Image Transmission Over LDPC Coded Massive-MIMO OFDM System
Fadhil Sahib Hasan, Daniya Amer Jassim
Abstract: In the recent time, the speed in transmission of data over wireless communication channel is considered as the requirement in the communication world. This paper is focused on the transmission of images using Low-Density-Parity-Check (LDPC) coded Massive Multiple Input Multiple Output (MIMO) systems based on Orthogonal Frequency Division Multiplexing (OFDM) system over selective Rayleigh fading channel. The Quality objective measures and Bit Error Rate (BER) are used to examine the performance of the system. Simulation results prove that the proposed system enhances the performance of the recovered images. Also LDPC Coded Massive MIMO OFDM system gives an excellent BER performance.
hen the image transmitted during a digital wireless communication channel many characteristics like high information rate, efficient power consumption, less propagation time, high spectral efficiency and high throughput over selective Rayleigh fading channel while obtained acceptable quality image, all these characteristics are needed for a better image transmission. To improve image quality different methods are implemented before send the data effectively. In ([1], [2]), Low Density Parity Check (LDPC) codes were used to transmit an image with high spectral efficiency and best quality. In [3], Set Partitioning In Hierarchical Trees (SPIHT) images were sent over Multi-Input Multi-Output (MIMO) diversity with serial and parallel modes. In this system, Turbo code is used as channel coding to improve the performance. In [4], Hierarchical Quadrature Amplitude Modulation (HQAM) is used for transmitting compressed picture under multipath fading channel. In [5], LDPC based Orthogonal Frequency Division Multiplexing (LDPC-COFDM) was used to transmit SPIHT images over fading channel in which the data was split into Inphase and Quadrature components. After that, the Discrete Cosine Transform (DCT) were used instead of Fast Fourier Transform (FFT). This approach will minimize the Peak-to-Average-Power-Ratio (PAPR). In [6], an approach for sending SPIHT images across LDPC-COFDM system with minimum PAPR value in which LDPC is combined with chaotic interleaver and OFDM system to improve the transmission of image over selective fading channel. In [7], the image is transmitted through composite fading channel using M-QAM OFDM system. In [8], Multicarrier Code Division Multiple Access (MC-CDMA) to send picture under fading channel. Two interleaver schemes that are Helical and chaotic interleaver with Linear-Minimum-Mean-Square-Error (LMMSE) were used to improve the system. In [9], the mage sending under wireless channel using M-QAM and QPSK modulation were investigated. In recent years, Massive MIMO system using OFDM (MIMO-OFDM) is the modern technique to achieve high information rate and enhance the link reliability over selective fading channel [10]. This technique can be combined with LDPC codes to more enhance the system [11,
12]. In this paper, employing LDPC coded OFDM with massive MIMO are suggested to transit image signals under selective Rayleigh fading channels. This technology is used to obtain large data rate and enhance the performance of the received picture. The remaining of this paper is organized as follows. LDPC Codes is studied in briefly in Section 2. The block diagram of the suggested system is depicted in Section 3. while simulation results are illustrated in Section 4. In the last, conclusions are depicted in Section 5.
2 LDPC CODES Many types of error correcting code are used to enhance the performance of Massive-MIMO with OFDM system [1], [3], [13]. LDPC is regarded as very good error correcting codes for LTE system and beyond [1]. LDPC code is considered as a Linear Block Code (LBC). LDPC code has a very sparse of parity check matrix where the elements in it are zeros with small number of one's. It consists of M rows and N columns with code rate, R, equals K/N where K equals (N – M) [6]. LDPC codes can be classified into regular and irregular LDPC codes. In regular LDPC code, the number of row weight is equal to the number of column weight. The structure of parity check matrices in regular LDPC codes are easier than irregular LDPC code [1]..
3 The Proposed Model LDPC coded Massive MIMO OFDM System In this paper, MIMO OFDM Nt × Nr system are displayed in Fig. 1, where Nt and Nr represents sender and receiver antennas number respectively. Assuming here Nr is larger than Nt. Firstly, convert the input source image to a stream of binary bits. Then, LDPC encoder with a rate = 1/2 is used to encode the stream of binary bits. The output of LDPC codes are mapped using QAM modulation system. Serial-to-parallel converter are used to obtain the parallel symbols from stream symbol and then transformed in each antenna to produce OFDM sequence using the inverse FFT (IFFT). For each OFDM signals, a Cyclic-Prefix (CP) uses to overcome the inter symbol interference during selective fading channel. The transmitted sequence send over the channel which is Frequency Selective Rayleigh Fading (FSRF) channels and Additive White Gaussian Noise (AWGN). At the reception side, the reverse operations as performed to recover the source image. At the receiver, after remove CP and applied FFT for each antennas, the equation of the received sequence can be
W
———————————————— Fadhil Sahib Hasan , Al - Mustansiriya University, Electrical Engineering
expressed as Xn = Hn . Sn + Wn , n=0,..,K-1, ……. (1) where Xn are the complex reception sequence in the frequency domain, Sn are the complex transmitted symbols, Hn is the n-th channel matrix in frequency domain, and Wn is the frequency domain of AWGN signals. The message information symbols are recovered using,
n = n . Xn = Sn + n Wn …… (2)
where n represents the pseudo-inverse of Hn and n is the
estimation value of original transmitted symbols. To detect the stream sending bits, LDPC decoder is employed.
Fig. 1. The block diagram of the transmitter and receiver for the proposed LDPC coded Massive MIMO with OFDM system.
4 SIMULATION AND RESULTS Cameraman image represents the input source image in Figure (1) which is 8 bits/pixel, 256 x 256 grayscale image. The simulation of the proposed algorithm is done in MATLAB version 2018. In this paper, the performance of the recovered picture is compared with the source picture with different parameters. Therefore, the precision of it is measured by the BER, the Peak-Signal-to-Noise Ratio (PSNR) and Root-Mean-Square-Error (RMSE) values as follow: 4.1 BER performance evaluation Fig. 2, Fig. 3 and Fig. 4 show the performance of BER performance for LDPC coded Massive MIMO with OFDM System in image transmission under FSRF and AWGN
channels for the cases of are
respectively each with
iterations (5, 10, and 15). It can be noticed that increases number of receive antenna, number of transmit antenna and number of iteration, BER values will be decreased.
4.2 Image quality measure One of the most important parameters to find the image quality is PSNR and RMSE measures. PSNR measure for the recovered image can be calculated in logarithmic scale according to equation below [3], [5], [8].
PSNR = 10 ……. (3)
where, peak value is equal to 255 (8 bits per pixel) of the source picture and MSE represents the mean square error between the source and the recovered image that is calculated according to
MSE = …… (4)
where represent the values of gray pixels
in the source image and recovered image respectively. W and H represented the image width and image height respectively. Figures (5-10) show the PSNR and RMSE simulation results
for the cases of are
and for each iterations (5, 10, and 15) respectively. From the results, it can be noticed that the PSNR values is increased and the RMSE values are decreased on the transmission of image with the increasing in number of iteration and receive antenna. Furthermore, increase SNR will enhance the results also.
Fig. 4. The BER performance of the proposed system for Nt =10
Nr =30.
Fig. 2. The BER performance of the proposed system for Nt =10 and Nr =10.
Fig. 3. The BER performance of the proposed system for Nt =10
and Nr =20.
INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 12, DECEMBER 2020 ISSN 2277-8616
The performance of the recovered image can be visualized. Fig. 11, shows the evaluation of the performance at 2dB, 3dB, and 5dB. It has been observed that the reconstructed image improved when increase number of receiver antenna (Nr) with increase SNR.
Fig. 11. Performance of the image recovering (cameraman image) at SNR = 2, 3, 5 dB.
Fig. 5. PSNR comparison vs. channel SNR with Nt=10 and Nr=10 for cameraman transmitted image.
Fig. 6. RMSE comparison vs. channel SNR with Nt=10 and Nr=10 for cameraman transmitted image
Fig. 7. PSNR comparison vs. channel SNR with Nt=10 and
Nr=20 for cameraman transmitted image.
Fig. 8. RMSE comparison vs. channel SNR with Nt=10 and Nr=
20 for cameraman transmitted image.
Fig. 9. PSNR comparison vs. channel SNR with Nt=10 and Nr= 30 for cameraman transmitted image.
Fig. 10. RMSE comparison vs. channel SNR with Nt=10 and
Nr= 30 for cameraman transmitted image.
INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 12, DECEMBER 2020 ISSN 2277-8616
5 CONCLUSION In this paper the image transmission performance is analyzed using LDPC coded Massive-MIMO with OFDM System. From the results, this model improved the simulation performance of the BER. The results prove that the LDPC code is excellent in image recovering. Also the image quality which are PSNR and RMSE values give better performance for low SNR. It has been observed that the BER in case of 15 iteration is better than the BER in case of 10 and 5 iteration. It has been concluded that the image recovering at 5 dB is better than that the image recovering at 2 dB and 3 dB respectively. Also, the process of image recovering will be better when the received antenna number and transmit antenna increase.
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