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Negotiation-Based Distributed Power Control in Wireless Networks with Autonomous Nodes

Negotiation-Based Distributed Power Control in Wireless Networks with Autonomous Nodes. Vaggelis G. Douros George C. Polyzos Stavros Toumpis. IEEE VTC2011-Spring 17 May 2011, Budapest, Hungary. Motivation (1). Deadline is today!. This is urgent!. The food is delicious.

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Negotiation-Based Distributed Power Control in Wireless Networks with Autonomous Nodes

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  1. Negotiation-Based Distributed Power Control in Wireless Networks with Autonomous Nodes Vaggelis G. Douros George C. Polyzos Stavros Toumpis IEEE VTC2011-Spring 17 May 2011, Budapest, Hungary douros@aueb.gr http://mm.aueb.gr/~douros

  2. Motivation (1) Deadline is today! This is urgent! The food is delicious Fantastic shirt! The problem: Some couples may not communicate efficiently 

  3. Motivation (2) • N couples of friends discuss in the same cafeteria • Each couple aims at achieving a (different) “minimum quality of discussion” • Discussions of other couples may prevent an efficient communication • N pairs of wireless nodes (e.g., BSs-MNs, APs-Clients) transmit their data sharing the same wireless medium • Each pair aims at achieving a (different) (SINR) target • Interference among wireless devices may prevent an efficient communication Competition for resources among multiple players, where the influence from each player is different≡ Weighted Congestion Game

  4. Fundamentals of SINR-Based Power Control (1) • Power control is a standard radio resource management method for interference mitigation • Analogy: A person that increases/ reduces his level of voice • The Simplified Foschini-Miljanic Formula (FM) [F&M, TVT ’93], [Bambos, IEEE Pers. Comm. ’98]: • (+) fully distributed algorithm • no need for cooperation among the nodes to apply FM • At steady state, for each node i: Pi(k+1)=Pi(k) • each node i has either achieved its SINR targetγior it is below its target and transmits with Pmax

  5. Fundamentals of SINR-Based Power Control (2) • The problem: Even in small topologies, there are cases that it is impossible for all wireless nodes to achieve their SINR targets

  6. Dealing with Infeasibility • Trunc(ated) Power Control [Zander, TVT ’92] • (-) Unfair for this node – no opportunity to achieve its target • More importantly: how to oblige an autonomous entity to power off? I am 15% below my SINR target  I am 40% below my SINR target I am 20% below my SINR target  I achieved my SINR target 

  7. The Bargaining Foschini-Miljanic Scheme (BFM) • A heuristic approach that aims at maximizing the number of links that have achieved their targets • Should be at most “N-1”≡ “(N-1)-feasible” solution • BFM works on top of FM, starting from its steady state • Unsatisfied links negotiate in pairs. Each one uses part of its budget to make an offer to the other I offer you 100 credits if you reduce your power 20% I am 15% below my SINR target  OK! No thanks! I am 20% below my SINR target  I achieved my SINR target  I am 40% below my SINR target

  8. Fundamentals of BFM (1) • How to choose who makes an offer? • How to choose to whom it offers? • Choose randomly one among the set of unsatisfied nodes • (-) This demands an external entity • A distributed approach: Each unsatisfied link decides independently whether it is a “Seller” or a “Buyer” and broadcasts its status to the network • Which is the desired percentage reduction Pred? • The minimum needed to achieve its target in the next round • If this is not feasible, it is not interested in making an offer • How a “Buyer” decides the level of its offer?

  9. Fundamentals of BFM (2) • Tx1 computes the reward R12 (i.e., the percentage of its budget B1) that is willing to offer to Tx2 • If Tx2 accepts its offer, then Tx1 updates its power according to the FM scheme and achieves its target • If Tx2 rejects its offer, then Tx1 voluntarily reduces a bit its current transmission power (0<c<1) • Otherwise, all nodes may stay at the same state

  10. A Toy Example

  11. FM: SINR Evolution

  12. Trunc FM: SINR Evolution

  13. Trunc FM: Power Evolution

  14. BFM: SINR Evolution

  15. BFM: Power Evolution

  16. “The Meat” • BFM: A heuristic approach for joint power control and bargaining that aims at maximizing the number of satisfied entities, in cases that it is impossible for all of them to achieve their SINR targets • (+) distributed implementation • (+) efficient – finds out a large number of solutions • (+) fair – statistical rotation of unsatisfied nodes • Ongoing Work: To cast this problem as a weighted congestion gameand apply findings from recent works of the algorithmic game theory

  17. Acknowledgment

  18.  Köszönöm!  Vaggelis G. Douros Mobile Multimedia Laboratory Department of Informatics Athens University of Economics and Business douros@aueb.gr http://mm.aueb.gr/~douros

  19. BACKUP

  20. Major Exrensions from Our Previous Work • Our scheme was firstly presented in ACM Mobiwac Symposium (Bodrum, Turkey, October 2010) • Major extensions from this work: • The algorithm was revisited and extended to support multiple negotiations during each transmission round • A distributed version of our scheme was proposed • We discuss the connection of our scheme with a weighted congestion game • We argue on why our algorithm leads to a statistical rotation of the unsatisfied nodes

  21. Fundamentals of BFM (3) • Tx2 computes through the reward R21 that would have given if Tx2 had asked for the same Pred • If R21≤ R12,Tx2 accepts the offer and transmits at Pred*P2(k) • If R21> R12,Tx2 rejects the offer and updates its power using the FM algorithm

  22. On the Number of “(N-1)-feasible” Solutions • Number of “(N-1)-feasible” solutions after the application of both BFM and Trunc FM for 50000 different scenarios • Similar Performance with Trunc FM • But Trunc FM is not suitable for autonomous nodes and it is unfair

  23. On the Long Term Fairness of BFM (1) • Application of BFM for the same set of nodes for 10000 transmission rounds • The budget at the start of the (m+1)th round is the one at the end of the mth round • For every period of 100 transmission rounds, we count how many times Tx5 and Tx6 (the only unsatisfied nodes in this particular example) do not achieve their targets

  24. On the Long Term Fairness of BFM (2) • There is an average ratio 3:2 per period • (+) This ratio represents well every transmission period • (+) All nodes get the opportunity to transmit their data • (-) In Trunc FM, the weakest node always powers off • (+) This ratio is independent of the initial budget of the nodes • Due to the dynamically adjusting mechanism that nodes follow when they either make or evaluate an offer

  25. Simulation Parameters(1)

  26. Simulation Parameters(2)

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