Wireless Networking & Mobile Computing
CS 752/852 - Spring 2012
Lec #8: MAC Sectorized Antennas
Tamer Nadeem
Dept. of Computer Science
Enhancing 802.11 Wireless Networks
with Directional Antenna and Multiple
Receivers
(Chenxi Zhu, Tamer Nadeem, and Jonathan Agre)
Introduction
• IEEE 802.11 WLANs have enjoyed tremendous popularity in
recent years.
• RTS/CTS/DATA/ACK packets assume omni-directionality
Introduction (cont’d)
• Channel reservation is made through carrier sensing
• All neighbors of source
and destination nodes
need to be silent.
• Limited number of
channels
and
unlicensed
spectrum
usage
Interference between transmissions is becoming a
serious problem.
Spatial Fairness of 802.11
• Different
nodes
different neighbors
have
 experience different
contention environments.
• Nodes at the overlapping
coverage area of the WLANs
suffer from lower throughput
Extend Bianchi’s discrete time Markov model to
understand Spatial Fairness
Spatial Fairness of 802.11
• Extend Bianchi’s discrete
time Markov model to some
simple multihop networks.
• Contention probability 
• conditional collision probability pc
• Beyond a single hop  different nodes are attached to
different ’spatial channels’  no longer share the same notion
of discrete time.
Need to revisit Bianchi’s discrete time model
Assumptions
• The carrier sensing range is the same as the communication
range;
• RTS/CTS messages are always used
• A collision (duration of RTS/CTS) takes the same amount
of time as an idle slot. DATA/ACK are free of collisions
• Duration of the RTS/CTS/DATA/ACK four way handshake
is a geometric random variable with average of 1/pt slots,
where pt is the probability that a data transmission
terminates in a slot;
• Every node always has a packet to send to one of its
neighbors.
Markov Model
•
•
•
•
•
Markov Model
•
The state (SA, SC, SB)
represents the status of the
nodes in group A,C,B in a slot,
where
•
The Markov chain has 5 states: (0; 0; 0), (1; 0; 0), (1; 0; 1),
(0; 0; 1), (0; 1; 0).
Markov Model
•
Transitional Probabilities:
Markov Model
• Stationary State Probabilities:
ps(0; 0; 0), ps(1; 0; 1), ps(0; 1;
0), and ps (1; 0; 0) = ps (0; 0; 1)
• Collision probabilities of the nodes in groups A,B and group C
• Contention probabilities 1; 2 of nodes in areas A/B and C
Fairness Analysis (NA=Nc=NB=20)
• Throughput vs. Packet size
• Stationary Probabilities
Fairness Analysis (NA=Nc=NB=20)
• Node Contention/Collision
• PaA = p*s(0; 0; 0) + p*s (0; 0; 1)
PaC = p*s(0; 0; 0)
Use of Directional Antenna
• Directional antenna is a
well known method to
reduce the interference
and to increase the range
and the capacity for
wireless networks.
• Fairness relieved through interference reduction
S-MAC
S-MAC: Sectorized Antenna
#3
#2
• Dedicated Rx per sector/antenna
• Tx can switch to different antennas
• Self-interference cancellation between
Tx and Rx in different sectors
• Consistent channel information at
different nodes
#4
#1
s
r
N
#8
#5
R
I
#6
#7
• No hidden nodes or deafness problem
Addresses the hidden node problem and the
deafness problem by continuously monitoring the
channel in all directions (sectors) at all time
S-MAC Architecture
Directional
Antennas
Separate queues
DUX
DUX
RX
RF
DUX
RX3
…
RX
RX2
RX1
TX symbol for
self-interference
cancellation
switching
fabric
RF
TX
RF
S-MAC:
SNAV=[NAVTX1,NAVTX2,
NAVRX1, NAVRX2, NAVRX3]
TX2
TX1
Base Band
TX
MAC and higher
Self-interference Cancellation Scheme
• Different TX and RX modules are all part of the
same PHY
– on-chip communication between them is possible.
• When TXi transmits signal Sti, RXj receives Sri. ;
– RXj cancels the interference caused by own TXi
– RXj can then decode signal from another node k
– This requires self-channel estimation from own i to j:
Gij:
Srik. = Sri - Gij* Sti.
Sectorized NAV and Carrier Sensing
• SNAV=[NAVTX1, NAVTX2, NAV1, NAV2, …,
NAVM].
– NAVTXi: status of TXi (busy period).
• Updated when S-MAC node is involved in a transmission using
TXi
– NAVj: status of medium in sector j.
• Updated when S-MAC node senses a change of medium status
in sector j (sending or receiving RTS/CTS/DATA).
• Fully interoperable with regular omni 802.11
nodes.
Operation of S-MAC (example I)
D
DMAC “Hidden Node due
to asymmetric gain”
H
A
RTS
CTS
RTS
E
B
G
F
Collision
C
Example adopted from R. Choudhury, X. Yang, R. Ramanathan, and
NH Vaidy, MobiCom 2002.
Operation of S-MAC (example I)
D
SMAC: “Hidden Node due to
asymmetric gain” avoidance
H
A
RTS
CTS
CTS from F rcvd
RTS not sent by A
E
B
F
G
C
Example adopted from R. Choudhury, X. Yang, R. Ramanathan, and
NH Vaidy, MobiCom 2002.
Operation of S-MAC (example II)
“Hidden Node due to unheard
RTS/CTS” avoidance
D
H
A
RTS
CTS
E
B
F
G
E waits for B-F to finish
C
Example adopted from R. Choudhury, X. Yang, R. Ramanathan, and
NH Vaidy, MobiCom 2002.
Operation of S-MAC (example II)
Deafness Prevention
D
H
A
E
B
F
G
E is aware C is Transmitting
C
Example adopted from R. Choudhury, X. Yang, R. Ramanathan, and
NH Vaidy, MobiCom 2002.
Markov Model for S-MAC
•
The state (SA, SC1, SC2, SB)
represents the status of the
nodes in group A,C,B in a slot,
where
• SA + SC1 <= 1, SB + SC2 <= 1, SC1 + SC2 <= 1
• The Markov chain has 8 states: (0,0,0,0), (0,0,0,1), (0,0,1,0),
(0,1,0,0), (0,1,0,1), (1,0,0,0), (1,0,0,1), (1,0,1,0).
Fairness Analysis (NA=NB=20, Nc1=Nc2=10)
• Throughput vs. Packet size
• Stationary Probabilities
Fairness Analysis (NA=NB=20, Nc1=Nc2=10)
• Node Contention/Collision
• PaAd = ps(0,0,0,0) + ps(0,0,0,1)
+ps(0,0,1,0)
PaCd = ps(0,0,0,0) + ps(0,0,0,1)
Performance Evaluation
• NS-2 simulator is used.
• 802.11b with transmission rate 11 Mbps.
• Transmission range of 250m and carrier sensing range
is 550m.
• All nodes are stationary.
• UDP traffics packets with average packet size 1000
bytes.
• Four way handshake (RTS/CTS/DATA/ACK) is used.
• Simulated duration of 50 seconds and each point is
averaged from 5 independent runs.
Simulation Scenarios
• Infrastructure
mode is used.
• APs are upgraded
with S-MAC of 4
sectors (1 Tx & 4
Rx).
• All STAs still use
omni directional
antenna (regular
802.11 MAC).
• Network of 2x2 grid of overlapping
• Each AP has 40 clients that are distributed uniformly
in its coverage area.
Simulation Results
• Improvement arises
from
reduced
interference
with
sector antennas and
reduced
collision
from the S-MAC
protocol.
• Total throughput does not change significantly as the
number of sectors increases from 2 to 4.
• No significant change was found with different antenna
orientations.
Conclusion
• S-MAC takes full advantage of directional
antenna:
– Avoids hidden node problem and deafness.
– Multiple sectors can be used simultaneously.
• Fully compatible with regular omni-antenna client
nodes.
– Easy to upgrade existing 802.11 networks with
enhanced access.
– Increase the network capacity with minimal cost.
– Extendable to utilize smart antenna systems
Ideas
• For ad hoc networks:
– Study effect of x% of nodes are S-MAC.
– Study the effect of location of S-MAC node  find
the optimum set of S-MAC nodes for best
performance
• For Infrastructure:
– Best Carrier Sense Threshold for optimal performance
• Mobility?
BACKUP SLIDES
Directional Antenna and DMAC (I)
N3
N1
N2
• Conflict between increased spatial reuse (higher capacity)
and increased collision (higher MAC overhead)
• Collision caused by directional antenna
– Hidden nodes due to asymmetry omni/directional gain
– Hidden nodes due to unheard RTS or CTS packets
– Deafness
Directional Antenna and DMAC (II)
N4
N3
N1
N2
• Conflict between increased spatial reuse (higher
capacity) and increased collisions (higher MAC
overhead)
• Collisions caused by directional antenna
– Hidden nodes due to asymmetry omni/directional gain
– Hidden nodes due to unheard RTS or CTS packets
– Deafness
MAC Assisted Self-calibration
• Self-calibration:
– Estimate the channel from antenna i to antenna k, both of
the same S-MAC node.
– Applicable to all PHY (a/b/g).
• Procedures
– Step 1: send RTS in every sector to silence all neighbor
nodes, so the SYNC sent next will not collide with other
packets.
– Step 2: send regular training symbols (SYNC) in every
sector.
• As SYNC is sent from antenna i, antenna k estimate the
channel Gik.
• Gik and Gki can be averaged: Gki= Gik:=(Gki+ Gik)/2.