General Theory of Wireless Networks with Side Information Ahmad Khoshnevis, Debashis Dash Rice University Nokia Seminar February 10, 2006
Jan 17, 2018
General Theory of Wireless Networks with Side Information
Ahmad Khoshnevis, Debashis DashRice University
Nokia SeminarFebruary 10, 2006
Wireless Networks
• High data rate– WiMax, Mesh– 802.11x– 4G
• Irony– Current protocols such as 802.11 cause 30-50% non-data
communication (overhead)
First question: Is existence of protocols necessary?
Why Protocols?
• Queues have time-varying state– Might be empty sometimes
• In effect, # of active nodes is time varying
• Design for Max # of user is conservative– Underutilized network for many traffic
“Active” management of queue states = Medium Access Protocols
q2
q1 S1
S2
q3
S3
How Much Overhead?
Second Question: What is the minimum amount of overhead? How can it be reduced?
Observation
• 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 “side information” aboutQueue states, increases the utilization
1
2q2
q1 S1
S2
D
Implementation of the idea
• Perfect carrier sense no collision
• While q1 and q2 non empty– TDMA guarantees no collision
• When – q1 and q2 are empty – |t1-t2| < – Collision happens
• Collision resolution takes resources– Modeled as wasted time, c
• Probability of Collision is determined by probability of q1=q2=
q1 S11
2q2
S2
D
t
S2
S1
t1t2
c
Performance
Generalization
• In general “side information”– Queue state– Number of nodes– Battery life, …
• Catch• The “side information” is not of interest, data is• Gathering “side information” requires resources
– Perfect information causes a lot of overhead– Partial information gives more room for data, but more uncertainty
Fundamental Tradeoff
There is a tradeoff between amount of side information and total throughput of a network.
What is the maximum data rate for a given amount of side information?
New Source Model
• There are two information need to be transmitted– The actual data, M– The source state, S
• The message– Conveys useful information– Need to be sent error free
• The source state– Can’t be sent perfectly (takes all the capacity)– The rate of source information is controlled by distortion between S
and S’
M
S S’X
S.Enc
C.E
nc
New Source Model
Channel Model
• Discrete memoryless channel
• The channel is described by P{Y|X1,X2}
Formulating the Problem
Additional Insights
• Particularly in our approach– Generalization of side information & being independent of
interpretation– Addressing penalty associated with knowing side information
• Considered in earlier models– Is extendable to a network with arbitrary number of users– Simultaneously can answer both question
• Total network throughput• Per user throughput
Road Map
• Improving the Model– More interesting case is conferencing
• Find I(M1,M2;Y|S’1,S’2)• Properties of solution space and possible solution for special
cases