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Page 1: ACOE 422 Network Design and Planning Issues and Performance Evaluation.

ACOE 422

Network Design and Planning Issues and Performance Evaluation

Page 2: ACOE 422 Network Design and Planning Issues and Performance Evaluation.

Outline

Network DesignRadio Network PlanningPerformance EvaluationCase Study 1:

WLAN Coverage Planning

Case Study 2: WLAN Performance Evaluation

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Introduction Wireless networks rely on an inexpensive but

prone to errors medium (air) with limited bandwidth

We require wireless networks to be Functional Affordable Scalable Flexible Manageable Secure Resilient and Reliable Meet the growing user demands (e.g. of bandwidth) Low cost of ownership consistent with these objectives

Network Design and Planning is very essential!

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Coverage

Figure 2.13 A predicted coverage plot for three access points in a modern large lecture hall. (Courtesy of Wireless Valley Communications, Inc., ©2000, all rights reserved.)

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Figure 2.15 A typical neighborhood where high speed license free WLAN service from the street might be contemplated [Dur98b].

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Figure 2.16 Measured values of path loss using a street-mounted lamp-post transmitter at 5.8 GHz, for various types of customer premise antenna [from [Dur98], ©IEEE].

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Network Design

Specify network architecture Define radio access network design and engineering Define core network design and engineering Provide detailed protocol design

Traffic Modeling Decide on voice and data applications

Mobility Modeling Mobility assessment and design is important

Complete area plans Provide performance and bottleneck analysis Specify security and redundancy plans

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Network Design: considerations

Services & Traffic: How much and where? Impact on network quality, efficiency and cost

What is best design strategy (given an imprecise demand forecast)? Coverage vs. capacity, cell breathing (UMTS) Ability to use existing sites (e.g. GSM)

Meet budget and cash flow constraints

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Radio Network Planning RNP includes:

Dimensioning Detailed Coverage & Capacity Planning Network Optimization

Dimensioning estimates: an approximate number of base station sites base stations and their configurations other network elements

based on the operator’s requirements and the radio propagation in the area

Dimensioning must fulfil certain requirements for: Coverage Capacity Quality of Service (QoS)

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Radio Network Planning

Coverage and Capacity Planning Determine the coverage regions, area type information

and propagation conditions Determine the available spectrum and traffic density

information Note: In W-CDMA networks (e.g. UMTS),

capacity and coverage are closely related both must be considered simultaneously in the planning

process Network Optimization

Provide optimal coverage probability, blocking probability and end user throughput

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Radio Network Planning

Outputs during RNP: Rough number of base stations and sites Base station configuration Site selection Cell specific parameters for RRM & adjusting of

RRM parameters to optimal values Analysis in the issues of capacity, coverage

and QoS

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Performance Evaluation

Takes place prior to the deployment of a system

Assesses a system’s capabilitiesEvaluates any new mechanisms the

system will useNote: System = a collection of related

entities that interact together over a time to accomplish a goal E.g. to deliver telecommunication services that

satisfy specific QoS requirements

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Performance Evaluation

Two ways to achieve performance evaluation of any system Experiment with the actual system Experiment with a model of the system

Experiment with the actual system Set up the system and run it Collect measurements that will aid in the assessment of

the system Exact results but costly Often the system is not available

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Performance Evaluation Experiment with a model of the system

The model can be physical or abstract abstract = representation of the system containing

structural, logical or mathematical relationships The physical model is evaluated similarly to an actual

system The abstract model may be evaluated in two ways:

Analysis (mathematical analysis) Simulation

Mathematical analysis Costly Requires specialized knowledge Often several approximations need to be made (for

complex systems) hard to generalize results Simulation becomes more and more popular

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Performance Evaluation

Simulation Simulation models may be categorized

according to the type of input data they accept Deterministic Stochastic

Simulation models may be categorized according to the factors that cause system state to change

Continuous (time-based) Discrete event-based (still requires a time-keeping

mechanism to advance from one event to another)

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Performance EvaluationEvaluation of a system

Experiment with the actual system

•Costly

•Often the system is not available

Experiment with a model of the system

Physical model Abstract model

Analytical evaluation (mathematical analysis)

•Costly

•Approximations due to complexity

Simulation

Categorized according to the type of input data it accepts:

•Deterministic

•Stochastic

Categorized according to the factors that cause system state to change

•Continuous

•Discrete Event Based

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Conclusions Review your existing applications and infrastructure

Incorporate, as needed, wireless access points, routers, gateways, security devices and middleware

Determine connectivity requirements for your network and mobile devices Integrate seamlessly with your current and future IT

infrastructure Evaluate performance, scalability, and availability metrics

Leverage simulation and modeling tools to help ensure consistent quality of service

Assess server capacity and network coverage Ascertain security and management requirements

Provide maximum security for the whole network infrastructure

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Case Study 1: WLAN Coverage Planning

Paper: WLAN Coverage Planning: Optimization Models and Algorithms, E. Amaldi, A. Capone, M. Cesana, F. Malucelli, F. Palazzo

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Case Study 1: WLAN Coverage Planning

WLAN medium access mechanism: “listen before talk” approach if a user terminal is covered by more than 1 AP

and is transmitting/receiving to/from one of them, the other APs cannot transmit/receive to/from other users.

causes limited system capacity when coverage areas overlap

Appropriate positioning of APs is crucial

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Case Study 1: WLAN Coverage Planning

Simple way to plan coverage consider a set of possible positions of user terminals in

the service area consider a set of AP candidate sites select a subset of sites in which to install APs so as to

guarantee a high enough signal level to all user terminals

Problem Minimizing the number of APs that cover the complete

set of user terminals is an NP-hard task (a.k.a. cardinality set covering problem)

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Case Study 1: WLAN Coverage Planning Heuristics are adopted to provide a sub-optimal

solutions Not all such solutions provide acceptable levels

of capacity and QoS Proposed solution:

2 phases: greedy approach & local search The greedy phase starts from an empty solution and

iteratively adds to the current solution the candidate site which maximizes a certain benefit function (calculated for each candidate site)

The local search phase takes as an input the solution provided by the greedy phase. Then the site “neighborhood” is explored for a better solution, using an objective function. The final solution is the one with the highest objective function.

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Case Study 1: WLAN Coverage Planning

Conclusions Coverage planning for WLANs is a hard task An optimal solution is NP-hard

A sub-optimal approach is usually taken Proposed approach uses heuristics and is

composed of two phases: the greedy phase and the local search phase

Results show that this approach achieves better overall capacity than the classical approach, which is based on the minimum cardinality set covering problem.

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Performance evaluationIEEE 802.11 (XIV)

Unicast data transfer

DIFS

data

ACK

otherstations

receiver

sender

t

data

DIFS

waiting time contention

SIFS

– station has to wait for DIFS before sending datastation has to wait for DIFS before sending data– receivers acknowledge after waiting for a duration of a receivers acknowledge after waiting for a duration of a

Short Inter-Frame Space (SIFS), if the packet was Short Inter-Frame Space (SIFS), if the packet was received correctlyreceived correctly

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Masters thesis

http://eeweb.poly.edu/dgoodman/fainberg.pdf

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Case Study 2: Performance Evaluation of Wireless LANs

Paper: Enhancements and Performance Evaluation of Wireless Local Area Networks, Jiaqing Song and Ljiljana Trajkovic.

Performance Evaluation is done using the OPNET simulation tool.

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Case Study 2: Performance Evaluation of Wireless LANs

Known problems with WLANs WLAN media is error prone (very high BER) Hidden Terminal problem decreases performance Carrier Sensing (for collision detection) is difficult

a station is incapable of listening to its own transmissions

Investigate 3 approaches for improving WLAN performance tuning the physical layer related parameters tuning the IEEE 802.11 parameters using an enhanced link layer (MAC) protocol

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Case Study 2: Performance Evaluation of Wireless LANs

OPNET WLAN models WLAN station

IEEE 802.11 WLAN station includes ON/OFF traffic

source includes sink includes WLAN interface includes receiver/

transmitter pair

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Case Study 2: Performance Evaluation of Wireless LANs OPNET WLAN models

WLAN workstation workstation with client/server

applications running over TCP/IP and UPD/IP

supports IEEE 802.11 connections at 1Mbps, 2Mbps, 5.5Mbps or 11Mbps (speed is determined by data rate of connecting link)

WLAN server server with applications running over

TCP/IP and UDP/IP supports IEEE 802.11 connections

at 1Mbps, 2Mbps, 5.5Mbps or 11Mbps (speed is determined by data rate of connecting link)

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Case Study 2: Performance Evaluation of Wireless LANs

OPNET WLAN models WLAN access point

wireless router Ethernet interface connects the wireless

network to wired networks

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Case Study 2: Performance Evaluation of Wireless LANs

Approach 1: tuning the physical layer related parameters Modified OPNET wlan_mac

process to introduce 4 parameters

Slot time SIFS time Minimum contention window Maximum contention window

To enable choose “customized” option for “Physical Characteristics”

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Case Study 2: Performance Evaluation of Wireless LANs

Approach 1: tuning the physical layer related parameters Scenario with 2 WLAN stations WLAN stations have no TCP or higher layers, therefore

reflect the performance of MAC layer protocols more accurately

First set of simulations demonstrates the effect of Slot time and Short Inter-frame Space (SIFS) on WLAN performance

Second set of simulations demonstrates the effect of Minimum Contention window on the average media access delay

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Case Study 2: Performance Evaluation of Wireless LANs

Approach 1: tuning the physical layer related parameters Simulation set 1 media access delay in first

node is collected media access delay =

queue delay + contention delay

Results: smaller slot time and SIFS decrease the average media access delay improved performance

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Case Study 2: Performance Evaluation of Wireless LANs

Approach 1: tuning the physical layer related parameters Simulation set 2 media access delay is

again collected Results: setting Min

contention window to a smaller value (in the case when there are few WLAN stations in the network) decreases media access delay improved performance

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Case Study 2: Performance Evaluation of Wireless LANsApproach 2: tuning the IEEE 802.11

parameters A BER generator was developed and

integrated in the wlan_station model Nine simulation scenarios with various

combinations of values for BER and Fragmentation threshold

to demonstrate the effects of the fragmentation threshold

Throughput is collected Throughput represents the rate of data successfully

received by other stations

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Case Study 2: Performance Evaluation of Wireless LANs

Approach 2: tuning the IEEE 802.11 parameters Results show that for low BER various fragmentation

threshold have no significant effect on the WLAN performance.

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Case Study 2: Performance Evaluation of Wireless LANs

Approach 2: tuning the IEEE 802.11 parameters Results show that for relatively high BER, a small

fragmentation threshold can significantly improve WLAN performance.

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Case Study 2: Performance Evaluation of Wireless LANs

Approach 2: tuning the IEEE 802.11 parameters Results show that for relatively low BER, a very small

fragmentation threshold can significantly deteriorate WLAN performance, because of the heavy packet overhead.

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Case Study 2: Performance Evaluation of Wireless LANs

Approach 3: using an enhanced link layer (MAC) protocol Adaptive back-off mechanism was examined This mechanism can be implemented on top of

the existing access scheduling protocol and does not introduce additional overhead.

The main idea of the mechanism is to estimate the shared channel by calculating the slot utilization ratio.

High utilization possible collision back-off

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Case Study 2: Performance Evaluation of Wireless LANs Approach 3: using an enhanced link layer (MAC)

protocol adaptive back-off mechanism was implemented and

integrated into the wlan_mac process model.

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Case Study 2: Performance Evaluation of Wireless LANs

Approach 3: using an enhanced link layer (MAC) protocol Three simulation scenarios with various numbers of

identical WLAN stations Data is sent at an average rate of 820kbps Destination stations are randomly chosen by the source

station Results collected for analysis include:

Throughput (rate of data successfully received by other stations)

Load (rate of data sent to other stations)

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Case Study 2: Performance Evaluation of Wireless LANs

Approach 3: using an enhanced link layer (MAC) protocol Results: with the adaptive back-off mechanism load can be greatly

reduced while throughput can still achieve the same or higher value. the mechanism can effectively reduce the number of collisions and data loss

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Case Study 2: Performance Evaluation of Wireless LANs Approach 3: using an enhanced link layer (MAC)

protocol Results: throughput/load behavior of WLAN with more nodes is

consistent

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Case Study 2: Performance Evaluation of Wireless LANs

Conclusions 3 methods for improving WLAN performance were

implemented in OPNET Tuning the physical layer characteristics can greatly

improve network performance Properly chosen values for fragmentation threshold

improves WLAN performance when BER is high The adaptive back-off algorithm in the MAC layer can

effectively reduce the number of collisions This case study used simulation as the performance

evaluation method and came to its conclusions after a series of simulation sets for different scenarios


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