Communication over Bidirectional Links

A. Khoshnevis, D. Dash, C Steger, A. Sabharwal

TAP/WARP retreat

May 11, 2006

Wireless Networks

• Higher throughput• TAP: 400 Mbps• WiMax/Mesh• 4G

Network of Unknowns

Queue

TopologyInterference

Channel

Battery

Medium Access Example

• If S1 knows q2 and S2 knows q1

– No need for handshaking

– TDMA scheduling– No collision

• As load increases– Probability of queue empty reduces– Network utility increases

Having the “knowledge” aboutQueue states, increases the utilization

1

2q2

q1 S1

S2

D

How to learn about unknowns

• There is gain in knowing unknown parameters

• The information can be gathered– Directly

• Feedback• Training• Dedicated link, information sharing

– Indirectly • Overhearing• Passive sensing

H +X W

Y

Q(H)

S1

S2

D

Need for Bidirectional links

• Indirect– Limited

– Highly depends on the topology and availability

• Direct– Amount of information can be controlled

An explicit sharing of information requires flow of information in both directions among all communicating nodes, hence

Communication over Bidirectional Links

Cost-Benefit of learning the unknowns

• Catch– We don’t care about the unknown

• Only care about sending data

– Time varying in nature• Periodic measurements • Spend resources for non-data

If considering the true cost of knowing the unknown,

is there still any gain left?

Our research

• Unknown Channel– Chris, Farbod, Ashu, Behnaam

• Allerton’05, ISIT’06, JSAC’06• Resource allocation algorithm

• Uncertainty of noise– Farbod, Dash, Ashu

• CTW’06, Asilomar’06• Coding scheme

• Randomness of source– Upcoming NSF proposal

• Access mechanism

S1

Dh

S1

S2

D

Multiple Access Channel: MAC

• The system is modeled by

• Information theory answers:

What is the maximum rate (R1,R2) at which X1 and X2 can transmit

with arbitrary small probability of error

X1

X2

Y

Standard solution method

• Finding an achievable upper bound– Achievability proof

– Converse proof

• Typical solution to MAC

R1

R2

MAC with Bidirectional links

• Time is slotted– Forward channel: multiple access

– Reverse channel: feedback from receiver

• Superposition coding

Decoded

Tx

Rx

Decodable

From Feedback

Un-decodable

New Information

Un-decoded

Our model

j,l I’,k’

Contribution and results

• Considering resources in feedback

– Time

– Power (Pf)

• Coding scheme to compress the feedback information

• Pf / eP

Interpretation of result

• In second timeslot– Both user help to resolve

uncertainty

Co-operation induced by feedback

Cooperative link

• Anticipate the exponential feedback power is resolved

• Under investigation– Rate region

– Coding strategies

X1

X2

Y

What if…

• Receiver has information for senders

• Superimpose feedback information with its own information

Achievable rate region

R3

A

B• A: = 0

– Only Broadcast

• B: = 1– Only MAC

Channel state vs. data feedback

• So far, receiver sends back unresolved information

• In fading environment using channel state– Power / rate control increases the throughput

• Feedback can be used to send back channel state information

h1h2

h1h2

Randomness of source

X1

X4

X3

X2

• Challenges:– K is random

– Under delay constraint

– Access mechanism is required

• Each node needs to know the number of active users

Recap

Ongoing work:

• Gaining information about the unknowns increases the throughput• Obtaining information is best when it is explicit and direct

– Requires resources (power and time) to be allocated to unknowns– Requires bidirectional communication link

• Capacity of MAC increases with “realistic” feedback– Power in the feedback link is large

Up coming:

• Cooperative link • Channel state vs. data feedback• Randomness of the source