Maximum achievable data rate for each user

5.4.2 Maximum achievable data rate for each user

The maximum achievable data rate for each user after sites allocation is shown figure 5.10.

downlink data rate 10 MbPs, yellow has downlink data rate 15 MbPs, orange has downlink data rate 20MbPs, red has downlink data rate 30 MbPs as shown in figure 5.11.

Figure 5.10- Maximum Achievable Data rate for each user Figure 5.10- Maximum Achievable Data rate for each user

Figure 5.11 Data Rates Color levels Figure 5.11 Data Rates Color levels

Table 5.2 shows the area of each downlink data rate range and it's percentage from the total area.

Figure 5.12 show that most areas have a rate between 5 to 30 MbPs.

Downlink Data

Outside range 2.5609 6.861104 2.029934

Table 5.2 DL Data rate Percentages Table 5.2 DL Data rate Percentages

Figure 5.12 Downlink rates percentages Figure 5.12 Downlink rates percentages 0

2 4 6 8 10 12 14 16

Percentage T

Percentage Total otal Area Area

Percentage Total Area

6

LTE KEY SON Features LTE KEY SON Features

This chapter illustrated the key SON features of LTE network, the first section is optimization description the second section reviews in brief SON in 3GPP.Section three discuses LTE SON framework that includes self-configuration, optimization and healing categories. The fourth section discusses general basic optimization Features related to mobility and handover.

6.1 Introduction to LTE Optimization 6.1 Introduction to LTE Optimization

As discussed in chapter one, LTE planning process is consecutive steps as shown in figure 6.1.

Optimization is very important step in LTE network planning which is the last one. The LTE specification inherently supports SON features.

Figure 0.1 Mobile Planning Process Figure 0.1 Mobile Planning Process

6.2 SON in 3GPP 6.2 SON in 3GPP

3GPP is an alliance and a standards body that works within the scope of the International Telecommunication Union (ITU) to develop 3rd Generation (3G) and 4th Generation (4G)

specifications based on evolved Global System for Mobile communications (GSM) standards [8].

Reduction of cost and complexity is a key driver for Long Term Evolution (LTE), since with its deployment the new network layer needs to coexist with legacy systems without additional operating cost. Thus, it is of vital interest for operators to introduce automated engineering functions that minimize Operational Expenditure (OPEX) and, at the same time, increase network performance by dynamically adjusting the system configuration to the varying nature of wireless cellular networks [8].

Deploying and operating cellular networks is a complex task that comprises many activities, such as planning, dimensioning, deployment, testing, prelaunch optimization, post launch optimization, comprehensive performance monitoring, failure mitigation, failure correction and general maintenance. Today, such critical activities are extremely labor intensive and, hence, costly and prone to errors, which may result in customer dissatisfaction and increased churn [8].

6.3 SON Framework 6.3 SON Framework

SON solutions can be divided into three categories: Self-Configuration, Self-Optimization and Self-Healing.

6.3.1

6.3.1 SELF-Confi SELF-Configuration guration

This is the dynamic plug-and-play configuration of newly deployed eNBs. The eNB will by itself configure the Physical Cell Identity, transmission frequency and power, leading to faster cell planning and rollout [9].

The interfaces S1 and X2 are dynamically configured, as well as the IP address and connection to IP backhaul. To reduce manual work ANR (Automatic neighbour relations) is used. Dynamic configuration includes the configuration of the Layer 1 identifier, Physical cell identity (PCI) and Cell global ID (CGID) [9] [10].

Self-configuration mechanism is desirable during the pre-operational phases of network elements such as network planning and deployment, which will help reduce the CAPEX [11].

6.3.2 Self

6.3.2 Self Optimization Optimization

Utilization of measurements and performance indicators collected by the User and the base stations in order to auto-tune the network settings. This process is performed in the operational state [8] [11].

Self-optimization mechanism is desirable during the operational stage so that network operators get benefits of the dynamic optimization, e.g., mobility load balancing to make network more robust against environmental changes as well as the minimization of manual optimization steps to reduce operational costs

6.3.3 SELF-HEALING 6.3.3 SELF-HEALING

Features for automatic detection and removal of failures and automatic adjustment of parameters are mainly specified in Release 10. Coverage and Capacity Optimization enables automatic correction of capacity problems depending on slowly changing environment, like seasonal variations. Minimization of drive tests (MDT), is enabling normal UEs to provide the same type of information as those collected in drive test. A great advantage is that UEs can retrieve and report parameters from indoor environments [8] [9].

6.4 SON Use Cases 6.4 SON Use Cases

This section discuss general basic optimization Features

6.4.1 Coverage and Capacity

6.4.1 Coverage and Capacity Optimizat Optimization (CCO) ion (CCO)

This optimization aims at maximizing the system capacity and ensuring there is an appropriate overlapping area between adjacent cells as shown in figure 6.2. The optimal parameter setting is acquired by cooperatively adjusting antenna tilt and pilot power among the related cells. This optimization should operate with some effect even if the measurement reports from UE do not include their data on their own location [12].

Figure 6.2 Coverage gap optimization Figure 6.2 Coverage gap optimization

3GPP specifies the following requirements on CCO [12]:

 Coverage and capacity optimization shall be performed with minimal human intervention.

 Operator shall be able to configure the objectives and targets for the coverage and capacity

 Operator shall be able to configure the objectives and targets for the coverage and capacity

 Optimization functions differently for different areas of the network.

 The collection of data used as input into the coverage and capacity optimization function shall be automated to the maximum extent possible and shall require minimum possible amount of dedicated resources

6.4.2 Mobility Robustness

6.4.2 Mobility Robustness Optimizat Optimization (MRO) ion (MRO)

Mobility Robustness Optimization (MRO) encompasses the automated optimization of parameters affecting active mode and idle mode handovers to ensure good end-user quality and performance, while considering possible competing interactions with other SON features such as, automatic neighbor relation and load balancing. Incorrect handoff parameter settings can negatively affect user experience and waste network resources due to handoff and radio link failures (RLF). While handoff failures that do not lead to RLFs are often recoverable and invisible to the user, RLFs caused by incorrect handoff parameter settings have a combined impact on user experience and network resources [13].

In addition to MRO, intra-frequency Mobility Load Balancing (MLB) objective is to intelligently spread user traffi aross the system‟s radio resoures in order to optimize system apaity while maintaining quality end-user experience and performance. Additionally, MLB can be used to shape the system load according to operator policy, or to empty lightly loaded cells which can then be turned off in order to save energy. The automation of this minimizes human intervention in the network management and optimization tasks [13].

There are multiple approaches towards load balancing for MLB. One of the approaches is described here and other approaches may exist that supplement this approach [13].

6.4.3 Mobility Load Balancing Optimization (MLB) 6.4.3 Mobility Load Balancing Optimization (MLB)

Self-optimization of the intra-LTE and inter-RAT mobility parameters to the current load in the cell and in the adjacent cells can improve the system capacity compared to static/non-optimized

cell reselection/handover parameters and can minimize human intervention in the network management and optimization tasks[14].

The load balancing shall not affect the user QoS negatively in addition to what a user would experience sat normal mobility without load-balancing. Service capabilities of RATs must be taken into account, and solutions should take into account network deployments with overlay of high-capacity and low-capacity layers where high-capacity layer can have spotty coverage.

Objective: Optimization of cell reselection/handover parameters to cope with the unequal traffic load and minimize the number of handovers and redirections needed to achieve the load balancing [14]

6.4.4 Intra-LTE Handover Feature 6.4.4 Intra-LTE Handover Feature

Intra-LTE Handover is the basic mobility function for UEs in active mode. When one or more neighbor cells are better than current serving cell the UE is ordered to handover to best cell. Best cell evaluation is based on measurements of neighbor cells, serving cell and evaluation algorithm controlling parameters set by eNodeB [15].

Figure 6.3 shows interfaces involved in intra-LTE handovers.

Figure 6.3: interfaces involved in intra LTE Handover Figure 6.3: interfaces involved in intra LTE Handover

The benefits of the Intra-LTE Handover feature are the following [15]:

• Network apaity is maximized y ensuring that UE are served y the est availale ell.

• Data rates to individual UE within the network are maximized y ensuring that the UE is

• Connected mode mobility within the network is possible with minimal interruptions to data flows during the handover process

6.4.5 Automated Neighbor Relations 6.4.5 Automated Neighbor Relations (ANR) (ANR)

Mobile devices can report cells that are not in the neighbor list to the base station they are currently served by. This information can then be used by the network to automatically establish neighbor relationships for handovers [16]

The Automated Neighbor Relation (ANR) feature in the RBS removes the need for initial configuration of neighbor relation lists and greatly simplifies the optimization of them. The feature will execute autonomously in the RBS and automatically

The process to detect and add a new intra frequency LTE neighbor is outlined below [16]:

1. The eNodeB sends each connected UE a list of neighbor PCIs with their cell individual offsets (Ocn) and configures the conditions that will trigger the events associated to the corresponding measurements.

2. When the UE detects that the received signal of a given cell becomes stronger than that of the serving cell by more than a certain offset, the PCI of that cell is reported to the eNodeB, together with the associated measurement report. UEs carry out this procedure independently of whether the reported PCIs are part of the NRT.

3. If a reported PCI is not in the NRT, the eNodeB orders the UE to decode the ECGI of the newly discovered PCI, as well as the Tracking Area Code (TAC) and all available Public Land Mobile Network (PLMN) IDs. For this to happen, the eNodeB may schedule idle periods to allow the UE to read the ECGI that is broadcasted by the new neighbor associated with the detected PCI.

4. After this process has been completed, the UE reports the ECGI of the new neighbor to the eNodeB.

5. The eNodeB processes this information and may decide to update its NRT. Eventually, it may setup (if needed) a new X2 connection towards the new neighboring eNodeB. This new NR has its default attributes configured in such a way that HO, X2 connection setup and ANR actions to remove this NR are allowed

The process is summarized in figure 6.4

Figure 6.4: the process to detect and add intra-frequency LTE Handover Figure 6.4: the process to detect and add intra-frequency LTE Handover

6.4.6 PCI Conflict Reporting 6.4.6 PCI Conflict Reporting

Every LTE cell has a PCI that is used during the cell search procedure to distinguish the transmissions of several cells on the same carrier from each other. Only 504 IDs are available and neighboring base stations should use a certain combination for easier detection. As it is sometimes difficult to predict all cell neighbors, auto-configuration functionality is highly desirable. The mobile is required to report to the network as to which cells it looks out for the automated configuration process [16].

When a new eNB is established, it needs to select Ph-IDs for all the cells it supports. The Ph_ID of one cell should satisfy the following two criteria so that no confusion is caused [9].

 The Ph_ID of one cell should not be the same as those of his neighbor cells.

 The Ph_IDs of the neighbor cells should not be the same.

Figure 6.5 shows an example of Physical Cell ID deployment. In this example, the eNB with red color is the one that is newly introduced. The automatic configuration of the physical Cell ID for the new cell proceeds as follows:

1. When the procedure starts, the new cell starts a timer for this configuration phase.

2. A set of Physical Cell IDs is defined as a set of temporary Physical Cell IDs. The new cell picks one temporary Physical Cell ID randomly.

3. According to ANR (Automatic Neighbor Relation) function, UE reports those detected cells with their Physical Cell IDs to its serving cell. So the cells around the new cell receive the report of the new cell and the new cell receives the report of its surrounding cells. By ANR function, they also get the Global Cell ID of those reported cells.

Figure 0-5 Physical Cell ID Deployment Figure 0-5 Physical Cell ID Deployment

Figure 6.5: Physical Cell ID deployment Figure 6.5: Physical Cell ID deployment

4. The new cell adds those reported cells to its neighbour cell list. It also looks up the IP addresses of those neighbor cells and establishes the X2 connection if necessary.

5. Those cells, which receive the report of the new cell, adds the new cell in their neighbor cell list, look up the IP address of the new cell and establish the X2 connections if necessary. Which trigger the X2 connection setup, the new cell or the surrounding cells, depends on which one detects the neighborhood relation first.

6. After X2 connection is set up, the surrounding cells exchange their neighbor cell lists with the new cell. As a result, the new cell also gets the neighbor relation information of its neighbor cells.

7. When the timer times out, the new cell collect all the information it gets, which includes its neighbor cell list and the neighbor cell lists of its neighbor cells. Then the new cell selects one Physical Cell ID that satisfies the two criteria, which has been explained before.

8. The new cell informs its neighbor cells that it has changed its Physical Cell ID .Those neighbor cells updates their neighbor relation table accordingly. During the configuration phase, some collisions may also happen. For example, two new cells select the same temper Physical Cell ID and they are neighbors. The collision will be detected during the configuration procedures and one of the configuration procedures will be restarted.

6.4.7 16-QAM uplink and

6.4.7 16-QAM uplink and 64-QAM Downlink 64-QAM Downlink

Under ideal transmission conditions, for example, when clear LOS exists between sender and receiver over very short distances, 64-QAM is used, which codes six bits on a single subcarrier.

Under harsher conditions, less demanding modulation schemes like 16-QAM [6].

Higher-order modulation enables high peak data rates to be achieved in scenarios with high SIR, such as in indoor hotspot cells. Multiple-input multiple-output (MIMO) antenna operation making HSDPA the first standardized cellular system to support the transmission of multiple data streams to each UE by means of multiple antennas at each end of the radio link. MIMO aims to exploit spatial multiplexing gain by making positive use of the multiple propagation paths to separate different data streams transmitted simultaneously using the same frequency and

code [6].

6.4.9 Support for 15km CPRI Link 6.4.9 Support for 15km CPRI Link

CPRI Link Increases support for long optical fiber between Radio and Baseband to 15 km. When using optical fiber and optical Small Form Factor Plugins (SFPs) the length of the optical fiber between the DUL and the radio can be up to 15 km long. It is possible to mix long and short

fibers as long as no fiber distance exceeds the maximum distance [13].

6.4.10 System Information Modification 6.4.10 System Information Modification

This feature makes it possible to modify the System Information broadcasted in the cell without doing a lock/unlock operation on the cell [13].

An operator will need to tune the LTE coverage when building it out and hence change neighbor relations and re-selection thresholds. With this feature, such changes can be done without disturbing the service .This gives the operator the possibility to change parameters in the system information, for example, cell selection related parameters, without affecting the in service performance [6].

6.4.11 Enhanced

6.4.11 Enhanced Observabilit Observability y

This feature provides the operator with increased visibility in network performance statistics, enabling more diverse monitoring of the LTE RAN.

The statistical granularity, including averages, peak/min values and distributions of key events and procedures, is increased within in the areas of Accessibility, Retain ability, Integrity, Mobility and Availability. New utilization type measurements, such as procedure times and processor load, are also introduced [17]

7

Site Specific Propagation Model Site Specific Propagation Model

In previous chapters we have complete LTE planning using Cost Hata propagation model. Other propagation models can be used which are more efficient and accurate than cost Hata. Models that will be studied are Hata and full 3D propagation model. This chapter includes four sections the first section is dissection about Hata, Cost and Full 3D model, the second one is a description for the tool that we used ,then in the third section our design description finally in the last section summarize our results .

7.1 propagation models 7.1 propagation models

Propagation models have been developed to be able to estimate the radio wave propagation as accurately as possible. Models have been created for different environments to predict the path loss between the transmitter and receiver. How much power needs to be transmitted using the BTS to be able to receive certain power level from the MS? The complexity of the model affects the applicability as well as the accuracy. Two well-known models are those of Okumura – Hata and cost Hata. The first mentioned is created for large cells, i.e. for rural and suburban areas [3], while the CostHata model is an enhanced version of Okumura hat model that includes 1800_

1900 MHZ [18].

7.1.1 Hata Model

7.1.1 Hata Model (Okum (Okumura Hata Model) ura Hata Model)

Hata's propagation model is the basis for several widely used propagation models in the cellular industry. The main attraction of Hata's model is its simplicity, and its main drawback is its constraints on the ranges of some parameters [19].

The Okumura-Hata model is a well- known propagation model, which can be applied for a macro cell environment to predict median radio signal attenuation. Having one component the model uses free space loss. The Okumura-Hata model is an empirical model, which means that it is based on field measurements [3]. Hata derived empirical formulas for propagation path loss based on Okumura's report containing graphs such as median field strength versus distance. This

empirical model simplifies calculation of path loss because it is a closed-form formula and is not based on empirical curves for the different parameters [19].

Hata's basic model includes path loss for an urbanenvironment and provides correction factors for other environments, such as suburbanand open areas. Caution should be exercised while using Hata's model because it isvalid only for specific cases. Hata's model makes the following assumptions: pathloss is between isotropic antennas and the terrain is quasi-smooth and regular [19].

7.1.2 Cost Hata Model 7.1.2 Cost Hata Model

Hata's basic model is valid in the frequency range of 150-1500 MHz. European COST 231 extended the validity of Hata's model to higher frequencies by analyzing Okumura's propagation

Hata's basic model is valid in the frequency range of 150-1500 MHz. European COST 231 extended the validity of Hata's model to higher frequencies by analyzing Okumura's propagation

In document Lte Planning and Specific Propagation Model Selection (1) (Page 35-99)