ACOE 422 Wireless Computer Networks - FITstaff.fit.ac.cy/com.ke/files/acoe422/6_planning and...ACOE 422 Wireless Computer Networks Network Design and Planning Issues and Performance

ACOE 422 Wireless Computer NetworksACOE 422 Wireless Computer Networks Network Design and Planning Issues and

Performance EvaluationPerformance Evaluation

Efthyvoulos C. Kyriacou (PhD) Assoc. Prof.

Computer Science and Engineering DepartmentResources: Mobile Communications, J. Schiller book slides

Prof. A. Pitsillides Univ. of Cyprus

OutlineOutline

• Network Design

• Radio Network Planning

• Performance Evaluation

• Case Study 1:• Case Study 1:– WLAN Coverage Planning

C St d 2• Case Study 2:– WLAN Performance Evaluation

IntroductionIntroduction• Wireless networks rely on an inexpensive but prone Wireless networks rely on an inexpensive but prone to errors medium (air) with limited bandwidth

• We require wireless networks to be– Functional– Affordable

Scalable– Scalable– Flexible– Manageableg– Secure– Resilient and Reliable

M t th i d d ( f b d idth)– Meet the growing user demands (e.g. of bandwidth)– Low cost of ownership consistent with these objectives

• Network Design and Planning is very essential!Network Design and Planning is very essential!

CoverageCoverage

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.)Wireless Valley Communications, Inc., ©2000, all rights reserved.)

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

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]at 5.8 GHz, for various types of customer premise antenna [from [Dur98], ©IEEE].

Network DesignNetwork Design

S if t k hit t• Specify network architecture– Define radio access network design and engineering– Define core network design and engineeringDefine core network design and engineering– Provide detailed protocol design

• Traffic Modelingg– Decide on voice and data applications

• Mobility Modeling– Mobility assessment and design is important

• Complete area plansP id f d b l k l i• Provide performance and bottleneck analysis

• Specify security and redundancy plans

Network Design: considerationsNetwork Design: considerations

• Services & Traffic: – How much and where?

– Impact on network quality, efficiency and cost

• What is best design strategy (given an imprecise g gy g pdemand forecast)?– Coverage vs. capacity, cell breathing (UMTS)g p y, g ( )

– Ability to use existing sites (e.g. GSM)

• Meet budget and cash flow constraintsMeet budget and cash flow constraints

Radio Network PlanningRadio Network Planning • RNP includes:

– Dimensioning– Detailed Coverage & Capacity Planning– Network Optimization– Network Optimization

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

b d th t ’ i t d th di• based on the operator’s requirements and the radio propagation in the area

• Dimensioning must fulfil certain requirements for:g q– Coverage– CapacityQ lit f S i (Q S)– Quality of Service (QoS)

Radio Network PlanningRadio Network Planning

C d C it Pl i• Coverage and Capacity Planning– Determine the coverage regions, area type information and propagation conditionsp p g

– Determine the available spectrum and traffic density information

• Note In W CDMA networks (e g UMTS) capacity• Note: In W‐CDMA networks (e.g. UMTS), capacity and coverage are closely related– both must be considered simultaneously in the planningboth must be considered simultaneously in the planning process

• Network Optimization– Provide optimal coverage probability, blocking probability and end user throughput

Radio Network PlanningRadio 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


T k l i h d l f• Takes place prior to the deployment of a system• Assesses a system’s capabilities• Evaluates any new mechanisms the system will use• Note: System = a collection of related entities that interact together over a time to accomplish a goal– E.g. to deliver telecommunication services that satisfy

ifi Q S ispecific QoS requirements


• 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

Performance EvaluationPerformance 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 relationshipslogical 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 analysisy• Costly• Requires specialized knowledge• Often several approximations need to be made (for complex• Often several approximations need to be made (for complex systems) hard to generalize results

– Simulation becomes more and more popular


• Simulation– Simulation models may be categorized according to the type of input data they accept

• Deterministic

• Stochastic• Stochastic

– Simulation models may be categorized according to the factors that cause system state to changefactors 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)

Performance EvaluationPerformance EvaluationEvaluation of a system

Experiment with the actual system Experiment with a model of the system

•Costly

•Often the system is not availablePhysical model Abstract model

Analytical evaluation (mathematical l i )

Simulation

Categorized according to Categorized according to analysis)

•Costly

•Approximations due to complexity

g gthe type of input data it accepts:

•Deterministic

g gthe factors that cause system state to change

•Continuous•Approximations due to complexity •Deterministic

•Stochastic

•Continuous

•Discrete Event Based

ConclusionsConclusions• 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• 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 servicequality of service

• Assess server capacity and network coverage• Ascertain security and management requirementsy g q

– Provide maximum security for the whole network infrastructure

Case Study 1: WLAN Coverage PlanningCase Study 1: WLAN Coverage Planning

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


• 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

t t it/ i t /f thcannot transmit/receive to/from other users.

– causes limited system capacity when coverage areas overlapoverlap

• Appropriate positioning of APs is crucial


• 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

l b f i i hi h i ll AP– select a subset of sites in which to install APs so as to guarantee a high enough signal level to all user terminals

• Problem• Problem– Minimizing the number of APs that cover the complete set of user terminals is an NP hard task (a k a cardinality setof user terminals is an NP‐hard task (a.k.a. cardinality set covering problem)

Case Study 1: WLAN Coverage PlanningCase 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 siteiteratively 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 functionis explored for a better solution, using an objective function. The final solution is the one with the highest objective function.


C l i• Conclusions– Coverage planning for WLANs is a hard task

A i l l i i NP h d– An optimal solution is NP‐hard• A sub‐optimal approach is usually taken

– Proposed approach uses heuristics and is composed ofProposed 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.

Performance evaluationPerformance evaluationIEEE 802.11 (XIV)

• Unicast data transfer

DIFSDIFS

ACK

senderdata

SIFS

data

ACK

otherstations

receiver

DIFS

stations twaiting time contention

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

InterInter--Frame Space (SIFS), if the packet was received correctlyFrame Space (SIFS), if the packet was received correctly

Masters thesis

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

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.


• 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


• OPNET WLAN models– WLAN station

• IEEE 802.11 WLAN station

• includes ON/OFF traffic source

• includes sink• includes sink

• includes WLAN interface

• includes receiver/ transmitter pair/ p


• OPNET WLAN models– WLAN workstation

• workstation with client/server applications running over TCP/IP andapplications running over TCP/IP and UPD/IP

• supports IEEE 802.11 connections at 1Mbps 2Mbps 5 5Mbps or 11Mbps1Mbps, 2Mbps, 5.5Mbps or 11Mbps (speed is determined by data rate of connecting link)

WLAN server– 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

ti li k)connecting link)


OPNET WLAN modelsWLAN access pointp

wireless router

Ethernet interface

connects the wireless network to wired networks


A h 1 i h• Approach 1: tuning the physical layer related parametersparameters– Modified OPNET wlan_mac process to introduce 4 pparameters

• Slot timeSIFS i• SIFS time

• Minimum contention window• Maximum contention windowMaximum contention window

– To enable choose “customized” option for “Physical Ch t i ti ”Characteristics”


A h 1 i h h i l l l d• Approach 1: tuning the physical layer related parameters

S i ith 2 WLAN t ti– Scenario with 2 WLAN stations– WLAN stations have no TCP or higher layers, therefore reflect the performance of MAC layer protocols more p y paccurately

– First set of simulations demonstrates the effect of Slot time d Sh t I t f S (SIFS) WLAN fand Short Inter‐frame Space (SIFS) on WLAN performance

– Second set of simulations demonstrates the effect of Minimum Contention window on the average media access u o te t o do o t e a e age ed a accessdelay


A h 1 i h• Approach 1: tuning the physical layer related parametersparameters– Simulation set 1– media access delay in firstmedia access delay in first node is collected

• media access delay = queue d l i d ldelay + contention delay

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


A h 1 i hApproach 1: tuning the physical layer related parametersparameters

Simulation set 2media access delay is againmedia access delay is again collectedResults: 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 performanceimproved performance


A h 2 t i th IEEE 802 11 t• Approach 2: tuning the IEEE 802.11 parameters– A BER generator was developed and integrated in the wlan stationmodelwlan_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 collectedh h h f d f ll d b• Throughput represents the rate of data successfully received by other stations


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

h h ld h f ff hthreshold have no significant effect on the WLAN performance.


• Approach 2: tuning the IEEE 802.11 parameters– Results show that for relatively high BER, a small f h h ld f lfragmentation threshold can significantly improve WLAN performance.


Approach 2: tuning the IEEE 802.11 parametersResults show that for relatively low BER, a very small f h h ld f l dfragmentation threshold can significantly deteriorate WLAN performance, because of the heavy packet overhead.


A h 3 i h d li k l (MAC)• Approach 3: using an enhanced link layer (MAC) protocol

Ad ti b k ff h i i d– Adaptive back‐off mechanism was examined– This mechanism can be implemented on top of the existing access scheduling protocol and does not introduceexisting 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


• Approach 3: using an enhanced link layer (MAC)• Approach 3: using an enhanced link layer (MAC) protocol– adaptive back‐off mechanism was implemented and p pintegrated into the wlan_mac process model.


• Approach 3: using an enhanced link layer (MAC) protocol– Three simulation scenarios with various numbers of identical WLAN stations

i f 820kb– Data is sent at an average rate of 820kbps

– Destination stations are randomly chosen by the source stationstation

– Results collected for analysis include: • Throughput (rate of data successfully received by otherThroughput (rate of data successfully received by other stations)

• Load (rate of data sent to other stations)


• Approach 3: using an enhanced link layer (MAC) protocolApproach 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.h h i ff i l d h b f lli i d d l• the mechanism can effectively reduce the number of collisions and data loss


A h 3 i h d li k l (MAC) t l• Approach 3: using an enhanced link layer (MAC) protocol– Results: throughput/load behavior of WLAN with more nodes is

consistent


• Conclusions– 3 methods for improving WLAN performance were

l dimplemented in OPNET• Tuning the physical layer characteristics can greatly improve network performancenetwork 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

Thi t d d i l ti th f– This case study used simulation as the performance evaluation method and came to its conclusions after a series of simulation sets for different scenariosof simulation sets for different scenarios

Documents

ACOE 422 Wireless Computer Networks - FITstaff.fit.ac.cy/com.ke/files/acoe422/6_planning and...ACOE 422 Wireless Computer Networks Network Design and Planning Issues and Performance