1. IEEE Student Paper Contest
11/17/2018
On The Throughput-Optimal Distributed Scheduling Schemes with Delay Analysis in Multi-hop Wireless Networks
IEEE Student Paper Contest
Seoul Section 2009
Presenter: Nguyen H. Tran

2. Outline Introduction Network Model and Examples
11/17/2018
Outline
Introduction
Network Model and Examples
Pick and Compare Scheduling Mechanism
Proposed Algorithm
Open Issues
Conclusion

3. 11/17/2018
Introduction
In wireless networks, how to design an efficient medium access scheme is an important issue.
In 1992, Tassiulas’ seminal paper triggers an avalanche of works dealing with throughput-optimal scheduling algorithms.
We develop a low-complexity, distributed scheduling scheme to achieve the optimal performance for K-hop interference model

4. Wireless Network Model and Examples
11/17/2018
Wireless Network Model and Examples
Example:
Wireless Network
One-Hop Inferference Model:
N = Node set = {1, 2…, N}
L = Link set = {1, 2, …, L}
Sl = Interference Set for
link l L
General Interference Set Model:
Sl = l U {links that interfere with link l transmission}

5. Wireless Network Model and Examples
11/17/2018
Wireless Network Model and Examples
Example:
Wireless Network
One-Hop Inferference Model :
N = Node set = {1, 2…, N}
L = Link set = {1, 2, …, L}
Sl = Interference Set for
link l L
Set Sl
General Interference Set Model:
Sl = l U {links that interfere with link l transmission}

6. Wireless Network Model and Examples
11/17/2018
Wireless Network Model and Examples
Example:
Wireless Network
Two-Hop Inferference Model :
N = Node set = {1, 2…, N}
L = Link set = {1, 2, …, L}
Sl = Interference Set for
link l L
Set Sl
General Interference Set Model:
Sl = l U {links that interfere with link l transmission}

7. Wireless Network Model and Examples
11/17/2018
Wireless Network Model and Examples
Queueing Dynamics:
-Slotted System: t = {0, 1, 2, 3, …}
-One Queue for each link l:
Ql[t] = # packets in currently in queue l (on slot t)
Al[t] = # new packet arrivals to queue l (on slot t)
ml[t] = # packets served from queue l (on slot t)
Al[t]
ml[t]
Ql[t]
Ql[t+1] = Ql[t] – ml[t] + Al[t]
R[t] ={Feasible Schedules}
ml[t] {0, 1}
ml[t] = 1 only if Ql[t]>0 AND no other active links w Sl

8. Wireless Network Model and Examples
11/17/2018
Wireless Network Model and Examples
Queueing Dynamics:
-Slotted System: t = {0, 1, 2, 3, …}
-One Queue for each link l:
Ql[t] = # packets in currently in queue l (on slot t)
Al[t] = # new packet arrivals to queue l (on slot t)
ml[t] = # packets served from queue l (on slot t)
Al[t]
ml[t]
Ql[t]
Ql[t+1] = Ql[t] – ml[t] + Al[t]
R[t] ={Feasible Schedules}
ml[t] {0, 1}
ml[t] = 1 only if Ql[t]>0 AND no other active links w Sl

9. Max Weight Scheduling Capacity Region:
11/17/2018
Max Weight Scheduling
Capacity Region:
L = {All rate vectors l = (l1,…, lL) supportable}
Capacity Region L
[Tassiulas, Ephremides 92]: Max Weight Match (MWM)
Maximize wl[t]ml[t] Subject to:
(Stabilizes Network, Supports all l interior to L)
m[t] R[t]

10. Max-Weight Complexity and Suboptimal Algorithms
11/17/2018
Max-Weight Complexity and Suboptimal Algorithms
Max-Weight Scheduling is a centralized and high-complexity algorithm
K=1: polynomial time-complexity
K>=2: NP-Hard
Some of distributed and suboptimal proposals: Maximal Matching, Constant-Time Complexity…
Capacity Region L
g-scaled region gL 10

11. 11/17/2018
Goal
We aim to design a scheduling algorithm for K-hop Interference Model with Distributed fashion yet still achieve Optimal Throughput.
Too Ambitious……..??
There is a solution: Pick and Compare Algorithm
What is the price: increasing Queuing Delay

12. Pick-and-Compare Algorithm
11/17/2018
Pick-and-Compare Algorithm
At each time-slot [t]

13. Pick-and-Compare Illustration

14. Delay Analysis
Theorem:
Pick-and-Compare algorithm can achieve the throughput-optimal performance if rate vector l = (l1,…, lL) lies in the capacity region, and we have:

15. Algorithm Description
Distributed Computation Model
m [t] R
Each time-slot [t] is divided into control phase (CP) and data transmission phase (DP)
Nodes are assumed to be synchronized and have unique IDs

16. Proposal: Pick Algorithm 1
Randomized Feasible Allocation Algorithm:
RTS n
O(1) m
node m choose n with probability P{
m [t]= R *
m [t] } ≥ u v

17. Proposal: Pick Algorithm 1
Randomized Feasible Allocation Algorithm:
CTS n
O(1) m
node m choose n with probability
COL P{
m [t]= R *
m [t] } ≥
CTS u v

18. Proposal: Pick Algorithm 1
Randomized Feasible Allocation Algorithm:
m [t] ={(m,n), (u,v)} m
O(1) n P{
m [t]= R *
m [t] } ≥
Remark:
Exponential growth of delay in network size u v

19. Proposal: Pick Algorithm 2
Randomized Maximal Matching Algorithm:
RTS
O(e log N) 4∆ K 2 n m
node m choose n with probability P{
m [t]= R *
m [t] } ≥ u v

20. Proposal: Pick Algorithm 2
Randomized Maximal Matching Algorithm:
CTS
O(e log N) 4∆ K 2 n m
node m choose n with probability
COL P{
m [t]= R *
m [t] } ≥
CTS u v

21. Proposal: Pick Algorithm 2
Randomized Maximal Matching Algorithm:
O(e log N) 4∆ K 2 m
ACK(m,n) n P{
m [t]= R *
m [t] } ≥
ACK(u,v) u v

22. Proposal: Pick Algorithm 2
Randomized Maximal Matching Algorithm:
m [t] ={(m,n), (u,v)}
O(e log N) 4∆ K 2 n m
node m choose n with probability
RTS
RTS P{
m [t]= R *
m [t] } ≥ u v

23. Proposal: Pick Algorithm 2
Randomized Maximal Matching Algorithm:
m [t] ={(m,n), (u,v), (i,j), (p,q)}
O(e log N) 4∆ K 2 n m p i P{
m [t]= R *
m [t] } ≥ j
Remark:
Polynomial growth of delay in network size q u v

24. 11/17/2018
Compare Algorithm

25. 11/17/2018
Results

26. 11/17/2018
Simulation Results

27. 11/17/2018
Open Problems

28. THANK YOU!!