LTE Wireless Virtualization and Spectrum
Management
Yasir Zaki*, Liang Zhao*, Carmelita Goerg*
*
TZI-ComNets/University of Bremen, Bremen, Germany
{yzaki/zhaol/cg}@tzi.de
Abstract— Many research initiatives have started looking
into Future Internet solutions in order to satisfy the ever
increasing requirements on the Internet and also to cope
with the challenges existing in the current Internet. Some
are proposing further enhancements while others are
proposing
completely
new
approaches.
Network
Virtualization is one solution that is able to combine these
approaches and therefore, could play a central role in the
Future Internet. It will enable the existence of multiple
virtual networks on a common infrastructure even with
different network architectures. Network Virtualization
means setting up a network composed of individual
virtualized network components, such as nodes, links, and
routers. Mobility will remain a major requirement, which
means that also wireless resources need to be virtualized. In
this paper the 3GPP Long Term Evolution (LTE) was
chosen as a case study to extend Network Virtualization into
the wireless area.
I.
INTRODUCTION
The Internet started in the Sixties as a US Defense
Department project mainly to investigate possibilities for
robust non-centralized digital communications. The main
reason for the project was that the telephone system
(which was the only communication system at that time)
had major problems of being dependent on central
switching stations which were points of failure for the
system. So the question was whether it would be possible
to design a network that could quickly reroute digital
traffic around failed nodes?
The solution was to build a datagram network called
"catenet", and use dynamic routing protocols that can
adjust the traffic flow through the network. The US
Defense Advanced Research Projects Agency (DARPA)
launched the DARPA Internet Program [24]. Over the
years the Internet has continuously been improved to grow
to a network with 1.668 billion users [22]. Services have
evolved from data and message exchange to today’s
worldwide web providing multi-channel communication
services and access to multimedia content. Further
challenges are visible: the Internet of Things [23]
requiring addresses and addressability of billions of
sensors and devices and multimedia services, such as
IPTV offering high quality video content over the Internet,
which are just some examples for future trends and
requirements on the Internet’s architecture and protocols.
Within today’s research communities, some investigates
new architectures and protocols for the Future Internet,
while others work on adapting the Internet protocols to
cope with all these future challenges as extensions to the
current Internet. Network virtualization is one alternative
migration path from the existing Internet to a new Internet
and thus leads to the new Internet with isolated networks
sharing common resources, like routers and links.
II. NETWORK VIRTUALIZATION
Virtualization itself is not something new; it is a well
known technique that has existed for years, especially in
the computer world like the use of virtual memory and
virtual operating systems. The new idea is to use
virtualization to create complete virtual networks. This
involves applying the current operating system
virtualization experience for network components, leading
to virtual network resources like virtual routers, virtual
links, and virtual base stations.
A number of research initiatives and projects all over
the globe have started focusing on Network Virtualization,
e.g. GENI [2] [3], PLANETLAB [4], VINI [5], CABO
[6], Cabernet [7] in the United States; 4WARD [8] [9] in
Europe, AKARI [10], AsiaFI [11] in Asia and many
others. This shows that the current direction in designing
the Future Internet is going in favor of having multiple coexisting architectures, instead of only one, where each
architecture is designed and customized to fit and satisfy a
specific type of network requirements rather than trying to
come up with one global architecture that fits all. That is
why Network Virtualization will play a vital role as it
helps diversifying the Future Internet into separate Virtual
Networks (VNets) that are isolated and can run different
architectures within.
III. WIRELESS NETWORK VIRTUALIZATION
Network virtualization will allow operators to share the
same physical infrastructure and have networks coexisting in a flexible, dynamic manner utilizing the
available resources more efficiently. This implies that the
physical infrastructure needs to be virtualized into a
number of virtual resources being offered to the different
virtual networks. In consequence resource virtualization
requires all entities to be virtualized: routers, servers, links
and host/end system. While virtualization for servers,
routers and wire line links has been extensively studied in
the literature [15] [19] [20] [21] [29] [30], the wireless
part has not yet received major consideration within
today’s research community. This has to be seen in view
of the importance of wireless access in today’s and future
networks.
Virtualization of wireless resources is a complex
challenge. First, the wireless resources at the Base Station
for example have to be shared and assigned to different
virtual network operators. This sharing needs to be fair.
Fairness in wireless systems can be defined differently:
fairness in terms of spectrum used, or power used, or a
product of these two or even fairness of QoS delivered to
end users. Furthermore, it is not sufficient to look at the
resources that are being shared, assigned or scheduled at
one base station, but also the interference caused by the
utilization of these resources need to be considered as
well. This is even more complex as neighboring base
stations of different (virtual) network operators could use
completely different transmission schemes. In this paper
we limit our investigation to only one base station and we
consider fairness to be defined as a fair sharing of
spectrum by means of bandwidth that will be assigned to
the different virtual operators based on predefined
contracts.
IV.
MOTIVATION BEHIND MOBILE NETWORK
VIRTUALIZATION
The Mobile networks are one of the fastest growing
technologies that are influencing major aspects of our life.
The idea of being able to virtualize these mobile networks
and share their resources will lead to a more efficient
utilization of the scarce wireless resources. Additionally
network virtualization can reduce the amount of the
required base station equipment and thus reduce the
required energy to run wireless networks.
Network virtualization will also allow completely new
value chains. Smaller players can come into the market
and provide new services to their customers using a virtual
network. This also allows completely new future
networks, e.g. isolating one virtual network (like a
banking network) from a best effort Internet access
network. Sharing the physical resources will enable these
new and smaller players to enter the market and provide
their services wherever required. In addition to all of the
previous, the idea of being able to share the frequency
resources among multiple operators is very appealing.
This gives operators the flexibility to expand/shrink their
networks and the air interface resources they use, and this
will lead to better overall resource utilization and reduced
energy consumption.
V. LONG TERM EVOLUTION (LTE)
As a case study for wireless virtualization we chose
LTE (Long Term Evolution) [12]. LTE is the latest
evolution of 3GPP [13] mobile phone standard. The LTE
air interface is based on OFDMA in the downlink and SCFDMA in the uplink (which also supports the use of
multi-antenna technologies (MIMO)). Since LTE is one of
the most promising future mobile networks and because of
the nature of OFDMA, it is a very good candidate to be
considered for applying network virtualization being the
focus of this paper.
From the specification LTE is an all IP network that
supports a downlink data rate of more than 100 Mbps and
an uplink data rate of more than 50 Mbps. The innovation
in the LTE system is that it can support a scalable
bandwidth from 1.4 MHz up to 20 MHz enabling it to also
operate in lower frequency ranges. LTE supports a new
network architecture based on a cost efficient two nodes
architecture, that consists of the Enhanced NodeB
(eNodeB) and the Access Gateway (AGW) as can be seen
in Figure 1. In comparison to earlier 3GPP architectures
(UMTS, HSPA) no dedicated Radio Network Controller
(RNC) is required anymore.
Figure 1. LTE network architecture
If network virtualization is applied to the LTE network
then the idea is to virtualize the infrastructure of the LTE
system (that is eNodeBs, routers, Ethernet links …) and
let multiple mobile network operators create their own
virtual network depending on their requirements and
goals, while using a common infrastructure. The
challenges of that are mainly how to virtualize the
physical infrastructure to support such scenarios, and what
kind of changes are required to be introduced to the LTE
system. We mainly foresee two different types of
virtualization processes: one is virtualizing the physical
nodes of the LTE system like for example the eNodeBs,
the routers, the Ethernet links and the second one is
virtualizing the air interface of the LTE system, which is
the focus of this paper.
A. LTE Air Interface Virtualization
In order to virtualize the LTE air interface, the eNodeB
has to be virtualized, since it is the entity that is
responsible for accessing the radio channel and scheduling
the air interface resources. Virtualizing the eNodeB is
similar to node virtualization. In node virtualization there
exist a number of solutions, where the physical resources
of the virtual machine (like the CPU cycles, memory, I/O
devices etc.) are being shared between multiple virtual
instances of virtual operating systems. XEN [15] for
example is a well known PC virtualization solution that
calls the entity that is responsible for scheduling the
physical resources a “Hypervisor”. Our proposal follows
the same principle, where a hypervisor is added to the
LTE eNodeB as shown in Figure 2.
Figure 2. Virtualized LTE eNodeB protocol stack
The LTE Hypervisor is responsible for virtualizing the
eNodeB into a number of virtual eNodeBs (where the
assumption is that each will be used by a different
operator); the physical resources are being scheduled
among the different virtual instances via the hypervisor
(similar to XEN hypervisor). In addition, the LTE
hypervisor is also responsible for scheduling the air
interface resources (that is the OFDMA sub-carriers)
between the different virtual eNodeBs. In this paper, we
only focus on the latter functionality of the hypervisor that
is the air interface scheduling; the first part that the
physical eNodeB is capable of running multiple instances
of virtual eNodeBs stacks is not the focus of this paper,
but similar solutions are already commercially available
like in VANU [14]. VANU MultiRAN is a solution used
to support multiple virtual base stations all running on a
single hardware platform. The multiple virtual base
stations share the antennas, hardware platform and the
backhaul. MultiRAN allows multiple operators to virtually
share a single physical network.
Figure 3. VANU MultiRAN mobile operator network architecture [14]
B. LTE Virtualization Hypervisor
The LTE Hypervisor is responsible for virtualizing the
LTE eNodeB, as well as scheduling the air interface
resources among the virtual operators. The Hypervisor
collects information from the individual virtual eNodeB
stacks, like user channel conditions, loads, priorities, QoS
requirements and information related to the contract of
each of the virtual operators. This information is used to
schedule the air interface resources between the different
virtual operators. LTE uses OFDMA in the downlink,
which means that the frequency band is divided into a
number of sub-bands each with a carrier frequency. The
air interface resources that the hypervisor schedules are
actually the Physical Radio Resource Blocks (PRB); this
is the smallest unit that the LTE MAC scheduler can
allocate to a user. A PRB consists of 12 sub carriers in the
frequency domain as well as 7 OFDMA symbols in the
time domain as can be seen in Figure 4.
requirements. This means that some mechanisms/contracts
guidelines has to be defined to guarantee the resources to
the operators which could be done by different options for
example setting a guaranteed amount for each operator
and leaving the rest of the resources to be shared. What is
also important here is the granularity that the hypervisor
operates with in order to guarantees the pre-defined
requirements.
In our paper, we concentrate on a contract based
hypervisor algorithm. In the algorithm the spectrum is
divided between the different virtual operators based on
predefined contracts that the virtual network operators
have made with the infrastructure provider. We mainly
define 4 different types of contracts that the infrastructure
provider offers to the virtual operators and these are:
a. Fixed guarantees: the operator requests a fixed
bandwidth that would be allocated to it all the
time whether it will be used or not
b. Dynamic guarantees: the operator requests a
guaranteed maximum bandwidth that would be
allocated to the operator if required; otherwise
only the actual need would be allocated which
could be less than the max value. The operator
might only pay based on the used bandwidth
which could save cost
c. Best effort (BE) with min guarantees: the operator
specifies a min guaranteed bandwidth which will
be allocated at all time; and a max value that
would act as an upper bound. The allocation will
be done in a BE manner.
d. Best effort with no guarantees: the operator would
only be allocated part of the bandwidth if the
current load permits i.e. in a pure BE manner
In order for the hypervisor to be able to allocate the
spectrum to the different virtual operators an estimation of
the current bandwidth need has to be provided. Each
operator will send back his bandwidth estimations at
frequent time intervals. The estimated bandwidth value is
calculated in terms of PRBs as follows:
Est (n)  Est n  1    PRBs _ TTI n   
Figure 4. LTE Downlink Physical Resources Structure
Scheduling the PRBs between the different virtual
eNodeBs actually means splitting the frequency spectrum
between the different eNodeBs of the different operators.
The hypervisor can make use of apriori knowledge (e.g.
users channel conditions, virtual operator contract, load ...
etc.) to schedule the PRBs.
OFDMA scheduling has been studied extensively in the
literature [25] [26] [27] [28], but what is new here is that
the frequency spectrum among the different operators has
to be scheduled. This is even more challenging because of
the additional degree of freedom that has been added. A
number of possibilities exist here, where the scheduling
could be based upon different criteria’s: bandwidth, data
rates, power, interference, pre-defined contracts, channel
conditions, traffic load or a combination of them. At the
end the hypervisor has to convert these criteria into a
number of PRBs to be scheduled to each operator, but the
challenge is to make sure that the allocated PRBs would
be fair and enable the operators to satisfy their
Where Est(n) is the averaged estimate count of PRBs
over n time interval that are either additionally required by
the operator or are not required that the operator wants to
give back; PRBs_TTI(n) is the current estimate count of
PRBs at the nth TTI, this is calculated by summing the
PRBs that were additionally needed to schedule the unserved users within this TTI minus the number of left
PRBs that were not used in that TTI; n is the number of
TTIs in the hypervisor time interval.
Est(n) can take both positive and negative values,
because the operator would either need more PRBs to be
allocated to it or would like to give some PRBs back to
the hypervisor that are not required. The hypervisor would
use this to calculate and allocate each virtual operator’s
spectrum, and this would be used specifically for contract
types b, c and d. For contract type b, this would serve as
the allocated bandwidth for that operator, upper bounded
by the contract max value. For the BE contracts (contract
c and d) the hypervisor will first allocate the c operators
with their min guaranteed value, and whatever PRBs that
are left at the end would be distributed between the BE
operators. The split would be based on a fairness factor
that is calculated as follows:
FFi  Est (n)i / Total _ Est
Where FFi is the fairness factor of operator i; Est(n)i is
the PRBs estimate of operator i; and the Total_Est is the
total BE PRBs estimate over all BE operators, and is
calculated as follows:
Total _ Est  i 1
# BEoperators
Est n i
The number of PRBs allocated for each BE virtual
operator is calculated as follows:
PRBs _ alloci  int FFi  Left _ PRBs 
Where the Left_PRBs is the number of PRBs left for the
BE operators after allocating the guaranteed operators
VI. LTE SIMULATION MODEL
The LTE simulation model used in this paper was
developed using OPNET [16]. The model is designed and
implemented following the 3GPP specifications, where
most of the LTE functionalities and protocols have been
implemented as can be seen in Figure 5.
The focus of this work was not on the node or link
virtualization, but rather on the air interface virtualization
and how to schedule the air interface resources among the
different virtual operators, no node/link virtualization was
simulated, instead the assumption was that we have a
perfect node/link virtualization. Figure 6 shows an
example scenario.
Figure 5. LTE simulation model overview
Figure 6. Virtualized LTE model (example with 3 Virtual Operators)
VII. SIMULATION SCENARIOS AND CONFIGURATIONS
Two different scenarios are investigated within this
paper, one without virtualization which will be called
“legacy” and one with virtualization which we will call
“virtualized”. In the virtualized scenario, 4 different
virtual network operators are configured each with a
different contract configuration as follows:
1.
Video streaming operator: configured with a
fixed guaranteed contract of 33 PRBs
2. VOIP operator: configured with a dynamic
guaranteed contract, with a max value of 33
PRBs
3. VOIP + BE Video on demand operator:
configured with the best effort with min
guarantees contract, with min and max value of
25 and 45 consecutively
4. Small VOIP operator: configured with BE and no
guarantees contract
As for the legacy scenario, only the first three operators
are configured, because there will be no chance for the 4th
operator since the spectrum cannot be shared. Each of the
three operators will get 33 PRBs. The rest of the
simulation is configured according to Table 1 and Table 2.
Table 1
Simulation parameters
Parameter
Assumption
Number of virtual
4 virtual operators (red, blue, green and
operators
yellow in Figure 6) each with one eNB
eNodeB coverage area
Circular with one cell
Radius = 375 meters
Total Number of PRBs
99 (which corresponds to about ~ 20
MHz)
Mobility model
Random Way Point (RWP)
Users are initially distributed uniformly
within the cell
Users speed
5 km/h
Number of active users
VO1: 12 video users
VO2: 40 VOIP users
VO3: 16 VOIP + 16 video users
VO4: 3 VOIP users
Path loss model
128.1 + 37.6 log10(R) dB, R in km [17]
Slow Fading model
Lognormal distributed
Mean value = zero
Standard deviation = 8 dB
Correlation distance = 50 meters
Fast Fading model
Jake’s model
CQI reporting
Ideal
Modulation schemes
QPSK, 16 QAM, 64 QAM
Link-to-System level
Effective Exponential SINR mapping
interface
(EESIM) [18]
Hypervisor resolution
1 sec
Estimate α and β
0.5 for each
Simulation run time
1000 sec
Table 2
Traffic models configurations
VOIP traffic model
Silence length
neg. exponential with 3 sec mean
Talk Spurt length
neg. exponential with 3 sec mean
Encoder scheme
GSM EFR
Conversational envir.
land phone – quiet room
Call duration
Uniform (1, 3 min)
Inter-repetition time
neg. exponential with 90 sec mean
Video streaming traffic model
Incoming/Outgoing stream inter Const (0.01 sec)
arrival time
Incoming/Outgoing stream frame Const (80 Bytes)
size
VIII. SIMULATION RESULTS
As discussed earlier the main focus of this paper is the
LTE air interface virtualization, and in order to show the
performance gains that could be achieved from
virtualizing the air interface two different scenarios are
compared against each other. The scenarios are
configured as stated earlier; one is similar to today mobile
network operator setup, where three different operators
are operating in the same region, each uses his own
frequency band. The second scenario is the futuristic
approach, where the operators are sharing the frequency
band dynamically depending on predefined contracts. In
addition, a 4th operator (smaller operator) will also join
the other three operators in this scenario.
the users have similar performance in both scenarios. This
is also applicable for the average per user application end2-end delays shown in Figure 11. The results confirm that
all users are being served with no problem and hence no
additional buffering delay is being introduced.
Figure 10. Operator 2 DL average per user air interface throughput
Figure 7. Cell1 per virtual operator (VO) allocated bandwidth (or PRBs)
Figure 7 shows the number of PRBs that each VO has
been allocated over time. It can be noticed that for the 1st
operator the PRBs allocation is fixed to 33 PRBs, since it
is using the fixed guaranteed contract. As for the other
three operators we can notice that the allocated number of
PRBs changes with time depending on the traffic load and
the contract details of each operator. The average per user
Air interface throughput for operator 1 can be seen in
Figure 8. The result shows that the operator has the same
performance with and without virtualization; this is
because this operator has a contract with a guaranteed
fixed allocation. Figure 9 shows the per user average
application end-2-end delay, and again the operator has
similar performance for both scenarios.
Figure 11. Operator 2 DL average per user application end-2-end delay
The results mean that operator 2 has the same
performance with and without virtualization. But, in the
“virtualized” scenario operator 2 is not wasting the air
interface resources and only uses the required number of
PRBs to serve its users as can be seen in Figure 12. This is
a big advantage since the operator will be able to cut cost
since the operator will only pay for the used resources.
Figure 8. Operator1 DL average per user air interface throughput
Figure 12. Operator 2 DL used number of PRBs vs. time
Figure 9. Operator1 DL average per user application end-2-end delay
The average per user air interface throughput for
operator 2 can be seen in Figure 10. One can notice that
The results for operator 3 can be seen in Figure 13 and
Figure 14. For users 1 – 16 (the VOIP users) we can see
that similar performance is achieved in both scenarios, this
is also confirmed by the end-2-end delay results. As for
users 17 – 32 (the video users) one can notice a slightly
better performance in the throughput results for the
“virtualized” scenario, but the end-2-end results shows
that these the users suffer from huge delay values for the
“legacy” scenario; whereas in the “virtualized” scenario
the users are having good performance. The reason why
the VOIP users are not affected in the “legacy” scenario
is the fact that these users are being served with higher
priority and the resources are enough to serve those users,
but not enough to serve the video users.
best effort manner with whatever resources are left rather
than wasting these resources; this can be seen from
operator 4 results shown in Figure 15.
Figure 15. Operator 4 DL per user app. throughput and end-2-end delay
IX.
Figure 13. Operator 3 DL average per user air interface throughput
Figure 14. Operator 3 DL average per user application end-2-end delay
This is a big advantage for the virtualization scenario,
where operator 3 makes use of the available PRBs left by
the 2nd operator to serve his users in a BE manner. This is
one of the main motivations to apply virtualization in the
wireless world, the idea of being able to share the
spectrum in a more efficient way and being able to have
some multiplexing gain achieved as well as cutting cost.
One additional advantage that can be achieved in the
“virtualized” scenario is the ability to serve small
operators with relatively smaller number of users in a pure
CONCLUSION AND OUTLOOK
The goal behind applying network virtualization in
LTE systems is the expectation that a better performance
can be achieved; the simulation results confirmed this
expectation. Based on the contract configurations and the
traffic load of each virtual operator the air interface
resources (PRBs) are shared among the different
operators. In this way, the overall resource utilization is
enhanced and in turn the performance of both network
and end-user perspective is better. Although the
simulation results are quite scenario-specific, the basic
findings are representative and show the advantages that
can be achieved by applying network virtualization to the
LTE system. The results demonstrated the additional
advantages that can be achieved by applying network
virtualization for the wireless world in addition sharing
the infrastructure and assigning resources dynamically.
Both operator 2 and operator 3 benefited from
virtualization mainly by being able to cut costs and
providing better performance for the users. The paper
also showed the possibility of opening the market to new
players (small operators) that can serve a specific role and
have in general small numbers of users.
This work is only a starting point on the LTE
virtualization, and more issues still need to be addressed,
e.g., interference coordination among multiple virtual
operators, signaling overhead due to the hypervisor,
defining guidelines and scheduling disciplines for the
hypervisor based on more enhanced criteria/contracts and
more diverse simulation scenarios.
Nevertheless, with LTE wireless virtualization,
operators can expect not only lower investments for
flexible network deployments but also lower costs for
network management and maintenance. End users can
expect better services with lower prices in the future.
ACKNOWLEDGMENT
We would like to thank all members of the 4WARD
project, especially those from the virtualization work
package.
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