The design of wireless communication system plays an important role in the performance of the network. There are basically two major parts in planning WLAN. First, finding the optimum number and locations of wireless access points (APs) to achieve best coverage area (with few or no gaps) by maximizing the signal strength. Second, the allocation of frequency channels to these APs that gives minimum channel interference and provides best throughput. In addition to these, the number of APs can be reduced to optimize the installation cost of the network. In this paper we present a multi-objective optimization ILP model that maximizes the signal strength, minimizes the co-channel and adjacent channel interference by allocating channels to APs with appropriate distances and minimizes total cost by optimizing the number of APs installed. Each objective is weighted with trade-off parameters which can be tuned to generate designs suitable for different wireless applications.

1. Optimal Network
and
Frequency Planning
for WLAN
Abhishek Verma Dr.-Ing. Michael Reyer
Lehrstuhl für Theoretische Informationstechnik
Univ.-Prof. Dr. rer. nat. Rudolf Mathar
RWTH Aachen University
Master’s Thesis Final Talk 18th Feb, 2016

2. Outline
 Introduction
 WLAN planning
 Objectives
 Design components
 ILP model
 Results
 Summary and future work
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3. Introduction
Why WLANs are popular?
 Easy to deploy
 Stable future migration
 Low cost
 Effective services for indoor/outdoor
 Easy access to Internet
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4. Problem Scenario
Works
?
• Limited coverage range of each access point.
• Limited frequency bandwidth/capacity.
• And so many users to satisfy!!
Access point (AP)
Needs some planning
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5. WLAN Planning
 For any given design requirements, planning a wireless network means to
determine
1. An optimal number of APs.
2. The locations of those APs.
3. Frequency channels associated to them.
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Inputs
• Access points : Candidate locations (j), # available APs (b) capacities (Pj), costs (mj)
• Users (i) : data rates (ti), priorities (pi)
• Received signal strengths (sij)

6. Demand Node Concept
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 Service area is divided into
demand point grid.
 Assumption – data rate demand
at each point is constant.
Candidate location for an AP j
Demand point i
Inputs
• Signal strength,
• Demand rate,
• Priority,
• AP Cost,
Where,
i : demand points
j : candidate APs

7. Objectives
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We have three competing objectives
Signal Strength
• sij
• Maximize the sum
of signal strengths
of all demand
points ‘i’.
Interference
• Minimize at each
demand point
from all
interfering APs.
• Depends upon
channel distance
between APs.
Cost
• mj
• Minimize the
number of
installed APs ‘j’.

8. WLAN Design Components
 Coverage area
 (in dBm): receiver sensitivity threshold.
 (in dBm) : calculated using the log distance path loss.
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Where, is the transmit power of AP j
• User traffic
• Provide minimum required data rate, .
• Varies with the type of user application.

9. WLAN Design Components
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• Limited number of channels.
• Need adequate channel distances between APs.
• (in dBm) : detection threshold.
Frequency spectrum
• Different channels --- different overlaps
• The effect of interfering signal depends upon the channel distance ‘d’
between the two APs. This attenuation factor is given by:
where, ko is a constant

10.  Objective function
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• Related Constraints
1.
2.
3.
4.
Integer Linear Program Model
5.
6.
7.

11. 11
• Related Constraints
1.
2.
3.
4.
Signal strength maximization
5.
6.
7.

12. Parameters
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• Number of demand points, N = 196
• Number of candidate AP locations, M = 12
• Number of available channels, K = 13
• Number of available APs, b = 10
• Demand rate, ti = 1
• Priority, pi = 1
• = 6.02 dBm
• = 7.95 dBm
• Transmit power, = 20 dBm

13. Results
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• Random channel selection possible.
• Overlapping of frequencies.
• Bigger dots represents ‘bad points’
suffering from interference more
than the .
• Lead to more collisions, increased
sensing time and low throughput.
• decreases with increasing distance.
• Demand points are assigned to APs
closer to them.

14. Minimizing Interference
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• Subtracting interference from the objective
• Related Constraints
1.
2.
3.
4.
5.
6.
7.

15. Results
15
• = 0.9, = 0.1
• Number of ‘bad points’ decreases.
• Number of APs drops to 6.
• Coverage area drops by only 2%.
• But this trade of between coverage
area and interference depends upon
the values of and .

16. Minimizing Cost
16
• Subtracting cost of APs from the objective
• Related Constraints
1.
2.
3.
4.
5.
6.
7.

17. Final Optimal Solution
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• = 0.6, = 0.1, = 0.3
• Number of APs further drops to 4
• Area covered = 82 %.
• # of ‘Bad points’ = 0.
How to choose values of ??

18.  The competing objectives are weighted by trade-off parameters .
Each triplet of lambdas may produce different optimal solutions of
the ILP model while maximizing the total objective sum.
 We can compare these solutions with one another in terms of :
 Area covered (in %)
 Number of ‘bad nodes’
 Number of APs installed
 are varied in the steps of 0.1 from 0 to 1 keeping .
 From the results, we can focus on those triplets which jointly gives
maximum coverage area and minimum number of ‘bad nodes’ and
number of APs.
 Resulting into the triplet, .
 But, a designer may choose a different set of lambdas depending the
results and wireless environment.
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Final Optimal Solution

19. Summary
 WLAN planning is important due to the huge popularity and large
scale deployment of wireless networks.
 3 major problems related to planning are finding the number of APs,
their locations and frequency allocation.
 The optimization objectives signal strength, interference and cost are
jointly included in our ILP model and are weighted by tunable trade-
off parameters.
 For the considered planning scenario, the values of the trade-off
parameters which jointly produces maximum signal strength,
minimum interference and minimum cost is
= 0.6, = 0.1, = 0.3.
 For a different planning scenario this may be different.
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20. Future Work
 The model assumes constant data rate throughout the basic service
area. Because of the IEEE 802.11 fallback protocols, the data rate may
change according to the SIR levels.
 We have assumed the effective AP capacities. Since, an access to a
wireless medium is a random event and depends upon the number
of users, we could use capacity analytical method to estimate more
practical AP capacity.
 The optimal trade-off values could not be found due the
computational limitations. Work can be done in developing more
efficient algorithms for solving linear optimization problems.
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21. 21
Thank you!

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Questions?