Lebanese University Faculty of Engineering - Branch III

Telecommunications

Scheduling in OFDMA based systems

Prepared by

Alaa KHREIS

Supervisor

Dr. Abed Ellatif SAMHAT

June, 2014

Acknowledgments

This report is the result of a mini-project, carried out at the Lebanese University, under the supervision and guidance of Dr. Abed Ellatif SAMHAT. I would like to sincerely thank my supervisor for his professional help and technical support.

Abstract

The new generation of wireless networks support many high resource consuming

services. The main problem of such networks is that the bandwidth is limited, besides to be subject to fading process, and shared among multiple users. Therefore, a combination of sophisticated transmission techniques (OFDMA…) and proper packet scheduling algorithms is necessary, in order to provide applications with suitable quality of service.

The objective of this mini-project is to build a reference model for the simulation

of various scheduling algorithms using the Network Simulator (NS3). To achieve this goal, an overview of Orthogonal Frequency division multiple access is presented. Then, we discuss the Long Term Evolution cellular network and apply various scheduling algorithms on the downlink to study their behavior.

We proceed by building an LTE model in order to test the LTE downlink

schedulers. The simulation results are analyzed. We compare and discuss the performance of various schedulers and our results in comparison with the theoretical performance of the schedulers.

Table of contents

Chapter I Overview on OFDM and OFDMA .............................................................. 1 1 Introduction ........................................................................................................... 1 2 OFDM ................................................................................................................... 1 3 OFDMA ................................................................................................................ 2 4 Downlink Packet Scheduling in LTE Cellular Networks ........................................ 3

Chapter II Overview on LTE Networks ....................................................................... 5 1 Introduction ........................................................................................................... 5 2 System Architecture and Radio Access Network .................................................. 5 3 Radio Bearer Management and Protocol Stack.................................................... 6 4 Physical Layer ...................................................................................................... 7 5 Radio Resource Management .............................................................................. 9 6 CQI Reporting ..................................................................................................... 10 7 AMC and Power Control ..................................................................................... 10 8 Physical Channels .............................................................................................. 11 9 HARQ ................................................................................................................. 12

Chapter III The Scheduling Process .......................................................................... 14 1 Introduction ......................................................................................................... 14 2 Different Scheduling Strategies for LTE downlink............................................... 15 3 Comparison and simulation of various MAC schedulers .................................... 15

Round Robin Scheduler ......................................................................................... 17 Proportional Fair Scheduler .................................................................................... 19 Maximum Throughput Scheduler ........................................................................... 21 Throughput to Average Scheduler ......................................................................... 22 Blind Equal Throughput Scheduler ......................................................................... 23 Token Bank Fair Queue Scheduler ........................................................................ 27 Priority Set Scheduler ............................................................................................ 28

Chapter IV Conclusions and perspectives ................................................................. 29 1 Conclusion: The simulation model and scenarios ............................................... 29 2 Conclusion: The schedulers performance .......................................................... 29 3 Perspectives: Completing the reference model .................................................. 29

The code of the simulation is available on GitHub https://github.com/AlaaKHREIS/Downlink-Scheduling-LTE-model/

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Table of Figures

Figure 1 Frequency-Time Representative of an OFDM signal ........................................ 2 Figure 2 OFDMA Subcarriers .......................................................................................... 3 Figure 3: The Radio Access Network .............................................................................. 3 Figure 4:The Service Architecture Evolution in LTE network .......................................... 6 Figure 5 Time-Frequence radio resource grid ................................................................. 8 Figure 6 Time Division Duplex frame configuration ......................................................... 9 Figure 7 Interconnections between LTE layers ............................................................. 10 Figure 8 Time/Frequency structure of the LTE downlink subframe in the 3MHz

bandwidth cases .................................................................................................... 11 Figure 9 Simplified model of a packet scheduler ........................................................... 14 Figure 10 Structure of LTE MAC scheduler in ns-3 ....................................................... 15 Figure 15 Round Robin Scheduler - scenario 1 ............................................................ 18 Figure 16 Round Robin Scheduler - scenario 2 ............................................................ 19 Figure 11 Proportional Fair Scheduler - scenario 1 ....................................................... 20 Figure 12 Proportional Fair Scheduler - Scenario 2 ...................................................... 21 Figure 13 Maximum Throughput Scheduler - scenario 2 ............................................... 22 Figure 14 Throughput to Average Scheduler - scenario 1 ............................................. 23 Figure 17 Frequency Domain Blind Equal Throughput Scheduler – scenario 2 ............ 24 Figure 18 Time Domain Blind Equal Throughput Scheduler - scenario 2 ...................... 25 Figure 19 Frequency Domain Blind Equal Throughput - scenario 1 .............................. 26 Figure 20 Time Domain Blind Equal Throughput - scenario 1 ....................................... 26

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Abbreviations

AMC: Adaptive Modulation and Coding BLER: Block Error Rate CQI: Channel Quality Indicator eNB: evolved NodeB EPC: Evolved Packet Core E-UTRAN: Evolved-Universal Terrestrial Radio Access Network FDM: Frequency Division Multiplexing GBR: Guaranteed Bit-Rate HARQ: Hybrid Automatic Transmission Request MCS: Modulation and Coding Scheme MIMO: Multiple-Input and Multiple-Output MME: Mobility Management Entity OFDM: Orthogonal Frequency Division Multiplexing OFDMA: Orthogonal Frequency Division Multiple Access PGW: Packet Data Network Gateway QoS: Quality of Service RB: Resource Block RBG: Resource Block Group RRM: Radio Resource Management SGW: Serving Gateway SINR: Signal to Interference plus Noise Ratio TDD: Time Division Duplex TTI: Transmission Time Interval UE: User Equipement

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Orthogonal frequency-division multiple Access (OFDMA) is a multiuser version of the popular orthogonal frequency-division multiplexing (OFDM) digital modulation Scheme. OFDM is a method of encoding digital data on multiple carrier frequencies. It has been developed into a scheme for wideband digital communication, whether wireless or over copper wires, used in applications such as digital television and audio broadcasting, DSL internet access, wireless networks, power line networks, and 4G mobile communications.

In OFDM, a large number of closely spaced orthogonal sub-carrier signals are used to carry data on several parallel data streams or channels. Each sub-carrier is modulated with a conventional modulation scheme (such as quadrature amplitude modulation or phase-shift keying) at a low symbol rate, maintaining total data rates similar to conventional single-carrier modulation in the same bandwidth. Conceptually, OFDM is a specialized Frequency Division Multiplexing (FDM), the additional constraint being: all the carrier signals are orthogonal to each other. This means that cross-talk between the sub-channels is eliminated and inter-carrier guard bands are not required, which simplifies the design of both the transmitter and the receiver. Unlike conventional FDM, a separate filter for each sub-channel is not required.

The orthogonality requires that the sub-carrier spacing is ∆𝑓𝑓 = 𝑘𝑘𝑇𝑇𝑈𝑈

Hertz, where

𝑇𝑇𝑈𝑈 seconds is the useful symbol duration (the receiver side window size), and k is a positive integer, typically equal to 1. Therefore, with N sub-carriers, the total pass-band bandwidth will be 𝐵𝐵 = 𝑁𝑁 ∗ ∆𝑓𝑓 . The following figure illustrates the main concepts of an OFDM signal and the inter-relationship between the frequency and time domains. In the frequency domain, multiple adjacent tones or subcarriers are each independently modulated with complex data. An Inverse FFT transform is performed on the frequency-domain subcarriers to produce the OFDM symbol in the time-domain. Then in the time domain, guard intervals are inserted between each of the symbols to prevent inter-symbol interference at the receiver caused by multi-path delay spread in the radio channel. Multiple symbols can be concatenated to create the final OFDM burst signal. At the receiver an FFT is performed on the OFDM symbols to recover the original data bits.

Chapter I Overview on OFDM and OFDMA

1 Introduction

2 OFDM

1

Figure 1 Frequency-Time Representative of an OFDM signal The orthogonality allows high spectral efficiency, with a total symbol rate near the Nyquist rate for the equivalent baseband signal. However, OFDM requires very accurate frequency synchronization between the receiver and the transmitter. With a frequency deviation, the sub-carriers will no longer be orthogonal, causing inter-carrier interference (ICI), cross-talk between the sub-carriers.

Multiple access is achieved in OFDMA by assigned subsets of subcarriers to individual users. This allows simultaneous low data rate transmission from several users. Based on feedback information about the channel conditions, adaptive user-to-subcarrier assignment can be achieved. If the assignment is done sufficiently fast, this further improves the OFDM robustness to fast fading and narrow-band co-channel interference, and makes it possible to achieve even better system spectral efficiency. Different numbers of sub-carriers can be assigned to different users, in view to support differentiated Quality of Service (QoS), to control the data rate and error probability individually for each user. OFDMA can also be described as a combination of frequency domain and time domain multiple access, where the resources are partitioned in the time-frequency space, and slots are assigned along the OFDM symbol index as well as OFDM sub-carrier index. OFDMA is considered as highly suitable for broadband wireless networks, due to advantages including scalability and use of multiple antennas (MIMO) friendliness, and ability to take advantage of channel frequency selectivity. In particular, OFDMA is used on the downlink between the eNB and UE in LTE cellular Networks.

3 OFDMA

2

Figure 2 OFDMA Subcarriers

A key feature of LTE is the adoption of advanced Radio Resource Management procedures in order to increase the system performance up to the Shanon limit. Packet Scheduling mechanisms play a fundamental role because they are responsible for choosing, with fine time and frequency resolutions how to distribute radio resources among different stations, taking into account channel condition and QoS requirements. This goal should be accomplished by providing, at the same time, an optimal trade-off between spectral efficiency and fairness. LTE access network, based on Orthogonal Frequency Division Multiplexing (OFDMA), is expected to support a wide range of multimedia and internet services even in high mobility scenarios. To achieve these goals, the Radio Resource Management (RRM) block exploits a mix of advanced MAC and Physical functions, like resource sharing, Channel Quality Indicator (CQI) reporting, link adaptation through Adaptive Modulation and Coding (AMC), and Hybrid Automatic Transmission Request (HARQ). The packed scheduler works at the radio base station, namely the evolved NodeB (eNB), and it is in charge of assigning portions of spectrum shared among users, by

4 Downlink Packet Scheduling in LTE Cellular Networks

Figure 3: The Radio Access Network

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following specific policies. In a wireless network scenario the packet scheduler plays an additional fundamental role: it aims to maximize the spectral efficiency through an effective resource allocation policy that reduces or makes negligible the impact of channel quality drops. In fact, on wireless links the channel quality is subject to high variability in time and frequency domains due to several causes, such as fading effects, multipath propagation, Doppler Effect, and so on. For these reasons, channel-aware solutions are usually adopted in OFDMA systems because they are able to exploit channel quality variations by assigning higher priority to users experiencing better channel conditions.

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LTE networks have been conceived with very ambitious requirements that strongly overtake features of 3G networks, mainly designed for classic voice services. LTE aims, as minimum requirement, at doubling the spectral efficiency of previous generation systems and at increasing the network coverage in terms of bitrate for cell-edge users. Table 1 Main LTE Performance Targets Peak Data Rate • Downlink: 100 Mbps

• Uplink: 50 Mbps Spectral Efficiency 2- 4 times better than 3G systems Cell-Edge Bit-Rate Increased whilst maintaining same site locations as deployed today User Plane Latency Below 5 ms for 5 MHz bandwidth of higher Mobility • Optimized for low mobility up to 15 km/h

• High performance for speed up to 120 km/h • Maintaining connection up to 350 km/h

Scalable Bandwidth From 1.4 to 20 MHz RRM • Enhanced support for end-to-end QoS

• Efficient transmission and operation of higher layer protocols Service Support • Efficient support of several services (web browsing, FTP, video-

streaming, VoIP …) This chapter gives an overview of the main LTE features. First of all the system architecture is described, including the main aspects of the protocol stack. The OFDMA air interface is also illustrated, with particular focus on issues related to scheduling. Finally, a comprehensive description of the fundamental RRM procedures is provided.

The LTE system is based on a flat architecture, known as the “Service Architecture Evolution”, with respect to the 3G systems. This guarantees a seamless mobility support and a high speed delivery for data and signaling. It is made by a core network, namely the Evolved Packet Core, and a radio access network, namely the Evolved-Universal Terrestrial Radio Access Network (E-UTRAN).

Chapter II Overview on LTE Networks

1 Introduction

2 System Architecture and Radio Access Network

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The Evolved Packet Core comprises the Mobility Management Entity (MME), the Serving Gateway (SGW), and the Packet Data Network Gateway (PGW). The MME is responsible for user mobility, intra-LTE handover, and tracking and paging procedures of User Equipments (UEs) upon connection establishment. The main purpose of the SGW is, instead, to route and forward user data packets among LTE nodes, and to manage handover among LTE and other 3GPP technologies. The PGW interconnects LTE network with the rest of the world, providing connectivity among UEs and external packet data networks. The LTE access network can host only two kinds of node: the UE (that is the end-user) and the eNB. Note that eNB nodes are directly connected to other and to the MME gateway. Differently from other cellular network architectures, the eNB is the only device in charge of performing both radio resource management and control procedures on the radio interface. In what follows we will focus only the LTE architecture related to the radio resource management, such as the radio access network, the radio bearer management and the physical layer design.

A radio bearer is a logical channel established between UE and eNB. It is in charge of managing QoS provision on the E-UTRAN interface. When a UE joins the network, a

3 Radio Bearer Management and Protocol Stack

GW: Packet Gateway MME: Mobility Management Entity SGW: Service Gateway E-UTRAN: Evolved Universal Terrestrial Radio Access Network

Other IP networks

SGW

MME

PGW

UE

UE

UE

eNB

Evolved Packet Core Radio Access Network

E-UTRAN

E-UTRAN

Figure 4:The Service Architecture Evolution in LTE network

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default bearer is created for basic connectivity and exchange of control messages. It remains established during the entire lifetime of the connection. Dedicated bearers, instead, are set up every time a new specific service is issued. Depending on QoS requirements, they can be further classified as Guaranteed Bit-Rate (GBR) or non-guaranteed bit rate (non-GBR) bearers. The general definition of QoS requirements is translated in variables that characterize performance experienced by users. A set of QoS parameters is therefore associated to each bearer depending on the application data it carries, thus enabling differentiation among flows. The RRM module translates QoS parameters into scheduling parameters, admission policies, queue management thresholds, link layer configurations, and so on. Table 2 Standardized QoS Class Identifiers for LTE CQI Resource

Type Priority Packet Delay

Budget [ms] Packet Loss Rate

Example services

1 GBR 2 100 10-2 Conversational voice 2 GBR 4 150 10-3 Conversational video (live

streaming) 3 GBR 5 300 10-6 Non-Conversational video

(buffered streaming) 4 GBR 3 50 10-3 Real time gaming 5 Non-GBR 1 100 10-6 IMS signaling 6 Non-GBR 7 100 10-3 Voice, video (live streaming),

interactive gaming 7 Non-GBR 6 300 10-6 Video (buffered streaming) 8 Non-GBR 8 300 10-6 TCP based (www, email, FTP,

P2P file sharing) 9 Non-GBR 9 300 10-6 LTE specifications introduced also specific protocol entities:

• The Radio Resource Control, which handles the establishment and management of connections, the broadcast of system information, the mobility, the paging procedures, and the establishment, reconfiguration and management of radio bearers.

• The Packet Data Control Protocol, which operates header compression of upper layers before the MAC enquiring.

• The MAC, which provides all the most important procedures for the LTE radio interface, such as multiplexing/demultiplexing, random access, radio resource allocation and scheduling requests.

LTE has been designed as a highly flexible radio access technology in order to support several system bandwidth configurations (from 1.4 MHz up to 20 MHz). Radio spectrum access is based on the Orthogonal Frequency Division Multiplexing (OFDM) scheme. In particular, Single Carrier Frequency Division Multiple Access (SC-FDMA) and OFDMA are used in the uplink and downlink directions, respectively. Differently from basic OFDM, they allow multiple access by assigning sets of sub-carriers to each individual user.

4 Physical Layer

7

OFDMA can exploit sub-carriers distributed inside the entire spectrum, whereas SC-FDMA can use only adjacent sub-carriers. OFDMA is able to provide high scalability, simple equalization, and high robustness against the time-frequency selective nature of radio channel fading. On the other hand, SC-FDMA is used in the LTE uplink to increase the power efficiency of UEs, given that they are battery supplied.

Radio resources are allocated into the time/frequency domain. In particular, in the time domain they are distributed every Transmission Time Interval (TTI), each one lasting 1 ms .The time is split in frames, each one composed of 10 consecutive TTIs. Furthermore, each TTI is made of two time slots with length 0.5 ms, corresponding to 7 OFDM symbols in the default configuration with short cyclic prefix. In the frequency domain, instead, the total bandwidth is divided in sub-channels of 180 kHz, each one with 12 consecutive and equally spaced OFDM sub-carriers. A time/frequency radio resource spanning over two time slots in the time domain and over one sub-channel in the frequency domain is called Resource Block (RB) and corresponds to the smallest radio resource unit that can be assigned to the UE for data transmission. As the sub-channel size is fixed, the number of RBs varies according to the system bandwidth configuration.

Figure 5 Time-Frequence radio resource grid

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The LTE radio interface supports two types of frame structure, related to the different de-multiplexing schemes. Under Frequency Division Multiplexing, the bandwidth is divided in two parts, allowing simultaneous downlink and uplink data transmissions, and the LTE frame is composed of 10 consecutive identical sub-frames. For Time Division Duplex (TDD), the LTE frame is divided into two consecutive half-frames, each one lasting 5 ms. In fact, a frame can be characterized by a very high presence of downlink (configuration 5) or uplink sub-frames (configuration 0). Note that there is in all the configurations a special downlink sub-frame that handles synchronization information. The selection of the TDD frame configuration is performed by the RRM module, and is based on the proportion between downlink and uplink traffic loading the network.

Besides resource distribution, LTE makes massive use of RRM procedures such as link adaption, HARQ, Power Control and CQI reporting.

5 Radio Resource Management

Figure 6 Time Division Duplex frame configuration

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The procedure of the CQI reporting is a fundamental feature of LTE networks since it enables the estimation of the quality of the downlink channel at the eNB. Each CQI is calculated as a quantized and scaled measure of the experienced Signal to Interference plus Noise Ratio (SINR). The main issue related to CQI reporting methods is to find a good tradeoff between a precise channel quality estimation and a reduced signaling overhead.

The CQI reporting procedure is strictly related to the AMC module, which selects the proper Modulation and Coding Scheme (MCS) trying to maximize the supported throughput with a given target Block Error Rate (BLER). In this way, a user experiencing a higher SINR will be served with higher bitrates, whereas a cell-edge user, or in general a user experiencing bad channel conditions will maintain active connections, but at the cost of a lower throughput.

6 CQI Reporting

7 AMC and Power Control

AMC: Adaptive Modulation and Coding RLC: Radio Link Control PDCCH: Physical Downlink Control Channel PUSCH: Physical Uplink Shared Channel CQI: Channel Quality Indicator HARQ: Hybrid Automatic Repeat Request RRC: Radio Resource Control PDSCH: Physical Downlink Shared Channel PUCCH: Physical Uplink Control Channel

Figure 7 Interconnections between LTE layers

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Downlink data are transmitted by the eNB over the Physical Downlink Shared Channel (PDSCH). As its name states, it is shared among all the users in the cell as, in general, no resource reservation is performed in LTE. Transmission of PDSCH payloads is allowed only in given portion of the spectrum and in certain time interval according to a scheme.

Figure 8 Time/Frequency structure of the LTE downlink subframe in the 3MHz bandwidth cases Downlink data and signaling information are time multiplexed within the sub-frame. The Physical Downlink Control Channel (PDCCH) carries assignments for downlink resources and uplink grants, including the used MCS. It is worth to observe the influence that the control overhead had on the downlink performance, because for every TTI a significant amount of radio resources is used for sending such information. PDCCH, in particular, carries a message known as Downlink Control Information (DCI), that conveys various pieces of information depending on the specific system configuration. In the uplink direction, two physical channels are defined: the Physical Uplink Control Channel (PUCCH) and the Physical Uplink Shared Channel (PUSCH). Due to single carrier limitations, simultaneous transmission on both channels is not allowed. When no uplink data transmission is foreseen in a given TTI, PUCCH is used to transmit signaling (example: ACK/NACK related to downlink transmissions, downlink CQI, and requests for uplink transmission). On the other hand, PUSCH carries the uplink control signals when the UE has been scheduled for data transmission. In this case data and different control fields, such as ACK/NACK and CQI, are time multiplexed in the PUSCH payload.

8 Physical Channels

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It is the retransmission procedure in a MAC layer, based on the use of the stop and wait algorithm. This procedure is simply performed by the eNB and UE through the exchange of ACK/NACK messages. A NACK is sent over PUCCH when a packet transmitted by the eNB is unsuccessfully decoded at the UE. In this case the eNB will perform a retransmission, sending the same copy of the lost packet. Then, the UE will try to decode the packet by combining the retransmission with the original version, and will send and ACK message to the eNB upon a successfully decoding.

The overall architecture of the LENA simulation model is depicted in the figure Overall architecture of the LTE-EPC simulation model. There are two main components:

• The LTE Model: This model includes the LTE Radio Protocol stack (RRC, PDCP, RLC, MAC, PHY). These entities reside entirely within the UE and the eNB nodes.

• The EPC Model: This models includes core network interfaces, protocols and entities. These entities and protocols reside within the SGW, PGW and MME nodes, and partially within the eNB nodes. It provides means for the simulation of end-to-end IP connectivity over the LTE model. In particular, it supports for the interconnection of multiple UEs to the internet, via a radio access network of multiple eNBs connected to a single SGW/PGW node.

Figure 9 Overall architecture of the LTE-EPC simulation model The model provides many objects and classes that we used to create our simulation. We mention the Helpers as an example (Discussing all the available objects is out of the scope of this project).

9 HARQ

10 The NS3 LTE model (LENA Projects)

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Two helper objects are used to setup simulations and configure the various components. These objects are:

• LteHelper, which takes care of the configuration of the LTE radio access network, as well as of coordinating the setup and release of EPS bearers.

• EpcHelper, which takes care of the configuration of the Evolved Packet Core. In our code we used the helpers multiple times. We define the LTE and EPC helpers: //Set LTE Helper and EPC Helper Ptr<LteHelper> lteHelper = CreateObject<LteHelper> (); Ptr<PointToPointEpcHelper> epcHelper = CreateObject<PointToPointEpcHelper> (); lteHelper->SetEpcHelper (epcHelper); We use the helpers to set the scheduler type: lteHelper -> SetSchedulerType ("ns3::FdMtFfMacScheduler"); //Setting the FD-MT Scheduler We use the helpers to set the antenna model: lteHelper->SetEnbAntennaModelType ("ns3::IsotropicAntennaModel"); We use the helper to set the path loss model: //Pathloss/Fading Model lteHelper->SetAttribute ("PathlossModel", StringValue ("ns3::FriisSpectrumPropagationLossModel")); We can also setup many other things and configure various components using the Helpers. Regarding the schedulers, multiple schedulers have been developed during the LENA projects and past Google Summer of Code. Those schedulers are currently available in ns-3.19. Besides those schedulers, we find validations suites. Their role is to run a simulation of the schedulers and verify that the obtained output matches the theoretical output of the scheduler algorithm and realistic cases, thus validating the schedulers’ functionality. Many publications have been issued regarding this topic.

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The main RRM modules that interact with downlink packet schedulers perform the following processes:

1) Each UE decodes the reference signals, computes the CQI, and sends it back to the eNB.

2) The eNB uses the CQI information for the allocation decisions and fills up a RB allocation mask.

3) The AMC module selects the best MCS that should be used for the data transmission by scheduled users.

4) The information about this user, the allocated RBs, and the selected MCS are sent to the UEs on the PDCCH.

5) Each UE reads the PDCCH payload, and in case it has been scheduled, accesses the proper PDSCH payload.

Figure 10 Simplified model of a packet scheduler

Chapter III The Scheduling Process

1 Introduction

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The simulated algorithms for scheduling differ in terms of input parameters, objectives and service targets. A simple classification of these strategies is the following:

• Channel-unaware • Channel-aware/QoS-unaware • Channel-aware/QoS-aware • Semi-persistent for VoIP support • Energy-aware (outside the scope of this project)

Most of these scheduling algorithms approach the radio resource allocation following either the time domain (TD) or the frequency domain (FD) approach. In the TD approach, the LTE scheduler assigns all radio resources in the current transmission time interval (TTI) to one UE. Conversely, in the FD approach, the LTE scheduler allocates the resources along both the frequency and time scales.

Figure 11 Structure of LTE MAC scheduler in ns-3 RRC: Radio resource control RRM: Radio resource management QAM: Quadrature amplitude modulation RLC: Radio link control

In the following section, various schedulers are discussed then simulated. We build a Network Simulator model to simulate the behavior of the schedulers. We implement two scenarios Scenario 1: All UEs are at the same distance from the eNB Scenario 2: UEs are at different distances from the eNB Following are the simulation parameters:

2 Different Scheduling Strategies for LTE downlink

3 Comparison and simulation of various MAC schedulers

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Table 3 Simulation Parameters Parameter Value AMC model PiroEW2010 Path loss model Friis Spectrum Propagation eNB antenna model type Isotropic Antenna Mobility model Constant position Bit Error Rate 0.000001 Number of User Equipment (UEs) 10 Number of Evolved NodeB (eNBs) 1 UE and eNB distance 3000 * i meters where i=0…9 Transmission power eNB: 30 dBm; UE: 23 dBm Noise figure eNB: 5 dB; UE: 5 dBm Inter Packet Interval 20 ms Simulation time 300 seconds This scenario consider some assumptions that make it feasible to determine the theoretical performance of the scheduling algorithms. Each UE will have the same SINR during the whole simulation, a UE will not change its position during the simulation. In other words, both wideband CQI and sub-band CQI of UEs are constant and their values are only related to the distance between UE and eNB and the Noise figure on the UE. We note that the transmission is subject to a BER of 0.000001. In the second scenario, each UE has a different distance to the eNB, so that each UE has different CQI parameters. The output is focused on throughput statistics since all of the algorithms have policies based on the assigned resources in terms of bitrate. Other statistics related to the delay, jitter and fairness can be studied in other scenarios. First we run the model, each time using a different scheduler and gather statistics regarding the throughput of each UE characterized by its International Mobile Subscriber Identity (IMSI), located at a specific distance from the eNB and having its own Noise Figure value. As a result, we get charts for each scheduler showing the throughput of all the UEs. These results are compared to the theoretical scheduler performance taking into account that this model is subject to noise effects. The Friis propagation loss model was first described in "A Note on a Simple Transmission Formula", by "Harald T. Friis". The original equation was described as: 𝑃𝑃𝑟𝑟𝑃𝑃𝑡𝑡

= 𝐴𝐴𝑟𝑟𝐴𝐴𝑡𝑡𝑑𝑑2𝜆𝜆2

with the following equation for the case of an isotropic antenna with no

heat loss as in our model: 𝐴𝐴𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖. = 𝜆𝜆2

4𝜋𝜋

The final equation becomes: 𝑃𝑃𝑟𝑟𝑃𝑃𝑡𝑡

= 𝜆𝜆2

(4𝜋𝜋𝑑𝑑)2

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In the implementation, 𝜆𝜆 is calculated as 𝑐𝑐𝑓𝑓 , where 𝑐𝑐 = 299792458 𝑚𝑚/𝑠𝑠 is the speed

of light in vacuum, and 𝑓𝑓 is the frequency in Hz which can be configured by the user via the Frequency attribute. A theoretical explanation of each scheduler’s functionality is followed by the simulation results. The built simulation model is based on the LTE LENA v8 documentation and lena-simple-epc.cc example provided with the ns3-1.9 LTE model. However, our scenario enable testing various schedulers under the same conditions and fading conditions which was not found while studying the literature of this topic. The purpose of the first scenario is to compare the simulation results with the discussed theory regarding each scheduler, and the second scenario permits the study of the scheduler behavior under different SINR conditions.

Round Robin Scheduler It performs fair sharing of time resources among users. Round Robin (RR) metric is 𝑖𝑖(𝑡𝑡) = 𝑡𝑡 − 𝑡𝑡𝑖𝑖 where 𝑡𝑡𝑖𝑖 refers to the last time the user 𝑖𝑖 was served. In this context, the concept of fairness is related to the amount of time in which the channel is occupied by users. Of course, this approach is not fair in terms of user throughput that, in wireless systems, does not depend only on the amount of occupied resources, but also on the experienced channel conditions. Furthermore, the allocation of the same amount of time to users with very different application-layer bitrates is not efficient. We notice that the users experiencing bad channel conditions receive lower throughput, since they are allocated the same amount of time as other users experiencing better channel conditions.

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Figure 12 Round Robin Scheduler - scenario 1 Since all the UEs in scenario 1 are subject to the same channel conditions (BER, SINR and are located at the same distance), the allocation of equal time slots to all UEs is enough to achieve fairness in resource sharing. The Round Robin Scheduler does not consider at all the past achieved throughput neither the channel indicator.

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Figure 13 Round Robin Scheduler - scenario 2 This chart verifies that the allocation of the same amount of time for UEs experiencing different SINR values results with an unfair distribution of throughput. This verifies that only equal time resource sharing does not achieve fairness.

Proportional Fair Scheduler The Proportional Fair (PF) scheduler works by scheduling a user when its instantaneous channel quality is high relative to its own average channel condition over time. Let 𝑖𝑖, 𝑗𝑗 denote generic users, let 𝑡𝑡 be the sub-frame index, and 𝑘𝑘 be the resource block index. Let 𝑀𝑀𝑖𝑖,𝑘𝑘(𝑡𝑡) be MCS usable by user 𝑖𝑖 on resource block 𝑘𝑘 according to what the AMC model reported. Finally, let 𝑆𝑆(𝑀𝑀,𝐵𝐵) be the TB size in bits for the case where a number 𝐵𝐵 of resource blocks is used. The achievable rate 𝑅𝑅𝑖𝑖(𝑘𝑘, 𝑡𝑡) in bit/s for

user 𝑖𝑖 on resource block 𝑘𝑘 at sub-frame 𝑡𝑡 is defined as 𝑅𝑅𝑖𝑖(𝑘𝑘, 𝑡𝑡) = 𝑆𝑆(𝑀𝑀𝑖𝑖,𝑘𝑘(𝑖𝑖),1)𝜏𝜏

where 𝜏𝜏 is the TTI duration. At the start of each sub-frame 𝑡𝑡, each RB is assigned to a certain user. In detail, the index 𝑖𝑖𝑘𝑘(𝑡𝑡) to which RB 𝑘𝑘 is assigned at time 𝑡𝑡 is

determined as 𝑖𝑖𝑘𝑘(𝑡𝑡) = 𝑎𝑎𝑎𝑎𝑎𝑎𝑚𝑚𝑎𝑎𝑚𝑚𝑗𝑗=1,..,𝑁𝑁𝑅𝑅𝑗𝑗(𝑘𝑘,𝑖𝑖)𝑇𝑇𝑗𝑗

where 𝑇𝑇𝑗𝑗(𝑡𝑡) is the past throughput

performance perceived by the user 𝑗𝑗. According to the above scheduling algorithm, a user can be allocated to different RBGs, which can be either adjacent or not, depending

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on the current condition of the channel and the past throughput performance 𝑇𝑇𝑗𝑗(𝑡𝑡).

The latter is determined at the end of the sub-frame 𝑡𝑡 using the following exponential

moving average approach: 𝑇𝑇𝑗𝑗(𝑡𝑡) = (1 − 1𝛼𝛼

)𝑇𝑇𝑗𝑗(𝑡𝑡 − 1) + 1𝛼𝛼𝑆𝑆(𝑀𝑀𝑖𝑖,𝑘𝑘(𝑖𝑖),𝐵𝐵𝑗𝑗(𝑖𝑖))

𝜏𝜏

where 𝛼𝛼 is the time constant (in number of sub-frames) of the exponential moving

average, and 𝑇𝑇𝑗𝑗(𝑡𝑡) is the actual throughput achievable by the user 𝑗𝑗 in the sub-frame

𝑡𝑡. 𝑇𝑇𝑗𝑗(𝑡𝑡) is measured according to the following procedure: First we determine the

MCS 𝑀𝑀𝑗𝑗(𝑡𝑡) actually used by user 𝑗𝑗 as the minimum one among the allocated RBGs.

𝐵𝐵𝑗𝑗(𝑡𝑡) is the total number of RBs allocated to user 𝑗𝑗. The expected behavior of the PF scheduler when all UEs have the same SINR is that each UE should get an equal fraction of the throughput obtainable by a single UE when using all the RBs. The second scenario test aims at verifying the fairness of the PF scheduler in a more realistic simulation scenario where the UEs have a different SINR (constant for the whole simulation). In these conditions, the PF scheduler will give to each user a share of the system bandwidth that is proportional to the capacity achievable by a single user alone considered its SINR.

Figure 14 Proportional Fair Scheduler - scenario 1

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Since all UEs in scenario 1 are experiencing the same channel conditions (same SINR and all UEs are located at the same distance from the UE). The PF scheduler allocates the same resources to all the UEs.

Figure 15 Proportional Fair Scheduler - Scenario 2 In this scenario the PF Scheduler assigns a Modulation and Coding Scheme that achieves lower bitrates to the UEs with MSI 8, 9 and 10. The PF scheduler works by scheduling a user when its instantaneous channel quality is high relative to its own average channel condition over time, but the CQI is always constant on run time in scenario 2. In this case, the scheduler won’t allocate more resources to the UEs 8, 9 and 10 because there relative channel quality indicator does not improve.

Maximum Throughput Scheduler The maximum throughput (MT) scheduler aims to maximize the overall throughput of an eNB. It allocates each RBG to the UE that can achieve the maximum expected data rate in the current TTI. Given N UEs, the priority metrics used in the frequency domain MT (FD-MT) and the time domain MT (TD-MT) are respectively

𝑖𝑖𝑘𝑘(𝑡𝑡) = 𝑎𝑎𝑎𝑎𝑎𝑎𝑚𝑚𝑎𝑎𝑚𝑚𝑗𝑗=1,..,𝑁𝑁𝑅𝑅𝑗𝑗(𝑘𝑘, 𝑡𝑡) 𝑖𝑖(𝑡𝑡) = 𝑎𝑎𝑎𝑎𝑎𝑎𝑚𝑚𝑎𝑎𝑚𝑚𝑗𝑗=1,..,𝑁𝑁𝑅𝑅𝑗𝑗(𝑡𝑡)

Where 𝑗𝑗 =1,…,N is the generic UE, 𝑖𝑖𝑘𝑘(𝑡𝑡) is the UE chosen by FD-MT for transmission on the RBG 𝑘𝑘 at the time interval 𝑡𝑡, and 𝑖𝑖(𝑡𝑡) is the UE chosen by TD-MT for transmission at the time interval 𝑡𝑡 over all the RBGs. Here 𝑅𝑅𝑗𝑗(𝑘𝑘, 𝑡𝑡) and 𝑅𝑅𝑗𝑗(𝑡𝑡) are the

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priority metrics used for FD-MT and TD-MT, respectively. In particular, 𝑅𝑅𝑗𝑗(𝑘𝑘, 𝑡𝑡) is the

achievable data rate for RBG 𝑘𝑘 determined based on the sub-band CQI of that specific RBG, while 𝑅𝑅𝑗𝑗(𝑡𝑡) is the achievable data rate determined based on the Wideband CQI. By using the above priority metrics, MT scheduler always allocates radio resources to UEs with the best channel quality. As such, the network utilization efficiency can be maximized since the network resources are always fully utilized. However, this opportunistic behavior cannot ensure fairness among UEs which is another key performance factor.

Figure 16 Maximum Throughput Scheduler - scenario 2 This scheduler achieved the highest total throughput in scenario 2, however the UEs located at 24 km and 27 km received a very low throughput. Those results validate the previous theoretical discussion.

Throughput to Average Scheduler Different form MT, the throughput to average (TTA) scheduler can be considered as the tradeoff between efficiency and fairness. The scheduling decision in TTA is performed

according to the following equation: 𝑖𝑖𝑘𝑘(𝑡𝑡) = arg𝑚𝑚𝑎𝑎𝑚𝑚𝑗𝑗=1,…,𝑁𝑁𝑅𝑅𝑗𝑗(𝑘𝑘,𝑖𝑖)𝑅𝑅𝑗𝑗(𝑖𝑖)

. The priority

metric of the TTA scheduler is 𝑅𝑅𝑗𝑗(𝑘𝑘,𝑖𝑖)𝑅𝑅𝑗𝑗(𝑖𝑖)

which in general varies with t, j and k. From this

consideration, it is evident that TTA is a FD scheduler.

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Since according to scenario 2 configuration the CQI of UEs are constant, the achieved data rate selected by adaptive modulation and coding (AMC) is also constant during the simulation. Therefore, for FD-MT, TD-MT and TTA all UEs have the same priority metrics.

Figure 17 Throughput to Average Scheduler - scenario 1 In scenario 1, all the UEs have the same CQI value which remains constant during the simulation time, therefore they receive equal throughput. However a more meaningful scenario would consists of varying the CQI indicator (by varying the position of each UE during the simulation time for example) to verify the TTA Scheduler properties.

Blind Equal Throughput Scheduler The blind equal throughput (BET) scheduler aims to provide an equal throughput to all UEs associated with the same eNB. Unlike MT and TTA, BET is channel unaware in the sense that both FD-BET and TD-BET use wideband CQI in packet scheduling. The scheduling decision for BET is performed according to the following equation:

𝑖𝑖(𝑡𝑡) = arg𝑚𝑚𝑎𝑎𝑚𝑚𝑗𝑗=1,…,𝑁𝑁1

𝑇𝑇𝑗𝑗(𝑡𝑡)

where 𝑇𝑇𝑗𝑗(𝑡𝑡) is the past average throughput of UE j at time t, which is calculated by

𝑇𝑇𝑗𝑗(𝑡𝑡) = 𝛽𝛽𝑇𝑇𝑗𝑗(𝑡𝑡 − 1) + (1 − 𝛽𝛽)𝑅𝑅𝑗𝑗(𝑡𝑡)

Here 𝛽𝛽 ( 0 ≤ 𝛽𝛽 ≤ 1 ) is the weight factor for moving average, and 𝑅𝑅𝑗𝑗(𝑡𝑡) is the achieved data rate of UE 𝑗𝑗 at time t as defined above. The TD-BET scheduler selects

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the UE with the largest priority metric and allocates all RBs in the current TTI to this UE. In contrast, the FD-BET scheduler first selects the UE with the lowest past average throughput (largest priority metric) and assigns one RBG to this UE. Then, the scheduler recalculates its expected throughput and continues to allocate more RBG blocks to this UE if the updated past average throughput is still not greater than other UEs. This procedure continues until the expected throughput of this UE is no longer the lowest. The scheduler assigns RBG blocks to other UEs in the same way until all RBGs are allocated. The idea behind this algorithm is to ensure an equal throughput among all UEs in every TTI. UEs are placed at different distance to eNB so that the AMC will assign different achievable data rate to each UE according to its wideband CQI. In this case, FD-BET will allocate a different number of RGBs to each UE. Besides, the time slot between two scheduling events for one UE in TD-BET is also variable. This will result in an equal throughput for all UEs.

Figure 18 Frequency Domain Blind Equal Throughput Scheduler – scenario 2 In this chart the difference between the highest throughput and lowest throughput is only 4.68 kbps. The Frequency Domain Blind Equal Throughput Scheduler succeeded to provide relatively equal throughput to all UEs.

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Figure 19 Time Domain Blind Equal Throughput Scheduler - scenario 2 We notice that the FD-BET is much better than the TD-BET in terms of equal throughput scheduling since the maximum difference in throughput here is 70.55 kbps. By comparing this to the result obtained previously and the results obtained using other schedulers, we deduce that the TD-BET almost achieves equal throughput distribution when the UEs are subject to different SINR. This result verifies that the FD approach can be more efficient in terms of equal resource sharing. In scenario 1, we expect equal throughput for all UEs using TD-BET and FD-BET and this is exactly the obtained result as shown below.

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Figure 20 Frequency Domain Blind Equal Throughput - scenario 1

Figure 21 Time Domain Blind Equal Throughput - scenario 1

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Token Bank Fair Queue Scheduler The token bank fair queue (TBFQ) is a QoS aware scheduler which derives from the leaky-bucket mechanism. In TBFQ, the traffic flow of a generic UE 𝑖𝑖 = 1, … ,𝑁𝑁 is characterized by the following parameters:

• 𝑡𝑡𝑖𝑖 : packet arrival rate • 𝑎𝑎𝑖𝑖 : token generation rate • 𝑝𝑝𝑖𝑖 : token pool size • 𝐸𝐸𝑖𝑖 : counter that records the number of token borrowed from or given to the token

bank by flow 𝑖𝑖. 𝐸𝐸𝑖𝑖 can be smaller than zero

Each 𝑘𝑘 bytes data consumes 𝑘𝑘 tokens. Also, TBFQ maintains a shared token bank B so as to balance the traffic between different flows. If token generation rate 𝑎𝑎𝑖𝑖 is bigger that packet arrival rate 𝑡𝑡𝑖𝑖 , then tokens overflowing from token pool are added to the token bank, and 𝐸𝐸𝑖𝑖 is increased by the same amount. Otherwise, flow 𝑖𝑖 needs to

withdraw tokens from the token bank based on a priority metric 𝐸𝐸𝑖𝑖𝑖𝑖𝑖𝑖

, and then 𝐸𝐸𝑖𝑖 is

decreased. Obviously, the UE that contributes more to the token bank has a higher priority to borrow tokens. On the other hand, the UE that borrows more token from the bank has a lower priority to continue to withdraw tokens. Therefore, in case of several UEs having the same token generation suffering from higher interference has more opportunity to borrow tokens from bank. In addition, TBFQ can police the traffic by setting the token generation rate to limit the throughput. Besides, TBFQ maintains following three parameters for each flow:

• 𝑑𝑑𝑖𝑖 : debt limit, if 𝐸𝐸𝑖𝑖 drops below this threshold, UE 𝑖𝑖 cannot further borrow tokens from bank. This is for preventing a UE to borrow too much tokens

• 𝑐𝑐𝑖𝑖: the maximum number of tokens UE 𝑖𝑖 can borrow from the bank each time • 𝐶𝐶: once 𝐸𝐸𝑖𝑖 reaches the debt limit 𝑑𝑑𝑖𝑖 , the UE 𝑖𝑖 must store C tokens to the bank

in order to borrow further tokens.

We implement two versions of the TBFQ scheduler: frequency domain TBFQ (FD-TBFQ) and time domain TBFQ (TD-TBFQ). In FD-TBFQ, the scheduler always selects the UE with the highest metric and allocates RBG with the highest sub-band CQI until there are no packets in the UE’s RLC buffer or all RBGs are allocated. In TD-TBFQ, after selecting the UE with maximum metric, all the RBGs are allocated to this UE by using the wideband CQI. In the current implementation, the token generation rate is configured according to the guaranteed bit rate (GBR) specified by the EPS bearer QoS parameters. In this model we set the following TBFQ parameters: the Debt limit is – 5 Mbytes (𝑑𝑑𝑖𝑖 =−625000), the Credit limit is 5 Mbytes (𝑐𝑐𝑖𝑖 = 625000) and the token pool size is 1 byte (𝑝𝑝𝑖𝑖 = 1).

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Priority Set Scheduler The Priority Set Scheduler (PSS) is another QoS aware scheduler which combines time domain (TD) and frequency domain (FD) packet scheduling operations into one scheduler. It controls the fairness among UEs by defining a specified target bit rate (TBR). In the TD part of the scheduler, PSS first selects those UEs with a non-empty RLC buffer, and then divides them into two sets based on the TBR:

• Set 1: UEs whose past average throughput is smaller than TBR. TD scheduler calculates its priority metric 𝑝𝑝𝑘𝑘1(𝑡𝑡) following the blind equal throughput (BET) approach:

𝑝𝑝𝑘𝑘1(𝑡𝑡) = 1𝑇𝑇𝑗𝑗(𝑖𝑖)

• Set 2: UEs whose past average throughput is larger (or equal) than TBR. TD scheduler calculates the priority metric 𝑝𝑝𝑘𝑘2(𝑡𝑡) following the proportional fair (PF) approach:

𝑝𝑝𝑘𝑘2(𝑡𝑡) = 𝑅𝑅𝑗𝑗(𝑘𝑘, 𝑡𝑡)𝑇𝑇𝑗𝑗(𝑡𝑡)

Here, 𝑅𝑅𝑗𝑗(𝑘𝑘, 𝑡𝑡) is the achievable data rate for UE 𝑗𝑗 at time t on the 𝑘𝑘 − 𝑡𝑡ℎ RBG and

𝑇𝑇𝑗𝑗(𝑡𝑡) is the past average throughput of UE 𝑗𝑗 at time 𝑡𝑡. The UEs belonging to set 1 are

considered with a high priority than those in set 2. PSS selects 𝑁𝑁𝑚𝑚𝑚𝑚𝑚𝑚 UEs with the highest metric in the two sets and forward those UEs to FD scheduler. In PSS, FD scheduler allocates a RBG to the UE with largest metric. The Carrier over interference to average algorithm (Colta) have been considered:

𝑀𝑀𝑘𝑘(𝑡𝑡) = 𝑚𝑚𝑎𝑎𝑚𝑚𝑗𝑗=1,…,𝑁𝑁 𝐶𝐶𝐶𝐶𝐶𝐶[𝑗𝑗, 𝑘𝑘]

∑ 𝐶𝐶𝐶𝐶𝐶𝐶[𝑗𝑗, 𝑘𝑘]𝑁𝑁𝑘𝑘=0

𝐶𝐶𝐶𝐶𝐶𝐶[𝑗𝑗,𝑘𝑘] is an estimation of the signal to interference plus noise ratio (SINR) on the RBG 𝑘𝑘 of UE 𝑗𝑗. Colta is used for decoupling the FD metric from TD scheduler. In addition, FD scheduler also provides a weight metric 𝑊𝑊[𝑛𝑛] for helping controlling

fairness in the case of a low number of UEs 𝑊𝑊[𝑛𝑛] = 𝑚𝑚𝑎𝑎𝑚𝑚(1, 𝑇𝑇𝐵𝐵𝑅𝑅𝑇𝑇𝑗𝑗(𝑖𝑖)).

Therefore, on RBG k, the FD scheduler selects the UE j that maximizes the product of the frequency domain metric by weight 𝑊𝑊[𝑛𝑛]. This strategy will guarantee the throughput of lower quality UEs tending toward their TBR.

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The reference model created in this project achieved successfully the simulation of the channel unaware and QoS unaware schedulers. The two scenarios (all UEs located at the same distance and various distances) helped to show many scheduler features especially for the Round Robin scheduler, Blind Equal Throughput Scheduler and others. Some output data is omitted (for the QoS aware schedulers) since it does not provide useful data using our model for the purpose of our project.

We deduce that equal time sharing in the Round Robin Scheduler does not achieve fairness in throughput. The Maximum Throughput Scheduler achieves better overall throughput without considering fairness among all UEs. The Proportional Fair scheduler allocates resources based on the change of the channel quality indicator in order to achieve fairness among the users. The Throughput to Average Scheduler is a tradeoff between efficiency and fairness, it takes in consideration the Channel Quality Indicator. The Token Bank Fair Queue Scheduler and Priority Set Scheduler take in consideration the CQI and QoS to achieve fairness and efficiency, however both schedulers do not perform well in a scenario where the CQI and QoS are constant.

While reading the literature about this topic, we found a lack of existence of an open source reference model in NS3 that can be used to compare the schedulers. Therefore, we started building this reference model. However, our model is not complete at the time of writing this report. The CQI must be varied during the simulation time, the model must provide various QoS requirements and multiple adjustments must be done to complete the reference model and add more scenarios.

Chapter IV Conclusions and perspectives

1 Conclusion: The simulation model and scenarios

2 Conclusion: The schedulers performance

3 Perspectives: Completing the reference model

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[1] Nicola Baldo, Marco Miozzo, Manuel Requena-Esteso, Jaume Nin-Guerrero, “An Open Source Product-Oriented LTE Network Simulator based on ns-3”, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), 2012.

[2] G. Piro, N. Baldo, and M. Miozzo, “An LTE module for the ns-3 network simulator,” in Proc. of the Workshop on NS-3 (in conjunction with SimuTOOLS 2011), Mar. 2011.

[3] F. Capozzi, G. Piro, L.A. Grieco, G. Boggia and P. Camarda, “Downlink Packet Scheduling in LTE Cellular Networks: Key Design Issues and a Survey”, “DEE - Dip. di Elettrotecnica ed Elettronica”, Politecnico di Bari, 2011

[4] Dizhi Zhou, Nicola Baldo and Marco Miozzo, “Implementation and Validation of LTE Downlink Schedulers for ns-3”, Centre Tecnològic de Telecomunicacions de Catalunya Barcelona, Spain, 2012

[5] ns-3 Tutorial,Release ns-3.19, ns-3 project, December 20, 2013 [6] Wikipedia, “Orthogonal frequency-division multiple access”,

“http://en.wikipedia.org/wiki/Orthogonal_frequency-division_multiple_access”, 2014

[7] Wikipedia, “Orthogonal frequency-division multiplexing”, “http://en.wikipedia.org/wiki/Orthogonal_frequency-division_multiplexing”, 2014

[8] Lena v8 user documentation, « http://lena.cttc.es/manual/lte-user.html », 2014 [9] Lena v8 testing documentation, « http://lena.cttc.es/manual/lte-testing.html »,

2014

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