ECE 4600 Design Project

Transcription

1 ECE 4600 Design Project Design and implementation of ZigBee-based cognitive radio for ISM band Group Number 9: Shuo Dong Willy Palahnuk Faculty Supervisors: Ekram Hossain, Ph.D. Date: October 1, 2012

2 1 Introduction A cognitive radio is an intelligent type of wireless transceiver that is capable of detecting available frequency bands in wireless spectrum and altering its transmission parameters to maximize the use of the frequency spectrum, both in terms of reliability and spectrum utilization. Cognitive radio has applications in taking advantage of underutilized frequency spectrums as well as allowing low transmission power secondary users to share frequency bands with high transmission power primary users, which do not utilize cognitive radio capabilities Zigbee is a wireless standard that provides a low cost, low power, and low data rate wireless network. Zigbee has a large breadth of applications, including; home automation, industrial automation, healthcare patient monitoring, and remote control. Zigbee operates in the ISM band is built on top of the IEEE standard. Current generation wireless devices, including Zigbee devices, do not provide cognitive radio abilities. The goal of this project will be to design and implement cognitive radio algorithms on programmable Zigbee units. The project will use existing cognitive algorithms that will be mapped out in order to convert them into the C code for the programmable microcontrollers on the Zigbee units. These algorithms will be tested by operating the modified Zigbee units in close physical range to Wi-Fi signals, which share access to the same frequency bands. The Wi-Fi signals will force the Zigbee units to utilize their cognitive functions to alter their transmission parameters in order to continue communicating optimally. Additional advanced algorithms will be modeled and implemented if time allows. 2 Specification 2.1 Cognitive Radio Design The main part of the project will consist of implementing cognitive radio algorithms in the programmable XBee Brand Zigbee modules. The cognitive algorithms enhance the wireless communication s reliability and frequency spectrum utilization. Cognitive algorithms are already defined, and the designing portion of the project will be creating layouts of the current algorithms so that they can be converted to C code in the XBee units microcontrollers.

3 2.2 Cognitive Radio Implementation The Implementation will be split into two main phases; a basic cognitive radio design, and an advanced cognitive radio design. The advanced design will be completed as time allows. The basic cognitive radio will feature the more straight forward and easier to implement features of a cognitive radio. The advanced design will consist of more advanced and difficult to implement algorithms. The advanced design will be completed depending on time remaining once the basic design has been implemented and tested Basic Cognitive Radio The first step in the project will be to implement the following cognitive algorithms in the Zigbee units. Once a basic implementation is complete, they can be modified later to improve its capabilities. Below are the three main portions of the basic cognitive radio Spectrum Sensing A cognitive radio must be able to sense activity or noise on selected frequency bands. The radio must be able to do this without interfering with other users. This is achieved by periodically scanning the selected frequency bands for holes or lack of use. Spectrum sensing also encompasses sharing of spectrum information between the different nodes of the cognitive network Spectrum Analysis Information obtained from sensing is acted upon by the spectrum analysis algorithms. The information is used to determine usage of the free frequency bands by the cognitive radio. Transceiver parameters, such as (transmit power, frequency band, bandwidth) are set in this stage. These parameters are optimized to decrease chance of collisions as well as increase the through put of the cognitive radio network Spectrum Decision The spectrum decision stage consists of the actual usage of the parameters determined by the spectrum analysis stage. When the decided upon spectrum is entered, the spectrum decision stage ensures no collisions occur between different nodes attempting to access the spectrum. This stage also determines the synchronization between receivers and transmitters.

4 2.2.2 Advanced Cognitive Radio Advanced cognitive radio functions will be added on as time allows. These are the more advanced functions that will prove more difficult to model and implement Machine Learning Machine learning will be utilized to improve the decision making stage of the basic cognitive radio. Machine learning allows the cognitive radio to remember states of the spectrum band, specifically information about primary user s, such as Wi-Fi, spectrum usage. This information can be stored and used to assist in the Spectrum Analysis function, aiding the cognitive radio in making decisions transmission parameters Networking Networking allows the cognitive radio network to reroute data packets based on routing algorithms, finding the optimal path to target nodes. Implementation of this function will require purchase of additional Zigbee units and will only be considered if the rest of the algorithms and functions have been properly implemented and tested. 2.3 Testing Heterogeneous Network Testing The Zigbee network will be tested while a Wi-Fi network utilizes the same frequency range. These tests will be performed both at the university where a large unpredictable network exists, and at a student s home where a controlled Wi-Fi network can be setup and controlled to test cognitive functions such as channel hopping. The Wi-Fi network will function as a primary user, which has priority in the selected spectrum, and the Zigbee network functioning as a cognitive secondary network attempting to utilize the frequency bands under the Wi-Fi network. The goal will be to force the cognitive Zigbee radios to utilize their cognitive algorithms to alter their current operating parameters to optimize use of the shared medium, with a more powerful wireless standard Homogeneous Network Testing Homogeneous network testing will consist of testing the cognitive radio algorithms with a larger homogenous Zigbee network. This would require additional Zigbee units to be purchased and will only be attempted if time allows. This test would consist of two separate Zigbee networks in close physical range to each other. These networks would not directly communicate with each other but their

5 individual transmissions would potentially interfere with each other. The cognitive algorithms would be utilized to prevent collisions and to detect the other network vying for the similar spectrum usage.

6 3. Milestones and Division of Tasks Task Primary Group Member Secondary Group Member Theory Zigbee Theory General Wireless Theory Cognitive Radio Shuo Willy Game Theory Shuo Literature Review C programming review Freescale HSC08 Willy Shuo Xbee Transceiver Willy Shuo CodeWarrior Willy Hardware Setup Zigbee development kit Configure and test Zigbee units Determine Capabilities Willy Willy Willy Software Pseudo-code cognitive radio algorithms: Spectrum Sensing Spectrum Analysis Spectrum Decision Machine Learning Networking Shuo Shuo Shuo Implement cognitive radio algorithms: Spectrum Sensing Willy Shuo Spectrum Analysis Willy Shuo Spectrum Decision Willy Shuo Machine Learning Networking

7 Theory Wireless Communication Review Zigbee Research Cognitive radio research 4. Gantt Chart October November December January February March Hardware Setup Zigbee Development Kit Test Basic Zigbee Capabilities Software Capability Test Software Design/Modelling - Phase 1 Spectrum Sensing Spectrum Analysis Spectrum Decision Implement Cognitive Radio Theory - Phase 1 Spectrum Sensing Spectrum Analysis Spectrum Decision Testing Cognitive Radio Implementation Spectrum Sensing Testing Heterogeneous Network Testing Homogeneous Network Testing Software Design/Modelling - Phase 2 Machine Learning Networking Implement Cognitive Radio Theory - Phase 2 Machine Learning Networking Final Testing

8 5. Budget Item Quantity Cost XBee-PRO ZB Programmable Development Kit - XBP24BZ7B-DK 1 $320 Included: 2 x Programmable XBee-PRO ZB - Zigbee Module 2 x XBIB-U-DEV - Development Board USB Multilink Debugger - Microcontroller debugger USB Xstick - Zigbee Network Analyzer Misc. Cables Additional Programmable XBee-PRO ZB* 1 $35 Total: $355 Students will provide their own laptops and Wi-Fi router for programming and testing. 6. References [1] A. Elahi and A. Gscwendwe, ZigBee Wireless Sensor and Control Network. Toronto: Prentice Hall, [2] I. Akyildiz, W. Lee, M. Vuran, and S. Mohanty, A Survey on Specture Management in Cognitive Radio Networks. Georgia Institute of Technology. April, [3] L. Jiao, V. Pla, and F. Li, Analysis on Channel Bonding/Aggregation for Multi-channel Connitive radio Networks. University of Ager, Grimstad, Norway [4] E. Hossain, D. Niyato, and Z. Han, Dynamic Spectrum Access and Management in Cognitive Radio Networks. University Press, Cambridge, UK