Cross-Layer Schemes for Antenna Array Based Wireless Ad Hoc Networks – Design and

Analysis

Jayakrishnan MundarathJointly Advised by :

Prof. Parmesh Ramanathan Prof. Barry Van Veen

Preliminary Examination Talk

Outline

Introduction – Ad Hoc networks and MIMO Design & Analysis Perspective Research Proposal – Unified Analysis Model Conclusion

Wireless Ad Hoc Networks

No infrastructure support

Nodes may rely on other nodes to forward packets on their behalf

Example: IEEE 802.11

Wide range of applications

Need higher bandwidths at lower energy expense

Popular Standard - IEEE 802.11

RTS-CTS-DATA-ACK framework

Single antenna – Single Spatial Reuse

When node A is communicating with node B all nodes in the neighborhood of A and B must remain idle

Limits aggregate network throughput

A

B

C

DE

F

G

RTS

CTS

DATA

ACK

Multi-Antenna Systems (MIMO)

Each node has N > 1 antenna

Can “beamform” transmissions (favorably or unfavorably) towards receivers

Can spatially multiplex multiple data streams

Can exploit array gain to lower energy consumption

Solution for ad hoc networks?

A B

C D

Outline

Introduction – Ad Hoc networks and MIMO Design & Analysis Perspective Research Proposal – Unified Analysis Model Conclusion

Preliminary Work

NULLHOC – MAC/PHY protocol that increases spatial reuse using nulling (IEEE Globecom’04, revision submitted to ACM Journal of Wireless Networks (WINET))

HYB – MAC/PHY protocol that exploits both spatial reuse and multiplexing (submitted to IEEE Trans. on Wireless Comm.)

QSAP – Allocates spatial reuse to satisfy QoS (submitted to IEEE/ACM Trans. on Networking)

DTNS Model – Markov chain model to predict protocol performance (submitted to IEEE/ACM Trans. on Networking)

Discrete Time Network State (DTNS) Model

Goal Analytical characterization of effects of cross-layer designs

on performance of multi-antenna wireless ad hoc networks

Accomplished Cross-layer analytical model to assess network throughput

for a class of ad hoc networks

Future direction Application to a wider class of networks Exploring wider range of performance metrics, e.g., energy

consumption, Quality of Service (QoS).

HYB – An illustrative example for DTNS

Spatial reuse + spatial multiplexing

Orthogonal control and data channels (CC and DC)

Single spatial reuse control channel

Multiple spatial reuse data channel

A B

C D

D E

DATA

CCDC

CONTROL

DATA

CONTROL

DATA

HYB : Network Evolution

Control

Data

Time

1 2 3 4 5 6 7 8 9

DTNS Considerations

Medium Access Framework : RTS-CTS-DATA-ACK

Channel knowledge at transmitter and receiver assumed (e.g. using two-way pilot sequence exchange)

Orthogonal Control and Data channels Proportion of bandwidth assigned to CC = α

DTNS Specifics (1)

Maximum spatial reuse dr

Maximum spatial multiplexing dm

Maximum EDB = kmax,α dm (1- α) kmax,α = maximum spatial reuse achievable with CC

bandwidth α

kmax,α =

rd,tB /L

)B) -/((1Lmin

chctrl

dat

DTNS Specifics (2)

Actual EDB < Maximum EDB due to MAC effects – e.g. collisions Physical layer effects – e.g. transmit power, poor SNR Possibly network/higher layer effects – packet

availability, QoS constraints etc.

To obtain actual EDB, model network time evolution using Markov chain

Given dr choose optimal αopt as solution to:

Then discretize time with one time slot = one control length

rd chctrl

dat

tB /L

)B) -/((1L

DTNS : Network Evolution Model

Data(3,2) (2,2) (1,2)

(3,2) (2,2) (1,2)

(3,2) (2,2) (1,2)(3,1) (2,1) (1,1)

(3,2) (2,2) (1,2)(0,0) (0,0) (0,0)

(3,2) (2,2) (1,2)

Time

1 2 3 4 5 6 7 8 9

Control

DTNS : Network State Representation

Data

(3,2) (2,2) (1,2)

(3,2) (2,2) (1,2)

(3,2) (2,2) (1,2)(3,1) (2,1) (1,1)

(3,2) (2,2) (1,2)(0,0) (0,0) (0,0)

(3,2) (2,2) (1,2)

(0,0)(0,0)(0,0)

(3,2)(0,0)(0,0)

(3,2)(2,2)(0,0)

(3,2)(2,2)(1,2)

(3,1)(2,2)(1,2)

(3,2)(2,1)(1,2)

(2,2)(1,1)(0,0)

(3,2)(1,2)(0,0)

(3,2)(2,2)(0,0)

Time

1 2 3 4 5 6 7 8 9

Control

DTNS Markov Chain

Transition probabilities derived from model of Channel statistics and physical layer scheme Bound on transmit power of each node MAC constraints such as collision Can accommodate other constraints

(1,2)

(2,2)

(3,2)

(0,0)

(1,2)

(2,2)

(2,1)

(0,0)

(1,2)

DTNS : Network Analysis

Network EDB given by kav(1-α)

kav is the average number of streams – obtained from steady state analysis of the DTNS Markov chain

Changing constraints amounts to modifying the transition probabilities

Ex.1 : Spatial Multiplexing on Eigen channels : MRATE

N antennas – transmit up to N data streams Simple extension to IEEE 802.11 RTS-CTS used for channel estimation Inverse water filling – allocate available power

among spatial channels to achieve equal SNR Fill from best to worst DTNS chain has N states Rayleigh flat-fading channel model

Ex.1 : Spatial Multiplexing on Eigen channels

MRATE – Results with adjusted back-offs

N = 8 Different total

available transmit powers

Ex.2 : HYB – Hybrid Protocol

N antennas – allocated for spatial reuse and spatial multiplexing

Maximum spatial reuse dr and maximum spatial multiplexing dm such that dm dr < N

Rayleigh flat fading channels used

Ex.2 : HYB – Hybrid Protocol

dm = 1, dr = 8 dm = 2, dr = 4

Ex.2 : HYB – Hybrid Protocol

dm = 4, dr = 2 dm = 8, dr = 1

Ex.2 : HYB – Results

Model captures trends accurately

Discrepancies in absolute value a consequence of some specific characteristics of the protocol

Sequence of different control messages have consequence on protocol performance

A coarse model for such effects accounts for ~70-80% of the discrepancies

Not included here since it requires elaborate description of HYB

Outline

Introduction – Ad Hoc networks and MIMO Design & Analysis Perspective Research Proposal – Unified Analysis Model Conclusion

Research Proposal

Multi-rate capable ad hoc networks – e.g IEEE 802.11a/b Different rate adaptation strategies Optimal MAC?

Multi-hop topology – can DTNS model performance in multi-hop topologies?

Quality of Service (QoS) – increasingly important in next generation ad hoc networks – best strategy?

P1 : Multi-rate protocols

IEEE 802.11a/b – supports transmissions at multiple rates

Strategies for rate adaptation exist in literature and practice

First goal is to assess schemes with practical physical layer models Model network as Markov chain – transitions depend on

Channel model and physical layer scheme Access strategy

State representation and exact nature of transitions?

Second goal is to analytically design an efficient rate adaptation strategy

Is there an optimal rate adaptation strategy for a given channel model? What is “optimal” in this context?

P2 : DTNS Model for Distributed Topology

Current DTNS models single hop topology

Multi-hop topology is more challenging

Performance metric –throughput per node Use flow contention graph to represent topology State representation – requires investigation

Generalize to statistical topology models

P3 : QoS in Ad Hoc Networks

QoS increasingly important in Ad Hoc Networks

Analytical model for QoS in MIMO networks can Provide insights for more efficient resource allocation Enable to take cross-layer effects into account

Possible approaches Represent network state at time k of N nodes as a N-vector of

deviations Vector u(k) represents allocation strategy at time k Model cost function and derive optimal “strategy” to

Minimize deviations Drive deviations to desired value

Outline

Introduction – Ad Hoc networks and MIMO Design & Analysis Perspective Research Proposal – Unified Analysis Model Conclusion

Conclusion

Analytical models important for next generation ad hoc networks

Research aims at achieving Deeper insights into performance limitations Identifying effects of cross-layer interactions Identifying optimal provisioning strategies Finding efficient designs

Thank you