Mark Weichold
Mounir Hamdi
Muhammad Zeeshan Shakir
Mohamed Abdallah
George K. Karagiannidis
Muhammad Ismail (Eds.)

156

Cognitive Radio
Oriented Wireless
Networks
10th International Conference, CROWNCOM 2015
Doha, Qatar, April 21–23, 2015
Revised Selected Papers

123


Lecture Notes of the Institute for Computer
Sciences, Social Informatics
and Telecommunications Engineering
156
Editorial Board
Ozgur Akan
Middle East Technical University, Ankara, Turkey
Paolo Bellavista
University of Bologna, Bologna, Italy
Jiannong Cao
Hong Kong Polytechnic University, Hong Kong, Hong Kong
Falko Dressler

University of Erlangen, Erlangen, Germany
Domenico Ferrari
Università Cattolica Piacenza, Piacenza, Italy
Mario Gerla
UCLA, Los Angels, USA
Hisashi Kobayashi
Princeton University, Princeton, USA
Sergio Palazzo
University of Catania, Catania, Italy
Sartaj Sahni
University of Florida, Florida, USA
Xuemin (Sherman) Shen
University of Waterloo, Waterloo, Canada
Mircea Stan
University of Virginia, Charlottesville, USA
Jia Xiaohua
City University of Hong Kong, Kowloon, Hong Kong
Albert Zomaya
University of Sydney, Sydney, Australia
Geoffrey Coulson
Lancaster University, Lancaster, UK


More information about this series at />

Mark Weichold Mounir Hamdi
Muhammad Zeeshan Shakir Mohamed Abdallah
George K. Karagiannidis Muhammad Ismail (Eds.)
•


•

•

Cognitive Radio
Oriented Wireless
Networks
10th International Conference, CROWNCOM 2015
Doha, Qatar, April 21–23, 2015
Revised Selected Papers

123


Editors
Mark Weichold
Texas A&M University at Qatar
Doha
Qatar

Mohamed Abdallah
Texas A&M University at Qatar
Doha
Qatar

Mounir Hamdi
Hamad Bin Khalifa University
Doha
Qatar


George K. Karagiannidis
Aristotle University of Thessaloniki
Greece and Khalifa University
United Arab Emirates

Muhammad Zeeshan Shakir
Texas A&M University of Qatar
Doha
Qatar

Muhammad Ismail
Texas A&M University at Qatar
Doha
Qatar

ISSN 1867-8211
ISSN 1867-822X (electronic)
Lecture Notes of the Institute for Computer Sciences, Social Informatics
and Telecommunications Engineering
ISBN 978-3-319-24539-3
ISBN 978-3-319-24540-9 (eBook)
DOI 10.1007/978-3-319-24540-9
Library of Congress Control Number: 2015950861
Springer Cham Heidelberg New York Dordrecht London
© Institute for Computer Science, Social Informatics and Telecommunications Engineering 2015
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the
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CROWNCOM 2015
Preface

2015 marks the 10th anniversary of the International Conference on Cognitive
Radio-Oriented Wireless Networks (Crowncom). Crowncom 2015 was jointly hosted
by Texas A&M University at Qatar and Hamad Bin Khalifa University in Doha, Qatar,
April 21–23, 2015. The event was a special occasion to look back at the contribution of
Crowncom toward the advancements of cognitive radio technology since its inaugural
conference in 2006 in Mykonos, Greece, as well as to look forward to the decades
ahead, the ways that cognitive radio technology would like to evolve, and the ways its
emerging applications and services can ensure everyone is connected everywhere.
Evolution of cognitive radio technology pertaining to 5G networks was the theme
of the 2015 edition of Crowncom. The technical program of Crowncom 2015 was
structured to bring academic and industrial researchers together to identify and discuss
recent developments, highlight the challenging gaps, and forecast the future trends of
cognitive radio technology toward its integration with the 5G network deployment.
One of the key topics of the conference was cognition and self-organization in the
future networks, which are now widely considered as a striking solution to cope with

the future ever-increasing spectra demands. Going beyond the theoretical development
and investigation, further practical advances and standardization developments in this
technology could provide potential dynamic solutions to cellular traffic congestion
problems by exploiting new and underutilized spectral resources. One of the challenging issues that Crowncom 2015 brought forward was to facilitate the heterogeneous demands of users in heterogeneous-type environments — particularly in the 5G
network paradigm, where the networks are anticipated to incorporate the provision of
high-quality services to users with extremely low delays and consider these requirements without explicit demand from users. Machine-type communications and Internet
of Everything are now representing emerging use cases of such ubiquitous connectivity
over limited spectra.
Crowncom 2015 strongly advocated that the research community, practitioners,
standardization bodies, and developers should collaborate on their research efforts to
further align the development initiatives toward the evolution of emerging highly
dynamic spectrum access frameworks. The biggest challenge is to design unified crosslayer new network architectures for successful aggregation of licensed and unlicensed
spectra, addressing the spectrum scarcity problem for ubiquitous connectivity and
preparing the ground for “The Age of the ZetaByte.”
Crowncom 2015 received a large number of submissions, and it was a challenging
task to select the best and most relevant meritorious papers to reflect the theme of the
2015 edition of Crowncom. All submissions received high-quality reviews from the
Technical Program Committee (TPC) members/reviewers and eventually 66 technical
papers (with an acceptance ratio of 56 %) were selected for the technical program of the


VI

CROWNCOM 2015

conference. The technical program of Crowncom 2015 is the result of the tireless
efforts of 14 track chairs, and more than 200 TPC members and reviewers. We are
grateful to the track chairs for handling the paper review process and their outstanding
efforts, and to the reviewers/TPC for their high-quality evaluations. We offer our
sincere gratitude to the Advisory Committee, local Organizing Committee (especially

colleagues at Texas A&M University at Qatar), and the Steering Committee members
for their insightful guidance. We would like to acknowledge the invaluable support
from European Alliance for Innovation and the Qatar National Research Fund for the
success of Crowncom 2015.
2015

Mark Weichold
Mounir Hamdi
Muhammad Zeeshan Shakir
Mohamed Abdallah
George K. Karagiannidis
Muhammad Ismail


Organization

General Chair
Mark Weichold
Mounir Hamdi

Texas A&M University at Qatar, Qatar
Hamad Bin Khalifa University, Qatar

Technical Program Chair
Muhammad Zeeshan Shakir
Mohamed Abdallah
George K. Karagiannidis

Texas A&M University at Qatar, Qatar
Texas A&M University at Qatar, Qatar

Aristotle University of Thessaloniki, Greece,
and Khalifa University, UAE

Advisory Board
Athanasios V. Vasilakos
Khalid A. Qaraqe
Jinsong Wu
David Grace
Naofal Al-Dhahir
Kaushik Chowdhury

Kuwait University, Kuwait
Texas A&M University at Qatar, Qatar
Bell Labs, China
University of York, UK
University of Texas, Dallas, USA
Northeastern University, USA

Special Session Chair
Alhussein Abouzeid

Rensselaer Polytechnic Institute, USA

Panel Chair
Maziar Nekovee

Samsung, UK

Publication Chair
Muhammad Ismail


Texas A&M University at Qatar, Qatar

Tutorial Chair
Mohamed Nafie

Nile University, Egypt

Exhibitions and Demos Chair
Majid Butt

Qatar University, Qatar


VIII

Organization

Web Chair
İslam Şafak Bayram

Qatar Environment and Energy Research Institute,
Qatar

Local Arrangements
Carol Nader
Mohamed Kashef

Texas A&M University at Qatar, Qatar
Texas A&M University at Qatar, Qatar


Track Chairs
Track 1: Dynamic Spectrum Access/Management
Mohammad Shaqfeh

Texas A&M University at Qatar, Qatar

Track 2: Networking Protocols for CR
Tamer Khattab
Amr Mohamed

Qatar University, Qatar
Qatar University, Qatar

Track 3: Modeling and Theory
Zouheir Rezki
Syed Ali Raza Zaidi

King Abdullah University of Science and Technology,
Saudi Arabia
University of Leeds, UK

Track 4: HW Architecture and Implementations
Ahmed El-Tawil
Fadi Kurdahi

University of California, Irvine, USA
University of California, Irvine, USA

Track 5: Next Generation of Cognitive Networks

Muhammad Ali Imran
Richard Demo Souza

CCSR/5G Innovation Centre University of Surrey, UK
Federal University of Technology - Paraná (UTFPR),
Curitiba - PR - Brazil

Track 6: Standards and Business Models
Stanislav Fillin
Stephen J. Shellhammer
Markus Dominik Mueck

National Institute of Information and Communications
Technology (NICT), Japan
Qualcomm Technologies, Inc., USA
INTEL Mobile Communications, Germany

Track 7: Emerging Applications for Cognitive Networks
Octavia A. Dobre
Hai Lin

Memorial University, Canada
Osaka Prefecture University, Japan


Contents

Dynamic Spectrum Access/Management
Fractional Low Order Cyclostationary-Based Spectrum Sensing
in Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Hadi Hashemi, Sina Mohammadi Fard, Abbas Taherpour,
and Tamer Khattab
Achievable Rate of Multi-relay Cognitive Radio MIMO Channel with
Space Alignment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Lokman Sboui, Hakim Ghazzai, Zouheir Rezki,
and Mohamed-Slim Alouini
Effective Capacity and Delay Optimization in Cognitive Radio Networks . . .
Mai Abdel-Malek, Karim Seddik, Tamer ElBatt, and Yahya Mohasseb

3

17

30

Auction Based Joint Resource Allocation with Flexible User Request
in Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wei Zhou, Tao Jing, Yan Huo, Jin Qian, and Zhen Li

43

Two-Stage Multiuser Access in 5G Cellular Using Massive MIMO
and Beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hussein Seleem, Abdullhameed Alsanie, and Ahmed Iyanda Sulyman

54

Detection of Temporally Correlated Primary User Signal with Multiple
Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hadi Hashemi, Sina Mohammadi Fard, Abbas Taherpour,

Saeid Sedighi, and Tamer Khattab
Non-uniform Quantized Distributed Sensing in Practical Wireless Rayleigh
Fading Channel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Sina Mohammadi Fard, Hadi Hashemi, Abbas Taherpour,
and Tamer Khattab
Downlink Scheduling and Power Allocation in Cognitive Femtocell
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hesham M. Elmaghraby, Dongrun Qin, and Zhi Ding

66

78

92


X

Contents

Networking Protocols for CR
Optimization of Collaborative Spectrum Sensing with Limited Time
Resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fariba Mohammadyan, Zahra Pourgharehkhan, Abbas Taherpour,
and Tamer Khattab

109

Stability and Delay Analysis for Cooperative Relaying with Multi-access
Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Mohamed Salman, Amr El-Keyi, Mohammed Nafie, and Mazen Hasna

123

An Efficient Switching Threshold-Based Scheduling Protocol for Multiuser
Cognitive AF Relay Networks with Primary Users Using Orthogonal
Spectrums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Anas M. Salhab, Fawaz Al-Qahtani, Salam A. Zummo,
and Hussein Alnuweiri

135

An Efficient Secondary User Selection Scheme for Cognitive Networks
with Imperfect Channel Estimation and Multiple Primary Users . . . . . . . . . .
Anas M. Salhab

149

Implementing a MATLAB-Based Self-configurable Software Defined
Radio Transceiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Benjamin Drozdenko, Ramanathan Subramanian, Kaushik Chowdhury,
and Miriam Leeser

164

Investigation of TCP Protocols in Dynamically Varying Bandwidth
Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fan Zhou, Abdulla Al Ali, and Kaushik Chowdhury

176


Opportunistic Energy Harvesting and Energy-Based Opportunistic
Spectrum Access in Cognitive Radio Networks. . . . . . . . . . . . . . . . . . . . . .
Yuanyuan Yao, Xiaoshi Song, Changchuan Yin, and Sai Huang

187

Channel Transition Monitoring Based Spectrum Sensing in Mobile
Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Meimei Duan, Zhimin Zeng, Caili Guo, and Fangfang Liu

199

Power Minimization Through Packet Retention in Cognitive Radio Sensor
Networks Under Interference and Delay Constraints: An Optimal Stopping
Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Amr Y. Elnakeeb, Hany M. Elsayed, and Mohamed M. Khairy

211

Modeling and Theory
Cooperative Spectrum Sensing using Improved p-norm Detector in
Generalized j-l Fading Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Monika Jain, Vaibhav Kumar, Ranjan Gangopadhyay,
and Soumitra Debnath

225


Contents


Kalman Filter Enhanced Parametric Classifiers for Spectrum Sensing Under
Flat Fading Channels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Olusegun P. Awe, Syed M. Naqvi, and Sangarapillai Lambotharan
Differential Entropy Driven Goodness-of-Fit Test for Spectrum Sensing. . . . .
Sanjeev Gurugopinath, Rangarao Muralishankar, and H.N. Shankar
Experimental Results for Generalized Spatial Modulation Scheme with
Variable Active Transmit Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Khaled M. Humadi, Ahmed Iyanda Sulyman, and Abdulhameed Alsanie
Low Complexity Multi-mode Signal Detection for DTMB System . . . . . . . .
Xue Liu, Guido H. Bruck, and Peter Jung
Best Relay Selection for DF Underlay Cognitive Networks with Different
Modulation Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ahmed M. ElShaarany, Mohamed M. Abdallah, Salama Ikki,
Mohamed M. Khairy, and Khalid Qaraqe
Spectrum-Sculpting-Aided PU-Claiming in OFDMA Cognitive Radio
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yi Ren, Chao Wang, Dong Liu, Fuqiang Liu, and Erwu Liu
Sensing-Throughput Tradeoff for Cognitive Radio Systems with Unknown
Received Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ankit Kaushik, Shree Krishna Sharma, Symeon Chatzinotas,
Björn Ottersten, and Friedrich Jondral

XI

235
248

260
271


282

295

308

Cooperative Spectrum Sensing for Heterogeneous Sensor Networks Using
Multiple Decision Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Shree Krishna Sharma, Symeon Chatzinotas, and Björn Ottersten

321

A Cognitive Subcarriers Sharing Scheme for OFDM Based Decode
and Forward Relaying System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Naveen Gupta and Vivek Ashok Bohara

334

Efficient Performance Evaluation for EGC, MRC and SC Receivers over
Weibull Multipath Fading Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Faissal El Bouanani and Hussain Ben-Azza

346

Power Control in Cognitive Radio Networks Using Cooperative
Modulation and Coding Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Anestis Tsakmalis, Symeon Chatzinotas, and Björn Ottersten

358


Symbol Based Precoding in the Downlink of Cognitive MISO Channel . . . . .
Maha Alodeh, Symeon Chatzinotas, and Björn Ottersten

370


XII

Contents

A Discrete-Time Multi-server Model for Opportunistic Spectrum Access
Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Islam A. Abdul Maksoud and Sherif I. Rabia

381

HW Architecture and Implementations
A Hardware Prototype of a Flexible Spectrum Sensing Node for Smart
Sensing Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ahmed Elsokary, Peter Lohmiller, Václav Valenta,
and Hermann Schumacher
Development of TV White-Space LTE Devices Complying with Regulation
in UK Digital Terrestrial TV Band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Takeshi Matsumura, Kazuo Ibuka, Kentaro Ishizu, Homare Murakami,
Fumihide Kojima, Hiroyuki Yano, and Hiroshi Harada
Feasibility Assessment of License-Shared Access in 600*700 MHz
and 2.3*2.4GHz Bands: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yao-Chia Chan, Ding-Bing Lin, and Chun-Ting Chou
Dynamic Cognitive Radios on the Xilinx Zynq Hybrid FPGA . . . . . . . . . . .

Shanker Shreejith, Bhaskar Banarjee, Kizheppatt Vipin,
and Suhaib A. Fahmy

391

405

417
427

Next Generation of Cognitive Networks
A Novel Algorithm for Blind Detection of the Number of Transmit Antenna . . . .
Mostafa Mohammadkarimi, Ebrahim Karami, and Octavia A. Dobre
Localization of Primary Users by Exploiting Distance Separation Between
Secondary Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Audri Biswas, Sam Reisenfeld, Mark Hedley, Zhuo Chen,
and Peng Cheng
Mitigation of Primary User Emulation Attacks in Cognitive Radio
Networks Using Belief Propagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Sasa Maric and Sam Reisenfeld
Femtocell Collaborative Outage Detection (FCOD) with Built-in Sleeping
Mode Recovery (SMR) Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dalia Abouelmaati, Arsalan Saeed, Oluwakayode Onireti,
Muhammad Ali Imran, and Kamran Arshad
Resource Allocation for Cognitive Satellite Uplink and Fixed-Service
Terrestrial Coexistence in Ka-Band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Eva Lagunas, Shree Krishna Sharma, Sina Maleki, Symeon Chatzinotas,
Joel Grotz, Jens Krause, and Björn Ottersten

441


451

463

477

487


Contents

XIII

SHARF: A Single Beacon Hybrid Acoustic and RF Indoor Localization
Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ahmed Zubair, Zaid Bin Tariq, Ijaz Haider Naqvi, and Momin Uppal

499

Massive MIMO and Femto Cells for Energy Efficient Cognitive Radio
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
S.D. Barnes, S. Joshi, B.T. Maharaj, and A.S. Alfa

511

Hybrid Cognitive Satellite Terrestrial Coverage: A Case Study for 5G
Deployment Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Theodoros Spathopoulos, Oluwakayode Onireti, Ammar H. Khan,
Muhammad A. Imran, and Kamran Arshad

Energy-Efficient Resource Allocation Based on Interference Alignment in
MIMO-OFDM Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . .
Mohammed El-Absi, Ali Ali, Mohamed El-Hadidy, and Thomas Kaiser

523

534

Standards and Business Models
Receiving More than Data - A Signal Model and Theory of a Cognitive
IEEE 802.15.4 Receiver. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tim Esemann and Horst Hellbrück
Prototype of Smart Phone Supporting TV White-Spaces LTE System . . . . . .
Takeshi Matsumura, Kazuo Ibuka, Kentaro Ishizu, Homare Murakami,
Fumihide Kojima, Hiroyuki Yano, and Hiroshi Harada
Strategic Choices for Mobile Network Operators in Future Flexible UHF
Spectrum Concepts? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Seppo Yrjölä, Petri Ahokangas, Jarkko Paavola, and Pekka Talmola

549
562

573

Spatial Spectrum Holes in TV Band: A Measurement in Beijing . . . . . . . . . .
Sai Huang, Yajian Huang, Hao Zhou, Zhiyong Feng, Yifan Zhang,
and Ping Zhang

585


TV White Space Availability in Libya . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Anas Abognah and Otman Basir

593

Emerging Applications for Cognitive Networks
Cognitive Aware Interference Mitigation Scheme for LTE Femtocells . . . . . .
Ismail AlQerm and Basem Shihada
Packet Loss Rate Analysis of Wireless Sensor Transmission with RF
Energy Harvesting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tian-Qing Wu and Hong-Chuan Yang

607

620


XIV

Contents

Distributed Fair Spectrum Assignment for Large-Scale Wireless DSA
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Bassem Khalfi, Mahdi Ben Ghorbel, Bechir Hamdaoui,
and Mohsen Guizani

631

Multiple Description Video Coding for Underlay Cognitive Radio Network . . .
Hezerul Abdul Karim, Hafizal Mohamad, Nordin Ramli,

and Aduwati Sali

643

Device-Relaying in Cellular D2D Networks: A Fairness Perspective . . . . . . .
Anas Chaaban and Aydin Sezgin

653

Interference Mitigation and Coexistence Strategies in IEEE 802.15.6 Based
Wearable Body-to-Body Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Muhammad Mahtab Alam and Elyes Ben Hamida

665

Workshop Cognitive Radio for 5G Networks
Distributed Power Control for Carrier Aggregation in Cognitive
Heterogeneous 5G Cellular Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fotis Foukalas and Tamer Khattab
Design of Probabilistic Random Access in Cognitive Radio Networks . . . . . .
Rana Abbas, Mahyar Shirvanimoghaddam, Yonghui Li,
and Branka Vucetic
On the Way to Massive Access in 5G: Challenges and Solutions for
Massive Machine Communications (Invited Paper) . . . . . . . . . . . . . . . . . . .
Konstantinos Chatzikokolakis, Alexandros Kaloxylos, Panagiotis Spapis,
Nancy Alonistioti, Chan Zhou, Josef Eichinger, and Ömer Bulakci

681
696


708

An Evolutionary Approach to Resource Allocation in Wireless Small Cell
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Shahriar Etemadi Tajbakhsh, Tapabrata Ray, and Mark C. Reed

718

Coexistence of LTE and WLAN in Unlicensed Bands: Full-Duplex
Spectrum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ville Syrjälä and Mikko Valkama

725

Research Trends in Multi-standard Device-to-Device Communication
in Wearable Wireless Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Muhammad Mahtab Alam, Dhafer Ben Arbia, and Elyes Ben Hamida

735

Implementation Aspects of a DSP-Based LTE Cognitive Radio Testbed. . . . .
Ammar Kabbani, Ali Ramadan Ali, Hanwen Cao, Asim Burak Güven,
Yuan Gao, Sundar Peethala, and Thomas Kaiser

747


Contents

Construction of a Robust Clustering Algorithm for Cognitive Radio

Ad-Hoc Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nafees Mansoor, A.K.M. Muzahidul Islam, Mahdi Zareei,
Sabariah Baharun, and Shozo Komaki
On the Effective Capacity of Delay Constrained Cognitive Radio Networks
with Relaying Capability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ahmed H. Anwar, Karim G. Seddik, Tamer ElBatt,
and Ahmed H. Zahran
Cooperative Spectrum Sharing Using Transmit Antenna Selection
for Cognitive Radio Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Neha Jain, Shubha Sharma, Ankush Vashistha, Vivek Ashok Bohara,
and Naveen Gupta
A Survey of Machine Learning Algorithms and Their Applications
in Cognitive Radio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mustafa Alshawaqfeh, Xu Wang, Ali Rıza Ekti,
Muhammad Zeeshan Shakir, Khalid Qaraqe, and Erchin Serpedin
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

XV

759

767

780

790

803



Dynamic Spectrum Access/Management


Fractional Low Order Cyclostationary-Based
Spectrum Sensing in Cognitive Radio Networks
Hadi Hashemi1 , Sina Mohammadi Fard1 , Abbas Taherpour1 ,
and Tamer Khattab2(B)
1

Department of Electrical Engineering, Imam Khomeini International University,
Qazvin, Iran

2
Electrical Engineering, Qatar University, Doha, Qatar


Abstract. In this paper, we study the problem of cyclostationary spectrum sensing in cognitive radio networks based on cyclic properties of linear modulations. For this purpose, we use fractional order of observations
in cyclic autocorrelation function (CAF). We derive the generalized likelihood ratio (GLR) for designing the detector. Therefore, the performance
of this detector has been improved compared to previous detectors. We
also find optimum value of the fractional order of observations in additive
Gaussian noise. The exact performance of the GLR detector is derived
analytically as well. The simulation results are presented to evaluate the
performance of the proposed detector and compare its performance with
their counterpart, so to illustrate the impact of the optimum value of
fractional order over performance improvement of these detectors.

Keywords: Cognitive radio
signal · Fractional low order

1


·

Spectrum sensing

·

Cyclostationary

Introduction

Increasing need for bandwidth in telecommunication and limited environmental
resources lead us to take advantage of other system’s spectrum. In spectrum
sensing, cognitive radio networks monitor the status of the frequency spectrum
by observing their surroundings to exploit the unused frequency bands. There
are several methods of spectrum sensing which need different and extra information about the primary user (PU) signal, such as accuracy and implementation
complexity [1]. The most important methods are matched filter, energy detection, eigenvalues-based detection, detection based on the covariance matrix and
cyclostationary based detection.
Among those, cyclostationary-based detector is one of the best way of spectrum sensing in terms of performance and robustness against environmental
parameters like ambient noise. In the context of cyclostationary-based spectrum
c Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015
M. Weichold et al. (Eds.): CROWNCOM 2015, LNICST 156, pp. 3–16, 2015.
DOI: 10.1007/978-3-319-24540-9 1


4

H. Hashemi et al.

sensing, in [2,3], this detector has been investigated for one specific cyclic frequency. The authors in [2] have reviewed collaborative case and have demonstrated channel fading effects in its performance. The authors in [4–6] have used

multiple cyclic frequencies for detection of PU signal and improvement the detection performance has been shown. Furthermore, several research such as [2,7,8]
have been conducted where the benefit of using cyclostationary-based detectors
in the collaborative systems are investigated. It is known that cyclostationarybased detectors have poor performance for situations where the environment is
impulsive noisy and to compensate, the CAF with fractional order of observations are used [9–11]. In these works, the problem of fractional order of observations, is investigated in Alpha stable noisy environment.
In this paper, we provide a spectrum sensing method which benefits of PU signal’s cyclostationary property and improve performance of cyclostationary-based
detector in different practical cases and noise models. We suggest using fractional
order of observed signals. We assume an additive Gaussian noise, thought the
results could be extended for the other model of ambient noises. For this purpose, we formulate the spectrum sensing as a binary hypothesis testing problem
and then derive the corresponding GLR detectors for the different practical scenarios. Then we investigate the optimum value of fractional order which results
in best performance in related cases.
The remaining of the paper is organized as follows. In Section 2, we introduce
the system model and the assumptions. In Section 3, we derive cyclostationarybased detectors in different scenarios for signal and noise prameters. In Section
4, we study the performance of the proposed detectors. The optimization of
the performance of the proposed detectors is presented in Section 6. The simulation results are provided in Section 7 and finally Section 8 summarizes the
conclusions.
Notation: Lightface letters denote scalars. Boldface lower-and upper-case letters
denote column vectors and matrices, respectively. x(.) is the entries and xi is
sub-vector of vector x. The inverse of matrix A is A−1 . The M × M identity
matrix is IM . Superscripts ∗, T and H are the complex conjugate, transpose and
Hermitian (conjugate transpose), respectively. E[.] is the statistical expectation.
N (m, P) denotes Gaussian distribution with mean m and covariance matrix P.
2
∞
du.
Q(x) is Q-function Q(x) = √12π x exp −u
2

2

System Model


Suppose a cognitive radio network in which PU and secondary user (SU)
equipped with a single antenna. For presentation, it’s assumed that the PU
signal is transmitted with linear modulation such that
∞

di p(t − iTP ),

s(t) =
i=−∞

(1)


Fractional Low Order Cyclostationary-Based Detector

5

where di is the PU data and p(t) is shaping pulse in the PU transmitter. We
suppose PU data, di , is a random variable with zero-mean Gaussian distribution, N (0, σs2 ). For the shaping pulse, a rectangular pulse with unit amplitude
and time spread TP is assumed. Received signal in SU has been sampled with
sampling rate of fs = T1s . The wireless channel between PU transmitter and
SU is assumed to be a flat fading channel with additive Gaussian noise and the
2
) denotes noise samples and
channel gain. The random variable w(n) ∼ N (0, σw
we assume noise and PU signal samples are mutually independent. Therefore
observed signal samples in SU under two hypotheses can be shown as follows,
H0 :


x(n) = w(n),

H1 :

x(n) = hs(n) + w(n),

(2)

where h is channel gain between the PU and SU antennas. It is assumed that the
channel gain is constant during the sensing time. CAF for the SU observed signal
samples is defined based on the correlation between samples and their complex
conjugate with lag time τi < TP . The CAF for fractional order is defined as,
α
Rxx
∗ (τi ) =

1
N

N −1

xp (n)x∗p (n + τi )e−j2παn ,

(3)

n=0

where p is fractional order 0 < p < 1, α ∈ { TkP , k = 1, 2, ...} is cyclic frequency
for linear modulation which is assumed to be known to SU and τi , i = 1, . . . , M s
is M lag times where the CAF is calculated.

We introduce vector rα
xx∗ consisting of CAF real parts for M different lag
times as,
α
α
T
rα
xx∗ = [Re(Rxx∗ (τ1 )), ..., Re(Rxx∗ (τM ))] .

(4)

By considering central limit theorem (CLT), since the CAF is summation of
N random variables, according to [12], for sufficiently large number of observation samples, each member of vector rα
xx∗ has Gaussian distribution . Thus, we
have,
rα
xx∗ ∼

N (µ0 , Σ0 ) for H0 ,
N (µ1 , Σ1 ) for H1 .

(5)

where µ0 and µ1 can be calculated for any given p. In Section 5 for the
known noise and signal variance these values are computed.

3

Cyclostationary-Based Detectors


SUs use different detection methods in spectrum sensing to make decision about
PU’s presence. In this section, we assume SU determines PUs situation based
on cyclostationary properties of PU signal in which the SU has knowledge about
cyclic frequency of observation signal by consideration of different scenarios.
These scenarios are investigated in following subsections.


6

H. Hashemi et al.

3.1

Known Signal and Noise Variance

Since in (5) covariance matrices under two hypotheses are unknown, we have
to use their estimations to construct the likelihood ratio (LR) function which
results in a GLR detector. Covariance matrices estimation have been calculated
under two hypotheses in Appendix. It has been shown that both of the covariance
matrices have same estimation. Thus, Σ0 = Σ1 = Σ. Now for the LR function,
we have,
H1

T

T −1
−1
µ0 − µT1 Σ−1 µ1 + 2rα
(µ1 − µ0 )} ≷ η.
LR(rα

xx∗ Σ
xx∗ ) = exp{µ0 Σ
H0

(6)

By incorporating the constant terms into threshold and taking logarithm in (6),
we obtain,
H1

T

−1
Tsub1 = rα
(µ1 − µ0 ) ≷ η1 ,
xx∗ Σ

(7)

H0

where µ0 and µ1 can be calculated. It can be seen that detector is the weighted
summation of CAF real part for different lag times τi , i = 1, 2, ..., M .
3.2

Known Noise Variance, Unknown Signal Variance

The mean of (4), when SU has just knowledge about noise variance, can be derived
under null hypothesis according to section 5.1. But as mentioned, signal variance
is unknown and thus, mean of the CAF real parts under alternative hypothesis

cannot be calculated. In this situation, we can use Hotelling-test [13,17], because
we definitely know that the mean under two hypotheses are different. Suppose, L > M + 1 given vector rα
xx∗ in a vector are considered together,
α
α
(1),
r
(2),
...,
r
(L)].
Statistical
distribution of this vector under
r = [rα
∗
∗
∗
xx
xx
xx
hypothesis Hj , j = 0, 1 can be written in the form below,
f (r|Hj ) =

exp {− 12 tr([ L1 Ψ + (r − µj )(r − µj )T ]Σ−1
j )}
(2π)

L

LM

2

L

|Σj | 2

,

(8)

L

α
α
T
where r = L1 i=1 rα
xx∗ (i) and Ψ =
i=1 (rxx∗ (i) − r)(rxx∗ (i) − r) , under
alternative hypothesis, r is estimate of µ1 and the statement inside the bracket
of function tr(.) is the estimation covariance matrix under two hypotheses. Thus
after eliminating the constants we have,
L

Λ=

| L1 (Ψ + L(r − µ0 )(r − µ0 )T )| 2
L
| L1 Ψ| 2

L


= |I + LΨ−1 (r − µ0 )(r − µ0 )T | 2 .

(9)

By using the matrix determinant lemma that computes the determinant of the
sum of an invertible matrix I and the dyadic product, Ψ−1 (r − µ0 )(r − µ0 )T ,
Λ = 1 + L(r − µ0 )T Ψ−1 (r − µ0 )

L
2

L

= (1 + Tsub2 ) 2 .

(10)


Fractional Low Order Cyclostationary-Based Detector

7

Since Λ is the strictly ascending function of Tsub2 , therefore, Tsub2 can be
considered as a statistic.
Tsub2 = L(r − µ0 )T Ψ−1 (r − µ0 )
3.3

(11)


Unknown Signal and Noise Variance

In this situation, by considering covariance matrices estimation as (A-4), we
have two Gaussian distribution by same covariance matrices and different mean
under two hypotheses. If estimation is used for means of CAF real parts under
both hypotheses, due to equality of estimation under two hypotheses the result
of GLR test does not give any information to make decision. Thus, mean of
CAFs for various lag time is considered as statistic and compared with a proper
threshold.
Tsub3

4

1
=
M

M

H1

α
Re(Rxx
∗ (τm )) ≷ η3 .

(12)

H0

m=1


Analytical Performance

In this section, we evaluate the performance of our proposed cyclostationarybased detectors in terms of detection and false alarm probabilities, Pd and Pfa ,
respectively.
4.1

Analytical Performance of Tsub1

We should derive statistical distribution of (7) under two hypotheses. We can
rewrite (7) as follows,
T

1

T

1

H1

−2
Tsub1 = (rα
)(Σ− 2 (µ1 − µ0 )) = rα
xx∗ Σ
xx∗ w ≷ η1 ,
H0

1


(13)

1

−2 α
where w = Σ− 2 (µ1 − µ0 ) and rα
rxx∗ which is distributed as Gaussian
xx∗ = Σ
under two hypotheses, i.e.,

rα
xx∗ |Hν ∼ N (mν , IM ), ν = 0, 1,

(14)

1

where mν = Σ− 2 µν . As we can see in (13), our detector is a linear combination
of independent Gaussian random variables mentioned in (14). Therefore, mean
of statistic is,
M

μTsub1 |Hν =

mν (i)w(i), ν = 0, 1.
i=1

(15)



8

H. Hashemi et al.

And similarly variance has been derived,
M
2
σT
=
sub1 |Hν

w2 (i), ν = 0, 1.

(16)

i=1

Then, the false alarm and detection probabilities can be calculated.
Pfa = P [Tsub1 > η1 |H0 ] = Q

η1 − μTsub1 |H0
σTsub1 |H0

(17)

If β is maximum acceptable probability false alarm, then threshold of detector
(β) = Q−1 (β) × σTsub1 |H0 + μTsub1 |H0 . Similarly for
can be set, η1 = FT−1
sub1 |H0
probability of detection, we have,

Pd = P [Tsub1 > η1 |H1 ] = Q
4.2

η1 − μTsub1 |H1
σTsub1 |H1

.

(18)

Analytical Performance of Tsub2

We should derive statistical distribution of (11) under two hypotheses. According
to [13], the asymptotic distribution of (11) under null hypothesis is central chisquared with M degrees of freedom. Thus, probability of false alarm is as follows,
Pfa = P [Tsub2 > η2 |H0 ] = 1 −

γ

M η2
2 , 2
Γ M
2

,

(19)

where Γ (.) and γ(., .) are Gamma and lower incomplete Gamma function, respectively. The asymptotic distribution of (11) under alternative hypothesis is noncentral chi-squared with noncentrality parameter, λ. Probability of detection is
as follows,
√ √

(20)
Pd = P [Tsub2 > η2 |H1 ] = Q M ( λ, η2 ),
2

where Q(., .) is Marcum Q-function and non-centrality parameter is, λ =
µ0 )T Σ−1
1 (µ1 − µ0 ).
4.3

L
2 (µ1 −

Analytical Performance of Tsub3

Because (12) is a linear combination of Gaussian random variables, therefore,
Tsub3 distribution is Gaussian under two hypotheses. According to Appendix 8,
mean and variance of (12) can be calculated. Thus, probability of false alarm
and detection are as follow,

Pfa = Q

η3 − μTsub3 |H0
σTsub3 |H0

,

(21)

Pd = Q


η3 − μTsub3 |H1
σTsub3 |H1

.

(22)


Fractional Low Order Cyclostationary-Based Detector

5

9

Calculation of rα
xx∗ Means

In this section, we have provided computations for expectation of rα
xx∗ under
two hypotheses when all variables are known.
5.1

Null Hypothesis

In this subsection, we investigate mean of rα
xx∗ under null hypothesis. By consideration of noise samples independency, expectation of (3) can be easily derived
for ith lag time as follows,
α
E[Rxx
∗ (τi )|H0 ]


1
=
N

N −1

E[wp (n)]E[w∗p (n + τi )]e−j2παn .

(23)

n=0

pth moment of Gaussian random variable has been calculated in Appendix, since
w(n) is zero mean Gaussian random variable, therefore,
e−jπα(N −1) sin(παN ) (−2)p πσn2p
.
N
sin(πα) Γ 2 1−p
2

(24)

sin(παN ) π(2σn2 )p
cos(π(α(1 − N ) + p)).
N sin(πα) Γ 2 1−p
2

(25)


α
E[Rxx
∗ (τ )|H0 ] =

Mean of (4) for i = 1, .., M ,
μ0 (i) =

5.2

Alternative Hypothesis

As mentioned earlier, each of the observation samples at SU is distributed as,
X = x(n) ∼ N (0, h2 p2 σs2 + σn2 )

N (0, σ12 ).

(26)

Now, we assume random variable Y to be the ith lag time of observation samples
which is distributed same as X, i.e., Y = x(n+τi ). It can be easily demonstrated
that correlation coefficient between X and Y is,
r=

E(XY ) − E(X)E(Y )
h2
h2 p2 σs2
= 2 E[s(t)s(t + τi )] =
,
σ1 × σ1
σ1

σ12

(27)

which reveals that X and Y are correlated. Thus, X and Y have joint Gaussian
distribution, N (0, 0, σ12 , σ12 , r). To determine the mean of CAF under alternative
hypothesis, we need to calculate E[X p Y p ] = E[Z p ] = E[T ]. First we must derive
probability density function (PDF) of Z which is product X and Y . i.e.,
∞

fZ (z) =
0

1
z
fXY (x, )dx −
x
x

0
−∞

1
z
fXY (x, )dx.
x
x

(28)



10

H. Hashemi et al.

2(1 − r2 ))p

(xσ1

2σ12

jp
−

e

−

k

∞

=

1 k
2

1−p
2


Γ

k=0

r2 x2
2σ12 (1 − r2 )

√

−p k
2

∞

x2
2 (1−r 2 )
2σ1

−
k!

Γ − p2

2jrx 1−p
2
√
σ1 1 − r2
−

A(r, σ1 , k, p)x2k+p − B(r, σ1 , k, p)x2k+p+1 e


k
3 k
2

×
k!

x2
2 (1−r 2 )
2σ1

k=0

(33)

In second step, we can declare distribution of T as function of Z PDF, as follows,
fT (t) =

1
1 p1 −1
t
fZ (t p ).
p

(29)

And thus, for computation of T mean, we have,
∞


E[T ] =
0

∞
−∞

1

1

tp
tp
fXY (x, )dtdx −
px
x

0

∞

−∞

−∞

1

1

tp
tp

fXY (x, )dtdx.
px
x

(30)

Common part of above equation is derived in following expression,
∞
−∞

2

x
1
1
exp − 2σ
2
tp
tp
√ 1
fXY (x, )dt =
2
px
x
px2πσ1 1 − r2

∞
−∞

1


1
p

t exp

−

t p − rx2

2

2x2 σ12 (1 − r2 )

dt.
(31)

Integral expression in equation (31) is in the form of p-th moment of Gaussian
random variable with respectively mean and variance rx2 and x2 σ12 (1 − r2 ) that
is calculated in Appendix. Therefore,
∞
−∞

1

1

(xσ1 (1 − r2 ))p
(2 − r2 )x2
tp

tp
exp − 2
Dp
fXY (x,
)dt =
2
px
x
4σ1 (1 − r2 )
j p 2πσ1

jrx
√
σ1 1 − r2

.

(32)

Result of replacement Apendix equations in (32) also some calculations and
simplifications, has led to (33), which is at the top of next page. In (33),
−p k
2

A(r, σ1 , k, p) =
Γ
√
B(r, σ1 , k, p) =

1−p

2

2p+1

2(1 − r2 ))p r2k

(σ1

1 k
2

1−p k
2

Γ − p2

,

k!

2σ12 j p (2σ12 (r2 − 1))k

(σ1

(1 − r2 ))p−2k−1 r2k+1

3 k
2

k!j p−1 (−2)k


(34)

.

(35)

Finally, from (36) and according to [14], mean of T is derived in the next page.
Therefore, ith member of µ1 for i = 1, ..., M is,
μ1 (i) =

sin(παN )
cos(πα(N − 1))E[T ]
N sin(πα)

(37)