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 Email: nguyenth@khu.ac.kr http://networking.khu.ac.kr

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

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

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}

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}

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}

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

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

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]

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

11/17/2018 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

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

Pick-and-Compare Illustration

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:

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

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

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

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

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

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

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

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

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

11/17/2018 Compare Algorithm

11/17/2018 Results

11/17/2018 Simulation Results

11/17/2018 Open Problems

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