basic functionality of RAN is to manage the radio
resources [12]- [13]. Thus, typical RAN involves two major
unit namely Radio Unit(RU) and Processing Unit(PU) as
shown in Fig. 2 .
•Radio Unit: Radio unit contains transceiver antennas and
it is responsible for transmission and reception.
,(((
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TABLE I
ABBREVIATIONS USED IN THE PAPER.
AI Artificial Intelligence
BS Base Station
CoMP Coordinated Multipoint
CP Central Processor
CRAN Cloud Radio Access Network
CU Control Unit
DU Distributed Unit
G Generation
IOT Internet of Things
IP Internet Protocol
MAC Medium Access Control
MEC Mobile Edge Computing
ML Machine Learning
MIMO Multi Input Multi Output
MME Mobility Management Entity
NVF Network Function Virtualization
OA Orchestration and Automation
O-RAN Open Radio Access Network
PDCP Packet Data Convergence Protocol
PU Processing Unit
QoS Quality of Service
RAN Radio Access Network
RIC Radio Intelligent Controller
RLC Radio Link Control
RRC Radio Resource Controller
RT Real Time
RU Radio Unit
SDAP Service Data Adaptation Protocol
UE User Equipment
Fig. 2. An illustration of basic RAN
•Processing Unit: Processing unit of RAN is responsible
for radio management, resource utilization/sharing and
some other operations like (pre-coding, encryption ,etc.).
Fig. 3 shows evolution of RAN over the time. Initially, the
number of users as well as data rate requirement was very
less. Due to the availability of some data restricted cellular
services (e.g., voice call, text messages, etc.), small number
of Base Stations(BSs) were sufficient to fulfil this demand. As
shown in Fig. 3(a), traditional RANs were equipped with the
integrated the RU and PU. Each BS was sufficient to cover the
significantly large area. Since the frequency reuse framework
was adopted, very less/no computation was required for inter-
ference avoidance. Later, RU and Distributed Unit(DU) were
separated as shown in Fig. 3(b). The RUs were equipped at
the height (usually at the top of tower to support large area)
and DUs used to be installed in the room underneath the BS.
Fiber optical cable were utilized to connect both the units.
Moreover, the introduction of data hungry applications and
increase in the number of UEs raised the demand of further
densification. However, the densification alone was unable to
support such huge data rate demand. Thus, the framework
has shifted towards the frequency reuse-1 scenario. Also, the
Fig. 3. Different generation of RAN condition
demand of millimeter wave (mm-wave) has been initiated
which subsequently raise the demand of connected framework
given in Fig. 3(c). The scenario given in Fig. 3(c) is also
referred as Cloud Radio Access Network (CRAN) in which
PUs of all the BSs are pooled to a standalone CP, formally
known as cloud processor [14]- [15].
A. Key Advancements in RAN
Some key advancements that have been occurred over the
time in the previous/existing RAN are:
1) BS centric to UE centric: Traditional RAN used to
associate a BS to a UE on the basis of received signal
strength from various BSs (the dominated one selected). This
sort of BS selection suffers from the fact that the interfering
power received by the cell edge users is usually comparable
to the power from serving BS. While, in the UE centric
approach, each UE is allowed to choose multiple BSs based
on the received signal strength and all these BSs works in
a coordinated manner to use/avoid the signal from adjacent
BSs. In this way, UE centric approach provides interference
free scenario irrespective of user’s position [16].
2) mmWave and beamforming: As discussed, BS densifi-
cation alone could not sufficient to fulfil the ever increasing
data rate requirement sufficiently. High data rate also need
an extra resources in terms of bandwidth. mmWave spectrum
being relatively unoccupied, so it would be a best solution [17].
Though, the incorporation of mmWave has provided several
fold increment in the available spectrum, it was challenging to
establish a reliable communication over mmWave due to high
attenuation and poor diffraction. As a result, multiple antennas
can be used at the RUs to form beams in the direction of the
intended user [18].
3) Single point to multi point transmission: The traditional
networks used BS centric approach to connect each user.
Consequently, the edge user suffered from severe inter-cell
interference. CRAN has shifted towards UE centric approach
which uses several BSs to reduce interference from the other
cells [19]. As a result, each UE is served by a cluster of BSs
or radio heads which are governed by the CP.
4) Coordinated Transmission: Earlier, several frequency
bands used to orthogonalize the adjacent cell users. Due to this,
the resource utilization technique was very poor . CRAN uti-
lizes multiple transmitting points to serve each user. This sort
of transmission is known as Coordinated Multipoint(CoMP)
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Fig. 4. Expended Version of Future RAN
Transmission. CoMP algorithms are run in the heart of CP
which provides coordination among the intra-cluster BSs.
Coordinated beamforming, distributed transmission and joint
transmission are among some of the popular CoMP techniques
[20]- [22].
III. OPEN RAN: OVERVIEW AND INFRASTRUCTURE
According to the latest version of RAN, RAN is basically
divided into two units as shown in Fig. 3(c). However,a
number of future applications require ultra low latency, more
reliable network. Fig.4 shows the internal scenario of RAN,
which contain dis-aggregated units like RU, baseband process-
ing function and packet processing function [23]- [26]. This
disaggregation enables each unit to perform specific functions
and thus, adds flexibility in the network. Basic working of
these units can be explained as:
A. Radio Functions at RU
RU contains transceiver antennas along with the special
radio hardware which perform physical layer operations (e.g.,
digital to analog conversion, filtering operation, modulation,
etc). In addition, it is also responsible for signal amplification
and regeneration [23]- [26].
B. Baseband Processing Function
This unit is responsible for upper layer functions (i.e., radio
link control, medium access control) which specifically per-
form carrier aggregation, soft combining, fast radio schedul-
ing, CoMP operations, etc. Moreover, it is also responsible
for selection of MIMO scheme, beam formation and antenna
selection [23]- [26].
C. Radio Control Functions
This unit strives to control resource distribution and load
sharing among different set of applications and system areas.
It is one of the most essential units of RAN and performs
virtualization and radio resource management. Basically, it
controls the overall performance on RAN based on radio
control algorithms [23]- [26].
D. Packet Switching Functions
Like radio control function, this layer plays a key role
in virtualization. Specifically, it performs packet processing
operations which involve multi-path handling, data scheduling,
dual connectivity management and encryption [23]- [26].
Due to dis-aggregation, these all units have a capability to
perform a specific task which provides agility to the network.
The ultimate goal of RAN is to connect RU (or UE) to the core
network. However, traditional network used to treat each appli-
cation indifferently. In contrast, the connecting path between
UE to the core network in O-RAN is decided by the nature of
service as depicted in Fig. 4. Some applications/services allow
to directly connect to the core network while some are diverted
through various stages of radio control function. Moreover,
as the depth of transmission increases, the transmission la-
tency also increases which specifically depends on the data
carrying capacity of back-haul unit at each intermediate stage.
Specifically, management units are responsible to define the
intermediate radio connectivity of each service. As a result, the
future RAN (i.e O-RAN) becomes highly complex and require
ultra large computational capability. However, to overcome
such limitations, some modern learning methods and MEC
capabilities are being indulged with O-RAN infrastructure.
MEC will not only reduce computational complexity at the
service plane but also reduce the overall latency [27].
Functions in the traditional RAN architecture that were
aggregated into a single node are dis-aggregated in the O-
RAN. This distribution increases the reliability by avoiding
any single point of failure. Moreover, allowing the separation
of control plane and user plane, the control plane function
can be implemented on all server platform while specific real
time functions can be implemented on the highly specialized
hardware. Furthermore, in the O-RAN, the control plane, user
plane and transport plane are intended to work independently
which increase the scalability and flexibility of the O-RAN. In
a nutshell, it can be inferred from the above discussion that O-
RAN is the dis-aggregated, virtualized, self driven, application
specific and software oriented network which is able to support
IoT network as well as modern high speed applications in a
stand alone flexible network. The ability to handle multiple
radio link protocol interoperability. In the advanced version,
some function that are presently handled by radio network
layer are planned to move in the IP layer.
The introduction of O-RAN will also have a great in-
fluence on market and operators. The deployment of O-
RAN could open the doors for public network operators to
achieve their core network independent of the existing access
network technology [29], which provides the operators the
leverage of their core service based network, across variety of
technological support. As per economic statistics, the market
for RAN based equipment is worth at $30 billion a year
and significantly more when smart buildings and vehicular
network is included. O-RAN seems to disrupt the network
by indulging several innovations and fast creation of flexible
inter-operable network. Finally, ORAN design could attract the
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Fig. 5. Reference Architecture of O-RAN
modern business models to incorporate latest wireless services
and next generation communication frameworks.
E. Advantages of O-RAN
With the dis-aggregation of hardware and software, O-
RAN creates a unified architecture unified architecture through
several advancements and brings several benefits (i.e., low
latency and network slicing). In addition to facilitating network
automation O-RAN provide several benefits given as [31]:
1) Agility: The unification of the software enabled archi-
tecture makes the network suitable for existing/past and future
generation.
2) Deployment Flexibility: Dis-aggregation and software
association makes the network flexible for installation and
upgradtion/extension.
3) Real time responsiveness: O-RAN is the software driven
service specific network which behaves on the basis of in-
tended service and thus prefers the real time services which
require very low latency over the less critical services.
4) Operating Cost Reduction: It is estimated that the plug
and play feature of O-RAN and modern learning methods may
reduce the maintenance cost upto 80%. Putting the software
at the heart of the network, the operators can unify the con-
nectivity gains of all the generations under the same umbrella.
Doing this, the operators can save millions of dollars.
F. O-RAN Architecture
Fig. 5 shows a reference O-RAN architecture which is based
on the principle of openness [28]- [30]. As discussed in the
previous sections that O-RAN is flexible, service oriented and
software defined network. Another add on this network is
the affiliation of artificial intelligence. Basically the reference
architecture of O-RAN includes various sub units. As shown
in Fig. 5 , non-real time functionalities are decoupled from the
real time function include service and model training for non-
real time functionality [28]- [30]. While trained models and
real time control functions (produced in real time) are included
in the RAN intelligent controller of the near-real time for
Fig. 6. RIC near-RT
run time execution. RIC near-RT utilizes the database (known
as radio network information base) which tracks the state of
the underlying network by using E2 and A1. E2 strives to
provide a standard interface between RIC near-RT and CU/DU
which feeds data that include various RAN measurements for
radio resources management. Specifically, the near-RT RIC
provides radio management tracked by AI/ML. In addition,
this layer is also responsible for operations like handover,
QoS management, etc as shown in Fig. 6 . Moreover, the
interface AI is responsible for conveying the AI enable policy
and ML based training models to the RIC of non-real time.
Basically, non-RT control functions strive to support non-real
time intelligence radio resource management and providing
guidance to support the operations of RIC near-RT functions
that are supported by AI interface include [32]:
•Useful data from network to the RIC non-RT to support
various requirements such as offline training, online on-
line learning AI/ML model, etc.
•Support for RIC near-RT functions such as deploying/
updating ML/AI model into the RIC of near-RT and
sometime feedback to ensure that the operators meet the
intended objectives.
Fig. 7 shows that dis-aggregated Control Unit stack which is
responsible to support various protocols (including 4G, 5G and
other protocols). RIC near-RT issues command to implement
basic functions (e.g., handovers) virtualization provides the
ability to distribute capacity across multiple elements. DU and
radio resources unit(RRU) are responsible to support radio
functions, radio processing, baseband processing etc [28]-
[30].
1) Future Challenges: Some challenges regarding the im-
plementation of model given in Fig. 5 include:
•It is challenging to deploy policies for the RIC near-RT
and non-real time control loop meeting the economical
and ecological aspects.
•Coordination, updation and training is difficult with the
modern learning techniques.(i.e., ML and AI).
•It is challenging to handle data (specifically cross layer
data) to support the intended operation while protecting
other internal operations.
IV. TECHNICAL ADVANCEMENTS AND OPPORTUNITIES
With the acceleration of 5G evolution and O-RAN im-
plementation, several advancements would take place in the
modern technologies. Applications associated to IOT devices
machine learning, mobile edge computing are expected to
reach at the peak in the coming years. Some Key technological
advancements and opportunities are discussed below:
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Fig. 7. Control Unit Stack
A. An IoT enabled Network
The launch of O-RAN will remove the restriction in the
IoT connectivity. It would provide a flexible architecture,
highly suitable for modern IoT connectivity. With this, more
things should be connected with various application including
health care, retail, security and many more [34].Some new
applications may now add on (e.g., digital locks, e-health, etc).
Fig. 8 shows some existing and possible applications [35].
Under these applications, IoT devices would be connected
over O-RAN framework. Furthermore, disaggregation of RIC
non-RT enables to support massive IoT connectivity to the
devices which require low throughput but large coverage and
low power consumption.
B. Enabling MEC computing
MEC is a form of architecture that enables an edge device to
perform computing tasks. Due to rapid increase in the number
of connected devices, the next generation RAN should be able
to manage the traffic far intelligently. MEC is supposed to a
key technique for the same [36] . Currently, most of the ap-
plications handle their content storage and online computation
on the remote sensors which usually lie far away from the end
user. MEC will bring those processes closer to the end user.
This shift will help to reduce the congestion on the mobile
network and cloud computer. In addition to reducing the
congestion, MEC will play a major role in reducing the latency
of 5G network [37]. Bringing the data closer to the end unit
and streaming it more directly at the end device, extremely low
latency can be achieved which enables to support applications
that require high speed data and computing.
C. Inclusion of Modern Learning Methods
Machine Learning enables a computer to learn without an
explicit program. ML is featured by learning useful informa-
tion from the input data set, which makes it suitable for the
applications in which processing environment is dynamic in
nature. Specifically, ML can enhance wireless framework in
the following ways:
•ML based resource management, mobility and network-
ing algorithms can significantly adapt the dynamic envi-
ronment.
•ML is considered as the key to realize the goals of self
realizable network.
Fig. 8 shows various ML enabled applications driven by
modern learning methods [38]. As per a survey, some machine
learning algorithms are already introduced for future wireless
Fig. 8. Applications of ML & IoT
networks, such as Q-learning for resource allocation and
interference coordination. In addition, Bayesian learning is
used or channel estimation in MIMO network.
V. C ONCLUSION AND FUTURE ASPECT
The co-existence of variety of applications require flexible,
application oriented and adaptive network which are difficult
to support on the existing infrastructure. As a result, ser-
vice providers and mobile operators are moving towards dis-
aggregation of existing RAN. Modern applications require a
flexible network which leads to the emergence of building
up a standard open interface enabled by AI based network
function virtualization. This article explained the evolution of
RAN along with background of O-RAN and its reference
architecture. The architecture given in this work is a step
towards software oriented network. Further we discussed var-
ious challenges associated with the O-RAN implementation.
Furthermore, opportunities created with the advent of O-RAN
have been discussed.
Current version of O-RAN is focused on identifying the
radio functions of RAN that can be grouped into functional
entities which can be embedded in the distributed system.
Specifically, researchers are working on pushing several lower
layer functions (i.e., QoS, mobility, management and security)
into the upper layer. However, there are some questions and
challenges; one issue in O-RAN is to standardize the opera-
tion, administration and management because it is difficult to
achieve inter-operability without standardization. Although O-
RAN seems to provide the required degree of inter-operability,
the details are to be work out.
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