Today mobile data traffic day by day increasing with public demands more
facility with highly applicable multimedia application in their mobile phones, laptops requires by customers. Data usage is advanced by a number of bandwidth hungry laptop applications including internet and intranet access, file sharing, streaming services to distribute video content and mobile TV and interactive gaming. In addition, service bundles of video, data and voice ' known also as triple play ' are entering the mobile market, also replacing the traditional fixed line voice and broadband data services with mobile services both at home and in the office. 
The high demand for higher data rates now-a-days for mobile wireless communication systems for supporting the wide range of multimedia, internet services has gained a significant attraction around the globe from mobile researchers and industries. The third Generation Partnership Project (3GPP) organization as an international collaboration project includes a tremendous number of members, mainly from both mobile industries and research institutes; it is dedicated to delivering a globally applicable third generation (3G) mobile phone system specification. It started on December 1998 and it was based on the
Global System for Mobile Communications (GSM) specifications, 2nd generation mobile systems (2G), and it is known generally now as Universal Mobile Telecommunications System (UMTS), 3rd generation (3G) mobile systems.
3GPP is also working to improve the UMTS standard to cope with the ever- evolving future requirements including efficiency improvement, lowering costs, services enhancements, exploiting the new spectrum opportunities, and better integration with other standards. Several complete-sets of technical specifications for these systems are produced by 3GPP.
The 3rd generation partnership project (3GPP) work on solutions and challenges came up with HSPA. The HSPA is currently used in 3G phones for such applications. It is expected that 80% in 2014 mobile broadband users se rved by HSPA and LTE networks.
The new enhancements for LTE is targets data rates up to 100 Mbps for Downlink (DL) and 50 Mbps for the Uplink (UL) over different frequency bands from 1.4 MHz (as smallest) up to 20 MHz (as largest).
1.2 Thesis Motivation
LTE is an enhancement to the previous releases of the 3GPP and has
received a significant amount of focus and research among several research institutes and mobile operators around the globe.
Conducting the proposed LTE state-of-the-art technology study by 3GPP with the new evolution system capabilities it indeed needs to be simulated first as a starting point to assuring its performance and towards deploying the new evolution system by mobile operators in the near future.
The motivation to work on this project comes from the fact that LTE is the future of mobile broadband. It is expected that in the future 80% of all mobile broadband users will be served by LTE .
Round Robin scheduling and Best CQI scheduling have been selected because of their characteristics. The Best CQI scheduling optimizes the user throughput by assigning the resource block to the user with the good channel
1.3 Objective of Thesis
This report will present implementation of scheduling methods in Long
Term Evolution (LTE). The study will begin with the basics of LTE and then, two scheduling algorithms will be introduced. My dissertation will focus on the analysis and implementation of Round Robin, proportional Fair and Best CQI scheduling algorithms will be presented subsequently.
This thesis is aimed towards the Comparative analysis of two scheduling algorithms in SISO & MIMO system Long Term Evolution (LTE) downlink. Estimating different parameters like Pedestrian & Vehicular ITU channel models. These parameters are very important as they define the performance characteristics of channel and fairness and user Throughput ultimately affect the performance of whole system.
LTE, Long Term Evolution, is a fourth-generation wireless technology. The
3rdGeneration Partnership Project (3GPP) Long Term Evolution (LTE) represents a major advance in cellular technology. LTE is designed to meet carrier needs for high- speed data and media transport as well as high-capacity voice support well into the next decade.
LTE Release 8 frozen in December 2008 provides very stable specifications and benefits to all subsequent 3GPP Releases.
LTE provide continuity in 3G system for future also it satisfied user demand of higher data rate and quality of services. It is packet switched optimised system, it provides cost reduction to many vendors and research institutes which participating in its standardizations.
LTE is designed to increase data rates and cell edge bitrates, improve spectrum efficiency (unicast as well as broadcast) and a llow spectrum flexibility (1.4,3, 5, 10, 15 and 20 MHz) for flexible radio planning. LTE has also to reduce packet latency, the main restriction for real-time services, such as Voice over IP or video conferencing, reduce radio access network cost as well as cost -effective migration from earlier 3GPP releases.
Orthogonal frequency division multiplexing (OFDM) has been adopted as downlink transmission scheme in LTE. A downlink transmission is from base station to mobile station. OFDM divides transmitted high bit stream signal into different sub streams and sends over many different sub-channels.
The downlink physical resource is represented as a time-frequency resource grid consisting multiple resource blocks (RB). A resource block is made o f many resource elements (RE). A scheduler is key element in BS and it assigns the time and frequency resources in cell.
Therefore, this thesis project is focused on studying the Round robin and Best CQI scheduling in LTE downlink. This estimation study has been carried out by means of computer-based simulation using MATLAB. The Round Robin and Best CQI scheduling algorithms have been simulated in MATLAB-based Downlink Link Level Simulator from Vienna University.
The implemented channel type of ITU defined pedestrian for LTE downlink level simulator currently operates over a variable 1.4MHz to 20 MHz frequency-
Band. for 5MHz bandwidth range Round Robin, Proportional Fair Best CQI
Scheduling schemes were implemented in SISO and MIMO systems.
1.5 Outline of Thesis
The remainder of this thesis is organized as follows.
CHAPTER 2 Overview of LTE.
CHAPTER 3 Detailed illustration of the scheduling algorithms based LTE
downlink design structure with comprehensive details.
CHAPTER 4 Detailed description of the implementation and mathematical analysis of both SISO and MIMO systems.
CHAPTER 5 Implementation of Vienna University LTE link level MATLAB simulator for scheduling techniques of the LTE Downlink and discusses parameters used also checking user Throughput in both scheduling schemes.
CHAPTER 6 Simulation results.
CHAPTER 7 Conclusion and future work of proposed report.
CHAPTER: 2 Long Term Evolution
This chapter introduces first the 3GPP LTE overview. We will begin with LTE
We will also introduce orthogonal frequency division multiplexing (OFDM) which used in LTE Downlink. Next we will see spectrum flexibility, then LTE downlink physical resource and downlink transport channel processing in preceding sections.
LTE, Long Term Evolution, is a fourth-generation wireless technology. The 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) represents a major advance in cellular technology. LTE is designed to meet carrier needs for high- speed data and media transport as well as high-capacity voice support well into the next decade .
3GPP started to work on the Evolution of the UMTS with the RANEvolution Work Shop in November 2004 and, recently, on December 2008, 3GPP has approved the functional freeze of LTE as part of Release 8. Therefore, this landmark achievement will allow the operators to realize their early deployment plans in deploying this technology. LTE is aimed at providing the true global mobile broadband experience for users with very high speed data rate and low latency compare to previous releases also places high priority on improving spectral efficiency and reducing cost .
In mid-2007, LTE performance has been evaluated in so-called checkpoints and the results were agreed on in a 3GPP meeting in South Korea. The specification work on LTE was completed inMarch2009 as the SAE specifications were included. Implementation based on the March 2009 version will guarantee backwards compatibility.
While the first mobile communications standards focused primarily on voice communication, the emphasis now has returned to the provision of systems optimized for data. This trend began with the 3rd Generation Wideband Code Division Multiple Access (WCDMA) system designed in the 3GPP, and is now reaching fulfilment in its successor, known as the Long-Term Evolution (LTE). LTE is the first cellular communication system optimized from the outset to support packet-switched data services, within which packetized voice communications are just one part.
LTE is an enabler. It is not technology for technology's sake, but techno logy with a purpose, connecting people and information to enable greater things to be achieved. It will provide higher data rates than ever previously achieved in mobile communications, combined with wide-area coverage and seamless support for mobility without regard for the type of data being transmitted. To provide this level of functionality and flexibility, it is inevitable that the complexities of the L TE system have far surpassed anything Marconi could have imagined.
2.1 An Overview of LTE
LTE requirements to achieve its goals, LTE must satisfy the following
' Data rates
LTE should support a data rate up to 100 Mb/s within a 20 MHz downlink
spectrum allocation and 50 Mb/s within a 20 MHz uplink or, equivalently, spectral efficiency Values of 5bps/Hz and 2.5 bps/Hz, respectively.
The downlink average throughput per MHz is about 3 to 4 times higher than in the release 6. The uplink average user throughput per MHz is about 2 to 3 times higher than in the release 6.
LTE allows bandwidth ranging from 1.4 MHz up to 20 MHz, where the latter
is used to achieve the highest LTE data rate. Furthermore, LTE operates in both paired and unpaired spectrum by supporting both Frequency-Division Duplex (FDD) and Time-Division Duplex (TDD).
The mobility is optimized for low terminals speeds ranging from 0 to 15
km/h. The connection should be maintained for very high UEs spee ds up to 350 km/h or even up to 500km/h.
The above targets should be met for 5 km cells and some slight degradation
in throughput and spectrum efficiency for 30 km cells. 100 km cells and up can't
meet the targets requirements.
2.2 Orthogonal Frequency Division Multiplexing
Orthogonal Frequency Division Multiplexing (OFDM) has gathered much
attention in recent years and has been adopted as the downlink transmission scheme for the 3GPP LTE. OFDM is a multicarrier transmission scheme becau se it splits up the transmitted high bit-stream signal into different sub-streams and sends these over many different sub channels .
In other words OFDM simply divides the available bandwidth into multiple narrower sub-carries and transmits the data on these carries in parallel streams. Each subcarrier is modulated using different levels of modulation, e.g. QPSK, QAM, 64QAM and an OFDM symbol is obtained by adding the modulated subcarrier signals.
2.3 Spectrum Flexibility
In LTE communication is available in different frequency bands, of different
sizes. Furthermore the communication can take place both in paired and unpaired bands. Paired frequency bands means that the uplink and downlink transmissions use separate frequency bands, while unpaired frequency bands downlink and uplink share the same frequency band. In LTE downlink transmissions are grouped in (radio) frame of length 10ms. One radio frame is formed of 10 sub frames of 1ms duration. Therefore there are ten sub frames in the uplink and ten frames in the downlink. Each sub frame is divided into two slots of 0.5 ms. duration. Each slot counts 6 or 7 OFDM symbols for normal or extended cyclic prefix used. The LTE frame structure is illustrated in the Figure 2.3.1
Figure 2.3.1 LTE Frame structures
The spectrum is very flexible and allows LTE to use different bandwidths ranging from 1.4MHz to 20 MHz the larger the bandwidth is, the higher the LTE
data rates .
2.4 Downlink physical resource
Orthogonal Frequency Division Multiplex (OFDM) is the core of LTE
downlink transmission . LTE downlink physical resource can be represented as a
time-frequency resource grid as depicted in the Figure 2.4.1. A
(RB) has a duration
of 0.5msec (one slot) and a bandwidth
of 180 kHz (12
subcarriers). It is a straightforward to see that each RB has 12x7 = 84 resource elements in the case of normal cyclic prefix and 12x6 = 72 resource elements in the case of extended cyclic prefix.
Figure 2.4.1 LTE downlink physical resource based on OFDM .
The resource grid refers to a number of resource blocks
in the available
bandwidth. Each entry of the resource block is called a Resource Element (RE) which represents one OFDM subcarrier during one OFDM symbol interval . The number of RB in a resource grid varies according to the size of the bandwidth. The OFDM subcarrier spacing is 15 KHz.
The table 2.4.1 shows the LTE bandwidth and resource configuration.
4 3 5 10 15 20
Blocks 6 15 25 50 75 10
occupied subcarriers 72 18
IFFT/FFT size 12
Spacing(KHz) 15 15 15 15 15 15
Table 2.4.1 Bandwidth and Resource blocks specifications.
2.5 Downlink reference signals
To perform channel estimation, reference symbols (reference signals) are
embedded in the Physical resource block (PRB) as shown in Figure 2.5.1. Reference signals are inserted in the first and fifth OFDM symbols of each slot in the case of the short CP and during the first and fourth OFDM symbols in the case of the long CP. Thus there are four reference symbols within one PRB.
Figure2.5.1 LTE downlink reference signal assuming normal CP
The Physical Resource Block (PRB) is the smallest element assigned by the base station scheduler.
2.6 Downlink transport channel processing
At beginning of the transport channel processing, a Cyclic Redundancy Check
(CRC) is computed and attached to each transport block (TB) for the detection of errors in the TB by the receiver. (See below fig.)
Transport block(s) of dynamic size
Delivered from MAC layer
CRC insertion per transp
Code-block segmentation a
Per code block CRC inserti
Rate matching and
Physical-layer hybrid ARQ Functionality
To OFDM modulation for each antenna
Figure 2.6.1 LTE downlink transport-channel processing .
After the CRC insertion, the data (TB + CRC) to be sent are turbo coded with a coding rate of 1/3. The task of the hybrid-ARQ is to take care of the retransmission if erroneously received packets are received. Retransmission must represent the same information bits as the initial message but the coded bits used for each retransmission can be different than the original message. Later the information to be transmitted is modulated using one of the following modulation schemes: QPSK,
16QAM, and 64QAM is representing two, four, and six bits per modulation symbol, respectively
The Antenna mapping block maps the transport block to different antennas. LTE uses up to four transmit antennas. LTE supports different multiple transmit antennas schemes: transmit diversity, beam forming and spatial multiplexing. The
goal of the resource block mapping is to map the data to be sent on each antenna to a set of resource blocks assigned by the scheduler. The Figure displays the downlink resource block mapping.
Figure 2.6.2 Downlink resource blocks mapping 
CHAPTER: 3 Scheduling methods in LTE downlink
3.1 Scheduling Methods
The scheduler controls the allocation of shared time-frequency resources
among users at each time instant. The scheduler is located in the base station and assigned uplink and downlink resources. The scheduler determines to which user the shared resources (time and frequencies) for each TTI (1ms.) should be allocated for reception of DL-SCH transmission.
3.2 Link adaptation
Link adaptation (LA) compensates the variations in the instanta neous
channel conditions. In situations with advantageous channel conditions, the data rate is increased and vice versa. To adjust the data rate, LA uses Adaptive modulation and coding (AMC). AMC matches the modulation and the channel coding scheme on resources assigned by the scheduler . In situations with advantageous channel conditions, AMC selects a higher modulation order and coding rate and vice versa. This principle is illustrated in Figure 3.2.1
Figure 3.2.1 Link Adaptations 
In principle the base station periodically receives information from the terminal in the form of Channel Quality Indicator (CQI). The higher the CQI, that provides better the channel. Thus based on the CQI received from the terminal, link adaptation can be performed.
The terminal reports the measured CQI to the BS by mapping the measured SNR according to Figure. LTE simulator the mapping of the SNR to the CQI for a BLER of 0.1 is approximated through a linear function as shown in figure 3.2.1.
Figure 3.2.2 SNR-CQI mapping model 
The Table 3.2.1 contains the CQI index, the modulation scheme and channel coding rate corresponding to the CQI value.
CODING RATE EFFICIENCY
0 Out of Range
1 QPSK 78/1024 0.1523
2 QPSK 120/1024 0.2344
3 QPSK 193/1024 0.3770
4 QPSK 308/1024 0.6016
5 QPSK 449/1024 0.8770
6 QPSK 602/1024 1.1753
7 16 QAM 378/1024 1.4766
8 16 QAM 490/1024 1.9141
9 16 QAM 616/1024 2.4063
10 64 QAM 466/1024 2.7305
11 64 QAM 567/1024 3.3223
12 64 QAM 666/1024 3.9023
13 64 QAM 772/1024 4.5234
14 64 QAM 873/1024 5.1152
15 64 QAM 943/1024 5.5547
Table 3.2.1 CQI table 
3.3 Best CQI scheduling
As the name implies, this scheduling strategy assigns resource blocks to the
user with the best radio link conditions as illustrated in Figure. In order to perform scheduling, terminals send Channel Quality Indicator (CQI) to the base station (BS).
Basically in the downlink, the BS transmits reference signal (downlink pilot) to terminals. These reference signals are used by UEs for the measurements of the CQI. A higher CQI value means better channel condition.
Figure 3.3.1 Best CQI scheduling 
Best CQI scheduling  can increase the cell capacity at the expense of the fairness. In this scheduling strategy, terminals located far from the base station (i.e. cell-edge users) are unlikely to be scheduled. The flowchart of the Bes t CQI scheduling is depicted in Figure 3.3.2
Figure 3.3.2 Best CQI scheduling Flowchart
3.4 Round Robin Scheduling
In this scheduling strategy the terminals are assigned the shared resources in turn (one after another). Thus every user is equally scheduled without taking the CQI into account as illustrated in Figure 3.4.1.
Figure 3.4.1 Round Robin scheduling 
advantage of Round Robin scheduling is
the guaranty of
fairness for all users. Furthermore Round Robin is easy to implemented, that is the reason why it is usually used by many systems. Since Round Robin doesn't take the
channel quality information into account, it will result in low user throughput. The flowchart of the Round Robin scheduling is shown in Figure 3.4.2.
In flow chart
we have seen that scheduling occurrence is one by one
manner. In Round Robin scheduling always schedules their user in first come first
serve basis which is
shown in flowchart figure. So it always provides very high
fairness comparatively Best CQI scheduling scheme. Here we can see that al l the participated users scheduled at end of scheduling task performed by base station to UEs end. The Round Robin scheduler always check that all user being scheduled or not at end.
Clearly we can see from the flowchart that Round Robin scheduler more
focused in fairness
of user where Best CQI scheduler more focuses on the
maximizing UEs Throughput and not on the fairness to UEs.
Figure 3.4.2 Round Robin scheduling Flowchart
3.5 Proportional Fair Scheduling:
In order to find a trade-off between throughput and fairness we design an
ewscheduling algorithm that operates somewhere between the Best CQI scheduling and the Round Robin scheduling. Then ewscheduling algorithm will result in an acceptable throughput and provides some fairness between users. We propose an ewscheduling algorithm that assigns the RB to the user that maximizes the CQI in the first slot period of each sub- frame; where as in the second slot period the scheduler assigns the RB in turn to each user. In this way thus a compromise between the fairness and the throughput can be reached. The granularity of the proposed new scheduling algorithm is 1 resource block (RB). A resource block is the smallest element of resource allocation assigned by the BS scheduler. We have seen in the section 2.3 that one LTE frame is divided in 10 sub-frames of 1 msec duration. One subframe contains two slots periods of 0.5msec duration. Figure4.1 illustrates the flowchart to the proposed new scheduling algorithm. At the beginning of the scheduling process the BS compares the CQI from different terminals and selects the user with the highest CQI. If there is more than one terminal with the highest CQI, a random one is picked by the scheduler. In the first timeslot the terminals with higher CQI are scheduled. In the second timeslot the terminals are scheduled cyclically in turn. At the end of the second slot period the process begins again. Thus in the first slot of the second sub-frame the terminal with the higher CQI is selected and in the second timeslot the terminals are assigned the RBs in turn.
Figure3.4.3 Flowchart of the proposed new scheduling algorithm
CHAPTER: 4 Mathematical Analysis of transmission schemes
LTE is part of UMTS standards but currently includes many changes and improvements identified by 3GPP. In this chapter we will see how LTE achieving its goal like to increase data throughput and the speed of wireless data using combination of different transmission schemes like SISO and MIMO systems.
The advanced use of multiple antennas at both the transmitter and receiver promises to be among LTE's largest advantages over other technology. Multiple antenna techniques can be grouped into mainly spatial diversity and Spatial multiplexing which will be described as following sections.
4.1 Conventional Radio SISO System
Conventionally systems use is with only one transmits and one receive
antenna. In terminology it is called single input single output (SISO) system shown in below fig.4.1.1.
Figure4.1.1.SISO antenna configurations
'Single Input Single Output' (SISO) systems were favoured for simplicity and
Low-cost but has some shortcomings: -
'Outage occurs if antennas fall into null.
'Energy is wasted by sending in all directions.
'It can cause additional interference to others.
'It is sensitive to interference from all directions.
'Output power limited by single power amplifier.
Shannon-Hartley theorem for SISO system: According to Shannon, the capacity C of a radio channel is dependent on bandwidth B and the signal-to-noise ratio S/N. The following applies to a SISO system:-
C ' B log (1' S )
4.2 Basic of MIMO systems
All radio communications systems, regardless of whether mobile radio
networks like 3GPP UMTS or wireless radio networks like WLAN, which provides higher data rates. In addition to conventional methods, such as introducing higher modulation types or providing larger bandwidths, this is als o achieved by using multiple antenna systems (Multiple input Multiple output -MIMO). Here we present basic concept and terminology of MIMO implementation in different radio
communications standards. MIMO refers to channel, thus the transmitter is the channel input and the receiver is the channel output.
Several different diversity modes are used to make radio communications more robust, even with varying channels. These include time diversity (different timeslots and channel coding), frequency diversity (different channels, spread spectrum, and OFDM), and also spatial diversity. Spatial diversity requires the use of multiple antennas at the transmitter or the receiver end. Multiple antenna systems are typically known as Multiple Input, Multiple Output systems (MIMO). Multiple antenna technology can also be used to increase the data rate (spatial multiplexing) instead of improving robustness.
In practice, both methods are used separately or in combination, depending on the channel condition.
SIMO (Single Input Multiple Output), as applied to wireless technology, refers to the smart antenna technology that uses a single antenna at the transmitter side and multiple antennas at the receiver side for improved performance and security.
Single Input Multiple Output (SIMO) is a form of smart antenna technology for wireless communications in which a single antenna at the transmitter and multiple antennas are used at the destination (receiver). An early form of SIMO, known as diversity reception, has been used by military, commercial, amateur, and shortwave radio operators at frequencies below 30 MHz since the First World War.
Figure4.2.1: SIMO antenna configuration
The other forms of smart antenna technology include Single Input Single Output (SISO), Multiple Input Multiple Output (MIMO) and Multiple Input Single Output (MISO).
Figure4.2.2: SISO, SIMO, MISO, and MIMO Systems
Availability of multiple antennas at the transmitter and/or the receiver, we can classified as Single Input Multiple Output (SIMO), Multiple Input Single Output (MISO) or Multiple Input Multiple Output (MIMO).thus in the scenario of a multi antenna enabled base station communicating with a single antenna UE, the uplink and Downlink are referred as SIMO and MISO respectively. Also a full MIMO term is sometimes also used in widest sense as including SIMO and MISO as special cases.
Multiple Input Multiple Output (MIMO) systems with multiple parallel radios improve the following:-
' Outages reduced by using information from multiple antennas.
' Transmit power can be increased via multiple power amplifiers.
''Higher throughputs possible.
' Transmit and receive interference limited by some techniques.
A MIMO system typically consists of m transmit and n receive antennas. By using the same channel, every antenna receives not only the direct components intended for it, but also the indirect components intended for the other antennas. A time-independent, narrowband channel is assumed.
The direct connection from antenna1 to 1is specified with h11, etc., while the indirect connection from antenna 1to2 is identified as cross component h21. From thesis obtained transmission matrix H with the dimensions xm.
Figure4.2.3: MIMO Channel model
The following formula results from receive vector y, transmit vector x and noise n:
y ' Hx ' n
In MIMO system increases the capacity theoretically by dividing data streams into M part which is less than or equal to no. of antennas.
For ex: 4X4system is used four transmit and four receive antenna so it has four or lesser streams. Thus theoretically capacity C is given for MIMO system as given in below equation:
C ' M * B log (1' S ) '''''''''''''''..(4.4)
4.3 MIMO Schemes in LTE Downlink
From theory we know that MIMO systems the multiple antennas at
transmitter and receiver can be used with two different modes, namely the spatial diversity and spatial multiplexing.
We can use receiver diversity or transmitter diversity but here focused only on transmit diversity (at the transmitter side). In receiver diversity is simply combining process of different streams replicas of the same transmitted signal, while transmit diversity requires a space time/frequency coding operation of the transmitted signal.
In these sections we defined different MIMO schemes mathematical
derivations and implementations.
4.3.1 Transmit diversity 2 x 1 space frequency block coding (SFBC)
The most popular open loop transmit diversity scheme is space- time (in LTE
space- frequency) coding, where particular code known to the receiver is applied at the transmitter.
Here in this section we specifically focus on space-frequency block codes
(SFBCs), which lend themselves to easy implementation and are supported in LTE.
Fig 4.3.1: Open loop transmit diversity (no feedback)
This simple code has become the most popular means of achieving transmit diversity due to its ease of implementation (linear at both transmitter and receiver), and its optimality with regards to diversity order is also referred as Alamouti code or the orthogonal space-time block code (OSTBC). Conceived for a narrowband fading channel, STBCs can easily be adapted to a wideband fading channel using OFDM by utilizing adjacent subcarriers rather than consecutive symbols. Mathematically and conceptually, there is no difference between STBCs and SFBCs: instead of adjacent
subcarriers as denoted below, generally STBCs use consecutive
symbols in time.
SFBCs are preferred to STBCs because they experience less delay and are less likely to suffer from channel variations. STBCs would require two OFDM symbols to be encoded and decoded over , which significantly increases delay while also increases likelihood of channel variation over the code block, which as we will see is contrary to the standard decoding model.
Simplest SFBC corresponds to two transmit antennas and a single receive antenna. If two symbols to be transmitted are S1and S2 , the Alamouti code sends the following over two subcarriers f1 and f2 :
Antenna 1 2
Subcarrier f1 s1s2
f2 - s2*
The 2 x 1 Alamouti SFBC is referred to as a rate 1 code, since the data rate is
Neither increased nor decreased; two symbols are sent over two adjacent subcarriers.
Rather than directly coding is to harness
increasing the data rate, the goal of space-frequency block the spatial diversity of the channel. Assuming a flat fading
channel on each subcarrier, then h1(f1) is the complex channel gain from transmit antenna 1 to the receive antenna and h2(f2) is the from transmit antenna 2. An additionally we assume that the channel is constant over the two adjacent subcarriers, that is h1(f1)=h2(f2)=h1. This is because forcing flat fading channel per subcarrier is one of the main purposes of multicarrier systems, and a prerequisite for efficiently suppressing ISI.
The received signal r(f) can be written as;
r(f1) = h1s1 + h2s2 + n(f1),
r(f2) = -h1s*
+ h s*
+ n(f ),
2 2 1 2
Where n(.) is
a sample of white Gaussian noise. The following diversity
combining scheme can then be used, assuming the channel is known at the receiver: Y1 = h* r(f ) + h r*(f ),
1 1 2 2
Y2 =h* r(f ) ' h r*(f ).
2 1 1 2
Hence, for example it can be seen that:
Y1 = h1* (h1s1 + h2s2 + n(f1)) +h2(-h1s*
+ h s*
+ n(f )),
2 2 1 2
= (|h1|2 + |h2|2) s1 + h1*n(f1)+ h2n*(f2). And proceeding similarly that:
Y2 = (|h1|2 + |h2|2) s2 + h2*n(f1)' h1n*(f2).
One important characteristics of such a code is that the transmitted signal
streams are orthogonal and a simple linear receiver is required for optimal performances. In our case we use ZF( zero forcing) linear receiver used for such a codes.
The resulting SNR can be computed as:
(| h |2 ' | h
|2 )2 '''''''x
| h |2 '2 ' | h
|2 '2 .2
(| h |2 ' | h
'| h |2
' i '1
It is seen that this is similar to the gain from MRC. However, in order to keep the transmit power the same as in MRC case, each antenna must halve its transmit
power so that we get total energy power per actual data symbol is ??x for both cases.
That is SFBC, E|s1|2 = E|s2|2 ='? /2 since each are sent twice.
4.3.2 Transmit diversity 4 x 2 frequency switched transmit diversity (FSTD)
In LTE 2 stacked SFBC approach is not supported. So SFBC codes
modified in order to be applied to the case of 4 transmit antennas. The new modified scheme of SFBC is known as Frequency Switched Transmit Diversity (FSTD).
The frequency switched code for 4 antennas is as follows:
' s1 s2
0 0 '
' 0 0
s3 s4 '
''s * s *
0 0 '
' 0 0
s * '
' 4 3 ' ''''''''''''''''(4.7)
This combination of SFBC and FSTD is a rate 1 diversity scheme, i.e. four modulation symbols are sent over four OFDM symbols using the following space - frequency encoder, where the columns corresponds to the subcarrier index and the rows to the transmit antenna.
The first and second symbols s1 and s2 are sent over antenna ports 0 and 2 on the first two OFDM subcarriers in the block. On the other hand two subcarriers, the third and fourth symbols are sent using antenna port 1 and 3 just like 2 x 2 SFBCs, this encoder is rate 1 and can be detected using a simple linear ML receiver.
There are main benefits of using spatial diversity can be exploited in different manners. It can increase the reliability of the radio link and it is quantified by so called diversity gain as consequence of the diversity gain error rate may decreases.
The data rate can also be improved logarithmically with respect to the number of the antennas as antenna diversity increases the SNR linearly as per eq. (4.1).
In addition, the coverage area can be improved or for the same coverage area. The required power can be reduced. The diversity gain in MIMO systems is usually characterised by the number of independent fading diversity branches, also called Diversity order. The diversity order is defined as the slope of the BLER vs. SNR curve on log-log scale. For a MIMO system with Nt transmit antennas and Nr receive antenna, it is said that the diversity order is Nd = Nt.Nr. The diversity order has a dramatic effect on the system reliability since the probability of one of the diversity branches having high SNR is higher compared to only one branch. IN LTE, the SFBC (2x1) and FSTD (4 x2) have a diversity order of 2 and 8 respectively.
To send two streams using four antennas, LTE uses an open-loop 4 x 2 spatial
multiplexing approach as given in further section.
4.3.3 Multiplexing scheme: 2 x 2 and 4x4 Close loop spatial multiplexing
In contrast to the diversity mode described in the previous section, the spatial multiplexing mode, which refers to splitting the incoming high data rate stream into Nt independent data stream is considered from a data throughput standpoint, as the most exciting type of MIMO systems. In MIMO system with N t transmit antennas, the nominal spectral efficiency can be increased by a factor of Nt if the streams can be successfully and independently decoded. The factor N t is known as Multiplexing Gain. In spatial multiplexing (Nt x Nr) MIMO system , the maximum data rate grows as :
min()Nlot g'(N1 r )
' ' ''''''''''''''''...(4.8)
When ?? is large.
Fig.4.3.2 A spatial multiplexing scheme to transmit multiple sub streams to increase data rate.
4.4 Throughput calculation in LTE Downlink
In SISO& MIMO systems with OFDM the maximum data throughput depends
on the available bandwidth and the parameter of the OFDM signal, like no. of subcarriers and the modulation order (QPSK, 16QAM, 64QAM). For a given frequency band (B) the maximum data throughput in bits per second can be approximated by the following simple equation:
Throughput (b) ps
' N FB .N SC .NOFDM .Nb .ECR Tsub
Where NFB is the number of frequency Block in the given frequency band (B); NSC is the number of subcarrier in one frequency Block; NOFDM is the number of OFDM symbols in one sub frame; Nb is the number of bits in one subcarrier; ECR is the effective code rate, and Tsub is the duration of the one sub frame equal to 1 ms.
Generally in LTE, NSC and NOFDM are fixed and equal to 12 and 14 respectively.
As per our simulation considering 5MHz bandwidth throughput calculation for both algorithms of scheduling which are Round Robin and Best CQI schedulers calculation is as per given below table.
Here in Round Robin we have fixed CQI=7 so we denoted it as RR7 and in
Best CQI we known it gives throughput at highest CQI=15 (In LTE).
CHAPTER 5 Simulation Basis
This chapter implementation and investigation of performance analysis of LTE link level simulator in terms of different scenarios like different scheduling algorithms, different no. of users, different antennas transmission systems, different channel models.
Also for research and development of signal processing algorithms for UMTS Long Term Evolution (LTE) requires a realistic, flexible and standard simulation environment. In further sections we also discuss about a MATLAB- based LTE Downlink level Vienna University simulator that openly and publicly provided for academic- industrial use for researchers.
First of all we presenting some of introductory part of Vienna University LTE downlink level simulator as specified in 3GPP release 8, Long Term Evolution (LTE) the targets for downlink and uplink peak rates is 100 Mb/s to 50
Mb/s respectively, when operating in a 20MHz spectrum allocation provide very much spectral flexibility. LTE has also used downlink transmission scheme is based on OFDMA ( orthogonal frequency division multiplexing access) which converts the wide band frequency selective channel into set of many flat fading sub channels, even LTE uses MIMO transmission system to provide high reliability and very high data rate with multiple antenna with low complexity and optimum receivers.
To the best of my knowledge this is (also authors knowledge) only free open source LTE simulator available in market up to now. This simulator is MATLAB based with some source code programming done in C++ and WM-SIM
platform with MEX functions. This simulator currently implements a standard
compliant LTE downlink with its main features being Adaptive Modulation and Coding (AMC), MIMO transmission, multiple users and scheduling. It is avail able for free and under an academic, non- commercial use license and allows researchers to compare algorithms in a standardised system .
5.1 LTE Link Level Simulator Overview
We can elaborate on possible applications of the LTE simulator in research.
Depending on that application we can distinguish mainly 3 different classes of simulations that differ mainly on computational complexity. We mainly use Link level simulations are carried out to evaluate the cell throughput.
Figure5.1.1 Overview of simulation scenarios in the LTE simulator
As seen from figure 5.1.1 it can show single-downlink, single- cell multi user, multi cell multi user scenarios.
The single-downlink basically covers link between base station and one user
It allows implementing following:
' Channel estimators,
' Channel tracking,
' Channel prediction,
' Synchronizing algorithms
' MIMO gains
' AMC feedback including feedback mapping optimization
' Receiver structures neglecting interference and impact of scheduling
' Modeling of channel encoding and decoding
' Physical layer modeling crucial for system level simulations.
All these simulation requires standard compliant like MIMO transmission scheme and gains measurement also measuring impact of different scheduling scenarios on user throughput.
5.1.2 Single cell Multi user
The single cell multi user covers the link between base station and multiple
users. This set up now additionally allows us to measure:
' Receiver structures also influence of scheduling into account,
' Multi user precoding,
' Multiuser gains,
' Scheduling and resource allocation.
It requires single downlink with fully optimised AMC. In case receiver structure we had problem of computational complexity so to avoid it by utilizing user interest as how many no of users should simulate for particular simulation.
Here in below fig 5.2 shows that overall simulation structure made of LTE TX means transmitter at one end connected with LTE RX (users) at other end. In between them there is channel and signalling line connected.
5.2 Simulation structure
The overall simulation model made of following functional parts like
transmitter, receiver and in between them a channel model. Their detailed descriptions with figures are given below for better understanding purposes.
5.2.1 Overall simulation structure
Below figure shows overall simulation structure with all their contacting
parts as given in fig. 5.2.1.
Fig 5.2.1. Overall simulation structure
LTE link level simulator consists of following parts:
equipment eNodeB, N receiver user equipments (UEs), a downlink shared channel
(DL-SCH) is transmitted, signalling with adjustable delay.
The transmitter structure is as shown below fig 5.3.
Figure 5.2.2: Structure of LTE transmitters.
As shown in fig LTE downlink transmission based on OFDMA. The LTE
downlink physical resource is made of time-frequency grid which one of resource
element in OFDM subcarrier during one OFDM symbol interval. The resource element consist of grouped of resource blocks (RBs) that consist of six to seven OFDM symbol depend on short cyclic prefix or extended cyclic prefix length. The normally 12 subcarriers corresponding to a nominal resource block bandwidth is around 180 KHz. This allows for a very flexible resource allocation in a multi user scenario.
In the transmitter first processing the user data generated depending on previous ACK if any. If previous acknowledgement not receiv ed then transport block then stored TB transport block retransmitted using a hybrid automatic repeat request (HARQ) scheme. Each block coded bits is interleaved and rate matching done based on received channel quality indicator (CQI) user feedback.
The encoding process is followed by data modulation for DL-SCH channel schemes like 4-QAM, 16-QAM and 64-QAM etc. used based on received CQI. On the basis of CQI scheme selected for corresponding RB.
The modulated symbols then mapped up to four transmit antennas. This antenna mapping depends on Rank Indicator (RI) feedback. Rank Indicator provides different antenna scheme like; transmit diversity (Txd), Open loop multiplexing (OLSM), and Closed loop Multiplexing (CLSM). Precoding matrix is applied to transmit signal and based on coded book optimum precoding matrix is chosen.
Now at last step individual symbols to be transmitted on each antenna that are mapped to different resource elements. Downlink reference symbols and synchronising symbols are also inserted in OFDM time and frequency grid. The assignment of RBs to user carried out based which algorithm for scheduling we are using. Scheduler has to check channel quality indicator (CQI)feedback from UEs.
Currently in Round Robin scheduler only implemented with static resource block assignment of a matrix, While Best CQI scheduler work with dynamic assignment of matrix.
5.2.2 Multi-Cell Multi user
The multi-cell multi user simulation is by far the most computationally
complex demanding scenario also it covers links between multiple base-stations to
the multiple users.
This allows investigations of following:
' Interference-aware receiver techniques
' Interference management , including cooperative transmission and
' Network based algorithms like joint resource allocation and scheduling.
In the above all scenario we choose second one as single downlink singl e cell multiple user for our simulation analysis and implementation of different impacts scheduling algorithms on multiple user throughputs.
5.2.3 Channel Model
For different scenarios and to check different environmental comparison
we need different types of channel models which is already defined by ITU (International Telecommunication Union) and 3GPP.
This channel type mainly work on power delay profile (PDPs) scenarios. ITU-2000 defined mainly on radio transmission technology structure. Below fig
5.2.3 shows the functional blocks of radio transmission and their interpretations.
' Radio transmission functional blocks
' Multiple access technologies are major impact on design of radio
' Modulation technology choice is depends on requirement of spectral efficiency and radio environment.
' Channel coding and interleaving depends propagation environment, spectral efficiency and quality requirement for different services. Application for large cell specifically for satellite application requires powerful coding, microcellular type cells which is used in pedestrian environment require less complex coding. A choice of channel coding with or without Interleaving depends on
Figure 5.2.3: functional blocks of radio transmission.
' Duplexing technology mainly affects the choice of RF channel bandwidth and frame length. Duplexing may be independent of access technology and may use frequency division duplexing (FDD) or time division duplexing (TDD) may be used with either TDMA or FDMA.
' Physical channel structure and multiplexing May defined in frequency, time and code domain.
' Frame structure defined on basis of access technology like FDMA, TDMA, and CDMA also with duplexing technology like FDD, TDD. To maximized frame structure whenever possible and also data field may defined for physical and logical channels.
' RF channel parameter defined parameter like bandwidth, allocation of data, and channel spacing.
' Source coder defined independently with access methods.
' Interworking function may define to converts standard data
services to use with internally transmission subsystem. The IWF
feed in transmitter side from channel coder and from channel decoder on the receiver side.
Generally, selected test environments for evaluation are defined below basis l ike
' Indoor office
' Outdoor to indoor and pedestrian
' Mixed cell pedestrian /vehicular
ITU channel model for outdoor to indoor and pedestrian test environment table 5.2.1 is given below their relative delay and average power required by them.
(ns) Average power
(dB) Relative delay
(ns) Average power
1 0 0 0 0
2 110 -9.7 200 -0.9
3 190 -19.2 800 -4.9
4 410 -22.8 1200 -8.0
5 -- -- 2300 -7.8
6 -- -- 3700 -23.9
Table no 5.2.1 ITU channel model for Outdoor to Indoor and Pedestrian
Another test environment for our simulation is Vehicular A channel type model is chosen. Which is shown with corresponding delay time in Nano seconds and also required power in dB are shown below Table 5.2.2
Tap Channel A Channel B
(ns) Average power
(dB) Relative delay
(ns) Average power
1 0 0 0 -2.5
2 310 -1.0 300 0
3 710 -9.0 8900 -12.8
4 1090 -10.0 12900 -10.0
5 1730 -15.0 17100 -25.2
6 2510 -20.0 20000 -16.0
Table no 5.2.2: ITU channel model for Vehicula environment.
Generally you may choose different delay values as given in table for both environment and also with average power required in particular environment.
Wimax forum recommend using only two channel type models for simulation environment conditions which are pedestrian B and Vehicular A channel type. As here I chosen pedestrian channel model B and Vehicular channel model A for my LTE downlink performance evaluation environment condit ion.
In this Vienna based MATLAB LTE Link Level Simulator currently contains tap-delay based models like PedA, PedB, VehA, VehB, TU etc. channel models to provide user a particular environment for simulation .
The receiver structure is as shown below fig 5.2.4.LTE receiver structure is
totally opposite to transmitter structure which is shown in figure 5.2.4 for simulationof LTE.
Figure 5.2.4:Structure of LTE receivers.
Each UE receives the signal transmitted by eNodeB and performs the reverse operation to the transmitter side. The receiver has to identify the RBs that carry designated data information. After receiving resource block grid of time - frequency grid first channel estimation is performed using reference signal available in the resource grid. Based upon channel estimated signal the channel quality indicator defines appropriate channel quality which utilizes the modulation and coding scheme with appropriate feedback given to transmitter side. The
Channel knowledge also plays an important part at demodulation process and also at soft- DE mapping of OFDM signal.
Finally, the UE performs HARQ process of combining and channel decoding. In order to cut down processing time every turbo iteration and CRC check of the decoded block is performed and corrected. Generally the CRC checking is negligible, as turbo decoder requires some computation time.
After all evaluation performed at receiver side the necessary information may be subtracted to evaluate the performance of user and cell Throughput, Bit error rate (BER), and also in case of LTE Block error ratio (BLER) may calculate.
CHAPTER 6 Simulation results & Analysis
In this chapter, we simulate LTE downlink level simulation for below given parameters using Vienna University based link level simulator model for LTE. We evaluate downlink scheduling algorithms and carried out performance analysis of different scenarios in the simulator. We measure evaluation on the basis of Throughput vs. SNR for different algorithms of scheduling, different users, different systems like SISO, MIMO(CLSM 2x2&4x4) also different environment of channel type like PedB, etc.
6.1 A parameter setting for simulation:
Table 6.1.1 summarizes the essential required parameter changes for performing simulation with different scenarios. Here we conclude all simulation scenarios for our simulation that must be configured well for simulation and desired results.
Our performance analysis of different scheduling algorithms is checked on the basis of theoretically values of Throughput vs. SNR plot. We have used
different channel model to check delay profile in simulation results.
Number of users 2 UEs
Number of base station 1
Channel type Pedestrian B
Simulation length 500 sub frames
Scheduling algorithms Round Robin, Proportional
Fair and Best CQI Scheduling
Transmission schemes SISO, MIMO(CLSM 2x2 &
Table no 6.1.1Simulation parameters.
6.2 Simulation scenarios
We have used different scenarios to check our performance analysis in LTE
simulator. So we here discuss different type's results with different parameters.
Case 1: Pedestrian B, SISO, CLSM 2x2 and 4x4 Round Robin
scheduling algorithms, 500 sub frames.
In this case we simulate two users with both the above algorithms scheduling. We have plotted the Throughput vs. SNR graph for SISO, CLSM 2x2 and 4x4. We selected duration of simulation 500TTI and selected bandwidth i s 5
MHz for LTE. The channel type is pedestrian B (PedB).
Figure 6.2.1: 2UEs RR SISO, CLSM 2x2 and 4x4PedB
Figure 6.2.1 shows user Throughputs vs. SNR graph for SISO, CLSM 2x2 and 4x4 system. We used Round Robin scheduling.
From the graph it is easily shown that Round Robin scheduling algorithms
which peak throughput 21Mbps in CLSM 4x4.
Case 2: Pedestrian B, SISO, CLSM 2x2 and 4x4 Proportional Fair scheduling algorithms, 500 sub frames.
In this case we simulate two users with both the above algorithms
scheduling. We have plotted the Throughput vs. SNR graph for SISO, CLSM 2x2 and 4x4 systems. We selected duration of simulation 500TTI and selected bandwidth is 5 MHz for LTE. The channel type is pedestrian B (PedB).
Figure 6.2.2: 2UEs PF SISO, CLSM 2x2 and 4x4PedB
Figure 6.2.2 shows user Throughputs vs. SNR graph for MIMO Txd 2x1 system. We used PF scheduling..
From the graph it is easily shown that PF scheduling algorithms has high throughput compare to Round Robin scheduling algorithms which peak throughput
60Mbps in CLSM 4x4.
Case 3: Pedestrian B, SISO, CLSM 2x2 and 4x4, Best CQI scheduling algorithms, 500 sub frames.
In this case we simulate two users with both the above algorithms scheduling. We have plotted the Throughput vs. SNR graph for SISO, CLSM 2x2 and 4x4system. We selected duration of simulation 500TTI and selected bandwidth is 5MHz for LTE. The channel type is pedestrian B (PedB).
Figure 6.2.3: 2UEs BCQI SISO, CLSM 2x2 and 4x4PedB
Figure 6.2.3 shows user Throughputs vs. SNR graph for SISO, CLSM 2x2 and 4x4system. We used Best CQI scheduling.
From the graph it is easily shown that Best CQI scheduling algorithms has high throughput compare to Round Robin scheduling and PF algorithms which peak throughput 70Mbps in CLSM 4X4.
CHAPTER 7 Conclusions and Future work
In this thesis a first overview of LTE (Long Term Evolution) UMTS
standard is presented. LTE mostly intends for 3GPP. It has wider useful application compare to previous standard with very high efficiency in packet data transmission. In LTE OFDM has been adopted as the downlink transmission scheme. LTE is the future of mobile broadband. It is expected to have 80% of subscribers in all around the world will be served by LTE.
I have done research in LTE downlink scheduling algorithms. The scheduler is very key parameter for base-station. It will assign the resource blocks to different users. I have worked on two scheduling schemes mainly focuses on Round Robin scheduler, PF and Best CQI scheduler. I have used Round robin scheduler because its characteristic to provide good fairness to users. Best CQI scheduler is used because its characteristics like provides very high throughput based on channel quality of user. Proportional Fair scheduler mainly focuses as trade-off between BCQI AND RR they provide user throughput high compare to RR and good fairness of user compare to BCQI. This thesis tells analysis the impact of schedulers on user throughput and fairness. As a simulation results we concluded that Round Robin scheduler assign resource blocks in turn one after another and mainly focuses on user fairness. In Best CQI scheduler assigns resource blocks to user which has very high CQI. So it gives very high user throughput and not focuses on user fairness.
We also uses three different MIMO systems Close loop spatial multiplexing
2x2 , 4x4 antenna system in comparison with SISO system. We use 5MHz bandwidth with two user in LTE downlink level simulation using Vienna university MATLAB based simulator. We also show difference between diversity mode and multiplexing mode. As in diversity mode we improves channel reliability not the data rate and in multiplexing mode because of multiplexing gain we got throughput almost double to SISO system. We also discusses about different channel type defined by ITU like Pedestrian channel model B to check channel delay impact on user throughput. We simulate Round Robin, PF and Best CQI scheduler techniques with PedB channel types. As expected Round Robin doesn't take channel condition in account. We know that Round Robin scheduler does not take AMC (adaptive
modulation coding) into account. While in Best CQI scheduler always takes channel condition in account so pf has both in it.
More and more research is going on LTE downlink scheduling algorithms
and also as per modern criteria it's very useful & interesting research field currently. Many vendors, operators and research institutes are currently doing research on scheduling techniques implementation. The mainly research are going on user fairness increment (as many as user allocating data resources) and also very high user throughput is demanding by customer nowadays.
On demanding of user we may design and implementing the scheduling algorithms. In this thesis we defined scheduling algorithms for only LTE downlink side but to check and implement in LTE uplink is also very interesting point.
We have implanted MIMO with mainly transmitting diversity and open loop multiplexing modes but with more advanced and complex techniques like closed
loop spatial multiplexing with multiuser scenarios can be deployed.
Mainly future one may continue to work on LTE uplink side with same scenarios and compare with these results.
Also one may try to develop different scheduling algorithms with also different delay channel types to check user throughput and delay effect in LTE.