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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.

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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

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Outline

1

Introduction

2

Multi-Class Service Transmission over Cognitive Radio Network

3

Cross-Layer Performance in Presence of Sensing Errors

S.M. Shahrear Tanzil (UBC) 2 / 33 September 5, 2013 2 / 33

SLIDE 3

Introduction

S.M. Shahrear Tanzil (UBC) 3 / 33 September 5, 2013 3 / 33

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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

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Introduction

Dynamic Spectrum Access

SUs can share the assigned spectrum with the PUs opportunistically

Underlay method Overlay method

Spectrum hole Spectrum hole Spectrum hole Spectrum hole Spectrum hole Frequency Time

Figure 2: An example of overlay spectrum access with spectrum holes

S.M. Shahrear Tanzil (UBC) 5 / 33 September 5, 2013 5 / 33

SLIDE 6

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

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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

f 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

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Introduction

CRN Architecture: Infrastructure-Based

SU-1 CR BS PU BS PU BS SU-2 SU-K

Figure 3: Infrastructure-based CRN, CR=cognitive radio, BS=base station, PU=primary user, SU=secondary user.

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SLIDE 9

Introduction

Operating Assumptions

PUs’ activity: ON/OFF Channel: Slowly time varying, Nakagami-m, finite state Markov channel Channel scheduling: Max rate

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SLIDE 10

Introduction

Cross-Layer Design

Physical layer Data link layer

Packets from higher layer

Queue 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

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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

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Multi-Class Service Transmission over Cognitive Radio Network

Rate Allocation Mechanism for a Particular SU

Buffer of delay sensitive service, Q(d) Buffer of best effort service, Q(b) Delay sensitive packet arrival from upper layer, β Best effort packet arrival from upper layer, α kth user rate allocator Allocated total transmission rate to user k Rate of delay sensitive service, R(d) Rate of best effort service, R(b)

Figure 5: Rate allocation for multi-class service transmission for kth SU.

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Multi-Class Service Transmission over Cognitive Radio Network

Optimal Rate Allocation Mechanism

Formulated the problem as a constrained Markov decision process (MDP) Objective minimize

x(S,A) [d(d,o)(S, A)]⊺x(S, A)

(1) subject to:[p(b,o)

loss

(S, A)]⊺x(S, A) ≤ p(b)

th

(2) [p(d,o)

loss

(S, A)]⊺x(S, A) ≤ p(d)

th

(3) p(b)

th and p(d) th

are target packet loss probabilities of BE service and DS service, respectively

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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

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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

2:

R(d) ← R

3:

R(b) ← 0

4: else 5:

R(d) ← number of packets in the DS buffer

6:

R(b) ← R − R(d)

7: end if

S.M. Shahrear Tanzil (UBC) 15 / 33 September 5, 2013 15 / 33

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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

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Multi-Class Service Transmission over Cognitive Radio Network

Numerical Results: Cumulative Distribution of Delay of DS Packets

10 20 30 40 50 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

X: 10 Y: 0.7156

time slots (X) Prob.(delay ≤ X) of DS packets

X: 10 Y: 0.8304

Suboptimal,K=2(ana) Optimal,K=2(sim) Suboptimal,K=3(ana) Optimal,K=3(sim) Suboptimal,K=4(ana) Optimal,K=4(sim)

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

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Multi-Class Service Transmission over Cognitive Radio Network

Numerical Results: Packet Loss Probability of DS service

1 2 3 4 5 0.01 0.02 0.03 0.04 0.05 0.06

X: 5 Y: 0.05227

Number of secondary users Packet loss probability of DS service

X: 4 Y: 0.03542

Suboptimal,(ana) Optimal,(ana)

Figure 7: Effect of number of SUs (K) on the packet loss probability of DS service (ana=analysis, sim=simulation)

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Multi-Class Service Transmission over Cognitive Radio Network

Numerical Results: Packet Loss Probability of BE service

1 2 3 4 5 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

X: 3 Y: 0.06752

Number of secondary users Packet loss probability of BE service

X: 2 Y: 0.008732

Suboptimal,(ana) Optimal(ana)

Figure 8: Effect of number of SUs (K) on the packet loss probability of BE service (ana=analysis, sim=simulation)

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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)

KD(d,s)

t,max

KP(d,s)

t,loss

KP(b,s)

t,loss

Ks 3 4 2 2

S.M. Shahrear Tanzil (UBC) 20 / 33 September 5, 2013 20 / 33

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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

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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.

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Cross-Layer Performance in Presence of Sensing Errors

Sensing Errors in Cognitive Radio Systems

Two types of sensing errors i.e., false alarm and miss-detection False alarm: CRN may detect a channel being used by PUs where in reality the channel is idle/PUs are not using the channel Miss-detection: CRN may not be able to detect an active PU

S.M. Shahrear Tanzil (UBC) 23 / 33 September 5, 2013 23 / 33

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Cross-Layer Performance in Presence of Sensing Errors

Random Transmission Protocol

Traditional deterministic protocol: If the channel is sensed as busy, CR base station (BS) decides to transmit with probability, 0 False alarm: Sensed as busy but in reality idle Random transmission protocol: If the channel is sensed as busy, CR BS decides to transmit with probability, P1

S.M. Shahrear Tanzil (UBC) 24 / 33 September 5, 2013 24 / 33

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Cross-Layer Performance in Presence of Sensing Errors

Random Transmission Protocol

Traditional deterministic protocol: If the channel is sensed as idle, CR BS decides to transmit with probability, 1 Miss-detection: Sensed as idle but in reality busy Random transmission protocol: If the channel is sensed as idle, CR BS decides to transmit with probability, P2

S.M. Shahrear Tanzil (UBC) 25 / 33 September 5, 2013 25 / 33

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Cross-Layer Performance in Presence of Sensing Errors

Queuing Analytic Model

Developed a queuing analytic model Analyzed the queuing analytic model as a QBD process Calculated QoS parameters of SUs e.g., packet loss probability and queuing delay as well as QoS parameters of PUs e.g., collision probability from the steady state probabilities of the QBD

S.M. Shahrear Tanzil (UBC) 26 / 33 September 5, 2013 26 / 33

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Cross-Layer Performance in Presence of Sensing Errors

Numerical Results: Packet Loss Probability

1 2 3 4 5 6 7 8 9 10 0.13 0.135 0.14 0.145

X: 8 Y: 0.1401

Number of secondary users (K) Packet loss probability P1=0,P2=1(ana) P1=0.1,P2=1(ana) P1=0,P2=0.9(ana)

Figure 9: Effect of the values of P1, P2 and SUs (K) on the packet loss probability (ana=analysis).

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Cross-Layer Performance in Presence of Sensing Errors

Numerical Results: Average Queuing Delay

1 2 3 4 5 6 7 8 9 10 74.5 75 75.5 76 76.5 77 77.5 Number of secondary users (K) Average queueing delay (time slots) P1=0,P2=1(ana) P1=0.1,P2=1(ana) P1=0,P2=0.9(ana)

Figure 10: Effect of the values of P1,P2 and SUs (K) on average queuing delay (ana=analysis).

S.M. Shahrear Tanzil (UBC) 28 / 33 September 5, 2013 28 / 33

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Cross-Layer Performance in Presence of Sensing Errors

Numerical Results: Collision Probability

1 2 3 4 5 6 7 8 9 10 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

X: 8 Y: 0.07117

Number of secondary users (K) Overall collision probability

X: 7 Y: 0.06684

P1=0.1,P2=1(ana) P1=0,P2=0.9(ana) P1=0,P2=1(ana)

Figure 11: Effect of the values of P1, P2 and SUs (K) on the collision probability (ana=analysis).

S.M. Shahrear Tanzil (UBC) 29 / 33 September 5, 2013 29 / 33

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Cross-Layer Performance in Presence of Sensing Errors

Application of the Developed Model: Example

Table 2: Transmission Probabilities (P1, P2) vs. number of SUs for given QoS requirements (pt,ploss = 0.14, Dt,avg = 77 (time slots) and pt,col = 0.07 )

(P1, P2) Kpt,ploss KDt,avg Kpt,col Ks (0,0.9) 6 9 15 6 (0,1) 7 11 13 7 (0.1,1) 8 12 8 8

S.M. Shahrear Tanzil (UBC) 30 / 33 September 5, 2013 30 / 33

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Cross-Layer Performance in Presence of Sensing Errors

Summary: Part II

Investigated the performance of a random transmission protocol Developed a queuing analytic model in presence of sensing errors Calculated different QoS parameters using the developed queuing model The queuing analytic model is also useful for admission control Selected numerical results have shown that random transmission protocol can support more SUs than the classical deterministic transmission protocol

S.M. Shahrear Tanzil (UBC) 31 / 33 September 5, 2013 31 / 33

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Cross-Layer Performance in Presence of Sensing Errors

Publication

S M Shahrear Tanzil and Md. Jahangir Hossain, “Cross-layer performance analysis for cognitive radio network with a random transmission protocol in presence of sensing errors”, in Proc. of Int. Conf. on Cognitive Radio Oriented Wireless Networks (CROWNCOM’13), Washington DC, USA, Jul. 2013.

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Cross-Layer Performance in Presence of Sensing Errors

Thank you!

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