A Simple and Effective Cross
Layer Networking System for
Mobile Ad Hoc Networks
Wing Ho Yuen, Heung-no Lee and
Timothy Andersen
Outline
Introduction to cross layer design
Proposed channel model
Rate adaptation scheme
Routing metrics utilizing MAC info
Simulation setup and results
Why Cross-layer?
Nature of wireless ad hoc networks

Limited capacity, constrained energy, mobility …
Application requirements

Real time, mission critical …
Current layered design paradigm is inflexible
and sub-optimal for wireless networks
Cross-layer design requires info exchanged
across layers, thus allows protocols to adapt
in a global manner, eventually achieves
optimal network performance
Cross Layer Example
Channel Model
Three signal strength attenuation
factors are considered, namely, pass
loss, shadowing and multipath fading
For channel-adaptive protocols, A good
time-varying channel model is needed
for simulation
A correlated shadowing channel model
is proposed
Correlated Shadowing Model
Correlated Shadowing Model
Shadowing attenuation Aj of a node j does not
change until node j moves out of a disc of radius d
from previously reference position
Suppose node j moves out of the disc, new
attenuation is A(n  1)  i A(n)  1  i2W
Suppose both node i and j move out of the disc
A(n  1)  ij A(n)  1  ij2 W
 ij  exp( 2 / D0 )
Where i  exp(  / D0 )
W is a zero mean Gaussian random variable
Rate Adaptation Scheme
Modification on IEEE 802.11 protocol
RTS,CTS and ACK packets sent at nominal
rate
SNR is estimated at when node receives RTS
Transmission rate is mapped from the
estimated SNR, and appended to the CTS
The sender transmits data at the adapted
rate
Rate Adaptation Scheme
An M-QAM scheme is used in which the
constellation size changed with SNR
Constellation size is decided by M ( )    K *
*

where K is a constant determined based on
the power constraints,  is SNR.
Threshold rule is used, if M j  M ( )  M j 1 ,
assign M j to 
NAV modification
Routing Metrics
Bandwidth awareness 1 Rij , Rij represents
the rate of link between node i and j
Interferences awareness  Dij , Where Dij is
the interval from the when the RTS packet is
sent to when the data packet is received
Congestion awareness  Qi , where Qij is the
queuing delay in the buffer of transmit node
Interference awareness is implemented
Implementation in DSR
DSR route maintenance unmodified since only low
mobility scenarios are considered
Received SNR information are appended in RREQ,
since RTS and CTS are not used when broadcasting
RREQ, no rate adaptation is used in RREQ packets
RREP packets are unicast packets to the source node
using rate adaptation based on the SNR information
along the route
Source node compute the MAC delay of every RREP
packets and choose the route with min delay
Simulation Setup
Ns-2 with wireless extensions by the Monarch
Project, CMU
Channel Model: Correlated shadowing,
implemented in C++
MAC layer: 802.11b at 914MHz, 2MHz
bandwidth
Network layer: modified (dynamic source
routing) DSR algorithm
Transport layer: UDP agent
Application layer: CBR application
Simulation Environment
50 nodes
Transmission range 250m
Scenario size 1500X300m
Channel model:
Path loss model: 2 ray ground reflection model
shadowing variance s=12 (severe shadowing)
Correlated fading (slow fading at low mobility)
Mobility model:
Random waypoint model
2 values of node mobility s=0m/s and 1m/s
corresponding to stationary and pedestrian scenarios
Simulation Environment
Each scenario has 20 flows (source destination pairs)
Packet rate varies from 10 to 60 packet/s
Each traffic flow starts at staggered time between 0s
and 100s
Performance metrics: throughput, delay, packet
delivery ratio
Three schemes are investigated:



Plain DSR
RA: rate adaptation
IARA: interference aware rate adaptation
Results:
Stationary
Scenario
Results:
Pedestrian
Scenario
Conclusions
Spectrally efficient rate adaptation
scheme leads to drastic improvement in
throughput
Simple routing metric incorporated to
the DSR protocol, leading to modest
decrease in packet delay