VLSI Implementation of OFDM Transceiver for 802.11n systems
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This document describes the VLSI implementation of a 4x4 MIMO-OFDM transceiver for 802.11n systems using a Spartan 6 FPGA. The transceiver uses OFDM modulation with 64-bit FFT and 1/2 rate encoding. It was designed and tested on a Diligent Atlys FPGA board with a wired channel. Performance was analyzed by measuring BER and data rate with varying SNR. Data rates up to 216 Mbps were achieved with 16QAM modulation. The design was implemented and tested using Matlab Simulink, Xilinx tools, and hardware co-simulation between Simulink and the FPGA board.
![Shreyas Kulkarni et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 5, ( Part -1) May 2015, pp.66-70
www.ijera.com 66|P a g e
VLSI Implementation of OFDM Transceiver for 802.11n systems
Shreyas Kulkarni, Aniket Phatak, Siddhesh Rahula, Dipak Gawade
B.E. Electronics and Telecommunication
Vidyalankar Institute Of Technology, Mumbai
Abstract
Orthogonal Frequency Division Multiplexing (OFDM) is the most widely used modulation technique for
wireless communication network. In this paper, 4 x 4 spatially multiplexed MIMO OFDM transceiver is
designed using 1/2 encoder and 64 bit FFT. The implementation has been carried out in hardware using Field
Programmable Gate Array (FPGA). Both the transmitter and the receiver are implemented on a single FPGA
board with the channel being a wired one. The FPGA board used is Diligent Atlys Xilinx Spartan 6. We have
analysed the effect of Bit Error Rate and Data rate with respect to Signal to Noise ratio.
Index Terms: OFDM, Field Programmable Gate Array(FPGA), Xilinx, Multiple Input Multiple Output, Fast
Fourier Transform.
I. INTRODUCTION
The growth in the use of wireless networks has
led to the need for new communication techniques
with higher data rates. OFDM is a modulation
technique used to achieve a high data rate and is able
to eliminate inter-symbol interference (ISI). It is
computationally efficient due to the use of Fast
Fourier Transform (FFT) techniques for
implementing modulation and demodulation
methodology [1]
.
In MIMO systems, at the expense of increased
hardware and computational complexity, a high
spectral efficiency can be achieved. This spectral
efficiency, which can be utilized as data rate,
capacity, or coverage improvement according to the
needs, Multiple-input multiple-output (MIMO)
technology provides extra degrees of freedom which
facilitate multiplexing gains and diversity gains.
Hence, MIMO has attracted a lot of research interest
in the past decade since it enables significant
performance enhancement without requiring
additional transmit power and bandwidth resources.
The combination of MIMO and OFDM is considered
a viable solution for achieving these high data rates.
In fact, the data rate improvement due to multiple
antennas is unlimited if we allow the numbers of
antennas employed at both the transmitter and the
receiver to grow[2]
.
II. OFDM
In classical data systems in which more data rate
was sought by exploiting the frequency domain,
parallel transmissions were achieved by dividing the
total signal frequency band into Nc non-overlapping
frequency sub-channels. This technique is referred to
as Frequency division Multiplexing (FDM). But this
method leads to inefficient use of available spectrum.
A more efficient use of bandwidth can be obtained
with parallel transmission if spectra of individual
sub-channels are permitted to overlap. This requires
specificorthogonality constraints that are imposed to
facilitate separation of sub channels at the receiver.
This is done using multi-carrier system, which is
called as Orthogonal Frequency Division
Multiplexing(OFDM).
III. 4 x 4 MIMO system
The 4 x 4 MIMO system is designed and
implemented on a high level mathematical modelling
of Matlab Simulink. The reason for selecting Matlab
Simulink is because of its real time environment
which resembles the real time design. The system is
made of two important parts of transmitter and
receiver. This is given in Fig. 4.
A. Spatial Multiplexing
The concept of spatial multiplexing (SM) is
different from that of space- time block coding
method. The SM provides high throughput as
compared to STBC at higher SNR. The spatial
multiplexing method uses multiple antennas at the
transmitter and receiver to provide a linearly
increasing capacity gain with increased number of
antennas. In this system a high rate bit stream is
decomposed into N independent bit sequences which
are then transmitted using multiple antennas.
These signals get mixed in the channel as they
use same frequency spectrum. At the receiver
individual bit streams are separated, estimated and
merged together to yield the original signal. Thus
MIMO transmits N streams through a single channel,
thereby can deliver N or more times the data rate per
channel without additional bandwidth or transmit
power. The 4 x 4 MIMO transceiver model in this
RESEARCH ARTICLE OPEN ACCESS](https://image.slidesharecdn.com/l505016670-150511080149-lva1-app6891/85/VLSI-Implementation-of-OFDM-Transceiver-for-802-11n-systems-1-320.jpg)
![Shreyas Kulkarni et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 5, ( Part -1) May 2015, pp.66-70
www.ijera.com 70|P a g e
Fig.7 Data Rate v/s SNR
Fig 8. Original vs Received signal
Fig. 9 RTL schematic
Table 1 Input Frequency and Data Rate
VI. Conclusion
A spatially multiplexed 4 x 4 MIMO OFDM
transceiver using 16 QAM wasimplemented on
Spartan 6 FPGA board with help of Matlab Simulink,
Xilinx and System Generator. As MIMO transmits
four data streams through a single channel, it can
deliver four or more times the data rate per channel
without additional bandwidth or transmit power. Data
rate up to 216 Mbps is achieved. Also there was
minimum distortion between transmitted signal and
received signal. The effect of Signal to Noise ratio on
Data rate was very less.
This can be enhanced by making a 16 x 16
MIMO system, in which data rate can be achieved in
Gbps. This design can be used for upcoming 5G
technology where emphasis is on both speed and
reliability.
REFERENCES
[1]. BER Analysis with OFDM using PSK/QAM
Techniques for Wireless Communication,
International Journal of Engineering
Research & Technology (IJERT), Vol. 1
Issue 7, September - 2012
[2]. Performance Enhancement of MIMO-
OFDMA: A Review Paper, International
Journal of Computer Science and
Communication Engineering, Volume 2
Issue 2 (May 2013 Issue)
[3]. Mujtabaet al., TGn Sync Proposal
Tech.Specification for IEEE 802.11 Task
Group 2005, IEEE 802.11-04/0889r3.
[4]. On the Capacity of OFDM-Based Spatial
Multiplexing Systems, IEEE Transaction on
Communication, Vol. 50, No.2, February
2002.
[5]. Outdoor MIMO Wireless Channels: Models
and Performance Predictions, IEEE
Transactions on Communication, Vol. 50,
No. 12, December 2002
[6]. Impact of the Propagation Environment on
Performance of Space-Frequency Coded
MIMO-OFDM, IEEE Journal on Selected
Areas in Communication, Vol. 21, No. 3,
April 2003.
[7]. Ma. J. Canet, F. Vicedo, J. Valls and V.
Almenar, “Design of a Digital Front-End
Transmitter For OFDM-WLAN Systems
Using FPGA”, Control, Communications
and Signal Processing 2004, First
International Symposium on, pp: 503–506,
2004.](https://image.slidesharecdn.com/l505016670-150511080149-lva1-app6891/85/VLSI-Implementation-of-OFDM-Transceiver-for-802-11n-systems-5-320.jpg)
VLSI Implementation of OFDM Transceiver for 802.11n systems
- 1. Shreyas Kulkarni et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 5, Issue 5, ( Part -1) May 2015, pp.66-70 www.ijera.com 66|P a g e VLSI Implementation of OFDM Transceiver for 802.11n systems Shreyas Kulkarni, Aniket Phatak, Siddhesh Rahula, Dipak Gawade B.E. Electronics and Telecommunication Vidyalankar Institute Of Technology, Mumbai Abstract Orthogonal Frequency Division Multiplexing (OFDM) is the most widely used modulation technique for wireless communication network. In this paper, 4 x 4 spatially multiplexed MIMO OFDM transceiver is designed using 1/2 encoder and 64 bit FFT. The implementation has been carried out in hardware using Field Programmable Gate Array (FPGA). Both the transmitter and the receiver are implemented on a single FPGA board with the channel being a wired one. The FPGA board used is Diligent Atlys Xilinx Spartan 6. We have analysed the effect of Bit Error Rate and Data rate with respect to Signal to Noise ratio. Index Terms: OFDM, Field Programmable Gate Array(FPGA), Xilinx, Multiple Input Multiple Output, Fast Fourier Transform. I. INTRODUCTION The growth in the use of wireless networks has led to the need for new communication techniques with higher data rates. OFDM is a modulation technique used to achieve a high data rate and is able to eliminate inter-symbol interference (ISI). It is computationally efficient due to the use of Fast Fourier Transform (FFT) techniques for implementing modulation and demodulation methodology [1] . In MIMO systems, at the expense of increased hardware and computational complexity, a high spectral efficiency can be achieved. This spectral efficiency, which can be utilized as data rate, capacity, or coverage improvement according to the needs, Multiple-input multiple-output (MIMO) technology provides extra degrees of freedom which facilitate multiplexing gains and diversity gains. Hence, MIMO has attracted a lot of research interest in the past decade since it enables significant performance enhancement without requiring additional transmit power and bandwidth resources. The combination of MIMO and OFDM is considered a viable solution for achieving these high data rates. In fact, the data rate improvement due to multiple antennas is unlimited if we allow the numbers of antennas employed at both the transmitter and the receiver to grow[2] . II. OFDM In classical data systems in which more data rate was sought by exploiting the frequency domain, parallel transmissions were achieved by dividing the total signal frequency band into Nc non-overlapping frequency sub-channels. This technique is referred to as Frequency division Multiplexing (FDM). But this method leads to inefficient use of available spectrum. A more efficient use of bandwidth can be obtained with parallel transmission if spectra of individual sub-channels are permitted to overlap. This requires specificorthogonality constraints that are imposed to facilitate separation of sub channels at the receiver. This is done using multi-carrier system, which is called as Orthogonal Frequency Division Multiplexing(OFDM). III. 4 x 4 MIMO system The 4 x 4 MIMO system is designed and implemented on a high level mathematical modelling of Matlab Simulink. The reason for selecting Matlab Simulink is because of its real time environment which resembles the real time design. The system is made of two important parts of transmitter and receiver. This is given in Fig. 4. A. Spatial Multiplexing The concept of spatial multiplexing (SM) is different from that of space- time block coding method. The SM provides high throughput as compared to STBC at higher SNR. The spatial multiplexing method uses multiple antennas at the transmitter and receiver to provide a linearly increasing capacity gain with increased number of antennas. In this system a high rate bit stream is decomposed into N independent bit sequences which are then transmitted using multiple antennas. These signals get mixed in the channel as they use same frequency spectrum. At the receiver individual bit streams are separated, estimated and merged together to yield the original signal. Thus MIMO transmits N streams through a single channel, thereby can deliver N or more times the data rate per channel without additional bandwidth or transmit power. The 4 x 4 MIMO transceiver model in this RESEARCH ARTICLE OPEN ACCESS
- 2. Shreyas Kulkarni et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 5, Issue 5, ( Part -1) May 2015, pp.66-70 www.ijera.com 67|P a g e paper implements spatial multiplexing scheme. It is given in Fig. 1 Fig.1 OFDM-based spatial multiplexing system (OMOD and ODEMOD denote an OFDM-modulator and demodulator, respectively). B.Transmitter Section The transmitter subsystem as in Fig. 2 mainly comprises of convolutional encoder, mapper, MIMO parser and IFFT. The sampled input voice bits are encoded using a half convolutional encoder, truncated, concatenated and is given to the mapper. The ROM_Imaginary(ROM_Imag), ROM_Real altogether forms QAM mapper. The ROM_Imag provides the value on imaginary axis while ROM_Real provides the value on real axis giving up the points on different quadrants. 1.Convolutional Encoder The data bits are encoded using a convolutional encoder. Convolutional encoding with Viterbi decoding is a FEC technique that is particularly suited to a channel in which the transmitted signal is corrupted mainly by additive white gaussian noise. 2. Mapper According to the modes of operation the data bits are punctured and mapped with QPSK constellations mentioned as per the standard. Puncturing is the process of removing some of the parity bits. This has the same effect as encoding with an error-correction code with a higher rate, or less redundancy. Thus puncturing considerably increases the flexibility of the system without significantly increasing its complexity. 3. MIMO Parser The MIMO parser performs different operations on input data bits. This operation is based on Space Time Coding (STC) or Spatial Multiplexing (SM). The multiple spatial data streams are applied to multiple interleavers. The ROM-Imag and ROM_Real are fed to the MIMO parser as In 1 and In 2 respectively. The main purpose of using a MIMO parser is to split the single stream input into two streams. C. Receiver Section In receiver subsystem as shown in Fig. 3 the outputs of four streams from transmitter are fed as input to OFDM blocks. In OFDM1 FFT performs the exact reverse operation as that of the IFFT. It extracts the ROM_Imag and ROM_Real inputs for MIMO de- parser. The OFDM 2 block performs exactly same as that of OFDM 1 block and extracts the ROM_Imag and ROM_Real inputs for MIMO de-parser. The OFDM 3 and 4 performs exact same as that of OFDM 1 and 2 respectively to be fed as inputs for MIMO de-parser. 1.MIMO De-Parser At the transmitter side the signal is divided whereas at the receiver signals are being combined together. The combination of two or more signals is deparsering. The MIMO De-parser uses two Time Division Multiplexers. Two ROM_Imag signals are combined to form a single ROM_Imagl while the two ROM_Real signals are combined to form a single ROM_Real signals. The output ofMIMODeparser and MIMO De-parser 1 are connected to MIMO De- parser 2 so that the splitted streams can be combined together and ROM-Imag and ROM_Real signals can be obtained.
- 3. Shreyas Kulkarni et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 5, Issue 5, ( Part -1) May 2015, pp. www.ijera.com 68|P a g e Fig 2 Transmitter Section Fig. 3 Receiver Section
- 4. Shreyas Kulkarni et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 5, Issue 5, ( Part -1) May 2015, pp. www.ijera.com 69|P a g e Fig. 4 MIMO-OFDM System including JTAG Co-Simulation IV. Methodology Here, we used JTAG Co-Sim block to burn the bitstream file onto Spartan 6 chip. This allows us to transmit Sine wave or audio wave in FPGA board which is passed through transmitter section then receiver section and received in Simulink through “PROG” port. In Simulink we can see the original wave and received wave. Fig. 5 AtlysDigilent FPGA board V. Results High data rates upto 216 Mbps are achieved by transmitting large number of bits per symbol. Thus for high data rates, higher order QAM is used.Thehardware co-simulation, VHDL codes, RTL Schematics, Test Bench, and data rate variations are obtained for the 4 X 4 MIMO OFDM model to verify the same. Graphs for BER v/s SNR, Data rate v/s SNR as well as the original signal and receiver signal scope output is given in Fig. 6, Fig. 7 and Fig. 8 respectively. Fig. 6 BER v/s SNR(dB)
- 5. Shreyas Kulkarni et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 5, Issue 5, ( Part -1) May 2015, pp.66-70 www.ijera.com 70|P a g e Fig.7 Data Rate v/s SNR Fig 8. Original vs Received signal Fig. 9 RTL schematic Table 1 Input Frequency and Data Rate VI. Conclusion A spatially multiplexed 4 x 4 MIMO OFDM transceiver using 16 QAM wasimplemented on Spartan 6 FPGA board with help of Matlab Simulink, Xilinx and System Generator. As MIMO transmits four data streams through a single channel, it can deliver four or more times the data rate per channel without additional bandwidth or transmit power. Data rate up to 216 Mbps is achieved. Also there was minimum distortion between transmitted signal and received signal. The effect of Signal to Noise ratio on Data rate was very less. This can be enhanced by making a 16 x 16 MIMO system, in which data rate can be achieved in Gbps. This design can be used for upcoming 5G technology where emphasis is on both speed and reliability. REFERENCES [1]. BER Analysis with OFDM using PSK/QAM Techniques for Wireless Communication, International Journal of Engineering Research & Technology (IJERT), Vol. 1 Issue 7, September - 2012 [2]. Performance Enhancement of MIMO- OFDMA: A Review Paper, International Journal of Computer Science and Communication Engineering, Volume 2 Issue 2 (May 2013 Issue) [3]. Mujtabaet al., TGn Sync Proposal Tech.Specification for IEEE 802.11 Task Group 2005, IEEE 802.11-04/0889r3. [4]. On the Capacity of OFDM-Based Spatial Multiplexing Systems, IEEE Transaction on Communication, Vol. 50, No.2, February 2002. [5]. Outdoor MIMO Wireless Channels: Models and Performance Predictions, IEEE Transactions on Communication, Vol. 50, No. 12, December 2002 [6]. Impact of the Propagation Environment on Performance of Space-Frequency Coded MIMO-OFDM, IEEE Journal on Selected Areas in Communication, Vol. 21, No. 3, April 2003. [7]. Ma. J. Canet, F. Vicedo, J. Valls and V. Almenar, “Design of a Digital Front-End Transmitter For OFDM-WLAN Systems Using FPGA”, Control, Communications and Signal Processing 2004, First International Symposium on, pp: 503–506, 2004.