Decentralized Predictive MAC Protocol for Ad Hoc Cognitive Radio Networks
Throughput Improvement in Ad hoc networks using the Channel MAC
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Throughput Improvement in Ad hoc networks using the Channel
MAC
Manzur AshrafITR, University of South Australia
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Contents
• Motivation: opportunistic communication
• The Channel MAC protocol
• Analytical model
• Discrete event simulation
• Challenges: Practical implementation
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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
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Channel MAC: Idea
Channel Randomness
Scheduling
Improved throughput,
fairness, etc
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Contd. (Fix a threshold & transmit)
threshold
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Contd.
threshold
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Contd.
threshold
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Contd.
threshold
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Contd.
threshold
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threshold
Probability that more than 1 channel becomes good at an instance is zero
Negligible Propagation delay minimize collision
Threshold
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Throughput calculation
Throughput of one channel
T
threshold
Using Monte Carlo Process, we can approximate the Throughput
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Rayleigh-faded (Jakes model) Channel Results
Node speed=10 km/h
Carrier Freq=2000 Mhz
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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
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Specific: 2-state channel model
In 1-user Channel MAC, the inter-arrival distribution is a shifted exponential dist.
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1-user Channel MAC
Proposition 1: In 1-user Channel MAC, the arrival point process is approximated by a Renewal process.
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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?
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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:
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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).
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Exp. Idle time
PACKET
time
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Throughput
Throughput = Exp. Packet transmission time
Exp. Packet transmission time + Exp Idle time
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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).
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• 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
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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.
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A chain multi-hop network
When p=0.85
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Random topologies
All single-hop flows;
Randomly distributed over 1500 X 1500 sq.meter
Monte Carlo approximation
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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?
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[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?
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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?
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[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.
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[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?
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