The Cognitive Radio Based Image Transmission Computer Science Essay

Published Date: 02 Nov 2017

V.R.Vijaykumar*, K.Ashok, and K.Nithya

Anna University - 641047, India

*Email: [email protected]

ABSTRACT

Cognitive Radio (CR) acts as a key component for reliable transmission of high speed multimedia data over the white spaces with reconfigurable wireless communication systems. An image is transmitted over MIMO employing minimum system power consumption through the unused spectrum, that to combat the multipath fading channels for cognitive radio is proposed. Prior to the transmission, spectrum holes are sensed by Modulated Wideband Converter (MWC). After extracting the available spectrum, that frequency is used as a carrier in Orthogonal Frequency Division Multiplexing, and the multimedia data (video or an image) is used as a message signal which is then transmitted. Sub-Optimal power allocation algorithms are developed to minimize the system power consumption in MIMO communication system and according to the target rate of the transmission; the number of antenna is activated for data transmission. The simulation results demonstrate that Sub-Nyquist sampling system used to sense the spectrum which ensures faster and low rate processing and significant system power savings up to 80% are achieved when compared to the conventional power allocation methods.

Keywords: Cognitive radio, Spectrum Sensing, Compressive sampling, MIMO, OFDM, System Power Minimization, Sub-Nyquist Sampling, Modulated wideband Converter (MWC).

1. INTRODUCTION

The goal for next generation wireless communication system seamlessly integrates a wide variety of communication services such as high-speed data, video and multimedia traffic as well as voice signals1. The increasing demand for wireless services and the advent of new wireless technologies imposes an impending limit on the amount of spectrum that will be available for these technologies to be deployed. According to studies as mentioned in2, usage of allocated and licensed spectrum is sparse. This information has yielded a solution to the limited spectrum, which involves sharing of the allocated spectrum between primary users who own the licensing for a particular band, and secondary users. CR is an intelligent wireless communication system in which the upper layer of Software Defined Radio (SDR) is aware of its surrounding environment. CR learns, understands and modifies the parameters of the signal that has to be changed to communicate efficiently without interfacing the licensed users. The secondary users require fast and accurate spectrum sensing, so that they can dynamically monitor the spectrum and rapidly tune their parameters to utilize the available spectrum, as well as to avoid interference causing to primary users3.

The conventional wideband spectrum sensing receives signals through the RF front end and then sampled at the Nyquist rate using the high speed analog-to-digital (A/D) converter with high resolution for detection of licensed user signal. The carrier frequencies in this aspect are over tens of GHz, for which sampling to the highest possible frequency exceeds above the capabilities of the commercial ADC devices4. Even so, it is practically infeasible to implement. A new technique called Compressive sampling (CS) is used to exploit the sparsity of signal frequency response. So there is a need to reduce the sampling frequency and reconstruct the signal effectively5. For better performance Sub-Nyquist base band, processing is used, which is the ability to extract the information bits from the band of interest directly from the samples, and it does not require knowledge of the frequency support in the recovery stage6. In our system spectrum sensing is done by Modulated Wideband Converter (MWC). The wideband signals are low pass filtered and sampled at a low rate. The rate can be as low as the expected width of an individual transmission. The window size is set to estimate the power spectral density of the received signal. Short window gives better averaging of additive noise, while longer one allows higher DFT orders. The Welch PSD estimation is used for calculating frequency resolution, minimal width of a single band and the shortest spacing between bands are considered. The regions that are shorter than the smallest spacing between bands is united and isolated regions with width smaller than the width of a single band are pruned. Moreover, the bandwidth is efficiently utilized by discarding the redundant information. After extracting the available spectrum, the frequency is used as a carrier in Orthogonal Frequency Division Multiplexing and the multimedia data (video or an image) is as a message signal which is then transmitted.  Before transmission of the signal, the power optimization in the system is carried out7. In addition, a CR not only learns channel conditions as in a conventional radio, but it is also aware of platform capabilities and characteristics that help to optimize the system power consumption. The rate of transmission in a mobile communication can be different for distinct user (Pedestrian, stable and mobile). Whenever the data is transmitted it checks with the rate to be achieved (a rate which an antenna can support), if the target rate is less than the achieved rate, the data is transmitted and if not the ratio of the antenna can be altered. The proposed system uses combined source-channel coders. The compressed image is transmitted using various sub optimal power models by adapting modulation, coding, antenna configuration according to the channel conditions and QoS requirements.

2. SYSTEM MODEL

In the rapidly growing world of wireless telecommunications, a number of trends are gaining widespread popularity. OFDM (Orthogonal Frequency Division Multiplexing), multi carrier modulation technique is identified as key technology for CR8. The proposed system architecture shown in Fig.1, the two major parts of the CR based transmission are spectrum monitoring mechanisms i.e. Senses the available frequency, gather about the interfering signals and adaptive transceiver architecture.  The systems use the unoccupied portions of the spectrum with a reasonable amount of guard intervals for secondary transmissions keeping the interference to primary users to a minimum. The detection of the spectrum hole is carried out by maximum – minimum eigenvalue9. The maximum- minimum Eigen value is the ratio of the maximum eigenvalue and minimum eigenvalue with a threshold. The sampled signal comes from the system interface to build the covariance matrix. The eigen values of the matrix are calculated with a specific algorithm to make the ratio maximum – minimum, with users the threshold is defined and the comparison with the eigenvalue ratio detects the signal presence. A MIMO system with 4X4 antenna is used for transmission and reception.

IMAGE

ENCODER

FEC ENCODER

SERIAL

TO

PARALLEL

INTERLEAVER

PILOT

INSERTION

QAM MAPPING

ANTENNA

SELECTION

IQ

MODULATOR

MODULATED

WIDE BAND

CONVERTER

(MWC)

PARALLEL

TO

SERIAL

IMAGE

DECODER

FEC DECODER

PARALLEL TO

SERIAL

DEINTERLEA-

VING

CHANNEL

CORRELATION

QAM

DEMAPPER

IQ

DEMODULATOR

MODULATED

WIDE BAND

CONVERTER

(MWC)

SERIAL

TO

PARALLEL

IFFT (TX)

FFT(RX)

ANTENNA

SELECTION

LNA

HPA

AFC CLOCK

RECOVERY

AGC AMP

INPUT IMAGE

RECONSTRUCTED

IMAGE

TIME AND

FREQUENCY

SYNCRONIZA-

TION

Fig. 1. System Architecture

Sub –Nyquist based wide band spectrum sensing acquires the sample, processed lower than the Nyquist rate, and by reconstructing the wideband spectrum with partial measurements is done with modulated wideband converter4.

Let ,be the continuous time received signal , where is possible primary user’s signal is noise and channel matrix.

(1)

The auto covariance matrix for in (2) is represented with the addition of auto covariance matrix for the signal and the auto covariance matrix for the noise multiplied with the identity matrix (I)

(2)

The eigen values of the matrix are calculated using Singular Value Decomposition (SVD) to measure the maximum – minimum value of the users by setting a threshold to identify the signal presence which doesn’t need a priori knowledge about the input signal.

Let the covariance matrix be

(3)

Using the SVD, Q in (3) can be factorized as, where U, V are the left and right singular vector and ∑ = diag (λ1, λ2, λ3….. λm) is a diagonal matrix whose non negative entries are square roots of the positive Eigen values of QQ*. These non negative values are singular values of Q. They are arranged in a decreasing order with the largest on in the upper left – hand corner. It is noted the QQ* = R so this method allows to calculate the Eigen values without to build the covariance matrix9.There is no information about the signal so it is very difficult to set the threshold based on detection probability, here false alarm probability is used. The decision statistic for the maximum-minimum eigenvalue (MME) detection is defined as the ratio of maximum to minimum eigen values of received signal covariance matrix. Based on the decision statistic in (4), the detection threshold must be estimated for a required probability of false alarm.

(4)

(5)

The power spectral density (PSD) estimation of x(n) is invoked in order to locate the energy concentration within each spectrum slice. By using Welch PSD estimation method, divides the input into overlapping sections with the overlap ratio of 50% filters each section by a hamming window, and it performs discrete Fourier transforms (DFT) on each section. The hamming window size is W ≥ 2B/ fres.

The frequency resolution is given by

(6)

The signal detector gives a binary output, when it is 0, an unused frequency band is found, and it is 1 when a signal is detected, and it is allocated to the secondary user. By calculating the threshold using (5), if the power is larger than the threshold, the channel is busy and represented as 1 otherwise represented as 0.

Then the input image is compressed using JPEG encoder and converted to bit streams. As shown in the fig.1 The bit streams are subsequently modulated with the OFDM technique. JPEG compression process starts with 8x8 block separation then DCT is coefficients are calculated for each block. The coefficients are then quantized and run length encoded. Coded data are fed into FEC encoding scheme. The IDFT and the Discrete Fourier Transform (DFT) are used for modulating and demodulating the data constellation on the orthogonal sub carriers.

The discrete sample of the OFDM symbol is given by

(7)

Where

By Following the parallel-to-serial (P/S) conversion, the baseband OFDM signal s(n) is up sampled and passed through the digital-to-analog converter (DAC). The baseband OFDM signal is then low pass filtered, up converted to the desired centering frequency using a mixer and a local oscillator (LO), and amplified for transmission using the power amplifier (PA). The receiver mixes the signal for base band processing.

The signal is then low pass filtered, converted to digital signal using an analog-to-digital converter (ADC) converter, and down sampled. After that the serial stream of sampled time signal is converted into parallel streams with a S/P converter, and the cyclic prefix is discarded from the received composite signal rm, n DFT in (7) is used to transform the time-domain data into the frequency domain.

(8)

Where

These parallel streams are then demodulated to yield digital data and are multiplexed together using the P/S converter to yield the serial bit stream, and delivered to the data sink. At the receiver, frame detection is an important task. Moreover, frequency and timing synchronization is required before the OFDM symbol can be correctly demodulated. A known preamble is sent before each OFDM frame to allow receiver synchronization and channel estimation, as well as an initial acquisition of the frequency offsets. Once the data streams have been separated one from another, the JPEG decoder is used to reconstruct the image.

In the transceiver the down converter convert the incoming signals to a lower central frequency or even baseband. So, a lower sampling rate easing the ADC selection is used with the help of MWC. The RF filtering is the first filtering performed on the incoming signal. It has to reject out of band signals and noise and provide as much selectivity as possible for the working bandwidth without losses. This bandwidth will define the flexibility of the system to operate at different frequencies under a range of the spectrum. The low noise amplifier LNA has to boost the signal to a manageable range without adding noise into the signal. The RF mixer is used to down convert the incoming signal and can be a source of intermediation distortions. These are due to a non-linear behaviour of the mixer that could be overcome by increasing the LO power to the mixer. But this could be prohibitive in mobile applications that need to save batteries. The local oscillator will generate the frequency used by the mixer to accomplish the down conversion and good phase stability is required. Phase noise at this part of the chain could be a significant source of interference. The automatic gain controller (AGC) has the function of maintaining the signal between the ADC ranges. To get the best of the quantization of the ADC, the signal should be amplified to use most of the range.

3. SUB OPTIMAL POWER ALLOCATION

Cognitive radio (CR) enables the wireless communication systems to sense the environment, learn and adapt the radio component characteristics. Based on the learned characteristics the system can be used to optimize system power consumption of multiple input multiple output (MIMO) communication systems by dynamically reconfiguring the radio based on the required Quality of Service (QoS), the channel condition, and the knowledge of the platform (component) capabilities and characteristics6.  The proposed system consists of theoretical frame work that should cooperate with the simulated environment. Four antennas are used; these antennas are connected with power amplifiers (PA). This PA may be class A amplifier or class B amplifier. The system should wait for the target rate that should be achieved for transmission. The more transmit branches the MIMO system uses; more power is consumed by the system. Therefore, the minimum number of branches PA as the system power consumption is linearly proportional to the number of active branches as long as the target rate can be achieved. Our system is based on the Quasi optimal algorithm9 as an optimal algorithm and Branch adaption and branch minimization are sub optimal 1 and suboptimal 2 respectively. The system power can be calculated by considering radiated power as in (9) of PA. The number of branches is increased only when the current number of active branches cannot satisfy the rate requirement. The total radiated power (10) is calculated for the system. The power amplifiers considered are class A and class B, and the power radiation for these amplifiers are calculated by (11). The radiated power from the transmit branches is

(9)

The total radiated power becomes

(10)

Where is the nth element of and the radiated power from transmit branch n. The system power consumption from branch can be expressed as

(11)

The system power consumption reduction is

(12)

Where Pcon is the system power consumption of MIMO system with a conventional power allocation scheme (i.e., the water-filling algorithm and the equal power allocation), and Pcog is the system power consumption of a MIMO system for the proposed Cognitive radio framework.

4. ALGORTHIM

Algorithm for Transmission

Step 1: Signal x(t) is sensed and sampled at sub- Nyquist rate x(n).

Step 2: Channel is estimated using SVD.

Step 3: Power Spectral Density is estimated for the sampled signal x(n).

Step 4: Frequency resolution (fres), window size (W) and threshold (µ) is calculated.

Step 5: The regions which are smaller than µ are united and those regions which are smaller width than bmin are isolated.

Step 6: If there is any isolated regions

Allocate the isolated region to the user.

Source Coding is performed using JPEG Compression technique.

Channel Coding is performed using OFDM with the channel parameter calculated by Step 2. Else goto step 1.

Step 7: Initialize the number of active antenna to one.

Step 8: Calculate and store the power allocation, target rate and achieved rate.

Step 9: Calculate the radiated power, consumed power and total consumed power.

Step 10: If power allocation achieves the target rate of the user

The signal is transmitted with that antenna configuration and achieved rate with minimum power consumption. Else increase the number of active antenna and goto step 8.

Algorithm for Reception

Step 1: Signal x(t) is sensed and sampled at sub - Nyquist rate x(n).

Step 2: Channel is estimated using SVD.

Step 3: Power Spectral Density estimated for the signal x(n).

Step 4: Frequency resolution (fres), window size (W) and threshold (µ) is calculated.

Step 5: The region of interest is isolated.

Step 6: If there is any isolated region. Channel Coding is performed using OFDM with the channel parameter calculated by Step 2. Else go to step 1.

Step 7: JPEG decompression is done and image is reconstructed.

5. RESULTS AND DISCUSSION

The proposed framework of MIMO - OFDM based Cognitive Radio to minimize the system power consumption was simulated using MATLAB 7.12.0. An image is used as source data and to be transmitted over the correlated Rayleigh fading channel. The Sub-Nyquist sampled frequency for a 10 GHz signal and a window size of [0, 0.5] MHz is considered, the sensed spectrum is divided in to 195 sub-channels corresponds to OFDM carriers are sensed by the Welch PSD. Based on the channel characteristics power allocation was performed, the resulting power allocation was used to transmit the bit streams. The bit streams are then modulated with the OFDM modulation, the Fig.2 shows the time response of the OFDM modulated signals, Fig.3. Show the spectrum of an unused carrier. The Fig.4 and Fig.5 shows the power savings that obtained for Class A and Class B PA respectively for quasi optimal, sub optimal 1 and sub optimal 2 algorithms. The reconstructed image shows that a PSNR value of 33.96dB. The orthogonality helps to transmits more data simultaneously.

D:\JOURNAL WORKS\results\4.tif

Fig. 2. Time Response of Band pass signal

D:\JOURNAL WORKS\results\5.tif

Fig. 3. Spectra of sensed OFDM Carrier

C:\Users\ash\Desktop\class a power amp.tif

Fig. 4. Power savings with Class A PA

C:\Users\ash\Desktop\class b.tif

Fig. 4. Power savings with Class B PA

Table 1. PSNR values for different images

Image Name

PSNR in (dB)

Peppers

30.96

Airplane

33.19

Araras

31.71

(b)

(c) (d)

(e) (f)

Figure 5 Original and Reconstructed Images (a) Original Peppers Image (b) reconstruct- ted Peppers Image (c) Original Airplane Image (d) Reconstructed Airplane Image (e) Ori-ginal Araras Image (f) Reconstructed Araras Image

6. CONCLUSION

The potential of Cognitive Radio is used in MIMO – OFDM for minimizing power consumption of the system. The unoccupied spectrum is identified by the MWC, and the image is transmitted. The antenna is selected only when it is needed. The power consumption of the system is minimized by optimizing the conventional Waterfilling algorithm. The quasi optimal algorithm, sub optimal 1 (Branch adaptation Algorithm) and sub optimal 2 (Branch Minimization algorithm) was also simulated and the percentage of power saving was compared. The system power consumption for a Class-A power amplifier is saved up to 80%, and for Class-B power amplifier is up to 38.5 % are compared with the conventional method. It achieves good performance for systems with Class A PAs. In future, the proposed model can be used to transmit a video over MIMO with the optimal and sub optimal power allocation method so that the power consumption of the system can be reduced, and the data rate can be increased with minimum SNR.

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