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Design, Analysis, Implementation and Evaluation of Real-time Opportunistic Spectrum Access in Cloud-based Cognitive Radio Networks

ABSTRACT

Opportunistic spectrum access in cognitive radio network is proposed for remediation of spectrum under-utilization caused by exclusive licensing for service providers that are intermittently utilizing spectrum at any given geolocation and time. The unlicensed secondary users (SUs) rely on opportunistic spectrum access to maximize spectrum utilization by sensing/identifying the idle bands without causing harmful interference to licensed primary users (PUs).

In this thesis, Real-time Opportunistic Spectrum Access in Cloud-based Cognitive Radio Networks (ROAR) architecture is presented where cloud computing is used for processing and storage of idle channels. Software-defined radios (SDRs) are used as SUs and PUs that identify, report, analyze and utilize the available idle channels. The SUs in ROAR architecture query the spectrum geolocation database for idle channels and use them opportunistically. The testbed for ROAR architecture is designed, analyzed, implemented and evaluated for efficient and plausible opportunistic communication between SUs.

ROAR SYSTEM ARCHITECTURE AND TESTBED VERIFICATION

Figure 2.1: System model of ROAR architecture

Figure 2.1: System model of ROAR architecture

In the ROAR system model shown in Figure 2.1, after the spectrums are sensed, idle spectrum are stored in the distributed database. The real-time spectrum availability information is encapsulated with time-stamp and geolocation information prior to storing the idle channel information in either private or public distributed cloud-based database. Here, the cloud-based database stores the information and cloud-computing deals with processing the spectrum requests from SUs during opportunistic spectrum access.

Figure 2.11: Agilent MXA N9020A signal analyzer reading of transmitter USRP to verify receiver USRP’s sensing ability

Figure 2.11: Agilent MXA N9020A signal analyzer reading of transmitter USRP to verify receiver USRP’s sensing ability

If the USRP receiver is indeed picking the right transmitter or if it is picking the noise in the environment. Thus, before ROAR architecture is developed, one more test is carried to validate the USRP reliability by introducing Agilent MXA N9020A signal analyzer as shown in Figure 2.11.

Figure 2.3: U-Blox LEA-6H GPS unit for coordinate acquistion

Figure 2.3: U-Blox LEA-6H GPS unit for coordinate acquistion

Along with NI-USRP, laptop and GPS units are being used for spectrum sensing and dynamic spectrum access for ROAR architecture. The transceivers designed uses laptops connected to USRP through gigabit Ethernet for faster reliable connection. The hardware components are individually programmed. The hardware components does not require any soldering or tampering to complete the research work.

SPECTRUM SENSING FOR ROAR ARCHITECTURE

Figure 3.4: Spectrum sensing testbed for ROAR architecture

Figure 3.4: Spectrum sensing testbed for ROAR architecture

The sensor testbed for ROAR architecture is shown in Figure 3.4. The unit consists of USRP, GPS unit with GPS antenna, remotely located cloud-based database and power supply unit. The USRP being used for the system design does not have its own GPS device built into it. Thus, a separate external GPS unit is used. The sensor reports to distributed database.

Figure 3.6: Block diagram representation of GPS LabVIEW program

Figure 3.6: Block diagram representation of GPS LabVIEW program

In the Figure3.6, the Block1 shows the initialization of the GPS USB V-Port, defining the timeout value and an implementation of timer. Once these values are defined, they are passed to NMEA-0183 coordinate acquisition program to retrieve GPS coordinates. The Block 2 is a conditional block where the NMEA-0183 data is extracted only if the satellite data is valid.

Figure 3.21: Spectrum analysis of higher resolution 1M sampling

Figure 3.21: Spectrum analysis of higher resolution 1M sampling

In the Figure 3.21 has similar resolution and signal strength (-80dB) as spectrum at 100k sampling rate.

CLOUD-ASSISTED DISTRIBUTED PROCESSING IN ROAR

Figure 4.4: Block diagram representation of database reporting from spectrum sensors implemented in LabVIEW program

Figure 4.4: Block diagram representation of database reporting from spectrum sensors implemented in LabVIEW program

The abstraction of the complete program implemented in LabVIEW as shown in the Appendix A.3 is shown in the Figure 4.4. Here, the Block 1 shows the process of setting the database parameters by setting the credentials. Once the connectivity to the database is established, the sensor data is passed into the tabular form. Block 2 processes the crude data (tuple) from the spectrum sensor into formatted data and then passes onto Block 3.

Figure 4.5: Spectrum reporting, processing, storage in Cassandra and heat-map visualization

Figure 4.5: Spectrum reporting, processing, storage in Cassandra and heat-map visualization

The idle spectrum information encapsulated with geolocation data is stored in Cassandra database and it can be visualized using Google maps API. The data stored can be plotted as heat-index. The complete process by which the idle spectrum information is processed, stored and visualized is shown in the Figure 4.5.

OPPORTUNISTIC SPECTRUM ACCESS IN ROAR

Figure 5.10: List of idle spectrum acquired from cloud-based database

Figure 5.10: List of idle spectrum acquired from cloud-based database

In Figure 5.10, the received spectrum list of SUTx is graphed in 3D. The graph shows the available spectrum retrieved at specific time and date. The graph also shows the signal strength at that particular frequency.

Figure 5.11: Quorum function block programming in LabVIEW

Figure 5.11: Quorum function block programming in LabVIEW

A custom built function block program is created in LabVIEW to serve for quorum-based channel selection as shown in Figure 5.11.

Figure 5.14: Different SUs communicating opportunistically in different contours

Figure 5.14: Different SUs communicating opportunistically in different contours

While using quorum-based common channel selection for opportunistic spectrum access, the SUs can only communicate if they are able to find common channels. If SUs are in the same contour as shown in the Figure 5.14, they are able to receive the list of available channels for that specific area. Implementing quorum-based common channel selection, they are able to find common channels. Since all channels might not be available to communicate, the SUs resort to using the common channels to communicate.

CONCLUSION AND FUTURE WORK

Conclusion and Future Work

This thesis introduced the concept of DSA in cognitive radio and presented the ROAR architecture for wideband (50MHz to 6GHz) spectrum sensing and opportunistic accessing. ROAR architecture is discussed and a testbed is created for ROAR architecture using USRPs, distributed cloud-assisted database and real-time opportunistic spectrum access for SUs to prevent causing interference to PUs. The testbed is then implemented, tested and evaluated for feasibility and practicality. ROAR architecture is perceived with system model and ideas are gathered to prototype the testbed.

It is intuitive to utilize opportunistic spectrum access in ROAR architecture to prevent interference to the PUs by allowing the SUs to dynamically access the idle channels. Prior to designing any sub-system for ROAR architecture, conceptual study is done in relevant topic to accumulate enough knowledge. A detail study and experimentation is performed on the USRP to test whether it is fit for ROAR architecture or not. Upon experimentation, it is found that USRP meets all the requirements and expectation as a ROAR architecture testbed and can be used for spectrum sensing, reporting and opportunistic spectrum access. The testbest is then created using GPS-units, SDR spectrum sensors and data reporting utilities.

The testbed worked collectively with distributed computing and database to report geolocation and spectrum information to cloud-based database. The database is installed in distributed cloud platform where Storm topology and Cassandra database are implemented for real-time access of the idle channels. The ROAR D2D and D2I transceivers are designed and implemented after successfully evaluating the spectrum sensing and idle channel reporting for the ROAR architecture.

Future Work

The research presented in this thesis can be further expanded by implementing Hidden Markov Model (HMM) to predict the spectrum where there are missing spectrum occupancy data. The research presented in this thesis does not have all the available spectrum information for every geolocation data. The HMM model makes it easy to predict the status of spectrum at any given geolocation based on past spectrum availability. Furthermore, mobility factor can be considered where the users are opportunistically searching for idle channels while moving from one geolocation to another. Security in opportunistic spectrum access in ROAR is another potential area to expand on the research.

Source: Georgia Southern University
Author: Nimish Sharma

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