1. Topology Control Protocol Using Sectorized Antennas in Dense 802
Topology Control Protocol Using Sectorized Antennas in Dense Wireless Networks
Theodoros Salonidis, Thomson
In collaboration with: Anand Subramanian, Henrik Lundgren, Don Towsley 1

2. Outline Introduction and Motivation
Problem formulation and protocol solution
Testbed and Experimental Evaluation
Conclusions 2

3. Sectorized antennas in dense 802.11 networks
Higher spatial reuse
Higher link budget gain
Throughput improvement

4. Directional Communication - Challenges
Just adding a sectorized antenna is not enough
New Challenges:
Sector Selection
Deafness
Directional Hidden Terminal Problem
Broadcast A
Node A is ‘deaf’ to node X
Deafness X B
Collision
Node A’s transmission to node B is hidden to node X
How to choose the best sector combination for a link?

5. Current Approaches Directional MAC protocols
Sector selection at packet time scale
Requires complex modifications to MAC to solve deafness and directional hidden terminal problem
Often requires multiple radios or time synchronization
Topology control approaches
Activate multiple sectors at a slow time scale
Minimal or no modifications to MAC protocol
Previous work on topology control
Uses idealized antenna radiation models
Assumes advanced antenna functionality (e.g. angle of arrival info)
Assumes binary pairwise interference
Only simulation-based evaluations
On one hand, we have several paper

6. Our Approach
Topology control using low-cost sectorized antennas on commodity hardware.
Propose and implement a measurement-based optimization procedure and attendant protocols
Evaluate in a dense reflection-rich wireless testbed

7. Outline Introduction and Motivation
Problem formulation and protocol solution
Testbed and Experimental Evaluation
Conclusions 7

8. Models A B C D Network model Antenna model Interference model
E’ is the set of links the network layer selects to carry traffic
Antenna model
s sectors =>2s -1 antenna patterns per node
RSSuvij = RSS on link (u,v) when node u Tx at antenna pattern i and v Rx at j
Interference model
I(u) set of nodes w that interfere with u: (u,w) and (w,u) do not belong to E’ A B C D
Sender side
Receiver side

9. Problem formulation where: subject to:
Decision binary variable Xui denotes whether antenna pattern i is assigned to node u
Connectivity constraint:
Equation (4) ensures that each node in the network is
assigned exactly one antenna pattern. Equation (5) ensures
that the RSS, using the assigned antenna patterns, of each
link carrying data traffic is within Cth of the RSS when
using omni-modes. Finally, Equation 6, ensures that Xui’s take
values 0 or 1.
The above optimization problem is a quadratically con-
strained quadratic optimization problem and is known to be
NP-hard. In the next section, we reduce this problem to a
integer linear program and then relax it to a linear program to
obtain a lower bound on the optimal solution.

10. Distributed solution Distributed measurement protocol
Provides the RSS measurements in all antenna patterns and links in the network
Greedy distributed topology control protocol
Uses RSS measurements and tries to solve the optimization problem in a distributed manner.

11. Distributed measurement protocol
Problem
Each node u can use K different antenna patterns xu
Measure the RSSuvxuxv of all antenna patterns (xu,xv) of all links (u,v) in the network.
Idea
Use broadcast probe packets.
Perfectly synchronized network with lossless channels requires NK2 measurements
Challenges
Nodes are half-duplex and must be coordinated to switch to the right sector assignment at the right time.
Implementation on top of CSMA MAC protocol.
Protocol
Each node u transmits broadcast probes tunes using antenna patterns xu using a predetermined sequence and period
Each node v estimates time and antenna pattern of next probe of u and schedules to switch and receive that probe at an antenna pattern xv at that time

12. Greedy Distributed Topology Control Protocol
Idea: Objective function can be decomposed
Distributed protocol iteration
Each node u, finds locally optimal antenna pattern xu that solves:
Minimize
subject to
Distributed protocol locking mechanism
Ensures that the node u with minimum Su(xu) among its neighbors switches to its optimal antenna pattern xu.

13. Outline Introduction and Motivation
Problem formulation and protocol solution
Testbed and Experimental Evaluation
Conclusions 13

14. Thomson Multi-sector Antennas
All sectors activated
omni-mode
Sector 4
Sector 3
Sector 1
Sector 2
4 Vivaldi sectors each covering 90o in the azimuth plane.
This antenna
can be used with any commodity IEEE wireless card
operating in the 5 GHz band. Each of the four sectors covers
a different quarter of the azimuth plane. The sectors at each
node can be simultaneously activated in any combination, thus
allowing 15 different antenna patterns. Omni-mode is achieved
by simultaneously activating all four sectors. Figures 1(b)
and 1(c) show the radiation patterns in the azimuth plane for a
single sector and omni-mode, respectively. Sector activation is
controlled by a switch integrated on the antenna system, which
we control through the parallel port of the host computer.
sector selection electronically through parallel port signaling
any combination of sectors can be activated (K=15)
max. directional gain = max. omni gain

15. Multi-sector antenna testbed
IEEE a (channel 52 in 5 GHz band)
No external interference. Night-time experiments
Default settings
TxPower 1dBm; Data rate 6Mbps; Cth 3dB
1024byte back-to-back UDP packets (broadcast and unicast)
Network topologies
15 node pairs in total. We use 6 node pairs which have 100% delivery
Form 20 ‘two-link’ topologies
We deploy a six-node testbed in an
indoor office environment as shown in Figure 2. This environ-
ment is rich in multi-path reflections, typical in dense home
or enterprise WiFi deployments and community wireless mesh
networks. Each node is a Dell D610 laptop running Linux
(Fedora Core 6) and uses the Madwifi driver (version 0.9.4)
to control an Atheros IEEE a/b/g wireless card.
In these topologies we run the greedy algorithm and find the optimal through exhaustive search.

16. Interference Reduction
Sender-side interference reduction.
Receiver-side interference.
In this figure we observe the interference reduction at the transmitter and receiver of each link. The x axis is the link index. The left graph shows that RSS
In all cases,
the Greedy and Optimal schemes generate less interference
than the Omni scheme and the Greedy scheme performs
close to the Optimal scheme.
The SINRs of the Greedy
and Optimal schemes are higher compared to the SINR of
the Omni scheme in most cases.
Our topology control scheme
thus reduces the receiver side interference compared to Omni-
Therefore, it reduces the probability of occurrence of
the directional hidden terminal problem and can thus leverage
on the higher spatial reuse achieved at the sender node.
CS threshold is -85dBm
Greedy: reduce 58% of the links to below CS threshold
Optimal: 65% of the links below CS threshold
Capture if SINR is above capture threshold (6 dB)
Greedy & Optimal: 78% of the links have SINR greater than capture threshold
Omni: 48%

17. Varying Network Density: Objective Value
1dBm transmit power.
4dBm transmit power.
In both cases, on the average
there is approximately 8 dB reduction in the objective value
(aggregate network interference) when using the Greedy or the
Optimal scheme compared to the Omni scheme.
Both Greedy and Optimal schemes result in higher aggre-
gate throughput compared to the Omni scheme. At 1 dBm
Tx power, the Greedy and Optimal schemes both result in
two-fold aggregate throughput improvement for half of the
topologies. At 4 dBm Tx power only a few topologies achieve
similar improvement. In such highly dense networks, finer
sectorization (sectors with smaller width) and joint Tx power
control may lead to higher spatial reuse.
On the average around 8dB reduction in objective value
Effect of density:
elevation of graph with 3-4dB,
but no significant difference in the shape of the curve

18. Varying Network Density: Throughput
1dBm transmit power.
4dBm transmit power.
Both Greedy and Optimal schemes result in higher aggre-
gate throughput compared to the Omni scheme. At 1 dBm
Tx power, the Greedy and Optimal schemes both result in
two-fold aggregate throughput improvement for half of the
topologies. At 4 dBm Tx power only a few topologies achieve
similar improvement. In such highly dense networks, finer
sectorization (sectors with smaller width) and joint Tx power
control may lead to higher spatial reuse.

19. Unicast Throughput Improvement
In this section, we
present throughput results for unicast traffic sent at 6 Mbps
data rate.
The Greedy and Optimal
schemes outperform the Omni scheme, though the relative
improvement is less compared to when using broadcast traffic
(cf. Figure 5(c)). There are two main reasons for this reduced
improvement. First, unicast traffic has ACK packets sent by
the receiver nodes that can reduce spatial reuse. Second, ACK
packets can collide with data packets on other links causing
increased directional hidden terminal problems. We observe an
average throughput improvement of more than 1.5 Mbps (33%
improvement over 5 Mbps) when using the Greedy scheme
compared to the Omni scheme
Average improvement is around 33%
ACK packets impact performance
reduces spatial reuse (transmitted from receiver)
can cause directional hidden terminal problems

20. Decreasing Measurement Complexity: RSS prediction
Throughput: Greedy scheme
Throughput: Optimal scheme
Performance using different prediction schemes: In this
section, we evaluate the performance impact of letting our
measurement protocol reduce measurement complexity by
applying a measurement method where multi-sector RSS is
predicted from single sector RSS measurements (’Method 2’
in Section VI). We compare this method with that obtained
from measuring in all sector combinations (’Method 1’ in
Section VI) and against the Omni scheme. Figure 8 shows
the aggregate throughput when using these three different
schemes. We observe that for both the Greedy and Optimal
schemes, Method 2 results in performance degradation com-
pared to Method 1. However, Method 2 still outperforms the
Omni scheme for several topologies in both cases.
Predict multi-sector RSS from single sector measurements
Reduces compexity from (2^s - 1)^2 to s^2
Leads to degradation compared to measuring in all sectors
But still better than omni

21. Different Network Architectures
WLAN scenario: We consider a WLAN scenario where
the access points (APs) are equipped with sectorized antennas
to reduce interference, while the clients have only omni-
directional antennas. We use eight different topologies, each
consisting of two APs with two clients each. We use iperf to
send downlink backlogged traffic, from APs toward the clients.
We expect the relative improvement of our topology control
protocol to be slightly less than in previous experiments. This
is because APs need to use wider sector selection to com-
municate with multiple clients. In addition, since the clients
use omni-directional antennas they cannot contribute to spatial
reuse improvement and, furthermore, they are more exposed
to directional hidden terminals. Despite these constraints, we
observe in Figure 9 that the Greedy scheme results in 15-25%
aggregate throughput improvement over the Omni scheme
except in topologies 1 and 3. In these cases, both schemes
resulted in the same sector assignments.
network throughput for the Greedy scheme and Omni scheme
in these eight topologies. The Greedy scheme yields 40-60%
improvement in four topologies, about 15% improvement in
two topologies, and performs similar to the Omni scheme in
the remaining topologies.
WLAN: 2 APs w/ 2clients each
broader sectorization needed to serve multiple clients
15-20% improvement in most cases
Mesh: 2-hop and 1-hop flows
broader sectorization needed for forwarding nodes
40-60% improvement in half of the topologies
15% in two other topologies

22. Outline Introduction and Motivation
Problem formulation and protocol solution
Testbed and Experimental Evaluation
Conclusions 22

23. Conclusions Our contribution Results Future work
Topology control using low-cost sectorized antennas on off-the-shelf cards.
Formulation of topology control problem using physical layer model based on RSS measurements.
A greedy distributed topology control protocol and a distributed RSS measurement protocol.
Results
Greedy protocol performs close to optimal
Sender-side and receiver-side interference reduction lead to higher spatial reuse and throughput.
Trade off between complexity and performance in sector measurements.
Density impacts the achievable spatial reuse.
Future work
Finer sectors
Combination with dynamic transmit power control 23