OpNET - LTE

1599 Planning LTE Network Deployments

Typical Modeled Network Architecture

UE with complete TCP/IP stack

eNodeBs (1, 3 and 6 sectors)

Evolved Packet Core (EPC) Network with IP/GTP Support


Data Traffic Flow in LTE Networks

Uplink data on radio bearer

IP packets entering the LTE network are mapped to GTP tunnels

Corresponding radio bearer carrying the downlink data

EPS Bearer

Radio Bearer

GTP Encapsulation/Decapsulation

S1 Bearer


Simulation Model Entities 



LTE eNodeB • lte_enodeb_atm4_ethernet4_slip4 • lte_enodeb_ethernet4 • lte_enodeb_slip4 • lte_enodeb_3sector_slip4 • lte_enodeb_6sector_slip4

LTE EPC • lte_epc_atm8_ethernet8_slip8



LTE Attribute Configuration Object • lte_attr_definer



LTE UE • lte_wkstn • lte_server • lte_ue_ethernet_gtwy (Router UE)


Agenda 

LTE Network Architecture, Features and Capabilities



Deploying Realistic LTE Networks in OPNET Modeler • Basic Configurations and Analysis



Lab 1: Deploying and Analyzing Performance of an LTE Network



Using OPNET Modeler for Planning Studies



Cell Planning Study with OPNET Modeler • Lab 2: Cell Planning Lab



Capacity Planning Study with OPNET Modeler • Lab 3: Capacity Planning Lab



Battery Life Planning Study with OPNET Modeler • Lab 4: Optimizing DRX Parameters Within Application Delay SLAs


LTE Network Deployment 

Fast Deployment: Wireless Network Wizard • Choice to select the bandwidth, number of cells, cell radius, etc. • Most of the other settings automatically taken care of



Deploying realistic conditions • Terrain Modeling Module • Mobility modeling  Trajectories, random mobility, programmatic mobility • Application/traffic modeling  Standard applications, custom applications, real application traces, application demands, IP traffic flows  Traffic must be mapped to EPS bearers to achieve QoS • Unmapped traffic will be handled by Default bearer • Interference modeling  Jammer nodes


LTE Network View

EPC ID


How to Configure Core Network Connectivity  OPNET

models both E-UTRAN and EPC

• A UE is connected to a core network with IP connectivity

 A UE

is allowed to connect to only one EPC

• The UE also cannot change EPC in the simulation  EPC

builds automatic GTP tunnels with an eNodeB for both uplink/downlink traffic communication with the UE • No attributes need to be configured


How to Analyze Core Network Connectivity  Usually

necessary only during troubleshooting when the UE does not receive any data when expected

 Associated  Also

eNodeB statistic (discussed later)

lte_emm specifically traces connectivity with the core The first attach Accept is received around 99 seconds

 Also,

UE’s NAS state can be checked in graphical debugger at any time Must be in EMM_Connected.


How to Configure the Serving Cell  Scanning

and Initial Cell Connectivity

• A UE can automatically detect nearby eNodeBs, possibly on different channels, and connect to them • Criteria: First suitable or the best eNodeB • Can also force eNodeB selection at a UE

Unique for each eNodeB

 What

if no eNodeB is found nearby?

• All uplink/downlink packets dropped • UE continues scanning for a new eNodeB


How to Identify UE’s Current Serving Cell  Note:

Connecting to an eNodeB also gives core network connectivity to that UE

eNodeB ID is configured at each eNodeB


How to Use the Serving Cell Knowledge for Rapid Analysis  UE

may undergo radio link failures and experience disconnection from LTE core network

“-1” stands for no eNodeB


How to Deploy QoS in LTE 

LTE Configuration node • Define the EPS bearers and their properties • Identify each by a unique name • QCI determines scheduling priority: {5} > {1-4} > {6-9}



UE • Deploy the EPS bearers by their names • Define which application packets are mapped to each bearer • Packets that are not mapped to any bearer (or all packets when no bearer defined) are mapped to “Default” bearer with QCI = 9


How to Analyze QoS in LTE  Per

EPS bearer stats are available for collection

Stats are annotated with bearer names


How to Configure the Physical Profile  Step

1: Define Physical Profile in the LTE Configuration Node

• FDD and TDD profiles can be configured in the LTE configuration node

FDD: UL and DL subframes are configured separately TDD: Common channel for UL and DL


How to Configure the Physical Profile (cont.)  Step

2: Deploy the required physical profile on the eNodeB

• Once a physical profile is deployed, it cannot be changed during the simulation


When to Use FDD and TDD ?  Use

FDD when the utilization of both UL and DL subframes is almost the same • Example: Voice or video conferencing traffic among the users in a cell

 TDD

when there is a asymmetric utilization of UL and DL subframes

• TDD Channel Index decides the UL:DL Division  Frame type cannot be changed during the simulation



TDD Channel Index

UL: DL Division

DL %

0

3:2

40%

1

2:3

60%

2

1:4

80%

3

3:7

70%

4

2:8

80%

5

1:9

90%

6

3:3:2:2

50%

Example: FTP or HTTP application traffic


How to Configure Downlink MIMO 

Step 1: Configure the MIMO Transmission technique • A UE may choose to use the MIMO Transmission technique configured on the eNodeB or use its own custom downlink MIMO transmission technique  By default “Downlink MIMO Transmission Technique” is set to “Use Serving eNodeB Setting”

• The MIMO transmission technique configured on the eNodeB is a cell wide setting for UEs without custom setting


How to Configure Downlink MIMO (cont.)  Step

2: Configure the number of transmit antennas on eNodeB and receive antennas on UE

• Number of transmit antennas supported by eNodeB : 1, 2 or 4 • Number of receive antennas supported by UE : 1, 2 or 4 • A minimum antenna criterion must be satisfied to support a spatial multiplexing MIMO transmission technique  If not satisfied then “Transmit Diversity” will be used as the MIMO technique MIMO Transmission Technique (Spatial Multiplexing)

Minimum number of transmit antennas at eNodeB

Minimum number of receive antennas at UE

2 Codewords- 2 Layers

2

2


4

4


4

4


How to Configure Downlink MIMO (cont.) 

Step 3: Downlink multipath channel model must be configured to realize detailed physical layer effects of MIMO in “PHY Enabled” efficiency mode

• Transmit and Receive diversity are supported only for “PHY Enabled” efficiency mode • Spatial Multiplexing is supported for “PHY Enabled” and “PHY Disabled” efficiency modes 



“PHY Layer Enabled” • MAC layer effects and detailed PHY layer effects of spatial multiplexing can be realized “PHY Layer Disabled” : Efficiency mode which bypasses the PHY Layer • Only MAC layer effects of spatial multiplexing can be realized • Packet drops can be modeled statistically


How to Configure Uplink MIMO 

Only receive diversity is feasible as only one UE Tx antenna is supported • Must use “PHY Enabled” efficiency mode



Step 1: Uplink Multipath Channel Model must be configured



Step 2: Configure the number of receive antennas

• Number of transmit antennas supported by UE • Number of receive antennas supported by eNodeB

:1 : 1, 2 or 4


MIMO Configuration  Default

configuration

• Downlink : Spatial Multiplexing 2 codewords to 2 Layers with 2 Tx and 2 Rx antennas • Uplink: Receive diversity with 1 Tx and 2 Rx antennas  Pros

and Cons

• MIMO Spatial Multiplexing increases throughput but is more prone to physical layer impairments  Can potentially degrade performance when the link quality is bad • MIMO Antenna Diversity (Transmit and Receive diversity) reduces the effects of multipath fading  Not so useful if the link quality is good  When the link quality is bad, using MIMO Antenna diversity technique yields better throughput than any other transmission schemes


How to Analyze the Impact of PHY in LTE 

Channel capacity depends upon: • Channel Bandwidth - The higher the bandwidth, the higher the capacity • Modulation and coding index (MCS) - The higher the MCS index, the greater the capacity • Spatial Multiplexing – When enabled increases the capacity



Capacity estimate available in an OT table at the end of the simulation for each cell for both uplink and downlink Capacity Estimate (Downlink) Mbps Channel Bandwidth (FDD only)

Transmit Diversity 2x2 Low Estimate (MCS 0)

High Estimate (MCS 28)

SM 2CW-2L Low Estimate (MCS 0)






1.4 MHz

0.15

3.27 - 4.09

0.33

6.52 – 8.12

0.48

8.63 – 10.54

0.66

11.50 – 14.04

3 MHz

0.39

8.86 – 10.65

0.81

17.78 – 21.33

1.20

24.45 – 28.19

1.62

32.61 – 37.40

5 MHz

0.68

14.88 – 17.84

1.38

29.84 – 35.70

2.06

41.20 – 47.03

2.77

55.01 – 62.73

10 MHz

1.38

30.13 – 36.20

2.79

60.80 – 74.22

4.18

81.84 – 95.48

5.58

109.31 – 127.55

15 MHz

2.09

45.20 – 54.24

4.14

90.53 – 108.50

6.22

120.99 – 140.69

8.27

161.36 – 187.60

20 MHz

2.79

60.80 – 73.06

5.54

122.58 – 145.52

8.34

163.96 – 192.27

11.09

218.61 – 256.99


Agenda 


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Lab 1: Deploy and Analyze an LTE Network  An

LTE “transit network” where static application users and an application server are connected to the Internet with LTE links

 Deploy

QoS in LTE to provide differentiated services to multiple applications

 Improve

throughput and performance for all users by analyzing

statistics  Time:

35 minutes


Lab 1: Conclusions  Deployment

of QoS improves the service offered to high priority traffic in congested LTE networks

 Spatial

Multiplexing may not benefit all users

• Use spatial multiplexing only when the signal quality is really good  Deployment

of TDD can help serve asymmetric traffic better and improves performance potentially for everyone


Agenda 


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Analysis Tools  Statistics

and ODB

• Discover the causal relationships between multiple observations  Application

delay tracking

• Can track standard applications end to end – including the LTE portion  Parametric

studies

• Parameter value that achieves the best performance • Distributed simulations: Run multiple simulations on multiple CPUs  Visualization

tools

• Time controller – helps correlating statistics with each other • Terrain viewer – helps understand pathloss and terrain profile quickly • Graphical ODB – reference session 1502  Reports

• Performance analysis web reports • OT tables


Typical Planning and Analysis Workflow

Set/revise assumptions

Deploy scenario

Analyze

Define constraints

Constraints not satisfied due to unrealistic assumptions

Find optimal configuration Constraints satisfied


Some Tips for Effective Planning Studies  LTE model

offers the “efficiency mode” that bypasses PHY for speedup

• Physical layer can be abstracted by properly configuring a global HARQ block error rate • UEs can be configured with static MCS indexes to reflect their typical link quality

 Raw

traffic: IP flows, application flows

 Large

packets: Fewer events

• Packets that are too large can cause undesirable effects; segmentation at TCP, IP, and MAC may diminish the benefits eventually  Jammer

nodes: Eliminating the need to model interfering neighbors explicitly • Great acceleration potential for studies requiring interference


LTE Analysis: Jammer Nodes Study Jammer nodes abstracting neighbor cell interference – one for the eNodeB (downlink) and one for all the UEs (uplink)

Statistically same results with scenario without jammers

Example Networks Project: LTE Scenario: video_perf_under_coch_interference_w_jammers

Explicit neighbor cells Jammer nodes

Time saving in large scale simulations for rapid analysis


Statistical Validity of the Planning Studies  Reference – Session

1576

 Good

practice to run the simulation with many random seeds in a typical planning study • Random seed acts as the promoted attribute • Parametric studies workflow  OPNET provides results with their mean values as well as the statistical confidence interval (95% by default or user entered)  Observations should be statistically indifferent • That is, their time series and mean values should look similar • Especially useful for R&D + planning studies  Incorrect custom algorithms might give a wrong notion because the results “by chance” look good  But different random seeds can reveal those problems  Reference example: Lab 2, Session 1941, OPNETWORK 2008


Agenda 


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The Cell Planning Problem  Provide

the required coverage while minimizing one of the resources and constraining the others: • Number of cells • Cell tower location/height • Transmission power

 Where

the following are assumed to be known

• Radio spectrum and the bandwidth • Number of users • Traffic per user • Density of users per square units of a given geographic area • Maximum transmission power of the users  Some

other variations of the cell planning problem are also available


How to Analyze the Cell Planning Problem  Cell

planning should mainly provide coverage

• “Coverage” can be defined as that point from the center of the cell where the UE’s performance is deemed “acceptable”  At a minimum, the UE should connect to the eNodeB  More performance criteria are defined as well  Cell

planning should account for mobility

• Need to plan cells so that handovers are as smooth as possible without service disruption  If the UE sees the strength of the current eNodeB is falling, it should find a new eNodeB in its vicinity


What Affects the Coverage of a UE? 

UE’s coverage is affected by physical layer effects such as • Terrain of the region • Frequencies used for communication  Interference from neighboring cells • Signal fading due to the pathloss • Signal variance due to the multipath

 Node

mobility affects the coverage of UE

• A UE may suffer radio link failures which causes a loss of coverage  Remedies

• MIMO transmit or receive diversity can be used to reduce the effect of multipath • eNodeB can make use of link adaptation to maintain a consistent link quality to reduce the physical layer effects • Have additional cells so that UEs can handover without experiencing radio link failures


Link Adaptation  UE’s

MCS index changes based on link quality

• Good signal – high MCS index, bad signal – low MCS index  Price

paid for low MCS index is consumption of extra radio resources lowering the data rate of the channel

 The

eNodeB balances signal quality and channel capacity by keeping the MCS index at a maximum possible level


Link Adaptation: In-Cell Mobility 

As the UE moves away: • MCS index reduces to sustain link quality • PDSCH utilization increases as more radio resources are required



Why did the traffic stop after some time?



UE was using a GBR bearer – must always be admitted



eNodeB can preemptively delete a GBR bearer if it can no longer guarantee the contract



Use “lte_adm” trace in ODB • mltrace lte_adm


Link Adaptation: Conclusions  If

the UE ventures farther from the base station, its MCS index is lowered and it consumes more resources as a result • Additionally, the UE will also spend more power to account for more transmissions • In some cases, the system may become overloaded and UE’s services may be dropped by the “Admission Control” module

 Thus

the cell should be planned such that UEs should be guaranteed a certain level of service in the worst case • The worst case could be defined by deciding a worst case MCS index value • What if the level of service cannot be guaranteed in the worst case?  UE must handover to another cell so that there is no interruption in the cell coverage


Mobility and Handovers  Handovers

• Allowed across cells (different eNodeBs) belonging to the same core network  eNodeB in any frequency or even different technology (FDD/TDD) is allowed • Not allowed across core networks (different EPC nodes)

EPC 0 EPC 0 EPC 1


How Handover Takes Place  Good

behavior:

• eNodeB triggers handovers based upon UE’s measurement reports when a certain condition is not satisfied (we will see this soon)  Bad

behavior

• UE encounters radio link failure, starts scanning for a new eNodeB and initiates attachment to the eNodeB that satisfies attachment criteria  Why

would UE encounter radio link failure?

• Measurement reports are not received by the eNodeB due to interference • There are no nearby altenative eNodeBs and the serving eNodeB cannot sustain communication with the UE • Too much interference causes frequent failures even when the UE is relatively close to the serving eNodeB  A good

eNodeB placement, reduction of interference and even data distribution across many eNodeBs can reduce the radio link failure problem


Why Does a UE Undergo Radio Link Failure? mltrace lte_rlf

 N310

timeout: Typical Typical when the UE is far away from the eNodeB eNodeB

 Other

factors causing radio link failure

• RACH access failure • RLC-AM maximum retransmission exceeded threshold  In

ideal situations, eNodeB should have handed over the UE before


Handover Triggers  Triggers

based upon two main statistics: RSRP and RSRQ

• RSRP: measures the strength of signal from the current eNodeB • RSRQ: measures the “quality” of signal from the serving cell by considering interference from neighbors Trigger handover if the strength of serving eNodeB <= -112 dBm Trigger handover if the quality of signal from the serving eNodeB <= -5 dB

 Above

trigger values are standard recommended

• But they can still be customized based upon a given scenario’s scenario’s requirements  Target eNodeB

has to satisfy entry threshold too

• Note: Selection threshold >= RSRP Threshold


Using Statistics to Analyze Handover Issues

 Radio

link failure

 RSRP

of the scanned eNodeB below threshold (-110 (-110 dBm)

 eNodeBs

are too far apart

• Either reduce the cell range or increase the transmission power


Using ODB to Analyze Handovers mltrace lte_handover_for_

 RSRQ

threshold exceeded for some time now (-7.00 dB)

 eNodeB

2 is the only eligible eNodeB

• RSRP and RSRQ values of that eNodeB mapped to an index • Index to value map: RSRP: (-140 + index) dBm, RSRQ: (index - 40)/2 dB


The Three Resources Affecting Cell Coverage  Tower

location/height

• Taller towers better LOS  Costs more  May increase pathloss beyond a point • Choose an optimal location based on the terrain  The location may be unavailable or expensive 

 Transmission

Power

• Higher transmission power More coverage  Also can increase interference at the cell edges 

 Number

of cells

• More cells UE’s can handover without radio link failure  Adding more cells can potentially increase interference  Adding more cells can be costly and yield diminishing returns 


The “Resources” for Cell Planning: Recap

e c n a m r o f r e P

Diminishing returns

Number of cells


Increasing coverage

Transmission Power


Increasing LoS

Increasing pathloss

Tower height

Increasing interference

OPNET Modeler helps in identifying the optimal operating points on similar performance curves under the presence of realistic terrain, mobility and physical layer data


Lab 2: Planning LTE Cell Placement to Provide Coverage and Minimize Interference  Deploy

an LTE network on a terrain in Nevada to provide coverage to 30

UEs • Start the deployment with a single cell and check if it is sufficient to provide coverage • By assuming a maximum of 50 meters of tower height, deploy multiple eNodeBs to provide 100% coverage  Adjust

the transmission power of one or more eNodeBs to minimize downlink interference and improve application traffic performance • Also find out if some eNodeBs are redundant

 Time:

35 minutes


Lab 2: Conclusions  The

terrain modeling module (TMM) allows us to model a realistic LTE cell deployment study

 Using

the in-built LTE statistics, it is easy to rapidly analyze the cell coverage

 Using

the TMM visualization tool, it is easy to check the line of sight (LoS) coverage and deploy multiple cells to provide the basic LoS coverage

 Parametric

studies tool allowed us to lower transmission power of two eNodeBs and improve performance • At first, interference was reduced • Later, we discovered that the eNodeBs were redundant and could be eliminated from the network


Agenda 


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The Capacity Planning Problem

Number of users

 Maximize

the number of users while • Satisfying the application delay constraint • Maximizing the throughput 

Sometimes, increasing number of users can affect throughput negatively due to TCP congestion window effects, increased interference, scheduling overheads and retransmissions, etc.

 Relationship

between capacity planning and cell planning • A well designed cell will also have a higher “capacity potential”


How to Analyze the Capacity Planning Problem  Need

to define “acceptable” application performance

• Service level agreements (SLAs) can help define constraints on application delay  Need

• • • •

to understand factors affecting channel capacity

Bandwidth: The higher the bandwidth, the higher the capacity MCS Index: The higher the MCS index, the higher the capacity Spatial Multiplexing: Increases the capacity when enabled Other factors: Overheads (MAC overheads, LTE physical overheads), retransmissions (TCP, RLC, HARQ)

 Need

to understand which channels can experience saturation

• Three channels: PDSCH (downlink shared), PDCCH (downlink control), PUSCH (uplink shared) • For good performance, all three channels should be below saturation level  OPNET

Modeler provides statistics, reports, and traces to help analyze channel capacity


Channel Capacity Reports 

Capacity reports published for both uplink/downlink for each eNodeB



Assumes a single-UE case • That is, a single UE saturates the channel with its traffic

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Overheads will consume some reported capacity; the rest is available for good throughput • MAC, RLC, PDCP • Hence application throughput will be lesser



If all UEs are approximately similar (same MCS indexes), it is easy to estimate channel capacity • With a mix of UEs, it is difficult, and that’s where planning studies are useful in OPNET Modeler


Channel Utilization Statistics  There

are Three channels of importance:

• PDCCH: Physical Downlink Control Channel • PDSCH: Physical Downlink Shared Channel • PUSCH: Physical Uplink Shared Channel  Utilization

statistics track how much channels are utilized to detect if they are overloaded

Overloaded downlink


ODB Tracing into an LTE Frame mltrace lte_frm



Complete breakdown of uplink and downlink subframes • Number of resource blocks and corresponding bits fitted for a given UE  Per codeword information in case of spatial multiplexing transmission in downlink sub frames • Higher layer payload in the LTE MPDU • Frequency information.

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It sometimes helps to look at the ODB output instead of just statistics • Per UE contribution to a subframe can be found • Bit carrying capacity per UE per subframe can be found


Role of Admission Control Module  Relevant

only for GBR bearers

• Before admitting into the system, admission control performs a “rough check” on radio resources to check if there is space to admit • Some things such as retransmissions cannot be anticipated apriori  Admission control can be made flexible using “loading factor”  To account for retransmissions, set the loading factor < 1 • Usually admitted GBR bearers have satisfactory traffic performance

All admitted, none preempted/rejected


Understanding Traffic Statistics Transmitter Total higher layer traffic sent to PDCP/RLC

Higher Layer

Higher layer traffic sent to PDCP/RLC per EPS bearer

Includes new transmissions , retransmissions and status reports

Receiver Delay

Higher Layer

PDCP/RLC

PDCP/RLC

MAC

MAC

PHY

PHY

Total higher layer traffic forwarded by PDCP

Higher layer traffic forwarded by PDCP per EPS Bearer - “Good throughput” per EPS Bearer

Recorded when HARQ decoding is successful

Includes new transmissions and HARQ retransmissions

Delay for all traffic that is delivered to the higher layer

Delay for traffic that is delivered to the higher layer per EPS bearer


Understanding LTE Traffic via Statistics (cont.) 

Examples • Collecting “ MAC Traffic sent ” at an eNodeB shows the traffic load on the downlink channel (PDSCH) • Applying the “Adder” filter for “Traffic received ” by the UEs in a cell can show traffic throughput for the downlink channel (PDSCH) • Applying the “Adder” filter for “Traffic sent ” by the UEs in a cell can show traffic load for the uplink channel (PUSCH) • Collecting “ MAC traffic received” at the eNodeB shows the throughput on the uplink channel (PUSCH)

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The difference between load and throughput is dropped traffic due to:  

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Congestion at the MAC Dropped packets at the physical layer due to interference/fading

Additionally traffic sent (bps, packets/sec), traffic received (bps/packets per second), and delay statistics is available p er GTP tunnel as well


Configurations of Physical Channels and Effect of Control Channels on Capacity 

Downlink • PDSCH: Physical Downlink Shared Channel • PDCCH: Physical Downlink Control Channel • The size of PDCCH is dynamic…Model automatically “resizes” PDCCH to maximize the number of resource elements left to PDSCH

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Uplink • PUSCH: Physical Uplink Shared Channel • PRACH: Physical Random Access Channel – bigger PRACH subtracts capacity from PUSCH • PUCCH: Physical Uplink Control Channel – bigger PUCCH subtracts capacity from PUSCH


“Resources” Affecting Channel Capacity Capacity Planning Resource Number of Users

Channel Bandwidth

Multiple Antennas (MIMO spatial multiplexing)

Transmission Power

Effect of more resource

Cost-benefit

More users  more load offered to the channel

Less users  less load offered to the channel

Having more users increases the offered load and can worsen performance for everyone

More users mean more revenue, so as long as the application SLAs are satisfied, number of users should be maximized

More bandwidth  More space to carry data

Less bandwidth  less space to carry data

More channel bandwidth means there is more space to carry the same amount of data which leads to lower channel utilization and better application performance

More bandwidth is clearly desirable, but it can cost more money to buy this resource

Enabling MIMO spatial multiplexing increase in capacity

Disabling MIMO spatial multiplexing lesser capacity

Enabling MIMO spatial multiplexing increases the subframe capacity without increasing the bandwidth

MIMO spatial multiplexing will only be beneficial if the link quality of the UE is really good otherwise there will be a lot of retransmissions as MIMO spatial multiplexing is very susceptible to physical layer effects.

More TX power  better coverage and higher MCS index unless interference is high

Less TX power  less coverage and lower MCS index

Increasing power can improve the UE’s MCS index and effective capacity is increased. However too much TX power can cause interference and the capacity gain is offset by retransmissions

Increasing power drains UE’s battery faster and can cause interference. Hence power should be minimized as long as coverage criterion is satisfied


The “Resources” for Capacity Planning: Recap OPNET Modeler helps in identifying the optimal operating points on similar performance curves under the presence of realistic terrain, mobility and physical layer data e c n a m r o f r e P


Application SLA

Bandwidth

Number of users


Increasing MCS Transmission Power

Operator budget

Increasing retransmissions

MIMO Spatial Multiplexing


MIMO Transmit Diversity

Link quality


Lab 3: Planning an LTE Cell to Determine Maximum Number of Users 

Deploy a single cell LTE network on a Nevada terrain region to determine the number of users that can be supported in that cell



Define an acceptable application service level agreement (SLA) criterion. Determine if 50 users can be supported with the given traffic profile at the beginning.

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If the SLA is not satisfied, progressively reduce the number of users. • Choose which users to eliminate intelligently; the users that achieve t he lowest MCS index should be eliminated to maximize the number of supported users



Determine using the above algorithm the maximum number of users that can be supported in the cell



Time: 25 minutes


Lab 3: Conclusions 

OPNET’s LTE solution provides an easy way to estimate the channel capacity (OT table reports) and channel utilization (in-built statistics) to rapidly analyze capacity planning problem

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The Top Statistic utility allowed us to find the UEs with the lowest MCS index rapidly

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The capacity planning problem was solved by finding the best 32 UEs that could be supported without compromising the application quality using the iterative algorithm


Agenda 


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Battery Life Planning Problem  Cell

planning and capacity planning studies are throughput oriented since we are attempting to solve • How to increase the system throughput by planning well placed cells covering a region • How to squeeze as many users as possible for maximum revenue

 However

to achieve those objectives, the UE may end up spending its battery by increasing its transmission power

 Why

would the battery life be important?

• Autonomous and unmanned sensor networks: Static UEs • Mobile UEs that do not have readily available charger 

Power saving feature in LTE: • “Discontinuous Reception” (DRX) in “RRC Connected” state • Idle mode


DRX in RRC Connected State 

A UE starts a DRX cycle when there is no activity on the medium for a certain t ime • A UE is in either “DRX Inactive” or “DRX” phase at all the times • If there is no activity on the medium for a duration of inactivity timer, “DRX” phase begins • UE runs DRX cycle in DRX phase  DRX cycle consists of an “active” period and “sleep” period  During “active” period, UE listens to PDCCH for downlink activity  At the beginning of a DRX phase UE runs short DRX cycle first • If the short DRX cycle completes successfully without transitioning to “DRX Inactive” phase, then from that point on UE will run only the long DRX cycle until it transitions to inactive period



DRX Configuration

Enables or Disables “DRX in RRC Connected State” for the UE Duration of active period Duration of short DRX cycle which includes active period Inactivity Timer Multiplication factor to the short DRX cycle gives the duration of long DRX cycle


Idle Mode  A UE

transitions to “idle mode” when there is no activity on the medium for a certain time

 When

in idle mode a UE…

• Is disconnected from the core • Runs “DRX” cycles to save power • Keeps track of the best eNodeB by performing “Cell Reselection” procedure  Sends a Tracking Area Update message to the core if the current eNodeB belongs to a different TA than the TA of the previous one • Reconnects to the core if uplink or downlink data activity is detected  Monitors the paging channel to detect downlink traffic


Idle Mode—Tracking Area Update and Paging  An

idling UE must update the EPC of its current tracking area

• A group of eNodeBs can be in the same tracking area  EPC

pages eNodeBs in UE’s current tracking area when there is a DL traffic • eNodeBs broadcast the page to UE

 Tracking

area size must be wisely chosen

• A larger tracking area may potentially have higher paging load and therefore lesser resources for PDSCH • If the tracking areas are small, idling UEs may need to send frequent tracking area updates, which reduces its battery life


Idle Mode Attributes 

Idle Mode Support • eNodeB Triggered, Enabled or Disabled • Enabled : eNodeB or UE Triggered



T3440 • UE Trigger • Upon expiry of this timer UE enters Idle mode if UE triggered





Cell Reselection attributes used only during idle mode

RRC Connection Release Timer • eNodeB Trigger • Upon expiry of this timer for a UE, the serving eNodeB starts UE context release with the EPC

Tracking area update attributes Attributes related to Paging


How to Analyze the UE’s Battery Power Expenditure 



Battery and power expenditure model

OT Report


Pros and Cons of DRX in RRC Connected State and Idle Mode  DRX

in RRC Connected State and Idle mode are two independent power saving features in LTE DRX in RRC Connected State

• UE is attached and bearers are active • No additional signaling required after the initial attachment • Usually used during short periods of inactivity



Idle Mode

•

A UE in idle mode is disconnected and bearers are inactive • Signaling required to go into and to come out of idle mode • Usually used during long periods of inactivity

OPNET Modeler helps can identifying the optimal operating parameters that will yield maximum battery life and still have good application performance


Lab 4: Maximize UE’s Battery Life Within Application SLAs  Deploy

a single cell sensor network where the UEs receive a command to send sensor data to a command center • Unless the command is received, sensor data is not sent • Sensor data is time critical • Sensors are costly to replace…hence their battery life is extremely important

 Define

an acceptable application SLA

 Verify

the battery life with just the idle mode only enabled and idle mode and DRX both enabled

 Conduct

a parametric study to find the optimal configuration resulting in most battery life

 Time:

30 minutes


Lab 4: Conclusions  With

default configuration we noticed that have idle mode and DRX enabled yielded the maximum power savings

 OPNET

Modeler allows the users to define the application SLA and the number of violation instances very easily.

 Using

the parametric studies feature and the distributed grid computing, one can easily determine the optimal value of the DRX sleep parameter for the given application that can satisfy the application SLA.

 Excise

caution when deciding the idle mode and DRX setting as they depend on the application behavior


Documentation References  Some

• • • • • • •

important 3GPP Standards

36213-880: for the physical layer 36300-910: for the overall description of E-UTRAN 36321-900: for the MAC operation 36322-870: for the RLC operation 36331-900: for the RRC protocol 23203-830: for the policy and control architecture 23401-860: for the EUTRAN access network

 OPNET

• • • • •

Published (LTE consortium website)

LTE Phase I, II, III, IV, V, and VI Requirements Documents Requirements document for the idle mode operation (LTE Phase VII) LTE Frame Generator and Scheduler Description LTE Modulation Models LTE Multipath Fading Models


User Community and Technical Support  Join

the OPNET products online user forum:

https://splash.riverbed.com/community/product-lines/opnet  Riverbed Technical

Support

• https://support.riverbed.com/

• 1.415.247.7381 or 1.888.782.3822 toll-free in the US or Canada International phone support numbers are available at: https://support.riverbed.com/contact/index.htm • Knowledge base: 

https://supportkb.riverbed.com/support/index?page=home


riverbed | splash for Modeler Users  riverbed |

splash is the place to connect with OPNET product

experts • Extensions — Download new product enhancements submitted by Riverbed’s OPNET product staff and customers • Community — Discuss your challenges; announce your successes  Login with your customer https://splash.riverbed.com/  Is

username and password at

there anything on riverbed | splash for Modeler users?

• Sure! For example, the contributed model for IEEE 802.15.3/3b WPAN technology can be found under this link: https://splash.riverbed.com/docs/DOC-3079


Related Sessions  1580:

Modeling Custom Wireless Effects

 1598:

Productivity and Code Efficiency Tips for OPNET Modeler

Users  1576:

Obtaining Statistically Valid Simulation Results: Generation, Interpretation, and Presentation

 1586:

Building Realistic Application Models for Discrete Event Simulation

 OPNETWORK

and Interfaces

2011 – 1581: Understanding LTE Models Internals


Take-Away Points  OPNET

Modeler supports easy deployment and auto-configuration of LTE networks using Wireless Network Deployment Wizard

 Terrain

modeling module can be used to model terrain and custom propagation models, which works seamlessly with LTE models 

Urban propagation model coming up shortly (Session 1574 -New and Improved Features for Modeling and Simulation)

 LTE

model library in OPNET Modeler is comprehensive with a variety of features which can be used in your planning and analysis studies • Cell planning, Capacity planning and battery life planning are some examples

 OPNET

provides extensive capabilities to provide what-if analysis

• Perform parametric studies with distributed execution • Evaluate protocol setting and network parameters to optimize performance

OpNET - LTE

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