Presented by Mohamad Haidar, Ph.D. May 13, 2009 Moncton, NB, Canada

Slides:



Advertisements
Similar presentations
$ Network Support for Wireless Connectivity in the TV Bands Victor Bahl Ranveer Chandra Thomas Moscibroda Srihari Narlanka Yunnan Wu Yuan.
Advertisements

VSMC MIMO: A Spectral Efficient Scheme for Cooperative Relay in Cognitive Radio Networks 1.
DBLA: D ISTRIBUTED B LOCK L EARNING A LGORITHM F OR C HANNEL S ELECTION I N C OGNITIVE R ADIO N ETWORKS Chowdhury Sayeed Hyder Department of Computer Science.
System Design for Cognitive Radio Communications
Doc.: IEEE /0806r0 SubmissionSlide 1 Date: Authors: aj (45 GHz) Channelization and Channel Operation Jul 2014 Bo Sun, ZTE Corp.
*Sponsored in part by the DARPA IT-MANET Program, NSF OCE Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks Rahul.
Summary of Path Loss in Propagation
On Spectrum Selection Games in Cognitive Radio Networks
1 Lecture on Mobile P2P Computing Prof. Maria Papadopouli University of Crete ICS-FORTH
2002 MURI Minisymposium Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments 2002 MURI Minisymposium Ameesh Pandya Prof.
COGNITIVE RADIO FOR NEXT-GENERATION WIRELESS NETWORKS: AN APPROACH TO OPPORTUNISTIC CHANNEL SELECTION IN IEEE BASED WIRELESS MESH Dusit Niyato,
Capacity of Wireless Mesh Networks: Comparing Single- Radio, Dual-Radio, and Multi- Radio Networks By: Alan Applegate.
MAXIMIZING SPECTRUM UTILIZATION OF COGNITIVE RADIO NETWORKS USING CHANNEL ALLOCATION AND POWER CONTROL Anh Tuan Hoang and Ying-Chang Liang Vehicular Technology.
POWER CONTROL IN COGNITIVE RADIO SYSTEMS BASED ON SPECTRUM SENSING SIDE INFORMATION Karama Hamdi, Wei Zhang, and Khaled Ben Letaief The Hong Kong University.
Utility Based Scheduling in Cognitive Radio Networks Term Project CmpE-300 Analysis of Algorithms Spring 2009 Computer Engineering, Boğaziçi University,
RELIABLE MULTIMEDIA TRANSMISSION OVER COGNITIVE RADIO NETWORKS USING FOUNTAIN CODES Proceedings of the IEEE | Vol. 96, No. 1, January 2008 Harikeshwar.
1 11 Subcarrier Allocation and Bit Loading Algorithms for OFDMA-Based Wireless Networks Gautam Kulkarni, Sachin Adlakha, Mani Srivastava UCLA IEEE Transactions.
Munawwar M. Sohul Dr. Taeyoung Yang Dr. Jeffrey H. Reed a
Mingyuan Yan, Shouling Ji, and Zhipeng Cai Presented by: Mingyuan Yan.
Cognitive Radio Networks
AUTONOMOUS DISTRIBUTED POWER CONTROL FOR COGNITIVE RADIO NETWORKS Sooyeol Im; Jeon, H.; Hyuckjae Lee; IEEE Vehicular Technology Conference, VTC 2008-Fall.
A Survey of Spectrum Sensing Algorithm for Cognitive Radio Applications YaGun Wu netlab.
CELLULAR NETWORK. Early mobile system Cellular Network Use of one powerful transmitter located on high location. Range of signals was upto 50km. These.
Tarun Bansal, Bo Chen and Prasun Sinha
Advanced Spectrum Management in Multicell OFDMA Networks enabling Cognitive Radio Usage F. Bernardo, J. Pérez-Romero, O. Sallent, R. Agustí Radio Communications.
Joint Scheduling and Power Control for Wireless Ad Hoc Networks Advisor: 王瑞騰 Student: 黃軍翰.
3 Introduction System Model Distributed Data Collection Simulation and Analysis 5 Conclusion 2.
Cognitive Radio for Dynamic Spectrum Allocation Systems Xiaohua (Edward) Li and Juite Hwu Department of Electrical and Computer Engineering State University.
Motivation: The electromagnetic spectrum is running out Almost all frequency bands have been assigned The spectrum is expensive Services are expensive.
Zaid A. Shafeeq Mohammed N. Al-Damluji Al-Ahliyya Amman University Amman - Jordan September
Cooperative MIMO Paradigms for Cognitive Radio Networks
Enhancement of Spectrum Utilization in Non- Contiguous DSA with Online Defragmentation Suman Bhunia, Vahid Behzadan and Shamik Sengupta Supported by NSF.
Spectrum Sensing In Cognitive Radio Networks
Dynamic Spectrum Access/Management Models Exclusive-Use Model Shared-Use Model.
STATE OF THE ART IN OPPORTUNISTIC SPECTRUM ACCESS MEDIUM ACCESS CONTROL DESIGN Pawelczak, P.; Pollin, S.; So, H.-S.W.; Motamedi, A.; Bahai, A.; Prasad,
Chance Constrained Robust Energy Efficiency in Cognitive Radio Networks with Channel Uncertainty Yongjun Xu and Xiaohui Zhao College of Communication Engineering,
Submission doc.: IEEE /0094r1 November 2015 Chen SUN, SonySlide 1 Coexistence Management Considering Interference Alignment Date: Notice:
Partially Overlapped Channels Not Considered Harmful Arunesh Mishra, Vivek Shrivastava, Suman Banerjee, William Arbaugh (ACM SIGMetrics 2006) Slides adapted.
Doc.: IEEE /0176r0 Submission Jan 2013 Bo Sun, ZTE/CWPANSlide 1 Date: Presenter: Proposal of Channelization for aj.
1 Spectrum Co-existence of IEEE b and a Networks using the CSCC Etiquette Protocol Xiangpeng Jing and Dipankar Raychaudhuri, WINLAB Rutgers.
Overcoming the Sensing-Throughput Tradeoff in Cognitive Radio Networks ICC 2010.
Ashish Rauniyar, Soo Young Shin IT Convergence Engineering
Network System Lab. Sungkyunkwan Univ. Differentiated Access Mechanism in Cognitive Radio Networks with Energy-Harvesting Nodes Network System Lab. Yunmin.
Adaptive Roaming between LTE and Wi-Fi 1 Daeguil Science high school, Daegu, Republic of Korea. 2 Daegu Gyeongbuk Institute of Science and Technology,
1 A Proportional Fair Spectrum Allocation for Wireless Heterogeneous Networks Sangwook Han, Irfanud Din, Woon Bong Young and Hoon Kim ISCE 2014.
A discussion on channel sensing techniques By James Xu.
Integrated Energy and Spectrum Harvesting for 5G Wireless Communications submitted by –SUMITH.MS(1KI12CS089) Guided by – BANUSHRI.S(ASST.PROF,Dept.Of.CSE)
Cognitive Radio Networks
SPECTRUM SHARING IN COGNITIVE RADIO NETWORK
Suman Bhunia and Shamik Sengupta
Cognitive Radio Based 5G Wireless Networks
Enhancing the capacity of Spectrum Sharing in Cognitive Radio Network
User Interference Effect on Routing of Cognitive Radio Ad-Hoc Networks
Cognitive Radio Network: Enabling New Wireless Broadband Opportunities
China MM-Wave (CMMW) Study Group - Introduction of CMMW PAR and 5C
Cognitive Radio Networks
Network Entry and Initialization
University of Arkansas at Little Rock
Submission Title: [Reliable Multicast for PAC]
Submission Title: Link Budget for m
Multicarrier Communication and Cognitive Radio
Project: IEEE P Working Group for Wireless Personal Area Networks (WPANs) Submission Title: [Intended IG Objectives] Date Submitted:
Spectrum Sharing in Cognitive Radio Networks
April 24, Study Group 1 A Regulatory Framework for Use of TV Channels by Part 15 Devices John Notor, Cadence Design Systems, Inc.
Video Streaming over Cognitive radio networks
Project: IEEE P Working Group for Wireless Personal Area Networks (WPANs) Submission Title: [Regulatory Update in Europe for Gigabit Application.
Channel usage in NGV: follow-up
Month Year doc.: IEEE yy/xxxxr0 August 2019
Chenhui Zheng/Communication Laboratory
Presentation transcript:

Presented by Mohamad Haidar, Ph.D. May 13, 2009 Moncton, NB, Canada Power Management and Bandwidth Allocation in a Cognitive Wireless Mesh Network Presented by Mohamad Haidar, Ph.D. May 13, 2009 Moncton, NB, Canada

Table of Content Introduction Problem statement Proposed Approach Problem Formulation Parameters and Assumptions Simulation Results Conclusion 12/4/2018

Introduction Cognitive Radio: It is a transceiver device that is able to understand and react to its operating environment. It is aware of channel conditions and activity. It changes its operating parameters to enable reliable, interference free, communications. 12/4/2018

Problem Statement Measurements consistently show that some bands are under-utilized in some areas at some times. Careful consideration should be given to the Primary User (PU) in order not to induce additional interference. 12/4/2018

Proposed Approach Heuristic algorithm: Sort Secondary Users (SUs) in ascending order according to highest BW request. This could be justified as giving a higher priority to SUs with high BW requests (Video and Voice vs. Data) Add SUs to the licensed spectrum as long as the interference level at the PU does not exceed a pre-defined threshold value and BW requests of SUs do not exceed the PU BW. In order to allow spectrum efficiency, power transmitted at SUs is dropped down as long as the data rate communication is still supported for the communication link between any two SUs. 12/4/2018

Problem Formulation NLIP Model: 12/4/2018

Parameters and Assumptions Locations of SUs and PUs are known. Communication activities between SUs are known. SUs operate on 2.4 GHz and PU operate on 700 MHz. Centralized infrastructure: The algorithm is run at the central server and decisions are multicast to intended SUs in order to instruct them about their respective frequencies or channels (along with the BW allocation on the licensed spectrum), and their transmitted power levels. Average data rate requests of each SU is known from which BW request can be calculated from the Shannon-Hartley theorem. Exchange of control information and signaling between SU nodes is beyond the scope at this moment. Information about the existence of the PU at the particular frequency and time is already known. SUs are assumed to be equipped with dual-antenna. Smooth switching between one antenna and the other. 12/4/2018

Results Scenario 1 Tx Power at all SU nodes and PU = 20 dBm PU BW = 12 MHz Interference threshold is -80 dBm SU Transmitter/Receiver Number SU Transmitter Power (dBm) SU Bandwidth Request (MHz) 3 / 1 8 2 14 / 13 1.9 13 / 11 11 1.8 1 / 16 6 0.4 tolerated interference at the PU is -80.57 dBm < -80 dBm BW Utilization ≈ 50% 12/4/2018

Results (cont’d) Scenario 2 SU Transmitter/Receiver Number SU Transmitter Power (dBm) SU Bandwidth Request (MHz) 12 / 7 3 4.80 14 / 13 4.10 13 / 11 7 3.00 tolerated interference at the PU is -82.36 dBm < -80 dBm BW Utilization ≈ 99.5% 12/4/2018

Results (cont’d) 12/4/2018

Conclusion and Future Work power management and spectrum utilization algorithm has been presented to allow SUs to opportunistically access the licensed spectrum. Future work: Control information and signalling Mobility of SUs Distributed approach rather than a centralized one. 12/4/2018