VLSI Implementation of Spatial Modulation MIMO System for Wireless communication Networks

VLSI Implementation of Spatial Modulation MIMO System for Wireless communication Networks Mohammad Irshad begum M.Tech.,(VLSID) Student Shri Vishnu Engineering College for Women Bhimavaram, Andhra Pradesh, India Dr. Pushpa Kotipalli Professor: ECE Department, Head of ATL Shri Vishnu Engineering College For Women Bhimavaram, Andhra Pradesh, India Abstract-- MIMO (Multiple Input Multiple Output) is an antenna technology for wireless communications in which multiple antennas are used at both the source and destination to send multiple parallel signals. Spatial Modulation (SM) is a transmission technique proposed MIMO systems, where only one transmit antenna is active at a time. In SM, information bits are conveyed through the index of the active transmit antenna in addition to the information bits conveyed through conventional modulation symbols. In spatial modulation, the stream of bits to be transmitted in one channel is divided into two groups. One group i.e., m- bit sequence chooses one antenna from a total of N t = 2 m antennas. A known signal is transmitted on this chosen antenna. The remaining N t -1 antennas remain silent. The second group determines the symbol to be transmitted from the chosen antenna. In this paper we present the VLSI implementation of Spatial Modulation MIMO system. The active antenna number detection algorithm called Iterative Maximal Ratio Combining (i-mrc) algorithm is presented This system is designed in VHDL language, simulated using Modelsim simulator and realized on SPARTRAN-3E FPGA Kit. Experimental results of the proposed technique shows the increased performance in terms of Accuracy. Index Terms Spatial Modulation (SM), Multipleinput- Multiple- output (MIMO), Interchannel interference (ICI), Complexity. IEE-754 Single precision Floating point format. 1. INTRODUCTION MIMO transmits and receives two or more data streams through a single radio channel. Thereby the system can deliver two or more times the data rate per channel without additional bandwidth or transmit power. The need to improve the spectral efficiency and reliability of radio communication is driven by the ever increasing requirement for higher data rates and improved Quality of service (QOS) across wireless links. MIMO technology is one solution to attain this by transmitting multiple data streams from multiple antennas. MIMO transmission strongly depends on transmit and receive 196 antenna spacing, transmit antenna synchronization and the reduction of interchannel interference (ICI) at the receiver input. An alternative transmission approach that entirely avoids ICI at the receiver input is used for BPSK and QPSK transmission respectively. The basic idea is to compress a block of N t symbols into a single symbol prior to transmission, where N t indicates the number of transmit antennas. Information is retained by this symbol and is mapped to one and only one of the N t antennas. The task of the receiver is twofold: First, to estimate the single symbol and second to detect the respective antenna number from which the symbol is transmitted. However this scheme suffers from a loss of Spectral efficiency. Traditional modulation techniques such as BPSK (binary phase shift keying), QPSK (Quadrature phase shift keying) etc. map a fixed number of information bits into one symbol. Each symbol represents a constellation point in the complex two dimensional signal planes. This is referred to as signal modulation. In this paper an alternative transmission approach is proposed in which this two dimensional plane is extended to a third dimension i.e., spatial dimension. This is referred as Spatial modulation. This new transmission technique will result in a very flexible mechanism which is able to achieve high spectral efficiency and very low receiver complexity. SM is a pragmatic approach for transmitting information, where the modulator uses well known modulation techniques (e.g., QPSK, BPSK), but also employs the antenna Index to convey information. Ideally, only one antenna remains active during transmission so that ICI is avoided. Spatial Modulation (SM) is a recently proposed spatial multiplexing scheme for Multiple-Input-Multiple-Output (MIMO) systems without requiring extra bandwidth or extra transmission power. SM does not place any restriction on the minimum number of receive-antennas. This is particularly beneficial for mobile handsets because of the limited available space and the cost constraints for these mass market devices. All these properties and requirements make SM a very attractive MIMO scheme for many potential applications. The idea of using the transmit antenna number as an additional source of information is utilized in spatial modulation. The number of information bits that can be transmitted using

spatial modulation depends on the used constellation diagram and the given number of transmit antennas. II. SYSTEM MODEL This paper is organized as follows: In section II System model is discussed, in section III hardware implementation is discussed, section IV is simulation results and section V is conclusion. We consider a generic N t N r Multiple-Input- Multiple-output (MIMO) system with N t and Nr being the number of transmit and receive antennas respectively. Moreover, we assume that the transmitter can send digital information via M distinct signal waveforms (i.e., the so-called signal-constellation). Fig.1. MIMO Network with N t Transmit antennas and N r Receive antennas Each spatial constellation point defines an independent complex plane of signal constellation points. 1. A symbol is chosen from a complex signal constellation diagram. 2. A unique transmit antenna index is chosen from the set transmit antennas in the antenna array. The principal working mechanism of spatial modulation is depicted in Fig:2. For illustrative purposes only two of such planes are shown in Fig.2. For i) N t = 4 and ii) M = 4. Legend: i) Re = real axis of the signal constellation diagram and ii) Im = imaginary axis of the signal constellation diagram. In Fig.2 the information bits are grouped into four bits. The left group indicates the antenna index and the right group indicates the information bits to be transmitted based on the used modulation technique. Fig.2. Illustration of the 3-D encoding of Spatial Modulation. The spatial modulation system model is shown in Fig 3. q (k) is a vector of n bits to be transmitted. The binary vector is mapped into another vector x(k). Symbol number l in the resulting vector x(k) is x l, where l is the mapped transmit antenna number l [1:Nt]. The symbol x l is transmitted from the antenna number l over the MIMO channel, H(k). H(k) can be written as a set of vectors where each vector corresponds to the channel path gains between transmit antenna v and the receive antennas as follows: H = [h 1 h 2 h 3.. h Nt ] (1) Where: h v = [h 1,v h 2,v h Nr,v ] T (2) Similarly for a N t x N r MIMO system the channel matrix is given as H (k) is the N t x N r discrete time invariant frequency response channel matrix. The received vector y(k) is given as y(k) = H(k)x l +w(k) (3) where w(k) is the Additive White Gaussian noise vector. The received vector y(k) is obtained as follows y 1 = h 11 x 1 +h 12 x 2 +h 13 x 3 +.+h 1N x 4 y2= h 21 x 1 +h 22 x 2 +h 33 x 3 + +h 2N x 4 y3= h 31 x 1 +h 32 x 3 +h 33 x 3 +.+h 3N x 4....... y M = H M1 x 1 +h M2 x 2 +H M3 x 3 +.h MN x N 197

Fig.3: Spatial Modulation system model The number of transmitted information bits n, can be adjusted in two different and independent ways either by changing the signal modulation and/or changing the spatial modulation. Different modulation techniques can be used for SM-MIMO such as BPSK, QPSK, 8QAM, 16QAM, 32QAM etc. These modulation techniques will be used to map the information bits to the symbols by using constellation diagrams. For example we consider only BPSK and QPSK modulation techniques. containing log 2 (N t )+log 2 (M) bits each with log 2 (N t ) and log 2 (M) being the number of bits needed to identify a transmit antenna in the antenna-array and a symbol in the signal constellation diagram respectively. Table 1: Symbol mapping table for BPSK and QPSK Modulation techniques. The transmitter of the SM-MIMO system has to transmit the symbol and also have to select the antenna for the transmission of the symbol from the group of antennas. A block of information bits is mapped into the constellation point in the signal and spatial (antenna) domain. From the binary source the serially generated binary data will be converted to parallel data. This binary data will be segmented into two groups 198 Fig.4. Spatial Modulation Mapper. The bits in the first sub-block are used to select the antenna that is switched on for data transmission, while all other transmit antennas are kept silent in the current signaling time interval. The bits in the second subblock are used to choose a symbol in the signal constellation diagram using SM mapper [4] as shown in Fig:4. In general the number of bits that can be transmitted using spatial modulation is given as follows n = log 2 (N t ) +m (4) Where m = log 2 (M) (5) Where M is the used constellation size

Fig.5. 3bits Transmission using BPSK and four transmit antennas and QPSK using two transmit antennas The transmission of binary data using spatial modulation is carried out over a Wireless Rayleigh Flat Fading Channel. The channel is a complex NtxNr Matrix. N t denotes the number of transmitting antennas and N r denotes the receiving antennas. It contains the channel path gains between N transmit and M receive antennas. The channel varies based on the number of transmit antennas and the used signal modulation. the serial to parallel converter. As the number of bits transmitted using SM depends on the used constellation size, for BPSK and QPSK we can transmit 3bits at a particular time instant. The transmitter using BPSK modulation is shown in Fig.7. III.HARDWARE IMPLEMENTATION OF SM-MIMO SYSTEM a) Spatial Modulation Transmitter The SM-MIMO transmitter has to perform two tasks of choosing the active antenna index and binary data has to be transmitted from that active antenna which is made active for the purpose of transmission as shown in Fig.6. Fig.7. SM Transmitter using BPSK and 4 Antennas. As shown in Fig.7 the 2 to 4 decoder is used to decode the antenna index bits into four indicating which antenna is made active for transmission based on the incoming bit stream. BPSK requires two bits to indicate antenna index and one bit to represent symbol. Fig.6. Spatial Modulation Transmitter The SM-MIMO transmitter is implemented in the hardware using N-bit register to store the N bits of binary data. This serial data is converted to parallel by Fig.8. SM Transmitter using QPSK and 2 Antennas. 199

The transmitter using QPSK modulation requires two bits to represent the modulated symbol and one bit to indicate the antenna index. Hence it requires only two transmit antennas as shown in Fig.8. Here 1 to 2 decoder is used to decode the active antenna which is set for transmission. The transmission gates are used as switches which is made ON/OFF based on the decoder output. transmitting zero energy. The channel matrix H(k) for the noise free transmission using BPSK modulation is given as follows: b) Spatial Modulation The of the SM-MIMO system is assumed to have full knowledge of the channel through which the transmission took place. The channel is a complex matrix consisting of complex elements consisting of both real and imaginary parts. These complex fractional numbers are finally converted to binary bits by using the IEEE-754 floating point format. The performs the complex multiplications and complex additions between the channel matrix H(k) and received vector y(k). The receiver chooses the transmit antenna number which gives highest correlation. The task of the receiver is twofold: i) To estimate the transmitted symbol and ii) To detect the respective antenna number from which the symbol is transmitted. Fig.9. Spatial Modulation The received vector y(k) at the receiver input is given as y(k) = H(k)x l Where 0.5377+0.1229i y(k) = 0.5450+0.0964i -0.4624+0.2680i -0.2854+0.1493i The resultant is obtained by applying maximum ratio combining to the received vector y(k) and results in g and is given as follows: g(k) = H conj (k)y(k), For j = 1:N t (6) where g = [ g 1 g 2 gn t ] T (7) The obtained resultant g for the received vector y(k) is given as follows: -0.3124-0.0146 g = -1.0000-0.1951+0.0719-0.1811 Hence we can observe from the above resultant vector that maximum correlation is obtained at antenna 2 and it is transmitting the BPSK symbol. Similarly, for QPSK modulated transmission of 3bits in the Spatial modulation, the channel matrix H(k) and the noise free transmission for QPSK modulation is given as follows: Assume the following sequence of bits to be transmitted, q(k) = [0 1 1]. Mapping this to BPSK symbol and four transmit antennas results in x(k) = [0,- 1,0,0] T. The vector x(k) is transmitted over the MIMO channel H(k). According to the given sequence the symbol -1 is detected at antenna 2 and maximum correlation is obtained at that antenna position. We have to note that only antenna number 2 will be transmitting the symbol x l and the remaining antennas will be 200 In the hardware the receiver is designed by first converting the complex fractional numbers to binary by using the IEEE-754 Hexadecimal Floating point format. The term floating-point refers to the fact that the

decimal point can float, that it is placed anywhere relative to the many digits of the amount. The single precision format is shown in Fig 10. 1 8 23 SIGN EXPONENT (E) MANTISSA (F) Fig.10. Single Precision Floating point Format. This format consists of 3fields- a sign bit(s), a biased exponent (E) and a mantissa (F). 1-bit sign, S: A value of 1 indicates that the number is negative, and a 0 indicates a positive number. Bias- 127 exponent, e = E + bias: This gives us an exponent range from E min = -126 to E max = 127 Fraction/mantissa: The fractional part of the number significand, which is 1 plus the fractional part. The leading 1 in the significand is implicit. The floating point numbers are represented by the equation which is given as follows: X = (-1)^ s*1.f*2^ (E-127) (8) Fig.12 Architecture for Floating point Multiplication Floating point addition has mainly 3 parts: 1. Adding hidden 1 and Alignment of the mantissas to make exponents equal. 2. Addition of aligned mantissas 3. Normalization and rounding the result Fig.11Flow chart for floating point Multiplication Floating point multiplication process can be given in the algorithmic form as follows: Multiply the significands i.e.(m1*m2) Placing the decimal point in the result. Adding the exponent i.e., (E1+E2-bias). Obtaining the sign, s1 xor s2 Normalizing the result Rounding of the result to fit in an available bit. 201 Fig.13 Architecture for Floating point Addition The initial mantissa is of 23-bit wide. After adding the hidden 1, it is 24 bit wide. First the exponents are compared by subtracting one from the other and looking at the sign (MSB which is carry) of the result. To equalize the exponents, the mantissa part of the number with lesser exponent is shifted right d times. Where d is the absolute value difference between the exponents.

The sign of the larger number is anchored. In Normalization, the leading zeroes are detected and shifted so that a leading one comes. Exponent also changes accordingly forming the exponent for the final packed floating point result. The Floating point adder or subtractor is used to add the partial products generated after each multiplication operation. Hence both multiplication and addition operations are performed on the real and imaginary parts of the complex numbers. For the purpose of simulation, a flat Rayleigh fading channel is assumed with additive white Gaussian noise (AWGN). The receiver is assumed to have full channel knowledge. Random binary data of length 10, 00,000 bits was generated. Let us consider first thirty information bits of transmission data. Fig15: Sampling index vs magnitude plot of first 30 bits of transmitting data. Fig 14. Architecture for SM-MIMO System. The sequence of N- input binary bits are divided into group of 3bits each. The left group indicates the active antenna index and the right group indicates the modulated symbol.this information is transmitted over the MIMO channel in the noise free environment. The is assumed to have full Knowledge of the channel and it is indicated as the channel matrix H (k). The received vector at the input terminals of the receiver is y (k). For the purpose of demodulation, the receiver performs the Complex multiplications and Complex Addition operations between the channel matrix H(k) and received vector y (k) as g (k) = H conj (k)y(k). Here g(k) is the resultant Symbol vector. The floating point multiplication and addition is carried out at the receiver to obtain the transmitted symbol matrix and the position of the active antenna. Here single precision floating pont format is carried out. IV.RESULTS A) MATLAB Simulation Results Fig16: Magnitude and phase plots of QPSK symbols Fig17: Magnitude and Phase plots of channel effected Symbols 202

BER BER BER International Journal of Engineering Technology, Management and Applied Sciences 10-1 BPSK Modulation B) VLSI Simulation Results 10-2 10-3 10-4 10-5 10-6 0 1 2 3 4 5 6 7 8 9 10 SNR in db Fig18: SNR V S BER Plot BPSK System QPSK Modulation 10 0 Fig21: Detection of BPSK symbol +1 at Antenna-1 by 10-1 10-2 10-3 10-4 10-5 0 1 2 3 4 5 6 7 8 9 SNR in db 10 0 Fig19: SNR Vs BER Plot of QPSK system Spatial Modulation BPSK QPSK Fig22: Detection of BPSK symbol -1 at Antenna-1 by 10-1 10-2 10-3 10-4 10-5 0 2 4 6 8 10 12 14 16 18 20 SNR in db Fig20: SNR Vs BER Plots for SM-BPSK and SM- QPSK 203 Fig23: Detection of BPSK symbol +1 at Antenna-2 by

. Fig24: Detection of BPSK symbol -1 at Antenna-2 by Fig27: Detection of BPSK symbol +1 at Antenna-4 by Fig25: Detection of BPSK symbol +1 at Antenna-3 by Fig28: Detection of BPSK symbol -1 at Antenna-4 by. Fig26: Detection of BPSK symbol -1 at Antenna-3 by Fig29: Detection of QPSK symbol +1+i at Antenna-1 by 204

Fig30: Detection of QPSK symbol -1+i at Antenna-1 by Fig33: Detection of QPSK symbol 1+i at Antenna-2 by. Fig31: Detection of QPSK symbol +1-i at Antenna-1 by Fig34: Detection of QPSK symbol -1+i at Antenna-2 by Fig32: Detection of QPSK symbol -1-i at Antenna-1 by 205 Fig35: Detection of QPSK symbol 1-i at Antenna-2 by

Fig36: Detection of QPSK symbol -1-i at Antenna-2 by Fig39: Technology Schematic of BPSK Transmitter C) RTL Schematics Fig37: Top module of BPSK Transmitter Fig40: Top module of QPSK Transmitter Fig38: Internal module of BPSK Transmitter Fig41: Internal module of QPSK Transmitter 206

Fig42: Technology Schematic of QPSK Transmitter Fig45: Top module of QPSK Transmitter Fig43: Top module of BPSK Fig46: Total Architecture of QPSK Fig44: Total Architecture of BPSK 207 Logic Utilization QPSK BPSK Number of Slices 4353 8826 Number of 4 input LUTs 8630 17492 Number of bonded IOBs 897 897 Number of MULT 18X18SIOs 4 4 Number of GCLKs 1 1 Combinational Path delay 143.524ns 93.547ns Fig 47: Comparison Table for BPSK/QPSK

V.CONCLUSION In this paper, we have implemented the hardware design of the Spatial Modulation MIMO with low complexity using VLSI technology. It employs the Complex number multiplication and Addition operations between channel matrix and received signal matrix. A novel high rate, low complexity MIMO transmission scheme called Spatial Modulation (SM) that utilizes the spatial information in an innovative fashion has been presented. It maps multiple information bits into a single information symbol and into the physical location of the single transmitting antenna. The task of the receiver is to detect the transmitted symbol and to estimate the respective transmitting antenna. Spatial modulation avoids ICI at the receiver input. In addition, only one RF (radio frequency) chain is required at the transmitter because at any given time only one antenna transmits. Hence the energy efficiency is achieved and the cost of the transmitter is significantly reduced. The of the SM-MIMO system has been deigned, which computes complex number multiplications with less amount of resources and with low complexity and thereby achieved high performance. REFERENCES [1] Caijun Zhong Capacity and Performance Analysis of Advance Multiple Antenna Communication Systems, London, March 2010 [2] Raed Y. Mesleh,, Harald Haas, Sinan Sinanovi c,chang Wook Ahn,, and Sangboh Yun,, spatial Modulation [3]R.Mesleh, H.Haas, Y.Lee, and S.Yun, Interchannel Interference Avoidance in MIMO Transmission by Exploitng Spatial Information, Proceedings of the International Symposium on Personal, Indoor and Mobile Radio Communications PIMRC 2005,September 11-September 14, 2005 [6] Y.Chau and S-H. Yu, Space modulation on Wireless fading Channels, Proc.IEEE VTC 2001, vol.3, pp. 1668-1671, October 2001 [7] J. Jeganathan, A.Ghrayeb and L.Szczecinski, Spatial modulation:optimal detection and performance analysis, IEEE Commun.Lett.Vol.12, no.8,pp.545-547, July 2009 [8] M.D.Renzo and H.Haas, Performance analysis of Spatial Modulation, In Proc. Int. ICST Conf.CHINACOM,Aug.2010,pp.1-7. [9] G.Even and P.M. Seidel, A comparison of three rounding algorithms for IEEE floating-point multiplication, Technical Report EES 1998-8,EES Dep., Tel-Aviv Univ.,1998 [10] S.Oberman, H. A1-Twaijry, and M.Flynn. The SNAP project: Design of floating point arithmetic units. In Proceedings of the 13th Symposium on Computer Arithmetic,volume 13, pages 156 165. IEEE, 1997 [11] dsplog-signal Processing for communication, www.dsplog.com [12] http://www.eng.tau.ac.il/utils/reportlist/reports /repfram.htm [13] www.xilinx.com [14] Convey Computer corporation, Convey computer, Richardson, TX, 2008-2010 [Online]. Available: http://www.conveycomputer.com [4] R.Mesleh and H.Haas, Spatial Modulation-A New Low Complexity Spectral Efficiency Enhancing Technique, Communication and Networking in China 2006. ChinaCom 06. First International Conference on 25-27, Oct 2006. [5] M. Di Renzo, Member, IEEE, H.Haas, Member, IEEE, Ali Ghrayeb, senior Member, IEEE, and Shinya Sugiura, senior member, IEEE, Spatial Modulation for generalized MIMO: Challenges, opportunities and implementation. 208