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Spectral Management for a Cognitive Radio Application with Adaptive Modulation and Coding Mohammed Amine Azza1*, Ali El Moussati1, Slimane Mekaoui2,Kamal Ghoumid1 1
Signals, Systems and Information Processing team, National School of Applied Sciences, Oujda, Morocco 2 Telecommunications 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].
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.
Among the operating parameters that may be adapted on cognitive radio systems, we find: Frequency, instantaneous bandwidth, modulation
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II. THEORY AND PRINCIPLES Cognitive radio 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 capacity to respond and adapt. 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 achieve the best performance. The principle of this application, included in the IEEE 802.22 and IEEE 802.16h standard, requires an alternative spectrum management which is as follows: the Secondary User (SU) may at any time access to frequency bands that are free, not occupied by the Primary User (PU) having a license on this band. The SU will assign it once the 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 coding depending on the link 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 sensing, and then we will detail the intelligence cycle ensuring adaptation in terms of modulation and coding.
since it requires no information about the detected needed signal.
For this purpose, we have adopted a blind spectrum sensing algorithm, called fast Fourier transform (FFT)-averaging-ratio (FAR), and proposed as depicted in Figure 1 given below.
Segmentation
FFT
&
&
&
Windowing
PSD Computation
Thresholding
Fig.1.
Ratio computation
Flow chart of FAR algorithm
The input to the FAR algorithm [5][6] is a baseband discrete-time signal sampled at frequency (fs) , while the output is a series of vectors of two-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 samples are segmented into T frames. Denote t-th frame of the input samples by Xt(n), the segmented frames are multiplied by a window function:
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.
1. A. Algorithm for spectrum sensing For the spectrum sensing, there are many possible techniques, such as energy detection, matched filter detection, cyclostationary feature detection, covariance-based detection, and wavelet-based detection [3][4]. In our case, we chose to use of the energy detection which guarantees a relatively increased level of efficiency, the output of which gives the decision variable. This variable is then compared with a threshold and if it is above the threshold, then the result of the detector is that a primary user is present. Energy detection is very useful
(1)
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(2)
The power spectral density (PSD) calculation follows the FFT operation.
P
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$ N & 1 (3)
The PSDs of T consecutive frames are used for averaging, yielding:
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∑
(4)
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INTERNATIONAL JOURNAL OF MICROWAVE AND OPTICAL TECHNOLOGY, VOL.9, NO.6, NOVEMBER 2014
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.
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(6)
k = 0, 1, . . . , N/2 , and the decisions on channel states are made according to the following rule:
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