Cognitive Radio Based Image Transmission Computer Science Essay

Published Date: 02 Nov 2017

Abstract- In this paper, we propose a novel scheme for transmission of image over MIMO channel using cognitive radio. The dynamic spectrum is sensed using the Modulated WideBand Converter (MWC). The Multicarrier MIMO-OFDM is used to modulate the compressed image. The sub-optimal algorithms are developed under the rate constraint to minimize the power consumption of the system. According to the target rate of transmission, the number of antenna is activated for data transmission. The simulation results demonstrate that SubNyquist sampling system used to sense the spectrum ensures with faster and low rate processing, significant power savings up to 80% are achieved when compared with the conventional methods.

Keywords: Cognitive radio, Compressive sampling, MIMO, OFDM modulation, Power Minimization, SubNyquist Sampling.

I. 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 signals. 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 mentioned in A, allocated and licensed spectrum is sparsely used. This information has yielded a solution to the limited available 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 which the upper layer of Software Defined Radio (SDR) which is aware of its surrounding environment. CR learns, understand and modify the parameters of the signal are 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 spectrum available, as well as avoid causing interference to primary users B.

The conventional spectrum sensing methods in a wideband cognitive radio are challenging to implement since they require very high sampling rates at or above the Nyquist rate. A new technique called Compressive sampling (CS) is used to exploit the sparsity of signal frequency response. In mobile communication, Analog to Digital Conversion (ADC) is used to convert the signals which are sensed from the mobile environment is analog, and the processing are carried out in digital. The carrier frequencies in this aspect are over tens of GHz, for which sampling according to the highest possible frequency exceeds by far the capabilities of commercial ADC devices. So there is a need to reduce to the sampling frequency and reconstruct the signal effectively. The MWC can do low rate computations. Sub Nyquist base band processing i.e. the ability to extract the information bits from the band of interest, directly from the samples, namely without performing any kind of interpolation to the Nyquist grid. The sampling frequency needs to sample the signal which is very less than Nyquist frequency. So the bandwidth is efficiently utilized by not sending 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) as message signal is transmitted. Before transmission of the signal, the power optimization in the system is carried out. Moreover, a CR cannot only learn channel conditions as in a conventional radio, but also be aware of platform capabilities and characteristics helps system power consumption optimization The power consumption of the system is optimized by making the antenna in the MIMO system (4x4) whenever needed. So, the High-power amplifier used at the front end of the transceiver which consumes more power in the communication system can be reduced. The rate of transmission in a mobile communication can be different for different user (Pedestrian, stable and mobile). Whenever the data is transmitted it checks with the achieved rate (a rate which an antenna can support), if the target rate is less than the achieved rate The data is transmitted if not the number of antenna for transmission is increased. In our system, we 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 requirements10.

II.SYSTEM DESCRIPTION

The System Architecture is shown in Fig. 1, with a description of different components given below.

JPEG ENCODER

FEC ENCODER

SERIAL

TO

PARALLEL

INTERLEAVER

PILOT

INSERTION

QAM MAPPING

ANTENNA

SELECTION

IQ

MODULATOR

MODULATED

WIDE BAND

CONVERTER

(MWC)

PARALLEL

TO

SERIAL

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

A. Compressive sampling in cognitive radio

Cognitive Radio can automatically analyze its radio spectrum environment to identify temporarily vacant spectrum and use it. Cognitive radio lies as a layer over the Software Defined Radio (SDR) system model that introduces intelligences to the radio systems. Simply modifying the software, CR can completely change SDR functionality or improve its performances without replace hardware. Changing the parameters of analog devices will allow modification at the physical layer by software change in filter characteristics, waveforms, bandwidth response etc.

Spectrum sensing is the technique in which the system analysis that the Primary User (PU) uses the spectrum and finds the holes in it. Detecting the presence of signals in the frequency spectrum is called spectrum sensing. The spectrum sensing is done by modulated wideband converter (MWC). MWC is used to convert the analog to digital signal. The system does not require knowledge of the frequency support in either the sampling or the recovery stage. The goal of the modulator is to alias the spectrum into baseband. The modulated output is then low pass filtered and sampled at a low rate. The rate can be as low as the expected width of an individual transmission. The received signal power spectral density is estimated and the window size is set to estimate the signal. Short window give better averaging of additive noise, while longer one allows higher DFT orders. The window should be greater than the twice the rate to the frequency resolution. For calculating frequency resolution the minimal width of a single band and the smallest spacing between bands. The regions that are smaller than the smallest spacing between bands is united and isolated regions with width smaller than the width of a single band are pruned. It has only N/2 pairs [ai,bi] and stop edges of the information bands and isolate the sequence upto N/2.

B. Modulation scheme

Orthogonal Frequency Division Multiplexing (OFDM) in cognitive radio systems uses multiple carriers to transmit data; each of these carriers could be BPSK to N-QAM. OFDM different from Frequency Division Multiplexing (FDM) by all the carriers are send data from one channel where as FDM allocate different data channel. A CR system requires spectrum sensing capabilities that are usually implemented by means of FFT. FFT is useful because size N improves frequency resolution which in turns helps in narrow band detection. OFDM already has an FFT machine that in many cases could be shared for spectrum sensing algorithms. The subcarriers have appropriate spacing and passband filter shape to satisfy orthogonality. OFDM requires high synchronization in frequency and time domain as well as for channel estimation. When transceivers are synchronized with each other, the subcarrier channels are orthogonal to each other and the resulting interference is insignificant. So, the pilots, preambles and cyclic prefix extension are used for synchronization. OFDM is flexible to modify power on individual carriers, suppressing any of them, modulation order and even spectrum shaping.

C. Antenna selection

The cognitive radio can be used to optimize system power consumption of MIMO communication systems by dynamically reconfiguring the radio based on the quality of services (QoS), the channel condition, and the knowledge of the platform (component) capabilities and characteristics. The equal power allocation technique is used for power allocation to the subchannel, the methods used are quasi-optimal algorithm, sub optimal algorithm 1 and sub optimal algorithm 2. These methods out performs the conventional method Waterfilling algorithm. Quasi optimal approach tests all antenna configuration and power allocation combinations for a given power allocation step size to find a solution that is negligibly different than the true optimal solution. To overcome this computational difficulty, we use a new optimization criterion called competitive optimality for solving the transmit-power. Considering a multiuser cognitive radio environment viewed as a non cooperative user, maximize the performance of each un-serviced transceiver, regardless of what all the other transceivers do, but subject to the constraint that the interference temperature limit not be violated. This formulation of the distributed transmit-power control problem leads to a solution that is of a local nature though suboptimum. In order to reduce the computational burden and the memory requirement, a suboptimal algorithm is proposed. The water-filling algorithm and equal power allocation are used to allocate power for each branch combination for problems of rate constraint, respectively. This algorithm differs from the conventional water-filling algorithm and the equal power allocation algorithm in a way that the branches in the conventional water-filling algorithm and the equal power allocation algorithm are fixed while the choice of branches in this proposed algorithm is adapted to minimize system power consumption. Therefore, we call this algorithm branch adaptation. The branch adaptation algorithm results in much less computational burden and smaller memory requirement as compared to the exhaustive search algorithm.

The suboptimal algorithm 1 can be further simplified based on the observation in the MIMO systems employing Class A PAs: the more transmit branches the MIMO system uses, the more power the system consumes. The power consumption of the Class A PA is the same no matter what the output power is. Therefore, it makes sense to use the minimal number of branches (PAs) as the system power consumption is linearly proportional to the number of active branches as long as the target rate can be achieved. In suboptimal algorithm 2, the number of active branches increases only when the current number of active branches cannot satisfy the rate requirement. As in branch adaptation algorithm, the water-filling algorithm and the equal power allocation algorithm are used to allocate power for each branch combination for rate constraint.

The total number of power allocation combinations for branch minimization algorithm is the same as that for the branch adaptation algorithm. The actual average number of combinations the branch minimization algorithm has to evaluate, depending on the distribution of the channel state and the target data rate, is lower than that of the branch adaptation algorithm.

D. Baseband processing

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 inter modulation distortions. These are due to a non-linear behaviour of the mixer that could be overcome by increasing the local oscillator 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. Clipping on the ADC must be avoided since for amplitude modulated signal as QAM, it will cause damage to the encoded information.

Due to the summation of the subcarriers at the transmitter, the composite OFDM signal in time domain could exhibit large envelope variations, which is characterized by a large PAPR. When high PAPR occurs, the D/A converter and PA of the transmitter must have large dynamic ranges to avoid amplitude clipping, thus increasing both power consumption and component cost of the transceiver. The power amplifiers used in the transceiver should be used efficiently so that power consumption of the system can be reduced.

The four transmitting antennas are connected with power amplifiers. This PA may be class A amplifier or class B amplifier is taken into account. When the target rate is achieved then only the transmission is allowed otherwise the transmission is not allowed. The more transmit branches the MIMO system uses, the 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. The number of branches is increased only when the current number of active branches cannot satisfy the rate requirement.

III . IMAGE TRANSMISSION OVER MIMO – OFDM CHANNEL

A MIMO system with 4X4 antenna is used for transmission reception. The random process x (n) is wide-sense stationary is taken for processing. The sequence observed is

(1)

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

(2)

The Singular Value Decomposition (SVD) is used for finding the eigenvalues. It is a reliable and accurate method for measuring eigen values. 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 the square roots of the positive Eigen values of QQ*. These non negative values are called singular values of Q and 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 matrix.

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

(4)

The threshold (µ) is determined by

(5)

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

Then the image is JPEG compressed and converted to bit streams. As shown in the fig.1 the bit streams are then modulated with the OFDM modulation 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.

The time discrete equivalent is the inverse discrete Fourier transform (IDFT). So, the IDFT and the discrete Fourier transform (DFT) are used for modulating and demodulating the data constellation on the orthogonal subcarriers. The discrete samples of the OFDM symbol is given by

(6)

Where

Following the parallel-to-serial (P/S) conversion, the baseband OFDM signal s(n) is upsampled and passed through the digital-to-analog (D/A) converter to convert the digital signal into an analog signal. The baseband OFDM signal is then low pass filtered, upconverted to the desired centering frequency using a mixer and a local oscillator (LO), and amplified for transmission by the power amplifier (PA). The receiver mixes the signal for baseband processing. Then, the signal is low pass filtered, converted to digital signal using an analog-to-digital (A/D) converter, and down sampled. 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. Then ,DFT in (7) is used to transform the time domain data into frequency domain

Where (7)

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 offset.

IV.SUB OPTIMAL POWER ALLOCATION

The system is provided with four antennas, these antennas are connected with power amplifiers. This PA may be class A amplifier or class B amplifier is taken into account. When the target rate is achieved then only the transmission is allowed otherwise the transmission is not allowed. The more transmit branches the MIMO system uses, the 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.

The power of the system is calculated by considering radiated power as in (8) 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 (9) 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 (10). The radiated power from the transmit branches is

(8)

The total radiated power becomes

(9)

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

(10)

The system power consumption reduction is

(11)

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 the system power consumption of a MIMO system with the proposed CR framework .

The analysis and implementation is carried out using MATLAB. The channel considered is a rayleigh fading channel and a memoryless channel. The transmitted signal is considered as a narrow bandwidth, so that the frequency response is flat.

5.1 COMPRESSIVE SAMPLING

The compressive sampling is the method in which the sampling rate is reduced for the sampling of the signal by exploiting the sparsity of the signal. The number of mixers and filters are considered as 6, the bandwidth is 50 MHz and nyquist frequency is considered as 10 GHz.

Figure 5.1 Original Signal Transmitted

Figure 5.2 Signal in the Mobile Environment

Figure 5.1 shows the cognitive radio spectrum sensing. The compressive sampling is used for sampling the signal sensed. This is the original signal have to be reconstructed. Figure 5.2 shows that the Additive White Gaussian Noise to the original signal. The data is transmitted in a rayleigh channel. The transmitted signal is corrupted by multiple access interface which is generated in a structured way rather than treating it as a Additive White Gaussian Noise (AWGN). The signal is further corrupted by AWGN at the front end of the receiver.

Figure 5.3 Reconstructed Signal

Figure 5.3 shows the reconstructed signal using Modulated Wideband Converter (MWC). MWC is used to convert the analog to digital signal. The system does not require knowledge of the frequency support in either the sampling or the recovery stage. The goal of MWC is to change the spectrum into baseband.

5.2 ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING

C:\Users\Kalaimagal Nithya\Desktop\New folder (2)\bandpass signal.bmp

Figure 5.4 Orthogonal Frequency Division Multiplexing

Figure 5.4 shows that the orthogonal frequency division multiplexing. Due to the summation of the subcarriers at the transmitter, the composite OFDM signal in time domain could exhibit large envelope variations.

C:\Users\Kalaimagal Nithya\Desktop\New folder (2)\fft.bmp

Figure 5.5 FFT of the Baseband Signal

Figure 5.5 shows that the FFT of the baseband signal. A CR system requires spectrum sensing capabilities that are usually implemented by means of FFT. FFT is useful because size N improves frequency resolution which in turns helps in narrow band detection. OFDM already has an FFT machine that in many cases could be shared for spectrum sensing algorithms.