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 co-existing 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 co-existing 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 SC-FDMA 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.

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

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:

nTTIPRBsnEstnEst _1)(

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 un-served 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:

EstTotalnEstFF ii _/)(

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:

sBEoperator

i inEstEstTotal#

1_

The number of PRBs allocated for each BE virtual operator is calculated as follows:

PRBsLeftFFallocPRBs ii _int_

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 operators

4 virtual operators (red, blue, green and 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 interface

Effective Exponential SINR mapping (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 arrival time

Const (0.01 sec)

Incoming/Outgoing stream frame size

Const (80 Bytes)

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.

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 8. Operator1 DL average per user air interface throughput

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 users have similar performance in both scenarios. This is also applicable for the average per user application end-2-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 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 12. Operator 2 DL used number of PRBs vs. time

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.

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

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. 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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