Some Cross-Layer Design and Performance Issues in Cognitive Radio Networks S.M. Shahrear Tanzil M.A.Sc. Student School of Engineering The University of British Columbia Okanagan Supervisor: Dr. Md. Jahangir Hossain September 5, 2013 S.M. Shahrear Tanzil (UBC) 1 / 33 September 5, 2013 1 / 33
Outline Introduction 1 Multi-Class Service Transmission over Cognitive Radio Network 2 Cross-Layer Performance in Presence of Sensing Errors 3 S.M. Shahrear Tanzil (UBC) 2 / 33 September 5, 2013 2 / 33
Introduction Introduction S.M. Shahrear Tanzil (UBC) 3 / 33 September 5, 2013 3 / 33
Introduction Fixed Spectrum Access A certain portion of radio spectrum is allocated/reserved for a certain group of users usually referred to as primary users (PUs) Other group of potential users, usually referred to as secondary users (SUs) are not allowed to access the spectrum, even if a particular portion of the spectrum is currently not being used by the PUs Recent studies on spectrum measurements have revealed that a large portion of the assigned spectrum is used sporadically by the PUs S.M. Shahrear Tanzil (UBC) 4 / 33 September 5, 2013 4 / 33
Introduction Dynamic Spectrum Access SUs can share the assigned spectrum with the PUs opportunistically Underlay method Overlay method Frequency Spectrum hole Spectrum hole Spectrum hole Spectrum hole Spectrum hole Time Figure 2: An example of overlay spectrum access with spectrum holes S.M. Shahrear Tanzil (UBC) 5 / 33 September 5, 2013 5 / 33
Introduction Cognitive Radio Facilitate dynamic spectrum access Joseph Mitola proposed the concept of cognitive radio (CR) technology in 1998 Senses the spectrum of the PUs Adapts various transmission and operating parameters including the frequency range, modulation type, and power according to the wireless environment S.M. Shahrear Tanzil (UBC) 6 / 33 September 5, 2013 6 / 33
Introduction Motivations Wireless channel quality not only varies with time but also the availability of radio spectrum depends on PUs’ activity Multi-class services e.g., video conferencing, email transfer and web browsing have diverse quality of service (QoS) requirements in terms of delay and packet loss probability One design challenge: How to develop innovative resource allocation mechanisms that can meet diverse QoS requirements of different classes of services transmitted over the cognitive radio network (CRN) Another design challenge: Channel sensing errors S.M. Shahrear Tanzil (UBC) 7 / 33 September 5, 2013 7 / 33
Introduction CRN Architecture: Infrastructure-Based SU-1 PU BS CR BS SU-2 SU-K PU BS Figure 3: Infrastructure-based CRN, CR=cognitive radio, BS=base station, PU=primary user, SU=secondary user. S.M. Shahrear Tanzil (UBC) 8 / 33 September 5, 2013 8 / 33
Introduction Operating Assumptions PUs’ activity: ON/OFF Channel: Slowly time varying, Nakagami- m , finite state Markov channel Channel scheduling: Max rate S.M. Shahrear Tanzil (UBC) 9 / 33 September 5, 2013 9 / 33
Introduction Cross-Layer Design Packets from higher layer Queue Data link layer Physical layer Packet are transmitted through the physical layer Figure 4: Cross-layer design Packet arrival follows batch Bernoulli random process Packets are stored in the data link layer’s buffer/queue Adaptive modulation and coding is employed S.M. Shahrear Tanzil (UBC) 10 / 33 September 5, 2013 10 / 33
Multi-Class Service Transmission over Cognitive Radio Network Multi-Class Service Voice, video streaming and web browsing have stringent delay constraints i.e., delay sensitive (DS) service Email has no stringent delay constraint i.e., delay non-sensitive/best-effort (BE) service S.M. Shahrear Tanzil (UBC) 11 / 33 September 5, 2013 11 / 33
Multi-Class Service Transmission over Cognitive Radio Network Rate Allocation Mechanism for a Particular SU Rate of delay sensitive service, R (d) Delay sensitive packet arrival from k th user upper layer, β Buffer of delay sensitive service, Q (d) rate Rate of best allocator effort service, R (b) Best effort packet arrival from upper Buffer of best effort service, Q (b) layer, α Allocated total transmission rate to user k Figure 5: Rate allocation for multi-class service transmission for k th SU. S.M. Shahrear Tanzil (UBC) 12 / 33 September 5, 2013 12 / 33
Multi-Class Service Transmission over Cognitive Radio Network Optimal Rate Allocation Mechanism Formulated the problem as a constrained Markov decision process (MDP) Objective x ( S , A ) [ d ( d , o ) ( S , A )] ⊺ x ( S , A ) minimize (1) subject to:[ p ( b , o ) ( S , A )] ⊺ x ( S , A ) ≤ p ( b ) (2) loss th [ p ( d , o ) ( S , A )] ⊺ x ( S , A ) ≤ p ( d ) (3) loss th p ( b ) th and p ( d ) are target packet loss probabilities of BE service and DS th service, respectively S.M. Shahrear Tanzil (UBC) 13 / 33 September 5, 2013 13 / 33
Multi-Class Service Transmission over Cognitive Radio Network Optimal Rate Allocation Mechanism x ∗ ( S , A ) denotes the probability of taking action A in state S that minimizes the average queuing delay of DS packets while satisfies packet loss probability constraints From the optimal values, x ∗ ( S , A ) one can calculate QoS parameters e.g., packet loss probabilities and queuing delay The optimal policies for constrained MDP are random S.M. Shahrear Tanzil (UBC) 14 / 33 September 5, 2013 14 / 33
Multi-Class Service Transmission over Cognitive Radio Network Suboptimal Rate Allocation Mechanism 1: if Available transmission rate, R ≤ number of packets in the DS buffer then R ( d ) ← R 2: R ( b ) ← 0 3: 4: else R ( d ) ← number of packets in the DS buffer 5: R ( b ) ← R − R ( d ) 6: 7: end if S.M. Shahrear Tanzil (UBC) 15 / 33 September 5, 2013 15 / 33
Multi-Class Service Transmission over Cognitive Radio Network Suboptimal Rate Allocation Mechanism Developed a queuing analytic model with the suboptimal rate allocation mechanism Analyzed queuing analytic model as a quasi-birth-death (QBD) process Calculated packet loss probabilities and queuing delay i.e., delay distribution from the steady state probabilities of the QBD S.M. Shahrear Tanzil (UBC) 16 / 33 September 5, 2013 16 / 33
Multi-Class Service Transmission over Cognitive Radio Network Numerical Results: Cumulative Distribution of Delay of DS Packets 1 X: 10 0.9 Y: 0.8304 0.8 Prob.(delay ≤ X) of DS packets 0.7 X: 10 Y: 0.7156 0.6 0.5 0.4 Suboptimal,K=2(ana) 0.3 Optimal,K=2(sim) 0.2 Suboptimal,K=3(ana) Optimal,K=3(sim) 0.1 Suboptimal,K=4(ana) Optimal,K=4(sim) 0 0 10 20 30 40 50 time slots (X) Figure 6: Effect of number of SUs ( K ) on the delay distribution of DS packets (ana=analysis, sim=simulation) S.M. Shahrear Tanzil (UBC) 17 / 33 September 5, 2013 17 / 33
Multi-Class Service Transmission over Cognitive Radio Network Numerical Results: Packet Loss Probability of DS service 0.06 X: 5 Y: 0.05227 0.05 Packet loss probability of DS service 0.04 X: 4 Y: 0.03542 0.03 0.02 0.01 Suboptimal,(ana) Optimal,(ana) 0 1 2 3 4 5 Number of secondary users Figure 7: Effect of number of SUs ( K ) on the packet loss probability of DS service (ana=analysis, sim=simulation) S.M. Shahrear Tanzil (UBC) 18 / 33 September 5, 2013 18 / 33
Multi-Class Service Transmission over Cognitive Radio Network Numerical Results: Packet Loss Probability of BE service 0.2 0.18 0.16 Packet loss probability of BE service 0.14 0.12 0.1 X: 3 0.08 Y: 0.06752 0.06 0.04 X: 2 0.02 Suboptimal,(ana) Y: 0.008732 Optimal(ana) 0 1 2 3 4 5 Number of secondary users Figure 8: Effect of number of SUs ( K ) on the packet loss probability of BE service (ana=analysis, sim=simulation) S.M. Shahrear Tanzil (UBC) 19 / 33 September 5, 2013 19 / 33
Multi-Class Service Transmission over Cognitive Radio Network Application of the Developed Queuing Model with the Suboptimal Mechanism: Example Table 1: Number of SUs for given QoS requirements ( D ( d , s ) t , max = 10 (time slots) with probability=0.8, P ( d , s ) t , loss ≤ 0 . 05 and P ( b , s ) t , loss ≤ 0 . 05) K D ( d , s ) K P ( d , s ) K P ( b , s ) K s t , max t , loss t , loss 3 4 2 2 S.M. Shahrear Tanzil (UBC) 20 / 33 September 5, 2013 20 / 33
Multi-Class Service Transmission over Cognitive Radio Network Summary: Part I Studied rate allocation mechanisms that allocate rate between two different classes of services of a particular SU Formulated the optimal rate allocation mechanism as a MDP Also proposed a low-complexity suboptimal rate allocation mechanism The performance of the suboptimal rate mechanism is quite similar to the optimal rate allocation mechanism Developed queuing analytic model with the suboptimal mechanism is useful not only for calculating QoS parameters but also in making a call admission control decision S.M. Shahrear Tanzil (UBC) 21 / 33 September 5, 2013 21 / 33
Multi-Class Service Transmission over Cognitive Radio Network Publication S M Shahrear Tanzil, Md. Jahangir Hossain, and Mohammad M Rashid , “Rate allocation mechanisms for multi-class service transmission over cognitive radio networks”, accepted in IEEE Global Commun. Conf. (Globecom’13) , Atlanta, USA, Dec. 2013. S.M. Shahrear Tanzil (UBC) 22 / 33 September 5, 2013 22 / 33
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