Leveraging the Trade-off Between Spatial Reuse and Channel Contention in Wireless Mesh Networks -Subhrendu Chattopadhyay, Sandip Chakraborty, Sukumar Nandi Subhrendu Chattopadhyay Dept of CSE IIT Guwahati January 13, 2016 Subhrendu Chattopadhyay (IIT Guwahati) Fair-JPRS January 13, 2016 1 / 24
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Leveraging the Trade-off Between Spatial Reuse and Channel ... · Content 1 Introduction 2 Motivation 3 Related Studies 4 System Model 5 Formulation of Optimization Problem 6 Proof
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Leveraging the Trade-off Between Spatial Reuse andChannel Contention in Wireless Mesh Networks
Pareto optimality: A solution of vector optimization problem is calledPareto optimal solution of Eqn. 9, if individual component of the vectorcan not optimized without affecting some other component.
min(f1(x), f2(x), . . . , fn(x)) (9)
S.T.:x ∈ X (10)
Say, S∗ is the Pareto optimal solution of Eqn. 9, and S be the set offeasible solutions, then
Every solution of the Problem 1 formulation yields a feasible transmissionscenario at each time slot.
Proof Idea: Each solution maintains SINR constraints along with hiddennode constraints. Therefore, yealds feasible transmission scenario.
Theorem (1)
All optimum solutions of Problem 1 generates a Pareto optimal powervector allocation based on the transmissions scheduled in each time slot.
Proof Idea: As the vector optimization uses no preference method, fromthe definition of Pareto optimality allocated power vectors are also Paretooptimal.
Solution method: Using KKT conditionAccording to Theorem 1, Problem 1 is proven to be convex optimization,.Therefore, it can further be simplified using KKT condition as following. 5
Problem (2)
λ1Puv
(Γ(α)
Txuv+
1− Γ(α)
Txαuv
)= λ3
Φ
rhσ(11)
λ2 + γ(rh)λ′4∑q
Guq = λ3Guv (12)
λ1 + λ2 + λ3 + λ4 = 1 (13)
However, the centralized solution requires global antenna and channel gainmatrix (G ) and communication matrix (X ) for calculating SINR andhidden node constraints. These information are not available in case ofWMN and MCCA suitable distributed implementation. Therefore, byexploiting the properties of Problem 2, a distributed heuristic can beformulated by approximating the local gain and local communicationinformation.
5Here λi denotes KKT variable and λi > 0Subhrendu Chattopadhyay (IIT Guwahati) Fair-JPRS January 13, 2016 12 / 24
Distributed Heuristic Proposal
Augmentation of MCCA
Each mesh STA v sends a beacon frame using Pmax and SINR forthat frame is captured in Suv . Each mesh STA broadcasts its Suvwith MCCAOP advertisement req message.
Data rate rh is decided such that γ(rh+1) > Suv and γ(rh) ≤ SuvTransmit power level is calculated using P
(h)uv ≥ γ(rh) Pmax
Suv .
A winner is decided based on the highest Suv .
Winner node decidesFor the winner if no prior schedule is available the it assigns MCCAOPduration= Txmax . Otherwise it estimates the value of Pij based on theavailable schedule information. Based on the estimated Pij solves
Problem 2 by assuming∑qGqv = 1
Pmax
(GuvPmax
Suv − η)
for finding Txuv .
MCCAOP offset= First available slotMCCAOP periodicity = no. of contending neighbour (∆).MCAOP duration= Txuv
Proposed Fair-JPRS improves performance in terms of fairness.
The required average power level and throughput remains almostsimilar.
Extension of the work:
For multiple interface with multiple channel caseDirectional antenna supportEffect of end to end throughput and delayTheoretical performance modelling of the proposed scheme
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