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EE726 Optimization in Communication Networks -Workshop- Throughput Maximization in Cognitive Network

EE726 Optimization in Communication Networks -Workshop- Throughput Maximization in Cognitive Network. KiSong Lee, ChungWoon Park, JaeGwang Lee http://143.248.230.222/~ee762project/project.html EE, KAIST. Contents. 01 Introduction and motivation ……………………………………… 3

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EE726 Optimization in Communication Networks -Workshop- Throughput Maximization in Cognitive Network

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  1. EE726 Optimization in Communication Networks-Workshop-Throughput Maximization in Cognitive Network KiSong Lee, ChungWoon Park, JaeGwang Leehttp://143.248.230.222/~ee762project/project.html EE, KAIST

  2. Contents 01 Introduction and motivation……………………………………… 3 02Big picture about the chosen papers………………..…… 5 03System model description…………………………………..……. 6 04Problem formulation…………………………………………………… 7 05 Algorithm/analysis/simulation results…………………… 8 06Conclusion and further work……………………………..…… 33 07 Reference………………………………………………………………..…34

  3. Introduction and motivation • What is Cognitive Radio?

  4. Introduction and motivation • What issues are in CR network? • Sensing (Primary user detection) • Spectrum sharing • Resource allocation • In this report • Focus on the throughput maximization of the secondary users • Motivation • CR is quite a recent technology • To taxonomize the way of solving the optimization problem in CR

  5. Big picture about the chosen papers • Big picture about the chosen papers

  6. System model description • System model description

  7. Problem formulation • Problem formulation

  8. 05Algorithm/analysis/simulation results • - Opportunistic scheduling

  9. Algorithm/analysis/simulation results Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks [1] • System model (check points) • Distributed cognitive network • One sub channel is allocated to only one CU • Assume imperfect sensing • Assume no infinite backlog • Mobility model • PU-static • CU-mobile • Problem formulation Maximize total admitted data in queue of SU Maximum collision constraint with PU

  10. Algorithm/analysis/simulation results Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks [1] • CNC Algorithm • Flow control • Scheduling • Theorem 1.1 and 1.2 • Prove queue stability • Theorem 1.3 • Prove throughput optimality by using Lyapunov function and drift • Theorem 2. • The Lyapunov drift satisfies • then, we have A fixed control parameter Larger queue backlog Low collision queue Compare the Lyapunov drift of CNC and STAT

  11. 05Algorithm/analysis/simulation results • - Power control (SISO)

  12. Algorithm/analysis/simulation results • System model(check point)

  13. Algorithm/analysis/simulation results Opportunistic Spectrum Access in Cognitive Radio Networks[2] • System model (check points) • # of channel < # of CR • The set of utilized channels for CR link i as Si • Power vector of CR link i over channels is denoted by • Pi = [Pi(1), Pi(2) ,…, Pi(K)], • Problem formulation • where Max. power constraint for CU Max. power constraint to give no disturbance to PU Price function

  14. Algorithm/analysis/simulation results Opportunistic Spectrum Access in Cognitive Radio Networks[2] • Analysis • Proposition • Compare Lagrangianand KKT condition of both optimal problem and proposed problem • KKT condition • Power allocation

  15. Algorithm/analysis/simulation results Maximizing Throughput of Cognitive Radio Networks with Limited Primary Users' Cooperation[3] • System model (check points) • Cognitive network consists of a BS serving a set of N CPEs • SINRs at PU and CU • Problem formulation • Power controls for PU and CU • Channel assignment Binary channel assignment SINR requirement for PU

  16. Algorithm/analysis/simulation results Maximizing Throughput of Cognitive Radio Networks with Limited Primary Users' Cooperation[3] • MDCA Algorithm • Distributed power control • i) Initialization : > • ii) Power updating • iii)Termination : one node approaches max. power constraint • Centralized channel assignment • the weighted bipartite graph • Prove three propositions • i) The power updating process will be terminated after a finite number of iterations, • ii) If the initial transmit and are selected as at then • iii) The SINR of each CPE increases after each power updating step.

  17. Algorithm/analysis/simulation results Optimal and Suboptimal Power Allocation Schemes for OFDM-based Cognitive Radio Systems [4] • System model (check points) • Co-existence scenario • OFDM-based(non-orthogonal subcarriers) • {B1, …, BL} are occupied by PUs Unoccupied L bands are divided by N subchannels • Problem formulation • Analysis • Consider two interferences which are related to the spectral distance (due to non-orthogonality) • : • : Total transmission rate maximization Interference limit constraint SU PU PU SU

  18. Algorithm/analysis/simulation results Optimal and Suboptimal Power Allocation Schemes for OFDM-based Cognitive Radio Systems [4] • Analysis (Continued) • Theorem: The total transmission capacity is maximized by • (Proof) • - Establish Lagrange dual problem • - Use KKT conditions • The power allocation policy above is indeed an water-filling type • More power should be allocated to the subcarrier which has relatively better channel quality and is relatively far away from the PU’s band • Simulation results where Ki(l) = Ii(l)/Pi Optimal scheme

  19. Algorithm/analysis/simulation results Stable Throughput of Cognitive Radios With and Without Relaying Capability[5] • System model (check points) • Two source-destination links • Independent & stationary packet arrival process, λP,λS • Independent & stationary Rayleigh flat-fading in each slot • Problem formulation • where • Analysis Stable throughput maximization of SU given PU’s average throughput Queue stability constraint Loyne’s theorem: Under the assumption that arrival and departure rates of a queuing system are stationary, if the average arrival rate λi is less than the average departure rate μi, λi < μi, then the ith queue is stable. Little’s law:L = λW

  20. Algorithm/analysis/simulation results Stable Throughput of Cognitive Radios With and Without Relaying Capability[5] • Analysis (Continued) • Proposition 1 (Conditions for PS such that the PU’s queue is stable) • (Proof) • - If QS(t)=0, the SU transmits “dummy” packets whenever sensed idle • The queue of the dominant system has always larger size than the original • Under saturation, two systems are indistinguishable • Analysis (Continued) • Proposition 2 (Stable throughput maximization problem with power constraint such that the queue of both PU and SU is stable) • (Proof) • Same manner as in Proposition 1with Little’s law • Consider dominant system and prove the stationary of the SU’s departure process • Then the Loynes’ theorem is applicable Original system transform Dominant system Loynes’ theorem cannot be applicable Has the same stability property as the original and Loynes’ theorem can be applicable

  21. Algorithm/analysis/simulation results • Summary

  22. 05Algorithm/analysis/simulation results • - Power control (MIMO)

  23. Algorithm/analysis/simulation results • System model(check point)

  24. Algorithm/analysis/simulation results Weighted Sum Rate Optimization for Cognitive Radio MIMO Broadcast Channels[6] • System Environment • Broadcast Channel • Non-convex Problem • Single SU TX – SU RX MIMO • Single SU TX – PU RX MISO • Problem formulation (Non-convex) • TX-RX signal model BS-SUi • Equivalent From • BC-MAC Duality Maximize the weighted sum rate of SU Interference Power Constraints Sum power constraint

  25. Algorithm/analysis/simulation results Weighted Sum Rate Optimization for Cognitive Radio MIMO Broadcast Channels[6] • DIPA • The achievable rate dual MAC • Objective Function(convex) • Equivalent to the minimization problem : Dual Objective Function • MAC-to-BC Covariance Mapping • Subgradient algorithm • SIPA Algorithm • BC-MAC + DIPA • Simulation results

  26. Algorithm/analysis/simulation results Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks[7] • System Environment • Multiple Access Channel • Convex Problem • Single SU TX – SU RX SIMO • Single SU TX – PU RX MIMO • Problem formulation(After QR) • TX-RX signal model SUs-BS • CML water-filling algorithm(PU1) • Lagrangian Function • KKT Conditions • Power Allocation SUi Maximize the Sum Rate of the SUs Individual peak transmission power const. Interference power constraints

  27. Algorithm/analysis/simulation results Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks[7] • Lemma 2: If , p(2) is the globally optimal solution. If then p(1) is the globally optimal solution. • Lemma 3 : Ifand • the globally optimal power vector • : interference constraints with equality. • Power allocation(KKT, Lagrargianfunc.) • Theorem 1: The optimal power allocation • Extended to Multiple PU constraints • 2 PU->generalized algorithm • Decouple • the original • problem • Lemma 1: No and

  28. Algorithm/analysis/simulation results Exploiting Multi-Antennas for Opportunistic Spectrum Sharing in Cognitive Radio Networks [8] • System Environment • Broadcast Channel • Convex Problem • Single SU TX – SU RX MIMO • Single SU TX – PU RX MISO/MIMO • Problem formulation • TX-RX signal Model SU-PU&SU • The eigenvalue decomposition • where • V : precoding matrix (eigenvectors of S) • ∑: diagonal matrix ( transmit powers for data stream) • One Single-Antenna Primary Receiver (MISO PU) • D-SVD, P-SVD • SVD of 2ndery users channel Maximize Transmit Rate Transmit-power constraint Total interference-power constraints

  29. Algorithm/analysis/simulation results Exploiting Multi-Antennas for Opportunistic Spectrum Sharing in Cognitive Radio Networks [8] • D-SVD : • LET V=U, • MIMO channel • Equivalent problem and multilevel WF solution • P-SVD : • LET V=Uㅗ • MIMO Channel • Multiple Primary Antenna • Hybrid D-SVD/P-SVD • After Projection D-SVD

  30. Algorithm/analysis/simulation results • Summary

  31. Conclusion and further work • Conclusion • Studied throughput maximization of SU • Opportunistic scheduling – Lyapunov drift & Queuing analysis • Power control in SISO – Optimization & probability analysis • Power control in MIMO - Optimization • Further work • Implementation issues • Sensing optimization • Guarantee QoS of SU, not only Guarantee QoS of PU • Cooperation between PU and SU

  32. References • References • RahulUrgaonkar and Michael J. Neely, ‘Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks’ IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 8, NO. 6, JUNE 2009 • Senhua Huang Xin Liu Zhi Ding,’ Opportunistic Spectrum Access in Cognitive Radio Networks’ IEEE INFOCOM 2008 • Anh Tuan Hoang, Ying-Chang Liang, Islam, M.H., ‘Maximizing Throughput of Cognitive Radio Networks with Limited Primary Users' Cooperation’, IEEE ICC 2008. • GauravBansal, Md. Jahangir Hossain and Vijay K. Bhargava, ‘Optimal and Suboptimal Power Allocation Schemes for OFDM-based Cognitive Radio Systems’, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 7, NO. 11, NOVEMBER 2008. • OsvaldoSimeone, Yeheskel Bar-Ness, and Umberto Spagnolini, ‘Stable Throughput of Cognitive Radios With and Without Relaying Capability’ IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 55, NO. 12, DECEMBER 2007 • Lan Zhang, Yan Xin, and Ying-Chang Liang, ‘Weighted Sum Rate Optimization for Cognitive Radio MIMO Broadcast Channels’ IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 8, NO. 6, JUNE 2009 • Lan Zhang, Ying-Chang Liang, and Yan Xin, ‘Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks’, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 26, NO. 1, JANUARY 2008. • Rui Zhang and Ying-Chang Liang, ‘Exploiting Multi-Antennas for Opportunistic Spectrum Sharing in Cognitive Radio Networks’ IEEE JOURNAL OF SELECTED TOPIC IN SIGNAL PROCESSING, VOL. 2, NO. 1, FEBRUARY 2008

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