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Designing Autonomic Wireless Multi-hop Networks for Delay-Sensitive Applications Peter Hsien-Po Shiang Advisor : Prof. Mihaela van der Schaar Electrical Engineering, UCLA Delay-sensitive applications are booming! Examples of


  1. Designing Autonomic Wireless Multi-hop Networks for Delay-Sensitive Applications Peter Hsien-Po Shiang Advisor : Prof. Mihaela van der Schaar Electrical Engineering, UCLA �

  2. Delay-sensitive applications are booming! Examples of delay-sensitive applications Video telephony Live audio Surveillance Live video Vehicular Video conferencing Games Battlefield sensing communications 1) Hard delay constraints 2) Prioritized multimedia traffic (graceful degradation desired) �

  3. Overall goal Building efficient multi-hop networks for delay-sensitive applications - Autonomic decision making framework • Gather local information • Learn • Make decisions and interact Control info. Autonomic node = Agent Data Application layer Decision making Network traffic requirements phase environment Info. exchange Information Learning gathering phase Channel condition phase �

  4. Autonomic network scenarios Network scenarios Dynamics Local information Multimedia Source traffic, Source rate, r1 r1 r1 S1 S1 transmission over channel transmission r5 r5 r2 r2 wireless mesh condition rate, packet r3 r3 S2 S2 S2 r4 r4 network error rate D2 D2 D1 D1 D1 Distributed resource Resource Primary users’ �� � �� ����� �� � � � management over availability loading, other � �� �� � � � cognitive radio secondary (spectrum holes) � �� � � � �� networks users’ actions � � � �� �� � � � Power control over Interference SINR ad hoc mobile coupling among ……… ……… networks transmitter- receiver pairs �

  5. Overview Multimedia transmission over mesh networks I. Exploiting information over space II. – information horizon Exploiting information over time III. – learning Conclusions IV. Data packets Multimedia 3. Decision phase characteristics 1. Information Control info. 2. Learning gathering phase phase �

  6. Focus: multiple multimedia applications over multi-hop wireless networks V 1 V 2 � � � � � � � � � � � Nodes: Edges: � � � � �� � � Applications: � � Classes: � � � � � ������ � � � � � � � � � � � � � � � � � � � Actions: � � � � � Utility: � Goal: Maximizing overall efficiency � � ��� � � � � � � � � � �

  7. Limitations of prior work (1/2) � � Function of resource, ��� � � � � �� � � � � e.g. throughput � � � � � � � �� ������� � � � Capacity constraint Centralized optimization for multimedia transmission • Wu and Chou (2005) • Setton, Yoo, Zhu, Goldsmith, and Girod. (2005) • Jurca, Frossard (2007) • Andreopoulos, Mastronarde, and Van der Schaar (2006,2007) Decision Complexity Information Adaptation maker overhead ability Traditional sol. Centralized High High Slow Proposed sol. Distributed Low Low Fast �

  8. Limitations of prior decentralized work (2/2) Flow-based optimized routing using link state information • Wei, Zakhor (2002) • Draves, Padhye, Zill (2004) Flow-based optimized routing using queue information feedback • Awerbuch and Leighton (1994) • Neely, Modiano, Rohrs (2005) • Gupta, Javidi (2007) • Gupta, Lin, Srikant (2007) Application Resource allocation Delay model constraint Traditional sol. Flow-based Predetermined Implicit Proposed sol. Packet-based Online adaptation Explicit �

  9. Challenges Heterogeneous characteristics of delay-sensitive applications Different priorities, hard delay deadlines, and loss tolerance Time-varying transmission environment Dynamic network conditions Informationally-decentralized environment Cost of information gathering Coupling among agents’ actions and utilities �

  10. Required solution features for multimedia transmission Fully distributed optimization that determines the � actions at each node , e.g. how to relay � Dynamic adaptation to the changing network/source conditions at each node and coupling between nodes’ actions From rate-constraint flow-based to explicit delay- constraint packet-based optimization ��

  11. Delay-sensitive application quality model Multimedia Packets Source Nodes Relay Nodes � � � � � � � � � � � : � � � � � � � � � � � � � � : � Classes � � � � � � � � � : � � � � � � � � � � � � � � : � � � GOP Transmission Time Quality at the sources: Quality at the destinations: � � � � � � ����� ������� � � � � � � � � � � �� � � �� � � � � � � � � � � � � � � � � � � � � � ��

  12. � Cross-layer transmission strategy (action ) � Priority queuing model provides Application an unified framework to analyze scheduling • Packet scheduling • Relay selection impacts packet Network arrival process relay selection • ARQ can be modeled as geometric service time distribution Data Link • MCS provides different physical retransmission limit transmission rates and packet error rates Physical Per-packet decisions with MCS selection explicit delay constraints ��

  13. Elementary structure (2-hop case) V users with distinct sources and destinations. M intermediate nodes. Information feedback from all the nodes of the next hop – fully distributed. ��

  14. Decisions at the PHY and MAC layer We show that in the delay constraint optimization, it is optimal to transmit the most important packet first with infinite retransmission limit � � � � ��� ���� � � � ���� ��� � � � � � � � � � � � � � � � � � � � ��� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � Knowing the next relay, the optimal modulation and coding scheme: � � � � � ��� � � � � � � ��� ��� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � Cascade the elementary structure to a multi-hop network ��

  15. Overlay multi-hop network structure Classes � � � � � Information feedback Directed Acyclic Multi-hop Network Can be applied to any physical network as an overlay network ��

  16. Decentralized optimization Centralized cross-layer optimization: � � !�� � � � � ��� ��� � � �� � � �� � � � � � � � � � � ��������������� ���� � � � � � � � ������ � � Delay constraints � � Decentralized optimization: !�� � � � � � ��� ��� " ���� � � � � � �� � � � � � � � � � � � � � � � � � � �� � ���� � � ����������������������� ���� � � � � � � ���� � � � � � � � � � � � � � � � Advantages: • No predetermined rate allocation – Low complexity – Fully distributed solution – Fast adaptation to network changes – " ���� � � Delay evaluation • � � � � ��

  17. Route selection at the network layer Local information: Transmission rates, packet error rates, expected delays Information to the previous hop relay selecting parameter Information from the next hop � � � � � � � � " ���� � � " # � � " ���� � � � � � � � � � � � � �� � � � � � � � � � � � � � � � � � � Proposed solution: self-learning algorithm [H. Shiang JSAC 2007] � � � � � � � � � �!��� � � � � �!��� � � � � � � � � � � � � � � � � " ���� � � � " ���� � � � � � � � � � � � � ���� � � � � � ���$%��� � � Property: Automatically avoid the congestion region Multi-path routing Our method: a generalization of the Bellman-Ford routing algorithm ��

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