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
Topology
Interference

Queue
Channel
Battery
Medium Access Example
• If S1 knows q2 and S2 knows q1
– No need for handshaking
– TDMA scheduling
– No collision
1
q1
2
q2
S1
D
S2
• As load increases
– Probability of queue empty reduces
– Network utility increases
Having the “knowledge” about
Queue states, increases the utilization
How to learn about unknowns
• There is gain in knowing unknown parameters
• The information can be gathered
– Directly
• Feedback
• Training
• Dedicated link, information sharing
X
W
+
H
Q(H)
– Indirectly
• Overhearing
• Passive sensing
S1
D
S2
Y
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
S1
h
D
– Chris, Farbod, Ashu, Behnaam
• Allerton’05, ISIT’06, JSAC’06
• Resource allocation algorithm
S1
• Uncertainty of noise
D
– Farbod, Dash, Ashu
• CTW’06, Asilomar’06
• Coding scheme
• Randomness of source
– Upcoming NSF proposal
• Access mechanism
S2
Multiple Access Channel: MAC
• The system is modeled by
X1
Y
• Information theory answers:
X2
What is the maximum rate (R1,R2) at which X1 and X2 can transmit
with arbitrary small probability of error
Standard solution method
• Finding an achievable upper bound
– Achievability proof
– Converse proof
• Typical solution to MAC
R2
R1
MAC with Bidirectional links
• Time is slotted
– Forward channel: multiple access
– Reverse channel: feedback from receiver
• Superposition coding
Decodable
Un-decoded
New Information
Tx
From Feedback
Un-decodable
Decoded
Rx
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
X1
Y
X2
• Under investigation
– Rate region
– Coding strategies
What if…
• Receiver has information for senders
• Superimpose feedback information with its own information
Achievable rate region
• A:  = 0
B
– Only Broadcast
• B:  = 1
– Only MAC
A
R3
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
h1
h2
h1
h2
Randomness of source
X2
X1
• Challenges:
– K is random
– Under delay constraint
– Access mechanism is
required
• Each node needs to
know the number of
active users
X3
X4
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