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Understanding Channel Access Control in Ad Hoc Networks with MIMO Nodes

Understanding Channel Access Control in Ad Hoc Networks with MIMO Nodes. J.J. Garcia-Luna-Aceves Hamid Sadjadpour * Marcelo Carvalho Renato Moraes Xiaohui Yu University of California, Santa Cruz. * Not funded by STC MURI. Outline. Quick summary of research output

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Understanding Channel Access Control in Ad Hoc Networks with MIMO Nodes

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  1. Understanding ChannelAccess Control in Ad Hoc Networks with MIMO Nodes J.J. Garcia-Luna-Aceves Hamid Sadjadpour* Marcelo Carvalho Renato Moraes Xiaohui Yu University of California, Santa Cruz * Not funded by STC MURI

  2. Outline • Quick summary of research output • Recent results on the capacity of ad hoc networks with MIMO nodes • Recent results on the performance of MAC protocols with MIMO nodes • Summary of research directions.

  3. Summary of Research Output • Papers accepted for publication: • 1-X. Yu, R. D. Moraes, H.R. Sadjadpour and J.J. Garcia-Luna-Aceves,, "Capacity of MIMO Wireless Ad Hoc Networks," IEEE WirelessCom 2005. • R. D. Moraes, H.R. Sadjadpour and J.J. Garcia-Luna-Aceves, "A New Communication Scheme for MANETs," IEEE WirelessCom 2005. • R.M. Moraes, H.R. Sadjadpour and JJ. Garcia-Luna, "Mobility-Capacity-Delay Trade-off in Wireless Ad Hoc Networks," Elsevier journal on ad hoc networks (to appear). • Papers submitted for publication: • R. D. Moraes, H.R. Sadjadpour and J.J. Garcia-Luna-Aceves, "Opportunistic cooperation: A new approach for scalable mobile ad hoc networks", submitted to INFOCOM2006. • R. D. Moraes, H.R. Sadjadpour and J.J. Garcia-Luna-Aceves, "Ergodic capacity of MIMO MANETs with opportunistic cooperation," submitted to INFOCOM2006. • R. D. Moraes, H.R. Sadjadpour and JJ. Garcia-Luna-Aceves, "Opportunistic cooperation: A new communication scheme for MANETs," submitted to Asilomar 2005. • R. D. Moraes, H.R. Sadjadpour and JJ. Garcia-Luna-Aceves, "Opportunistic Cooperation: A new approach for scalable Mobile Ad hoc networks," submitted IEEE Transactions on Information Theory. • R. D. Moraes, H.R. Sadjadpour and JJ. Garcia-Luna-Aceves, "Taking full advantage of Multiuser diversity in Mobile Ad hoc networks," submitted to IEEE Transactions on Communications. • M. Carvalho and J.J. Garcia-Luna-Aceves, “Modeling Ad hoc Networks with Directional Antennas,” submitted to INFOCOM 2006.

  4. Summary of Research Output • Ph.D. sudents: Renato Moraes and Marcelo Carvalho will be graduating before the end of the Fall 05 quarter.

  5. Key Research Areas(from kickoff meeting) • Help the understanding of the fundamental limitations to the scaling of ad hoc networks with cross-layer optimization (number of nodes, energy consumption, bandwidth utilization). • Study the impact of the physical layer on communication protocol stack. • Modular protocol stacks to bridge the gap between the applications of large ad hoc networks and the new hardware available with ST coding and other technologies. Emphasis on the MAC layer. Complementing MURI research with other ongoing research work at UCSC

  6. Network Capacity

  7. D Conventional close straight line path S Least resistance path Motivation for MURI Research(from kickoff meeting) • How can we improve the throughput performance by sending packet via “least resistance” paths? • We need cross layer optimization! … Constant hop count and constant interference per hop

  8. Single-copy forwarding Multi-copy forwarding r0 r0 n total users r0 r0 n total users First relay reaching destination delivers the packet (More than one relay looking for destination) Only one relay looking for destination Multi-copy Forwarding(from kickoff meeting)

  9. source 1 source 2 Compete in phase 1 other relay 1 relay 2 “Cooperate” while storing packet interference Compete in phase 2 destination 1 destination 2 Conventional Communication Models • Conventional communication models are based on competition-driven approaches. • Two-phase forwarding model by G&T clearly increase the capacity!: Competition leads to scaling problems (G-K model) Uses storage as bandwidth, a form of cooperation

  10. r3 D1 r1 s1 r2 s2 r2 D2 s3 r3 D3 r1 New Vision: Opportunistic Cooperation • Why should nodes “fight” for the channel? • Can we exploit MIMO processing to allow nodes to cooperate? • How much do we gain? • What type of cooperative protocols should we be designing? Many-to-Many Communication Model Start by exploiting MIMO in the two-phase delivery model of G-T.

  11. Opportunistic Cooperation with MIMO Systems • Assume: • n mobile nodes • Each square cell has same area size (a_cell) • Each node picks an arbitrary destination • Uniform mobility model. • A node transmits at power P to another node. • Each node has M antennas • Sources do not have CSI • Simple path propagation model. • Node knows its location and the “freq map” Consider a simplified FDMA/MIMO system as an instantiation of the many-to-many communication concept. MISO 9A frequency bands

  12. Ergodic Channel Capacity(submitted to Infocom 06)

  13. Capacity Behavior

  14. Performance Analysisof MAC Protocols

  15. Summary of Work • Used our interference matrix model as the baseline model: • M. Carvalho and J.J. Garcia-Luna-Aceves, ``A Scalable Model for Channel Access Protocols in Multihop Ad Hoc Networks,'' Proc. ACM Mobicom 2004, Philadelphia, Pennsylvania, Sept. 26--Oct. 1, 2004. • Developed first analytical model of wireless ad hoc networks that considers realistic antenna-gain patterns and applied it to DVCS protocol. • Developed first analytical model of wireless ad hoc networks that considers MIMO Space-Time Coding (STC) technology. • Space-time coding based on space-time block coding (STBC) • Alamouti scheme for MT = 2 transmit antennas and MR receive antennas • New Markov model for the IEEE 802.11 DCF MAC: • Impact of carrier-sensing mechanism. • Impact of transmission errors in both control and data frames: previous models assumed errors only in control frames (data frames were assumed to be transmitted error-free!). • Applicable to other back-off schemes with carrier sensing.

  16. Modeling Approach • PHY: • The probability of successful reception of a data packet and its acknowledgment, based on effect from all transmissions (which depend on scheduling by the MAC) and PHY parameters • MAC: • Scheduling rates based on feedback from the PHY regarding the success of transmissions and the state of the channel (e.g., busy, idle) • Topology: • Consider the effect of all nodes based on where they are and their transmission attempts • “Linearize” the problem exploiting the fact that any MAC protocol will tend not to schedule transmissions when feedback from the PHY indicates unsuccessful transmissions or the channel is “busy.”

  17. Modified IEEE 802.11 DCF MAC New Markov Model Errors in control and data frames are considered: previous models disregarded errors in data frames; Single retry counter: it increments if either a control or a data frame transmission is unsuccessful; Previous models did not consider the IEEE 802.11 finite retry limit and carrier sense activity jointly; impact of physical layer Linearized transmission probability:

  18. 50-Node Topology Preliminary Results (MIMO) Tx = 1, Rx = 1 (SISO) Tx = 2, Rx = 1 (MISO) Tx = 2, Rx = 2 (MIMO) Tx = 2, Rx = 4 (MIMO) Rician Fading Random Topologies Case Studies: 100-Node Topology Use of multiple antennas without the need of “fine-tuning” MAC still increases throughput in ad hoc networks; MISO and MIMO systems are robust to bad channel conditions (when K decreases); Throughput gains compared to SISO at K = 5: MISO: 65% (50 nodes) and 160% (100 nodes) MIMO (2x2): 86% and 220%, respectively MIMO (2x4): 113% and 285%, respectively Higher gains for MIMO due to diversity and array gains, as opposed to MISO, which gives only diversity gain. MIMO with 2 transmit and 2 receive antennas presents the best trade-off between throughput and system complexity;

  19. Results for Directional Antennas(Directional Virtual Carrier Sensing) Simulation versus Analytical Model: 10 random 100-node topologies Realistic Antenna Gain Patterns

  20. Realistic versus Simplified Antenna Gain Models Throughput Comparison: 10 random 100-node topologies Pie-slice Antenna Gain Model

  21. Next Steps

  22. Next Steps • Network capacity: • Analyze fundamental trade-off limits of throughput, delay, and receiver complexity under opportunistic cooperation for MIMO systems • We need a lower bound for capacity. • Consider different instantiations of opportunistic cooperation for MIMO systems • Consider different mobility models and static networks. • Performance analysis: • Compare the performance of “cooperative” MIMO MAC protocols with the performance of non-cooperative MIMO MAC protocols. • Compare the performance of contention-based and schedule-based MIMO MAC protocols. • Study the effect of PHY parameters on MIMO MAC protocols. • Study the effect of “opportunistic cooperation parameters.”

  23. Next Steps • Subsequent: • Develop MIMO MAC protocol(s) based on the opportunistic cooperation framework. • Address the interaction between MAC and network layer. • Develop cross-layer designs that exploit opportunistic cooperation in MIMO systems. • Example: Forwarding taking advantage of MIMO MAC

  24. Thanks!

  25. Transceiver

  26. Ergodic Channel Capacity

  27. Interference Effect Ergodic Channel Capacity(submitted to Infocom 06)

  28. Interference Effect

  29. Interference Effect

  30. Interference Effect

  31. signal 2 Constellation Mapper Receiver ST Block Code Information Source [ s1 s2 ] s1 -s2* s2 s1* signal 1 . . . MRReceive Antennas Space-Time Block Coding (STBC):The Alamouti Scheme • Scheme supports maximum-likelihood detection based on linear processing at the receiver (as opposed to space-time trellis coding (STTC), which has exponential complexity); • Part of W-CDMA and CDMA-2000 standards; • Channel information not necessary at the transmitter side; • Channel assumed to be known at the receiver side (easier to obtain); • Energy evenly divided among transmit antennas no extra power required! • Basic Alamouit scheme allows MIMO systems with 2 transmit antennas and MR receive antennas.

  32. MIMO Case: 2 Tx and 2 Rx Antennas Channel Matrix: Signals received at antenna array over consecutive symbol periods: are zero-mean AWGN samples Receiver forms the signal: is orthogonal irrespective of channel realization, i.e., If we get where The effective received symbols are with received SNR

  33. Bit Error Rate under Multipath Fading • Because the received SNR is equal to the sum of SNRs on each path: • SNR shape suggests the method of the Moment Generating Function (MGF) for computation of the symbol error probability under multipath fading • Example: DBPSK modulation under Rician fading: K is the Rician factor = ratio of the power of LOS signal to the power of NLOS signals; is the average SNR extracted from each path (dominated by path-loss propagation effects only);

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