Spectral Management for a Cognitive Radio Application with ... · The power spectral density (PSD) calculation ... 1 BPSK 1/2 6 -5 56.2 2 BPSK 3/4 9 -8 39.8 3 QPSK 1/2 12 -10 31.6

INTERNATIONAL JOURNAL OF MICROWAVE AND OPTICAL TECHNOLOGY,

Spectral Management for a Cognitive Radio Application with Adaptive Modulation and Coding

Mohammed Amine Azza1*, Ali El Moussati1, Slimane Mekaoui2,Kamal Ghoumid1

1Signals, Systems and Information Processing team, National School of Applied Sciences, Oujda, Morocco 2Telecommunications Department, Faculty of Electronics and Informatics, USTHB, Algiers, Algeria

E-mail: [email protected], [email protected], [email protected]

Abstract— Cognitive Radio allows Software Defined Radio terminal to perceive its environment and then interact with it. In other words, cognitive radio can collect information from its surroundings, model them and try to adapt its behavior according to them.

The main aim of this paper is to shed light on the concept of spectrum sensing and adaptive cognitive radio and propose an application implemented on Small Form Factor (SFF) Software Defined Radio (SDR) development platform. This application includes both a spectrum sensing algorithm based on Fast Fourier Transform which is basically insensitive to noise level and an Adaptive Modulation and Coding (AMC) scheme. The validation of the proposed cognitive radio application and the efficiency of the AMC and the spectrum sensing algorithms are shown through experiments and measurement results.

Index Terms- Spectrum Sensing, Cognitive radio, Software Defined Radio,Adaplive modulation and coding, SDR Platform.

1. I. INTRODUCTION

Radios that sense all or part of their environment are considered aware systems. Awareness may drive only a simple protocol decision or may provide network information to maintain a radio’s status as aware. A radio must additionally autonomously modify its operating parameters to be considered adaptive. When a radio is aware, adaptive, and learns, it is a Cognitive Radio CR [1].

Among the operating parameters that may be adapted on cognitive radio systems, we find: Frequency, instantaneous bandwidth, modulation

scheme, error correction coding, channel mitigation strategies such as equalizers or RAKE filters, system timing (e.g., a Time Division Multiple Access (TDMA) structure), data rate (baud timing), transmit power, and even filtering characteristics.

In the same way, Frequency adaptation (spectrum sensing), modulation and coding adaptation are referred to several research and various applications, where the spectrum sensing is the task upon which the entire operations of cognitive radio rests, even less, a few studies dealing adaptability in cognitive radio especially the adaptive modulation and coding, thus the combination of these two principles in one application remain the goal of our recent work. Our goal in this work is to develop in a single cognitive radio implementation a better spectrum management, and a better adaptation in terms of modulation and coding.

To this end, we propose, in this paper an application involving spectrum management with a spectrum sensing algorithm and adaptive modulation and coding scheme for whatever be the communication channel chosen.

The paper is organized as follows: The first section summarizes all the theory behind these two principles namely spectrum management, its technical and adaptive modulation and coding scheme, in the second section we present the experimental results and steps of implementation, in the last section, we discuss the experimental results.

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INTERNATIONAL JOURNAL OF MICROWAVE AND OPTICAL TECHNOLOGY

II. THEORY AND PRINCIPLES

Cognitive radio and adaptation is now the trendin all applications of wireless communications,this trend is based on the desire to obtain acommunicating terminal that has a consciencetowards his environment and an enormouscapacity to respond and adapt. As weearlier, cognitive radio can be described as a nodein a network that can sense his operatingenvironment and adapt his implementation toachieve the best performance.

The principle of this application, included in theIEEE 802.22 and IEEE 802.16requires an alternative spectrum managementwhich is as follows: the Secondarymay at any time access to frequency bands thatare free, not occupied by the Primary User (PU)having a license on this band. Theit once the service ended or after a PU has shownthese attempts to connection. This applicationallows also to the terminal at any time to have thecapability of using multiple modulations andchoosing the most appropriate modulation andcoding depending on the link quality (IEEE802.11a standard), this concept is known asAdaptive Modulation and Coding AMC

In the following, we will try to present the wholetheory behind this application, these twoobjectives, the algorithm used for spectrumsensing, and then we will detail the intelligencecycle ensuring adaptation in terms of modulationand coding.

1. A. Algorithm for spectrum sensing

For the spectrum sensing, there are manypossible techniques, such as energy detection,matched filter detection, cyclostationarydetection, covariance-based detection, andwavelet-based detection [3][4].

In our case, we chose to usedetection which guarantees a relativelylevel of efficiency, the output of which gives thedecision variable. This variable is then comparedwith a threshold and if it is above the threshold,then the result of the detector is that a primaryuser is present. Energy detection is very


PRINCIPLES

and adaptation is now the trend in all applications of wireless communications, this trend is based on the desire to obtain a communicating terminal that has a conscience towards his environment and an enormous

As we mentioned earlier, cognitive radio can be described as a node in a network that can sense his operating environment and adapt his implementation to

The principle of this application, included in the IEEE 802.22 and IEEE 802.16h standard, requires an alternative spectrum management

Secondary User (SU) may at any time access to frequency bands that

Primary User (PU) having a license on this band. The SU will assign

e service ended or after a PU has shown these attempts to connection. This application allows also to the terminal at any time to have the capability of using multiple modulations and choosing the most appropriate modulation and

k quality (IEEE 802.11a standard), this concept is known as Adaptive Modulation and Coding AMC [2].

In the following, we will try to present the whole theory behind this application, these two objectives, the algorithm used for spectrum

detail the intelligence ensuring adaptation in terms of modulation

A. Algorithm for spectrum sensing

For the spectrum sensing, there are many possible techniques, such as energy detection, matched filter detection, cyclostationary feature

based detection, and

In our case, we chose to use of the energy guarantees a relatively increased

level of efficiency, the output of which gives the ble is then compared

with a threshold and if it is above the threshold, then the result of the detector is that a primary

ergy detection is very useful

since it requires no idetected needed signal.

For this purpose, we have adoptedspectrum sensing algorithm, called fast Fouriertransform (FFT)-averagingproposed as depicted in Figure

Fig.1. Flow chart of FAR algorithm

The input to the FAR algorithm [baseband discrete-time signal sampled atfrequency (fs) , while the output is a series ofvectors of two-class decisions that represent theavailabilities of the channel.

The input signal is in real numbers. Firstly, ineach time slot, a block of basesamples are segmented into T frames. Denote tframe of the input samples by Xsegmented frames are multiplied by a windowfunction:

Xw,t(n) = Xt(n).W(n)

N : is the number of samples in a frame.T : is the number of frames.

Then, the FFT is applied to the windowed frame.

��

��

The power spectral density (PSD) calculationfollows the FFT operation.

P�� |X��|�

The PSDs of T consecutive frames are used foraveraging, yielding:

��

Segmentation

&

Windowing

FFT

&

PSD Computation


since it requires no information about the

we have adopted a blind spectrum sensing algorithm, called fast Fourier

averaging-ratio (FAR), and as depicted in Figure 1 given below.

Flow chart of FAR algorithm

The input to the FAR algorithm [5][6] is a time signal sampled at

frequency (fs) , while the output is a series of class decisions that represent the

availabilities of the channel.

The input signal is in real numbers. Firstly, in each time slot, a block of base-band signal amples are segmented into T frames. Denote t-th

frame of the input samples by Xt(n), the segmented frames are multiplied by a window

(n).W(n) (1)

N : is the number of samples in a frame. T : is the number of frames.

Then, the FFT is applied to the windowed frame.

� �� ! "

(2)

The power spectral density (PSD) calculation follows the FFT operation.

� � 0 $ � $ �N & 1� (3)

The PSDs of T consecutive frames are used for

∑ �� (4)

PSD Computation

Ratio computation

&

Thresholding


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Let Pm be the mean of Pavg(k) calculated across all frequency tones.

�) � � � ��

�

*� (5)

In order to be robust to the noise level, the decision variable r(k) is formed as a ratio.

+�� /�- (6)

Finally, thresholding is applied to r(k) for

k = 0, 1, . . . , N/2 , and the decisions on channel states are made according to the following rule:

+�� . / 012��3 455678�9+�� : / 012��3 2;2832<3� (7)

B. Adaptive modulation and coding.

The main concept of adaptive coding and modulation is to maintain a constant performance by varying transmitted power level, modulation scheme, coding rate or any combination of these schemes .This allows us to vary the data rate without sacrificing BER performance.

Fig.2. Variation of SNR versus time and choice mode based on thresholds

The selection of modulation mode for the next transmission heavily depends on the current channel quality estimation. If the channel quality can be measured accurately, ideal switching between different modes is available. Under these circumstances and if, the switching

thresholds are carefully selected; the system can then reach the highest performance. [7] (Fig.2).

We used the physical layer of the 802.11a standard [8] as adaptive implementation of our system on the SFF SDR platform. The Table 1 shows for each mode the modulation type, rate coding, throughput, the relative error of constellation and Error Vector Magnitude.

Table 1: modulation and coding dependent parameters in IEEE 802.11a standard

Mode Modulation Code Rate

Data rate

(Mbps)

Relative constellation

error (dB)

EVM (%

rms) 1 BPSK 1/2 6 -5 56.2 2 BPSK 3/4 9 -8 39.8 3 QPSK 1/2 12 -10 31.6 4 QPSK 3/4 18 -13 22.3 5 16-QAM 1/2 24 -16 15.8 6 16-QAM 3/4 36 -19 11.2 7 64-QAM 2/3 48 -22 7.9 8 64-QAM 3/4 54 -25 5.6

The system consists of a transmitter, a receiver and a Rayleigh communication channel figure 3.

Fig.3. Block diagram of IEEE 802.11a transceiver architecture

The transmission part consists in a block for generating the binary data and a block which contains multiple modulation and coding schemes to select the correct pattern.

The OFDM time signal is generated by an inverse FFT and is transmitted over the Rayleigh fading channel after the cyclic extension has been inserted.

The received signal is serial to parallel converted and passed to a FFT operator, which converts the


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signal hack to the frequency domain. This frequency domain signal is coherently demodulated. Then the binary data is decoded by the Viterbi hard decoding algorithm.

Signal to noise ratio is estimated at receiver and then transmitted to the transmitter through feedback channel. Then, the transmitter according to the estimated SNR selects the appropriate modulation scheme and the coding rate that maintains a constant bit error rate lower than the requested BER [9].

III. IMPLEMENTATION OF SPECTRUMSENSING AND (AMC) ON SFF SDR

PLATEFORM

The cognitive radio transceiver with a spectrum sensing algorithm and adaptive modulation and coding is implemented on the SFF SDR DP [10], which is composed of three functional modules: radio frequency (RF) module, data conversion module, and digital processing module, constitute the SFF SDR DP. The application development is made with the help of Simulink from Matlab Source Software, this type of development provides more robustness and relevance.

A. Hardware interface.

The platform is built around the digital processing module. The latter is designed around the TMS320DM6446 (also called DM6446) Digital Media Processor (DMP) System on Chip (SoC) [5] from TI and Virtex-IV XC4VX35 FPGA from Xilinx. DM6446 combines an Advanced Very Long Instruction Word (VLIW) 64x+ DSP and Reduced Instruction Set Computer (RISC) ARM926J-S cores, where the ARM microcontroller is mainly set to run the INTEGRITY Real-Time Operating System (RTOS) while DSP performs complex data processing. The data conversion module is equipped with a 125 MSPS, 14- bit dual channel ADC and a 500 MSPS 16-bit dual channel interpolating DAC provided by TI. The RF module is configured to have either 5 or 20 MHz bandwidth with working frequencies of 200-930 MHz for the transmitter and 30-928 MHz for the receiver.

The cognitive radio application implementation (Figure 1) is divided into different tasks each consisting of several modules. The SFF SDR platform gives the designer the option to choose the silicon device that is most suitable to the task being developed. We use the INTEGRITY and SMSHELL API provided by Lyrtech to target the board while we develop our signal processing tasks on the DSP core and the FPGA. The division of tasks between the DSP and the FPGA was made based on the availability of resources, the inherent characteristics of these cores, and the extra functionalities offered by TI and Xilinx. We used the readily available Xilinx Logicore Blocksets for FPGA and the optimized DSP libraries written for vectors of complex numbers for C64x+ core.

Interfacing between the DSP and FPGA is done using the Video Processing Sub-system (VPSS) data port. The VPSS consists of a Video Processing Front End (VPFE) and a Video Processing Back End (VPBE).

B. Software interface of FAR algorithm.

Included with the hardware is the board support design kit (BSDK) that includes the software drivers to support TI Code Composer Studio (CCStudio) Integrated Development Environment (IDE) and Green Hills INTEGRITY RTOS and MULT IDE for TMS320DM6446 DSP SoC and Xilinx ISE foundation development tool for the FPGA. Support for these tools through the BSDK enables application development partitioned and targeted independently for the DSP SoC and the FPGA.

The support for high-level model based software design flow using the model-based design kit (MBDK) allows for ease of development and easy partitioning of, software functions across a multiprocessing architecture and takes the board support package to the next level. The SDR DP includes both the BSDK and the MBDK that enables seamless integration of The MathWorks model-based design tools to the lower level DSP tools (CCStudio) and FPGA tools (ISE Foundation). With the model-based flow,


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developers can use either C/HDL or MATLAB torapidly develop and test proof-ofand then optimize the architecture for cost andpower for a specific application.

The distribution of the FAR algorithm and theadaptive modulation and coding cyclecomponents between the DSP core and the FPGAis shown in Figure 4. The VPBE and VPFE areused to transfer the data streams back and forthbetween the two modules while the customregisters are used for handshaking [

Fig.4. System data of the sensing application.

C. FPGA design for Adaptive modulation andcoding.

We implemented the IEEE 802.11a transceiveron the Lyrtech Small Form Factor (SFF)Software Defined Radio (SDR) DevelopmentPlatform. The receive path of the Lyrtech RFmodule down-converts the received signal to anintermediate frequency at 30 MHz. This meansthat the received IEEE802.11a signal at the inputof ADC has a maximum frequency of 40 MHzand so the minimum sampling rate should be 80MHz in accordance with the Shannoncriteria.

The system generator [11] implementationtransmitter is divided into fiveshown in the Fig. 6. The first subsystem which isnamed as process Control is designed forcontrolling the signal flow from the MAC layer


developers can use either C/HDL or MATLAB to of-concept designs

and then optimize the architecture for cost and

The distribution of the FAR algorithm and the adaptive modulation and coding cycle components between the DSP core and the FPGA

. The VPBE and VPFE are sed to transfer the data streams back and forth

between the two modules while the custom ters are used for handshaking [10].

. System data of the sensing application.

Adaptive modulation and

02.11a transceiver on the Lyrtech Small Form Factor (SFF) Software Defined Radio (SDR) Development Platform. The receive path of the Lyrtech RF

converts the received signal to an intermediate frequency at 30 MHz. This means

EE802.11a signal at the input of ADC has a maximum frequency of 40 MHz and so the minimum sampling rate should be 80

h the Shannon-Nyquist

implementation of the transmitter is divided into five subsystems as

. The first subsystem which is is designed for

controlling the signal flow from the MAC layer

to the DAC. The second subsystem is used toimplement the bit based operations, such asconvolution encoding and interleaving.the Interpolation block synchronizes the IFFToutput with the DAC operating at 120 MHz.

Fig.5. System Generator model of the transmitter

The system generator implementation of thereceiver is divided into nine subsystems as shownin Fig. 6. The first subsystem, named as“Down_Converter”, converts the IF signalreceived from the ADC into basecomponents. Then, the frame detection, timingand frequency synchronization processes areperformed by the “preamble_Decode”subsystem. After obtaining the frequency domainsubcarrier symbols, the “chan_Equalizer”subsystem performs the channel equalization andcarrier phase tracking algorithms“V iterbi_Decoder” subsystemstream that has been encoded by convolutioncodes. Finally, the decoded bits, except for thesignal field, are fed tosubsystem to reconstruct the transmittedmessage. The decoded bits of the Signal Field arealso sent to the “Adaptive_Control” block toextract the frame parameters suchtype, number of transmitted bytes and the numberof OFDM symbols in the frame.

Modulation

Filter

Process_Control Fec_interleaver


to the DAC. The second subsystem is used to implement the bit based operations, such as

and interleaving. Finally, the Interpolation block synchronizes the IFFT output with the DAC operating at 120 MHz.

. System Generator model of the transmitter

The system generator implementation of the receiver is divided into nine subsystems as shown

. The first subsystem, named as “Down_Converter”, converts the IF signal received from the ADC into base-band I and Q components. Then, the frame detection, timing and frequency synchronization processes are performed by the “preamble_Decode”

ter obtaining the frequency domain subcarrier symbols, the “chan_Equalizer” subsystem performs the channel equalization and carrier phase tracking algorithms. The

iterbi_Decoder” subsystem decodes the bit has been encoded by convolution

. Finally, the decoded bits, except for the signal field, are fed to the “descrambler”

reconstruct the transmitted The decoded bits of the Signal Field are

also sent to the “Adaptive_Control” block to extract the frame parameters such as modulation type, number of transmitted bytes and the number of OFDM symbols in the frame.

Modulation

IFFT

Interpolation

DAC_I

DAC_Q


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Fig.6. System Generator model of the receiver

IV. EXPERIMENT DEMONSTRATIONRESULTS DISCUSSION

The SFF SDR platform is considered as asecondary user. The frequency band covered bythe RF module of the platform is divided into avariety of channels, and a spectrum detectionalgorithm is adopted for detecting the availabilityof each channel. according toresults of the channel, a signal with afrequency belongs to the unoccupied channel issent simultaneously by considering the signal tonoise ratio of the channel as a metric , fordetecting the primary user input to the channel. Ifthis is the case, the secondary user must assignthe channel, and the carrier frequency of theemitted signal will be changed by anotherbelonging to an available channel (frequencyhopping is performed). The process repeatuntil the end of the transmission.

In the demonstration, an FRS handset (used inthe frequency band of 462 Mhz) and a talkiewalkie PMR446 (used in the frequency band of446 Mhz) are used as PUs call each other , Whilethe platform sends a signal said secondary withcarrier frequency of 545 Mhz, and one SFF SDRDP running FAR algorithm senses the spectrumin real-time. The experiment is conduindoor environment as illustrated in figure 7

As mentioned before, the platform is consideredas being a secondary user transmission and areceiver at a time. Below, we review all possiblecases for the three channels, and the resultsobtained in each case.


. System Generator model of the receiver

DEMONSTRATION AND

DISCUSSION

The SFF SDR platform is considered as a frequency band covered by

the RF module of the platform is divided into a variety of channels, and a spectrum detection algorithm is adopted for detecting the availability

according to the detection results of the channel, a signal with a carrier frequency belongs to the unoccupied channel is sent simultaneously by considering the signal to noise ratio of the channel as a metric , for detecting the primary user input to the channel. If this is the case, the secondary user must assign

annel, and the carrier frequency of the emitted signal will be changed by another belonging to an available channel (frequency hopping is performed). The process repeated until the end of the transmission.

In the demonstration, an FRS handset (used in Mhz) and a talkie-

walkie PMR446 (used in the frequency band of Mhz) are used as PUs call each other , While

the platform sends a signal said secondary with Mhz, and one SFF SDR

enses the spectrum time. The experiment is conducted in an

illustrated in figure 7.

As mentioned before, the platform is considered transmission and a

elow, we review all possible cases for the three channels, and the results

Fig.7. Schematic illustrating the use scenario.

The figure 8 is a compound figure that representsthe power spectral density of the 3 signalsbelonging to the 3 channels (Channel 1:Mhz, Channel 2: PMR 446 Mhz, Channel 3: 545Mhz). These three signals are derived from ourimplemented transmitter and received by thereceiving antenna via which acts in this case as a spectrum analyzer(The colors used in the figuretell the difference between the signals

Fig.8. Compilation of the power spectral density ofthe three separately received signals.

The use of the power spectral density (DSP) isnecessary, because it is the unit used incalculating the ratio r(k) to apply thresholding asnoted above in the definition of the FARalgorithm.

Note clearly the presence of a peak in thetransmission frequency band that has a relativelygreater power spectral denratio r(k) greater than the detection threshold.


. Schematic illustrating the use scenario.

is a compound figure that represents the power spectral density of the 3 signals belonging to the 3 channels (Channel 1: FRS 462 Mhz, Channel 2: PMR 446 Mhz, Channel 3: 545

ree signals are derived from our implemented transmitter and received by the

via our designed receiver which acts in this case as a spectrum analyzer

he figure 8 are intended to tell the difference between the signals).

Compilation of the power spectral density of the three separately received signals.

The use of the power spectral density (DSP) is necessary, because it is the unit used in calculating the ratio r(k) to apply thresholding as noted above in the definition of the FAR

Note clearly the presence of a peak in the transmission frequency band that has a relatively greater power spectral density, that makes the

reater than the detection threshold.


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In the remainder of the testing phase,tried to transmit a signal in a channel (example of figure 9: Channel 1: FRS 462 Mhz)in the presence of the signal from the(Channel 2: PMR 446 Mhz).

Fig.9. The power spectral density of the receivedsignals.

The result of the spectrum sensing in this caseindicates the occupation of thesethen, the FRS is used to inject a signal which willbe received by the platform, this causes tilting ofthe carrier frequency to another channel judgedfree channel.

After examination of all possible cases in theproposed scenario, we note that the algorithm hasall the expected results, because the objectivewas to exploit opportunistically the spectrumholes that are detected automatically inconjunction with the transmission.

In order to test our IEEE802.11a physical layerprototype on the Lyrtech SFF SDR DevelopmentPlatform, we also developed a simple MAC layerrunning on the platform and a user interfacprogram on the PC.

We measured the BER performance of ourprototype just for 5 modes (1, 3, 5,these cases we have the better performance of BitError Rate. The obtained BER curves aredepicted in Fig. 10 for these modulation typeswith the associated coding rates.


der of the testing phase, we have al in a channel (See the

el 1: FRS 462 Mhz) presence of the signal from the PMR

The power spectral density of the received

esult of the spectrum sensing in this case se two channels,

hen, the FRS is used to inject a signal which will this causes tilting of

the carrier frequency to another channel judged

After examination of all possible cases in the proposed scenario, we note that the algorithm has all the expected results, because the objective was to exploit opportunistically the spectrum

es that are detected automatically in conjunction with the transmission.

In order to test our IEEE802.11a physical layer prototype on the Lyrtech SFF SDR Development Platform, we also developed a simple MAC layer running on the platform and a user interface

We measured the BER performance of our prototype just for 5 modes (1, 3, 5, 7, 8) as in

the better performance of Bit Error Rate. The obtained BER curves are

for these modulation types

Fig.10. BER Performance of each mode inDispersive channel with practical estimation.

We can see in Figure 1mode gives a lower BER 10

Table 2: MCS depends of SNR

SNR values (dB) [-∞,7] [7,18]

[18,29] [29,34] [34,+∞]

In Figure 11 measurements of the BER as afunction of signal to noise ratio at the receptionare illustrated. In the same Fthroughput depending on the SNR.

Fig.11. BER and throughput pAdaptive Coded Modulation in Dispersive channelwith practical estimation


. BER Performance of each mode in Dispersive channel with practical estimation.

We can see in Figure 10 that below 11 dB, no mode gives a lower BER 10-3.

MCS depends of SNR interval thresholds

MCS Mode 1 Mode 3 Mode 5 Mode 7 Mode 8

measurements of the BER as a function of signal to noise ratio at the reception

. In the same Figure we have the hroughput depending on the SNR.

throughput performance of Adaptive Coded Modulation in Dispersive channel


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From figure 11 we can observe that in the case of adaptive modulation and with an SNR level greater than 11 dB is selected, our experiment allows keeping a BER lower than 10-3 by choosing the best and most appropriate coefficient for the spectral efficiency. These results were already confirmed by simulations done in previous studies [12].

Consider a channel that has a deep high level of fading, the options here are to use one of five modulation modes, which differ in spectral efficiency and robustness. If the fading is considered to be extremely deep, perhaps half of all bits will be interpreted as error bits. Here, it is advantageous to send fewer bits because the total number of errors will be decreased, which influences bit error rates much more than total number of bits sent. When the channel is not in the case of a fading, then many bits are wanted to be sent. In this situation, the BER is lowered by increasing the number of bits sent because errors become less frequent. It is the combination of these two principles that allows the BER performance of adaptive systems to be more robust than static systems while simultaneously providing better spectral efficiency at most ranges of SNR.

V. CONCLUSION

In this paper, the implementation of an application that combines two main objectives of the cognitive radio spectrum sensing and adaptive modulation and coding is presented. The selection of solutions for critical processing task is made; the FPGA implementation of the IEEE802.11a physical layer on the Xilinx Virtex-4 sx35 chip is detailed.

We have analyzed the performance of energy detection based spectrum sensing techniques using FAR algorithm, compared to other energy detection techniques ,the FAR algorithm has clearly better performance in identifying spectral holes between spectrally well-contained PUs.

Moreover, a real-time demonstration of spectrum sensing and adaptive modulation and coding has been conducted, and very encouraging

experiment results have been obtained. However, main project goals were achieved and solid grounds for the future research.

REFERENCES

[1] J. Polson, “Cognitive Radio Applications in Software Defined Radio,” in SDR Forum Technical Conference and Product Exposition, November 15–18, 2004.

[2] Nevio Benvenuto and Filippo Tosato, “On the selection Of Adaptive Modulation and Coding Modes Over OFDM”, IEEE Communications Society, 2004.

[3] S. Haykin, D. Thomson, and J. Reed, “Spectrum sensing for cognitive radio,” Proceedings of the IEEE, vol. 97, no. 5, pp. 849–877, May 2009.

[4] T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys & Tutorials, March 2009.

[5] M. A. Azza, A. El Moussati and R. Barrak, “Implementation of Cognitive Radio Applications on a Software Defined Radio Plateform” in ICMCS'14. Marrakech. April 14-16 2014

[6] Zhe Chen, Nan Guo, and Robert C.Qiu, “Demonstration of real-time spectrum sensing for cognitive radio” IEEE Communications Letters, vol. 14,no. 10,pp. 915-917,2010.

[7] A. Homayoun and B. Razavi, "A 5-GHz 11.6-mW CMOS receiver for IEEE 802.11a applications" in CICC IEEE, 2013.

[8] IEEE 802.11b, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, Sept. 1999.

[9] Hyung Suk Chu, Byung Su Park and Chong Koo An ,"Wireless Image Transmission based on Adaptive OFDM System",IEEE, 2007.

[10] “Lyrtech SFF SDR development platform technical specs,”Lyrtech Inc.

[11] Xilinx Inc., "XtremeDSP for Virtex-4 FPGAs UserGuide."http://www.xilinx.com/support/documentation/user_guides/ug073.pdf, 2013.

[12] S. N. Abdullah, Z. M. Abid, “Adaptive Coded Modulation for OFDM System”, Journal of Engineering, Vol. 2, Feb. 2012.


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Documents

Spectral Management for a Cognitive Radio Application with ... · The power spectral density (PSD) calculation ... 1 BPSK 1/2 6 -5 56.2 2 BPSK 3/4 9 -8 39.8 3 QPSK 1/2 12 -10 31.6