An Efficient Mi-Lmmse Channel Estimation in Coded OFDM Communication Sysytem

An Efficient Mi-Lmmse Channel Estimation in Coded OFDM Communication Sysytem

Ajay Kumar [1], Dr. Abhishek Sharma [2]

M.E. Student [1] , Asst. Professor [2]

Department of Electronics & Telecommunication Engineering Jabalpur Engineering College

Jabalpur, M.P., India

Abstract – OFDM is prominent choice for wireless communication systems due to its high data rate transmission capability with high bandwidth efficiency and its robustness to multi-path delay. The main objective of MI-LMMSE channel estimation algorithm is to transmit the data with low BER and error free transmission in noisy environment. This algorithm has been proposed to achieve the future requirements such as less BER, robustness to noise. Also, it is shown that the resulting steady state MSE of the proposed algorithms is quite insensitive to changes in input SNR.The performance of proposed algorithms is analyzed in terms of BER, SNR, and MSE. We also propose a robust OFDM schemes in coded systems, where feedback from the channel decoder is utilized to improve channel estimation performance. Simulation results show that the proposed algorithm achieve significant performance improvement over the conventional schemes.

Keywords: Channel estimation, MSE, LMMSE, MI-LMMSE, BER, SNR, MSE and OFDM.

1. INTRODUCTION

   OFDM is an efficient multicarrier modulation scheme which is resilient to the effect of multipath fading channels. Although all the OFDM subcarriers are modulated by waveforms that are limited in the time-domain, in practice, there is unavoidable power leakage in the frequency-domain and because of this, the guard band has to be placed so as to minimize adjacent channel interference to other coexisting wireless systems. Furthermore, OFDM's robustness against multipath propagation relies on the insertion of cyclic prefix (CP) which is a loss of spectrum efficiency [1].

   In last decades, OFDM based communication system has been identified as one of key transmission techniques for next generation wireless communication systems. The main attractions of OFDM are handling the multi-path interference, and mitigate inter-symbol interference (ISI) causing bit error rates in frequency selective fading environments. Wireless mobile communication systems of the 21st century have to confirm a wide range of multimedia services such as speech, image, and data transmission with different and variable bit rates up to 2 mbps. It is all recognized that there is a great impact of channel coding on the performances of OFDM based wireless communication system to provide high data rates over severe multipath channels [2].

   Channel equalization is the process in which the transmitting signal affected by the unwanted signals during transmission process is trying to become noise free. The ISI

   (Inter Symbol Interference) imposes the main obstacles for achieving increased digital transmission rates with the required accuracy. Traditionally, inter symbol interference problem is resolved by channel equalization. A channel equalizer is an important component of a communication system. The equalizer depends upon the channel characteristics. The adaptive equalizer and the decision device at the receiver compensate the ISI created by the channel. Thus it may be necessary for the channel equalizer to track the time varying channel in order to provide reasonable performance. The main key goal of this adaptive filter based equalizer is to minimize the mean square error of equalized signals before reaching to the receiver [3].

   We focus on Channel Estimation (CE) and Channel Length Estimation (CLE) for OFDM systems. CE plays a fundamental role in modern communication systems, especially for wireless devices. For a coherent communication, the channel must be estimated at the transmitter and/or receiver side. Knowledge of the Channel State Information (CSI) at the transmitter side is usually the most favorable condition, since the transmitter can apply smart techniques in advance, to adapt the communication to the environment conditions [4].

   Channel estimation with convolution code in OFDM systems is investigated. The main objective of this thesis is to investigate the performance of channel estimationin OFDM systems. The new algorithms have been proposed for channel estimation in this paper.

2. LITERATURE REVIEW

   SU HU et.al: This paper is focused on training sequence design for efficient channel estimation in multiple input multiple-output filter bank multicarrier (MIMO- FBMC) communications using offset quadrature amplitude modulation (OQAM). MIMO-FBMC is a promising technique to achieve high spectrum efficiency as well as strong robustness against dispersive channels due to its feature of time-frequency localization. In this paper, authors proposes a new class of training sequences, which are formed by concatenation of two identical zero-correlation zone sequences whose auto-correlation and cross correlation are zero within a time-shift window around the in-phase position [1].

   The system model considers imperfect channel estimation, pilot contamination (PC), and multicarrier and multipath channels. Analytical expressions are first presented on the mean square error (MSE) of two classical channel estimation algorithms [i.e. least squares (LS) and minimum mean square error (MMSE)] in the presence of PC. Then, a simple H-infinity (H-inf) channel estimation approach is proposed to have good suppression to PC [2].

   Yu Zhu et.al: In this paper, author considers a robust SC-FDE design with imperfect channel knowledge at a receiver due to the channel estimation error. Based on a statistical model for channel estimation, the optimal equalization coefficients are derived under the criterion of minimizing the mean square error conditioned on a given channel estimate. The bit error rate is further analyzed and a tight performance approximation is proposed. Two robust FDE schemes in coded systems were also proposed, where feedback from the channel decoder is utilized to improve the equalization and/or channel estimation performance [3].

   Sunho Park et.al: Authors proposed a new decision-directed channel estimation technique dealing with pilot shortage in the MIMO-OFDM systems. The proposed channel estimator uses soft symbol decisions obtained by iterative detection and decoding (IDD) scheme to enhance the quality of channel estimate. Using the soft information

   y = X*h

   and the error e is the expected output. The square error (S) can be defined as [5]

   S = ||2

   = ( )2

   = ( ) * ( )t

   = ( ) * ( )t

   where t stands for the complex transpose of matrix. The equation can be minimized by taking its derivative w.r.t h and equating it corresponding to zero. The final equation is [5],

   = (XtX)-1 Xty

   where = hls = X-1y.

   3.2 MMSE channel estimation

   The MMSE estimator minimizes the mean-square error. Mean square error = mean ( )2 = E( )2

   Notion of expected value and correlation can be utilized to derive the equations for locating the channel response. The estimated channel is

   from the decoders, the proposed channel estimator selects reliable data tones, subtracts interferences, and performs re- estimation of the channels. Authors analyze the optimal data

   where,

   = F*(

   1 )

   tone selection criterion, which accounts for the reliability of symbol decisions and correlation of channels between the data tones and pilot tones. From numerical simulations, we show that the proposed channel estimator achieves considerable improvement in sytem performance over the conventional channel estimators in realistic MIMO-OFDM scenarios [4].

3. CHANNEL ESTIMATION

   Channel estimation is a method to characterize the impact of the physical medium on the input sequence. The key aim of channel estimation is to evaluate the impact of the channel on known or partially known group of transmittances. OFDM systems are specifically equipped for channel estimation. The sub carriers are closely spaced. The channel is estimated on the basis of the training sequence that will be known to both transmitter and receiver. The receiver can employ the known training bits and the respective received samples for estimating the channel.

   3.1 LSE channel estimation

   LSE estimator reduces the square error between estimation and detection to estimate channel h[n]. In matrix form, the actual output can be written as

   RYY = X*F*X*F + Variance of the noise *

   Identity matrix.

4. PROPOSED METHOD

   Firstly the data input is applied to the FEC i.e. forward error correcting code in which convolutional coding with interleaving is used .A convolutional encoder first encodes the binary input data. Coded bits are sent to interleaving and then the binary values are represented on BPSK modulator. To be able to adjust the signal in the receiver for a possible phase drift, pilot carriers can be inserted. In the Serial to Parallel block, the serial input symbol-stream is transformed into a parallel stream. These parallel symbols are modulated onto the sub carriers by applying the Inverse Fast Fourier Transform. Following the IFFT block, the parallel output is converted back again to serial and guard interval, cyclic prefix of the time domain samples, is appended to eradicate ISI. In the receivers, the guard interval is removed and the opposite processing is carried out to transmitter like time samples are converted by the FFT into complex symbols. In the channel estimation technique Enhanced iterative LMMSE algorithm is used. Demodulated symbols are block deinterleaved. These bits are forwarded to Viterbi decoder. Decoded bits are going to be assigned to a specific user and then extracted utilizing the required bit rate information of the user.

   Data source

   FEC coding

   Modulation Scheme

   Add CP

   OFDM

   Transmitter

   Data sink

   FEC

   decoding

   Demodulation Scheme

   Remove CP

   OFDM

   Receiver

   Channel

   Channel Estimation

   Figure 1: BLOCK DIAGRAM OF PROPOSED METHOD

5. SIMULATION RESULTS:

   In this section, we compare the performance of the proposed channel estimation technique with the conventional approaches through computer simulations. To confirm the effectiveness of the proposed methods, two metrics, that is, MSE and BER are adopted for performance evaluation. The simulation results of comparison of transmitted signal and received signal are shown in figure (2)

   TABLE 1: Simulation Parameters

   S.NO.	PARAMETERS	VALUES
   1.	FFT Size	64,128, 256,512
   2.	CP	¼
   3.	Coding	Convolutional coding
   4.	Constraint length	7
   5.	SNR	-15 to 15 dB
   6.	Modulation	BPSK, 16 QAM, 64 QAM
   7.	Code rate	1/2
   8.	Channel length	4,8

   1. Mean Square Error (MSE) Comparison of proposed algorithm between conventional Algorithm

      2 2

      				LMMSE-CE VirtuaL Pilot based CE Threshold Based-CE FDE-NP CE CTSD-CE Proposed method

      				LMMSE-CE VirtuaL Pilot based CE Threshold Based-CE FDE-NP CE CTSD-CE Proposed method

      10 10

      0 0

      10 10

      -2 -2

      10 10

      -4 -4

      MSE

      MSE

      10 10

      -6 -6

      10 10

      -8 -8

      10 10

      -10

      10

      -12

      10

      -15 -10 -5 0 5 10 15

      SNR(dB)

      -10

      10

      -12

      10

      -15 -10 -5 0 5 10 15

      SNR(dB)

      1. (b)

   10

   2

   				LMMSE-CE VirtuaL Pilot based CE Threshold Based-CE FDE-NP CE CTSD-CE Proposed method

   2

   				LMMSE-CE VirtuaL Pilot based CE Threshold Based-CE FDE-NP CE CTSD-CE Proposed method

   10

   0

   0

   10 10

   -2

   -2

   10 10

   -4

   -4

   MSE

   MSE

   10 10

   -6

   -6

   10 10

   -8

   -8

   10 10

   -10

   10

   -12

   10

   -15 -10 -5 0 5 10 15

   SNR(dB)

   -10

   10

   -12

   10

   -15 -10 -5 0 5 10 15

   SNR(dB)

   (c) (d)

   Figure 2: MSE V/S SNR plot for 64-QAM modulation (a) N= 64, L=4, (b) N= 128, L= 4, (c) N=256, L= 4 & (d) N= 512, L=4

   2

   2

   				LMMSE-CE VirtuaL Pilot based CE Threshold Based-CE FDE-NP CE CTSD-CE Proposed method

   				LMMSE-CE VirtuaL Pilot based CE Threshold Based-CE FDE-NP CE CTSD-CE Proposed method

   10 10

   0 0

   10 10

   -2 -2

   10 10

   -4 -4

   MSE

   MSE

   10 10

   -6 -6

   10 10

   -8 -8

   10 10

   -10

   10

   -12

   10

   -15 -10 -5 0 5 10 15

   SNR(dB)

   -10

   10

   -12

   10

   -15 -10 -5 0 5 10 15

   SNR(dB)

   (a) (b)

   2

   2

   				LMMSE-CE VirtuaL Pilot based CE Threshold Based-CE FDE-NP CE CTSD-CE Proposed method

   				LMMSE-CE VirtuaL Pilot based CE Threshold Based-CE FDE-NP CE CTSD-CE Proposed method

   10 10

   0

   0

   10 10

   -2

   -2

   10 10

   -4 -4

   MSE

   MSE

   10 10

   -6 -6

   10 10

   -8 -8

   10 10

   -10

   10

   -12

   10

   -15 -10 -5 0 5 10 15

   SNR(dB)

   -10

   10

   -12

   10

   -15 -10 -5 0 5 10 15

   SNR(dB)

   (c) (d)

   Figure 3: MSE V/S SNR plot for 64-QAM modulation (a) N= 64, L=8, (b) N= 128, L= 8, (c) N=256, L= 8 & (d) N= 512, L=8

   0

   				BPSK 16 QAM 64 QAM

   10

   -1

   10

   -2

   10

   -3

   10

   BER

   -4

   10

   -5

   10

   -6

   10

   -7

   10

   -8

   10

   0 5 10 15 20 25

   Eb/N0 (dB)

   Figure 4: PLOT OF BER V/S Eb/No. FOR DIFFERENT MODULATION TECHNIQUE

   0

   				Virtual pilot based CE Threshold Based CE FDE-NP CE CTSD-CE Proposed method

   10

   -2

   10

   -4

   BER

   10

   -6

   10

   -8

   10

   -10

   10

   -15 -10 -5 0 5 10 15 20

   Eb/N0 (dB)

   Figure 5: BER Performance of the conventional and proposed Method for 64 QAM Modulation scheme

6. CONCULSION:

In this paper, we introduce a new Modified iterative- Linear minimum mean square error (MI-LMMSE) channel estimator technique in the Coded-OFDM systems. The performance can be improved by applying FEC codes in contrast to uncoded system. It is observed from simulation results that the proposed algorithm outperforms than that of other conventional algorithm. Proposed algorithm helps to transmit the data with low bit error rate with low error rate in the noisy environment.

REFERENCES:

1. SU HU et.al: Training Sequence Design for Efficient Channel Estimation in MIMO-FBMC Systems IEEE access Vol. 5 2017.

2. Yu Zhu et.al: Robust Single Carrier Frequency Domain Equalization with Imperfect Channel Knowledge IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 15, NO. 9, SEPTEMBER 2016.

3. Sunho Park: Iterative Channel Estimation Using Virtual Pilot Signals for MIMO-OFDM Systems IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol. 63, NO. 12, JUNE 15, 2015.

4. Peng Xuet.al: Analysis and Design of Channel Estimation in Multi cell Multiuser MIMO OFDM Systems IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, Vol. 64, NO. 2, FEBRUARY 2015 page no. 610-620.

5. Petros S. Bithaset.al: An Improved Threshold-Based Channel Selection Scheme for Wireless Communication Systems IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, Vol. 15, NO. 2, FEBRUARY 2016.

6. Han Wang et.al: A New Sparse Adaptive Channel Estimation Method Based on Compressive Sensing for FBMC/OQAM Transmission Network MDPI, sensors 2016 page no. 2-12.

7. Dimitrios Katselis et.al: Preamble-Based Channel Estimation for CP- OFDM and OFDM/OQAM Systems: A Comparative Study IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol. 58, NO. 5, MAY 2010.