Advances in Wireless and Mobile Communications. ISSN 0973-6972 Volume 5, Number 2 (2012), pp. 71-78 © Research India Publications http://www.ripublication.com/awmc.htm
An Effective Signal Decoding Algorithm for Wireless MIMO Receivers used in Mobile Communication #2 #3 #4 Sayan Dey#1 , Supratim Dutta , Pinaki Ghosh , Sandipan Chowdhury , Siddhanta Debnath#5 and Souradipa Das#6
Dept. of Electronics & Comm. Engineering, Techno India College of Technology Newtown, Rajarhat, Kolkata-700156 India E-mail:
[email protected],
[email protected], 3
[email protected],
[email protected], 5
[email protected],
[email protected]
Abstract In the field of modern wireless communication, Multiple Input Multiple Output (MIMO) systems are being used to increase the channel capacity and data transfer rate of wireless communication system to a great extent. But with the usage of MIMO systems, the signal decoding is becoming very complex and requires complex algorithms for signal recovery. This results in bulky hardware which is complex and not very cost efficient. The present study deals with a simple MIMO decoding algorithm using the concepts of correlation and its comparison with the existing decoding algorithms like Least Mean Square (LMS), Maximum Likelihood Sequence Estimation (MLSE) etc. The MIMO system in this study consists of a simple series of Code Division Multiple access (CDMA) transmitters, a series of distinct channels (‘n’ channels in case of nxn system) having individual transfer functions and a set of receivers. Each receiver receives a combination of ‘n’ signals and so requires an effective algorithm to find out the “best fit” signal. In this study, a correlation MIMO decoder is designed in such a way so as to minimize the bit error rate when compared to the other equalization algorithms. This decoder is low power consuming and the algorithm has comparatively lesser time and computational complexity in comparison to the other algorithms under observation. Keywords: MIMO, Correlation, LMS, MLSE, CDMA
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Introduction A MIMO system is a communication system having multiple transmitters and multiple receivers. A multi antenna system can be considered as a MIMO system.[1,2,3] Unlike single input single output (SISO) systems, the MIMO systems require special decoding algorithms for signal recovery.[4.5] Several researchers and scientists have developed MIMO decoding algorithms for detection of MIMO signals, but efficiency of those algorithms is less as they have high bit error rate.[6,7,8] The algorithm suggested in this paper offers comparatively low bit error rate. The hardware implementation of this algorithm is much simpler as compared to the other existing decoders. In the present study, a 2nd order MIMO system is considered. The basic spread spectrum technique is used i.e. a CDMA transmitter is used. The MIMO transmitter consists of two individual CDMA transmitters transmitting simultaneously through two individual channels. The receiver system consists of two antennas each connected to an individual CDMA receiver. Each antenna accepts a combination of the two transmitted signals. Using the correlation algorithm, the signals received by the two receivers are correlated with each other. The resultant correlated output is again cross correlated with a digital signature of the original data to produce the approximated or recovered data. The other equalization algorithms are also implemented and compared with the correlation decoder in this study. It was found that the correlation decoder gives the best result in terms of the bit error rate or bits lost per frame. A survey of the literature suggests that very few studies based on design of low power MIMO decoders have been performed. [9,10,11,12] Also, the studies done in the past does not effectively reduce the computational complexity of the decoding algorithm and increase the accuracy of the received signal. [13,14,18,19]Thus, the present study deals with the following: • To develop a simple MIMO decoding algorithm having less time and computational complexity. • To design and implement a low power system on chip for the decoding algorithm. • To perform a comparative study with the existing algorithms.
Methods and Materials The present study aimed at suggesting an effective MIMO decoding algorithm. A simple 2X2 MIMO system can be considered. Two individual signals are transmitted simultaneously from the two antennas of the transmitter. After passing through two distinct channels having distinct transfer functions, the signals are received in the receiver. The receiver consists of two individual CDMA demodulator (as CDMA modulation technique is applied in this study), each of which receive both the damaged signals. Now, the receiver has to detect the best output out of the multiple received signals. For this, the study is done in certain parts which are as follows The Spread Spectrum Technique Every wave, if represented in the frequency domain, has a certain spectrum covering a certain range of frequencies. This range of frequency is termed as the bandwidth of
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the signal. Similarly, for carrier modulation, there is a certain bandwidth of the carrier signal. But, with the increase of user pressure, the bandwidth requirements gradually increased and new techniques to use bandwidth effectively became an absolute necessity.[15] A noise is an unwanted signal having a very large bandwidth. Theoretically, its bandwidth ranges from -∞ to +∞. If any data signal is modulated with noise, the spectrum of the resultant signal i.e. the modulated signal spreads over the entire range of frequency of the carrier, in this case, noise. This technique of transmitting data by noise modulation is known as the Spread Spectrum Technique. Mathematically, it may be represented as follows: The baseband signal [Sbaseband(t)] is multiplied with PN sequence [SPNsequence [16] transmitting end (t)] and thus we get the modulated signal [M(t)]. M (t) = Sbaseband(t) x SPNsequence transmitting end(t) The correlation decoding algorithm When the MIMO receivers receive the two signals, each receiver has to estimate and recover the exact signal from the mixture of damaged signal. The figure 1 below shows a 2X2 MIMO receiver which consists of two antennas and each antenna receiving the two transmitted signals. The basic correlation states that to what extent, the two signals under observation are similar to each other. Mathematically, Let us consider two signals m(t) and n(t). If these two signals are correlated, the resultant will be: c(t) = m(t)*n(t) =
m t n t
Ƭ dƬ
(1)
Now, clearly, this signal consists of the common frequency components of the two received waves. Thus, the first round predicted signal can be obtained from this operation. Next, this recovered signal is again cross correlated with the digital signature of the actual data to make the estimation more precise and exact. Let the digital signature of the actual data be d(t). Then, the final recovered data after second round of correlation will be: r(t) = d(t)*c(t) =
d t c t
p dp
Putting the values from the equation (1), we get, =
d t
m t
p n t
=
d t
m t
p n t
Ƭ Ƭ
p dƬ dp p dƬ dp
(2)
Here, ‘Ƭ’ and ‘p’ are the two time delays respectively of the first stage and second stage correlation operations.
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Thus, the resultant signal consists of the component of the original signal d(t) which can be filtered out using band pass filters and thus, the original transmitted message signal can be recovered. A Fourier transform of the equation (2) has three components viz. D(f), M(f) and N(f). Thus, the original signal bandwidth can be very easily recovered. This algorithm has two iterations of similar operation i.e. integration. Thus, the computational complexity is very less and it is very effective for multiple signal decoding.
Figure 1: A 2X2 MIMO System
The design was made using MATLAB SIMULINK block sets and the Field Programmable Gate Array (FPGA) implementation was done using Xilinx System Generator. The design consisted of two transmitters with Pseudo Random Noise (PN) sequence as the carrier. Each transmitter was designed to transmit the modulated CDMA signal individually through different channels. After the signals reach the receiver, each receiver consist of the first set of correlators which perform the cross correlation of the two received signal. The second set of correlators correlate the result of the first stage of correlators with the digital signature of the data to estimate the actual data transmitted. There can be another set of correlators to correlate the best output out of the series of received signals but this is optional and valid only in the case when all the MIMO transmitters transmit the same signal. The figure 2 shows the block diagram of the correlation algorithm which is discussed in the present study. The Xilinx system generator is used for the implementation of the MATLAB design in the FPGA kit. The other standard decoding algorithms were also implemented and the results were compared with the correlation algorithm proposed in this paper. The other algorithms include the LMS equalizer, MLSE equalizer and CMA equalizer.
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Figure 2: Block Diagram of Correlation Algorithm
Results & Discussion The different MIMO algorithms were studied implemented and a comparison was carried out. The Figure 3 represents the BER comparison chart of the different MIMO decoding algorithms. The blue line indicates the BER of CMA algorithm, the red line indicates the LMS algorithm, the green represents the MLSE algorithm and the violet indicates the correlator algorithm. It is clear from the graph that the BER or bits lost per frame is the least in case of the correlation algorithm when compared to the other standard detection algorithms. Thus, the effectiveness of the proposed algorithm can be well justified. The table 1 shows the comparative study of the different VLSI design parameters of the different decoders. It is evident from the table that the power consumption, number of Combinational Logic Blocks (CLBs) used; operating temperature etc. is less when compared to the other algorithms. This shows that the hardware of the proposed algorithm has a simpler design as compared to other conventional designs. Hence, invariably the time delay of the decoder will be less and hence the design will have less time complexity.[17,18,19,20] Moreover, the iterations in case of the proposed algorithm is less as compared to the other algorithms under observation. Hence it can be also considered as less computationally complex and hence can be well stated as an effective design.
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0.01 0.008 0.006 0.004 0.002 0 0
50
100
SNR (IN dB)
Figure 3: BER Comparison of the Decoding Algorithms
Thus, it can be clearly stated that the proposed algorithm is much more efficient than the existing algorithms and so can be used effectively for decoding of MIMO signals and recovering the best output out of simultaneously received multiple signals. This would reduce the problem of fading to a great extent and will help to make the communication systems more flexible, versatile and make them fit for real time operations mainly in the defense and other military activity where high speed and accurate data transmission is an absolute necessity for successful operation. Table 1: Comparison of VLSI Design Issues VLSI Parameters No. of CLB used Static Power Consumption (in Watts) Quiescent Power (in milli Watts) Operating Temperature (in oC) Time delay (in µsecs)
CMA Algorithm 38
LMS Algorithm 32
MLSE Algorithm 36
Correlation Algorithm 24
1.59
0.978
0.5912
0.089
0.25
0.19
0.09
0.01
28.9 65.1
27.5 36.3
27.3 25.9
26.8 16.8
References [1] J. Ruggiero, T. Bonnema, D. J. Inman, application of SISO and MIMO modal analysis technique.
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[2] A. Khattab, A. Sabharwal, Edward W. Knightly, Fair Randomized Antenna Allocation in Asynchronous MIMO Multi-hop Networks ECE Department, Rice University, Houston , TX 77005 [3] Demystifying MIMO, a Bluesocket BluePaper. [4] A feature of MIMO systems discussed in a Bell Labs Technical Journal article in 1996. [5] M. Viberg_, T. Boman, U. Carlberg, L. Pettersson, S. Ali_, E. Arabi_, M. Bilal_ and O. Moussa, Simulation of MIMO Antenna Systems in Simulinkand Embedded Matlab, Department of Signals and Systems, Chalmers University of Technology, Göteborg, Sweden. [6] “Wireless Performance Evaluation,” Belkin Corp. Wireless “pren” Router, The Tolly Group FLEXChip Signal Processor (MC68175/D), Motorola, 1996. [7] Kuo, S.M., Morgan, D.R. Active Noise Control Systems. John Wiley & Sons Inc., 1996. A. Karnik, “Performance of TCP congestion control with rate feedback: TCP/ABR and rate adaptive 6 TCP/IP,” M. Eng. thesis, Indian Institute of Science, Bangalore, India, Jan. 1999. [8] Hult, T.; Mohammed, A.; Nordebo, S. Active Suppression of Electromagnetic Fields using a MIMO Antenna System. In 17th Int. Conf. on Appl. Electromagnetics and Commun. ICECom 2003, 2003. Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification, IEEE Std. 802.11, 1997. [9] N. Seifi, A. S. Tehrani, and M. Viberg, ““Simulation of a Wideband Reconfigurable Multi-Antenna System with Space- Time Coding”,” in Nordic Matlab Users Conference, Stockholm, Sweden, Nov. 2008. [10] T. Hult, A. Mohammed, Suppression of EM field using MIMO antenna system, Sweden. [11] G. Foschini and M. Gans, ““On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas”,” Wireless Personal Communications, vol. 6, pp. 311– 335, March 1998. [12] S. Alamouti, ““A Simple Transmit Diversity Technique for Wireless Communications”,” IEEE J Sel. Areas in Comm., vol. 16, no. 8, pp. 1451– 1458, Oct. 1998. [13] H. Huang, “Spatial channel model for multiple input multiple output (MIMO) simulations” http://www.3gpp.mobi/ftp/specs/htmlinfo/25996.htm, Dec. 2004, as read 2008-09-12. [14] O. Moussa and M. Bilal, ““Impact of Matching Network on the Performance of Antenna Arrays”, ”Master’s thesis, Chalmers University of Technology, Department of Signals and Systems, Mar. 2008. [15] E. Arabi and S. Ali, ““Modeling and Simulation of RF Front- End”,”Master’s thesis, Chalmers University of Technology, Department of Signals and Systems, Mar. 2008. [16] Johanna Ketonen, Markku Juntti and Joseph R. Cavallaro, “ PerformanceComplexity Comparison of Receivers for a LTE MIMO-OFDM System”, IEEE Transactions on Signal Processing, Vol. 58, No.6, June 2010.
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[17] S. Mondal, A.M. Eltawil, and K.N. Salama, “Architectural optimizations for low power K-best MIMO decoders,” IEEE Trans. Veh. Technol., Vol. 58, no. 7, pp. 3145-3153, sep.2009. [18] C. Studer, A. Burg, and H. Bolcskei, “Soft output sphere decoding: Algorithms and VlSI Implementation,” IEEE J.Sel. Areas Communcation., Vol. 26, no. 2, pp. 290-300, Feb. 2008. [19] “System Architecture and Implementation of MIMO sphere Decoders on FPGA”, Xinming Huang, member, IEEE, Cao Liang, Student member IEEE, and Jing Ma, member, IEEE, IEEE TRANSACTION ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, VOL. 16, NO.2, FEBRUARY 2008. [20] K. Cmpton and S. Hauck, “ Automatic design of area efficient configurable ASIC cores,” IEEE Trans. Comput., vol 56, no.5, pp. 662-672, May 2007.
