An Adaptive Modulation and Efficient Power ...

International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 12, December 2016

An Adaptive Modulation and Efficient Power Allocation Technique for V-BLAST 2X2 MIMO OFDM System Rajvirsinh Rana, PhD Scholar, School of Engineering, RK University, Rajkot, India. Jagdish M Rathod, Associate Professor, Electronics Department, Birla Vishwakarma Mahavidyalaya, Vallabh Vidyanagar, India. 

digital modulation scheme to simplify the equalization in frequency selective channels is orthogonal frequency division multiplexing (OFDM). The OFDM technique employs closely spaced narrowband orthogonal subcarriers for data transmission, thus avoiding the problem of inter symbol interference associated with wideband frequency selective channels [3]. With the introduction of orthogonal frequencydivision multiplexing and multiple antennas in cellular networks, there are new opportunities to adapt the transmission for propagation and interference conditions [4].The key radio techniques which have to be considered in developing such systems include multiple-input multipleoutput (MIMO) communication based on multiple antennas, multi-carrier modulation, as well as adaptive modulation and coding [5]. In closed-loop systems, transmit pre-coding reacts to channel conditions in order to improve the system capacity or bit error rate (BER). For instance, closed-loop MIMO OFDM allows transmit pre-coding on frequency-selective channels to preprocess signals at the subcarrier level and facilitates the utilization of capacity or performance gain. However, a carrier frequency offset may exist because of mismatch of oscillators and/or the Doppler shift caused by the relative motion between the transmitter and receiver or movement of other objects around transceivers. Such frequencies offsets distort the orthogonality between subcarriers and result in inter carrier interference (ICI) [6]. The complete knowledge of channel state information (CSI) at the transmitter will resolves this interference problem and optimizes the performance of the system. However, a perfect CSI knowledge is indeed a rather impractical assumption due to channel feedback delays, limited feedback conditions, estimation errors, and/or quantization errors. Hence, a partial CSI at the transmitter is a more realistic assumption between the two extremes, perfect CSI and no-CSI [7]. With the knowledge of CSI the transmission rate of the system can be improved by the adaptive modulation method. Also MIMO systems with channel coding are quite different from the single antenna ones. So the adaptive algorithms with channel coding in single antenna systems cannot be used in MIMO systems directly and needs additional considerations [8].

Abstract— Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is the foundation for most advanced wireless local area network and mobile broadband network standards because it achieves the greatest spectral efficiency and, therefore, delivers the highest capacity and data throughput. Based on the detection method used in this technique it can be classified into several systems among them Vertical Bell Labs Layered Space-Time (V-BLAST) MIMO OFDM found to be the high speed MIMO OFDM in which VBLAST algorithm is used for the detection purpose. In this paper, optimal power allocation is proposed based on the information estimated from the channel such as Signal to Interference plus Noise Ratio (SINR) and Bit Error Rate (BER). Based on these parameters the power is allocated optimally to the subcarriers using Cuckoo Search algorithm as well as Greedy power allocation and the corresponding modulation is adapted subsequently to achieve better BER. The proposed method is implemented on the MATLAB platform and also it is compared with existing methods with adaptive modulation and optimal power allocation in MIMO OFDM. Index Terms— Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing, (MIMO-OFDM), Vertical Bell Labs Layered Space-Time (V-BLAST) detection, Optimal Power allocation, Cuckoo Search (CS) algorithm, Greedy power allocation.

I. INTRODUCTION

R

CENTLY,

increasing interest has been concentrated on modulation techniques that provide high data rates over broadband wireless channels for applications, including wireless multimedia, wireless Internet access, and futuregeneration mobile communication systems. The promising Rajvirsinh C Rana did B.E from A. D. Patel Institute of Technology & M.S. in Electronics & Communication from University of Bridgeport, USA. Currently pursuing PhD in RK University, Rajkot, Gujarat, India and working as an Assistant Professor in BVM Engineering College, Vallabh Vidyanagar, Gujarat, India(e-mail: [email protected]). Jagdish M Rathod did B.E from G E C Modasa & M.E in Electronics communication systems from D D University and Ph D from S P UniversityIndia. He has published 30 papers in conference proceeding and 25 papers in journals at national and international level (e-mail: [email protected]). Paper is submitted on 6th December,2016 for review.

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will employ and based on the SINR the optimal power will be allocated. The multi objective optimization technique will be utilized for the optimization of power allocation based on SINR, either the cuckoo search or artificial bee colony algorithm will consider for performing the optimization. The proposed system will also enhance the performance in terms of spectral efficiency, outage probability, power efficiency and bit error rate. The proposed system will implemented in the working platform of Matlab and the results are compared with the conventional techniques for the adaptive modulation and power allocation. The system architecture for the proposed method is given in figure 1. In figure 1, the architecture of the proposed V-BLAST MIMO OFDM system is depicted. The data generated from the data source and modulated and allocated with different subcarriers having equal amount of power at first. In general, different sub-channels can contain different numbers of bits and each of them can be independently encoded, interleaved and modulated to form the modulated symbol. For simplicity, we assume that an ordinary M-QAM signal constellation is being used for the modulation [21].Then the modulated data in serial form will be converted into parallel form and passed through the Inverse Fast Fourier Transform (IFFT)for mapping the data stream on different sub carriers. Next cyclic prefix addition is performed to reduce the effects of Inter Symbol Interference (ISI) and Inter sub carrier Interference (ICI). Finally the data streams are transmitted through M number transmitting antennas over the Rayleigh fading channel and they are received by the N number of receiving antennas (Normally N ≥ M for V-BLAST based MIMO OFDM system). After receiving the data streams at receiver section they are passed through the FFT modules after removing the cyclic prefix and with the assumption that the delay between the signals reaching the receiver antennas are same in addition to the perfect symbol time synchronization. Then the parallel transformed data streams are combined by means of parallel to serial converter. From the combined signal the channel condition will be estimated using Linear Minimum Mean Square (LMMSE) method and the estimated channel information is used in the process of adaptive modulation and power allocation to the carriers. The optimum power allocated to the carriers and the corresponding modulation type adapted can be determined by means of Cuckoo Search algorithm with the consideration of Signal to Interference Noise Ratio (SINR) and by greedy power allocation algorithm using BER and SNR from channel estimation. The estimated channel information also given to the V-BLAST signal processing and detection block in which V-BLAST algorithm will be performed to recover the transmitted signal together with the Minimum Mean Square Error (MMSE) equalizer. The basic idea for power allocation is to transmit power and signal by adjusting transmission power and signal bit rate, so that the SNR Eb/No of the receiving end maintains a constant value[23]. For multiuser MIMO-OFDM V-BLAST communication system, it is necessary to allocate power and bits between sub-carrier and sub-channel to make sure that

The performance achieved through MIMO technology entails a considerable increase in signal processing complexity in receiver and, there exists a major challenge in designing low-complexity receivers for multichannel wireless receivers. A significant development for MIMO communications is VBLAST receiver, and two areas of research related to this technology are reduction of complexity, i.e. avoidance of matrix inversions, and extension to broadband implementation [14]. Bell vertical layered space-time coding (V-BLAST) is a promising technique due to its high spectral efficiency and simple structure. The channel of multiple inputs and multiple outputs (MIMO) V-BLAST system is assumed as flat fading when it is originally proposed. Most algorithms in the VBLAST system require the knowledge of the accurate channel state information (CSI) and have a constraint that the number of receive antennas must be larger than or equal to the number of the transmit ones [15]. There have been great efforts to study the adaptive bit and power allocation for MIMO systems. Although the Singular Value Decomposition (SVD) based adaptive modulation can achieve a theoretical bound, the computation complexity is very high and the feedback information is large. As an alternative, the use of the VBLAST algorithm for bit and power allocation through greedy algorithm was proposed. All of the adaptive algorithms thus provided are derived without channel coding and still with relatively high complexity [8].

II. MATHEMATICAL MODELING

Fig. 1. Architecture of the proposed V-BLAST MIMO OFDM system.

The V-BLAST system is one of the motivated technologies in MIMO OFDM for achieving high spectrum efficiency. In V-BLAST MIMO system the estimation of channel is the biggest challenge. In recent period the better channel estimation have achieved by the recent research work, but the modulation and power allocation is still a complex task, so it become a current task in the field of V-BLAST MIMO OFDM. Hence in this work we will develop optimal power allocation technique of V-BLAST MIMO OFDM system. For the adaptive optimal power allocation we will calculate the bit error rate (BER) and signal to interference plus noise ratio (SINR), based on the BER an adaptive modulation technique 67



SNR Eb/Nois constant after demodulation[24].

good level. The Signal to Noise Ratio (SNR) for the M-ary QAM [30] can be calculated on knowing BER from equation (22) and it is given in equation (25).

III. EFFICIENT POWER ALLOCATION The power distribution among subcarriers depends on the optimization criterion. The sum capacity of the V-BLAST MIMO OFDM is given as in equation (21).

SNR 

C   log 2 (1  SINR) (21)

BER of the ith transmitted symbol in terms of the post detection SINR can be calculated as in equation (22) if the symbol cancellation is perfect.

BERkn  Ekn  f (SINR)

B. Greedy Power allocation in V-BLAST MIMO OFDM The greedy power allocation algorithm for allocating the optimal power to the subcarriers contains two stages. They are Initial stage and Data transmission stage. In the initial stage the bit and power allocation is determined using greedy algorithm. In the transmission stage the carriers will be allocated with optimal power and bits, found from the initial stage. The main aim of greedy power allocation is to allocate the maximum power to the subcarrier through strongest sub channel and lower power to the weaker one. For achieving this the condition of the channel is estimated from channel estimation through SNR value and then the channels are sorted in the ascending order of their strength. After that the power and bit will be allocated optimally to the carriers as determined from the initial stage[22,25-28]. Hence from the estimated channel information the technique of adaptive modulation and optimal power allocation can be done in the V-BLAST MIMO OFDM with the use of Cuckoo search algorithm as well as greedy power allocation algorithm. The capacity of the system will be also improved and it yields good BER performance.

(22)

Where, f is the prediction function. For the independent transmit symbols the overall BER can be calculated as the mean of each symbol’s BER (equation (23)).

BER 

1 M

M

 BER i 1



(25) Where Q-1(x) is Inverse Q function. From the values calculated from the equations (22) and (25) the modulation can be chosen adaptively as given by the table 1.

K 1 N 1 k 0 n 0



2 1 1 Q ( BER / 4) (2 c  1) 3

ki

(23)

The Cuckoo search algorithm is used to find the transmit power that minimizes the BER. The cost function can be thus expressed as in equation (22). Thus the equation (22) is used as the objective function in the Cuckoo Search (CS) algorithm to find the optimal powers to be allocated to the carriers under the total power constraint and the Greedy power allocation can also be used to optimally allocate the power to the subcarriers. A. Cuckoo search (CS) algorithm The basic principle of the Cuckoo Search algorithm [29] is that each cuckoo bird lays a single egg at a time which is discarded into a randomly chosen nest. The optimum nest with great quality eggs is carried over to next generations. The number of host nests is static and a host can find an alien egg with a probability (Pa) [0, 1], whose presence leads to either throwing away of the egg or abandoning the nest by the host bird. One has to note that each egg in a nest represents a solution and a cuckoo egg represents a new solution where the objective is to replace the weaker fitness solution by a new solution.The steps involved in the CS algorithm is given in [29]. In Cuckoo search algorithm,The levy flight behavior in the CS algorithm can be calculated as in equation (24).

IV. RESULTS In this paper we have proposed an adaptive modulation and optimal power allocation scheme for the high speed V-BLAST MIMO ODFM system through channel estimation conditions in which the optimal power allocation can be done with the help of Cuckoo Search algorithm. We are analyzing the performance of the proposed model using 2X2 V-BLAST MIMO OFDM. The data used for communication through the system is generated by means of Pseudo Random sequence generator with the data rate of 64 kbps. The total power constraint in the system for the transmission of signals is 1W which is allocated to the subcarriers. The total channel bandwidth is 20 MHz and is divided into 64subcarriers each with random amount of allocated power. The signals to be transmitted are encoded with convolutional encoding and they are mapped into symbols by means of M-ary QAM. Next they are converted into time domain through IFFT with the size of 64 and the sampling rate is 20MHz. After that cyclic prefix is added to the symbols with the length of 16 that is 16 bits from the tail of the sequence is appended before the start of the bit sequence. At the receiver side the signals are demodulated by removing the cyclic prefix and converting it back to the frequency domain. Then the corresponding signals are used for channel estimation and V-BLAST decoding to recover the

Pi (t  1)  Pi (t )    Levy( ),   0 Levy( )  t ( ),1    3

(24) Based on the results calculated from the cuckoo search algorithm the power will be allocated optimally to the subcarriers. Next depending on the power allocated to the subcarriers modulation can be chosen adaptively for modulating the symbols. The basic concept in adaptive modulation is to transmit at high data rate when the subcarrier power is good and at lower data rate when the same is not at 68



transmitted signal. In the Channel estimation stage cuckoo search algorithm is implemented for optimal power allocation and adaptive modulation. The results shows that the proposed system achieves better BER and SNR performance before and optimal power allocation and it is given in the following table I. TABLE I COMPARISON OF BER AND SNR BEFORE AND AFTER OPTIMAL POWER ALLOCATION

Before Optimal power allocation SNR

BER

After Optimal power allocation SNR

BER

17.29

10

-1

18.33

10-1

21.35

10-2

22.34

10-2

25.32

10-3

26.33

10-3

29.33

10-4

30.24

10-4

Fig. 3. Greedy Power allocation and cuckoo search based power allocation in 2X2 V-BLAST MIMO OFDM.

The BER and SNR performance graph of the proposed system is given in the figure 2. From the figure it is clear that the system achieves higher SNR at lower BER values after allocating the power optimally rather than before optimal power allocation. Thus the receiver receives higher quality signal with minimum number of bit errors.

Fig. 4. BER vs. SNR for Greedy power allocation in 2X2 V-BLAST MIMO OFDM for 4 QAM with LS and LMMSE channel estimation.

Fig. 2. BER as a function of SNR for the proposed method before and after optimal power allocation.

The power allocation to the subcarriers in the 2X2 V-BLAST MIMO OFDM using Greedy power allocation and the Cuckoo Search algorithm are compared in the following figures 3. In the figure 3, the power allocated to the first three carriers are low with greedy power allocation compared to the power allocation using cuckoo search algorithm and for the fourth carrier the power allocation using both the methods are same. Hence it is clear that both the algorithms providing proper power allocation to the carriers with respect to the total power constraint in the system. The BER versus SNR performance graph with different channel estimation techniques and different power allocation algorithms are shown in the following figures 4 to 9. Fig. 5. BER vs. SNR for Greedy power allocation in 2X2 V-BLAST MIMO OFDM for 16 QAM with LS and LMMSE channel estimation. 69



Fig. 6. BER vs. SNR for Greedy power allocation in 2X2 V-BLAST MIMO OFDM for 64 QAM with LS and LMMSE channel estimation.

Fig. 9. BER vs. SNR for Cuckoo Search based power allocation in 2X2 V-BLAST MIMO OFDM for 64 QAM with LS and LMMSE channel estimation.

V. CONCLUSION In this paper we have proposed a high speed V-BLAST MIMO OFDM system with optimal power allocation and adaptive modulation from the parameters measured from channel estimation conditions. Initially the subcarriers are allocated with the random amount of power owing to the total power constraint and then optimal power to be allocated for the carriers is determined by the Cuckoo Search algorithm depending on the condition of the carriers measured from the channel estimation. After allocating the optimal power to the subcarriers we are choosing the modulation adaptively as per the strength of the carriers with target BER and the measured SNR and with different coding rates. The results shows that our proposed method achieves better performance in terms of BER and SNR compared with the existing methods used for improving the performance of the MIMO OFDM.


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Rajvirsinh Rana received the B.E. from ADIT college & M.S. in Electronics & Communication from University of Bridgeport, USA. Currently pursuing PhD in RK University and working as an Assistant Professor in BVM Engineering College, Gujarat, India.

Dr Jagdish M Rathod received the B.E. from G E C Modasa & M.E in Electronics communication systems from D D University and Ph D from S P UniversityIndia. He has published 30 papers in conference proceeding and 25 papers in journals at national and international level.

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