Throughput Improvement in Ad hoc networks using the Channel

MAC

Manzur AshrafITR, University of South Australia

Contents

• Motivation: opportunistic communication

• The Channel MAC protocol

• Analytical model

• Discrete event simulation

• Challenges: Practical implementation

Motivation: Opportunistic communication

Channel variations can be exploited by transmitting information opportunistically when and where the channel is strong.

• Goldsmith et al. (97) Point-to-point communication• David Tse, et al. (98) Multi-user communication

Multi-user diversity

impacts

resource allocated to strong user at a time

problem

Limited gain in slow fading/poor channel fluctuations

Beamforming using dumb antennas (same signal xmitted with time-varying phase and power to produce scattering)

solution

Contrary to Space-time code concepts

Channel MAC: Idea

Channel Randomness

Scheduling

Improved throughput,

fairness, etc

Contd. (Fix a threshold & transmit)

threshold

Contd.

threshold

Contd.

threshold

Contd.

threshold

Contd.

threshold

threshold

Probability that more than 1 channel becomes good at an instance is zero

Negligible Propagation delay minimize collision

Threshold

Throughput calculation

Throughput of one channel

T

threshold

Using Monte Carlo Process, we can approximate the Throughput

Rayleigh-faded (Jakes model) Channel Results

Node speed=10 km/h

Carrier Freq=2000 Mhz

Channel-model

l1 l2l3

OFF, Idle time

ON

Arrival points

time

Arrival-rate= r =Level crossing rate

Mixture of Weibull distribution:

assumptions

Any general Inter-arrival distribution function?

> 0

Specific: 2-state channel model

In 1-user Channel MAC, the inter-arrival distribution is a shifted exponential dist.

1-user Channel MAC

Proposition 1: In 1-user Channel MAC, the arrival point process is approximated by a Renewal process.

Proposition 2: The shifted exponential distribution function (by Prop. 1) results in a non-Poisson renewal arrival process.

How to get the inter-arrival distribution for the Superposition of a number of non-Poisson renewal processes?

Solution: Approximation approach

• If points of each individual processes are (a) suitably sparse and (b) no one process dominates the rest, then the distribution of the point process is close to Poisson.

Ref: B. Grigelionis, “On the convergence of sums of random step processes to a poisson process”, Theory Prob. Appl., No. 8, pp 177-182, 1963

Fairness of true random arrival processes

A Poisson process is often a good approximation for a superposition process if many processes are being superposed.

Ref: P. Keuhn, “Approximate analysis of general queuing networks by decomposition”, IEEE transactions of communications, Vol. com-27, No. 1, 1979, pp 113-126.

Grigelionis theorem, 1963:

N-user Channel MAC

Since, points of a homogeneous Poisson process in an interval are independently and uniformly distributed.

Proposition 3: The arrival points of the Superpositioned n-user Channel MAC converges asymptotically to a Poisson point process as per our assumptions.

Proposition 4: In a Poisson Point process, if n number of arrival points occur in an interval of T, the expected delay of the first arrival point in T is T/(n+1).

Exp. Idle time

PACKET

time

Throughput

Throughput = Exp. Packet transmission time

Exp. Packet transmission time + Exp Idle time

Discrete event simulation using NS-2

• NS version 2.27• Nodes communicate using half-duplex radio

based on the the Channel MAC mechanism at 1 Mbps.

• The transmission range of a node is set to 250 m and the career sense threshold is set to 550 m.

• For simplicity, the ARP (Address Resolution Protocol) is assumed to have the hardware address for the destination (i.e. ARP broadcasting is absent).

• Static routing technique is used incorporating the NOAH (No Ad hoc routing) extension of NS2.

• CMU-extension for Ricean fading (time-correlated)

DATA ACKACKDATA

SIFS

PIFS

Packets and overheads

Single-hop network

P. Pham, S. Perreau, A. Jayasuriya, “New cross layer design approach to ad hoc networks under rayleigh fading”, IEEE journal on selected areas in communications: special issue on wireless ad hoc networks, 2005.

A chain multi-hop network

When p=0.85

Random topologies

All single-hop flows;

Randomly distributed over 1500 X 1500 sq.meter

Monte Carlo approximation

Challenges for the practical Implementation

[Selecting threshold]How can we select “p” (probability of a good

channel) at the transmitter? Issues include: how to calculate the received mean power? how many symbols are required to be transmitted to calculate the received mean power?

[Scalability]

How long the channel can be predicted (with reasonable accuracy) for transmitting data considering a Rayleigh (or any other suitable) channel-fading model? Is the channel prediction scheme is scalable with any number of nodes?

Do we need another control channel for periodic broadcast of the channel information?

With the large number of users, is it feasible to use multiple channel in a coordinated way?

With small number of users, is it feasible to use multiple channels with the Channel MAC mechanism applied to each sub-channel?

[Routing]

How the broadcasting technique can be improved considering the ‘channel fading’ (deriving the CSI)? At the moment we only focus on point to point type communications with channel MAC, but to implement routing we need a proper broadcast mechanism.

[Rate adaptive MAC: Again Channel Prediction!]Design of a rate-adaptive Channel MAC: ARF, RBAR, OAR,MOAR etc are rate-adaptive protocols

in the IEEE 802.11 domain. [ Receiver calculates rate based on SNR/ Received

signal strength ]

Is it feasible to implement similar rate adaptation technique for the Channel MAC?

How the channel prediction inaccuracy will affect the rate-adaptation performance?

Manzur.Ashraf@postgrads.unisa.edu.au