Low Complexity MAC Scheduling Algorithms with Performance Guarantee

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# Low Complexity MAC Scheduling Algorithms with Performance Guarantee - PowerPoint PPT Presentation

Low Complexity MAC Scheduling Algorithms with Performance Guarantee . Soohwan Lee EE, KAIST shlee@lanada.kaist.ac.kr. Hyeon -je Cho Math , KAIST geniijhj@gmail.com. Contents. 1. Introduction 2 . System model 3. RPC (Random Peak and Compare) 4. GMM (Greedy Maximal Matching)

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## Low Complexity MAC Scheduling Algorithms with Performance Guarantee

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### Low Complexity MAC Scheduling Algorithms with Performance Guarantee

Soohwan Lee

EE, KAIST

Hyeon-je Cho

Math, KAIST

geniijhj@gmail.com

Contents
• 1. Introduction
• 2. System model
• 3. RPC (Random Peak and Compare)
• 4. GMM (Greedy Maximal Matching)
• 5. Conclusion

### 2. System Model

Network and Traffic Model
• Network model
• Single-hop wireless network (for focusing on scheduling)
• K - hop interference model
• Each link has own queue : Ql (t)
• Queuing dynamics
• Traffic model
• Assume that unsaturated system,
• E[Al (t)] = ¸l
• Arrival vector : ¸ = (¸1,…,¸L) 2¤ ,where ¤ is throughput region
Performance Metric
• Objective
• Throughput maximization (unsaturated system)
• Utility maximization (saturated system)
• Optimal algorithm :
• Throughput maximization
• Max-differential backlog routing + MW scheduling
• Utility maximization
• Congestion control + Max-differential backlog routing + MW scheduling

where R: routing vector,

x : source rate vector,

### 3. RPC (Random Peak and Compare)

RPC (Random Pick and Compare)
• Key idea
• For the complexity reduction of MW scheduling,compute optimal schedules infrequently
• Algorithm
• At each time slot,
• Step 1: Generate the random schedule S’(t) satisfying C1
• Step 2: Schedule S(t) defined in C2
• C1: (Pick) There is a 0 < δ· 1 s.t. P[S’(t) = S|Q(t)] ¸δ, for some schedule S, where W(S) ¸γW*(t), γ> 0
• C2: (Compare) S(t) = argmaxS={S(t-1),S’(t)}W(S)
Characteristic of RPC
• Method of reducing complexity of MW scheduling
• Solve this NP problem → Pick the random S’
• Reduce the complexity
• 추후 그림 삽입
• → Compare to previous S(t-1) and selectS(t) which has larger weight
• Tracking the optimal scheduling with long term time scale: infrequently solve
• RPC can achieve γ-optimal schedule with polynomial time complexity
• If γ=1, throughput region of RPC is exactly same as throughput region of MW with polynomial complexity
• RPC is a method of infrequent computation of optimal schedule
• Because throughput region ¤is defined by long term time scale
• But, infrequent computation of optimal schedule involves delay
• RPC tracks the optimal scheduling with long term time scale so, it is obvious
• In conclusion, RPC pays the delay for reducing complexity
• Tradeoff between complexity and delay