Communication over Bidirectional Links A. Khoshnevis, D. Dash, C Steger, A. Sabharwal TAP/WARP...
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Transcript of Communication over Bidirectional Links A. Khoshnevis, D. Dash, C Steger, A. Sabharwal TAP/WARP...
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