Channel Estimation of MIMO OFDM System K.Ram Nayak M-Tech (Embedded Systems) S.R Engineering College, Warangal Telangana, India Abstract Wireless Communication Technology has developed many folds over the past few years. One of the techniques to enhance the data rates is called Multiple Input Multiple Output (MIMO) in which multiple antennas are employed both at the transmitter and the receiver. Multiple signals are transmitted from different antennas at the transmitter using the same frequency and separated in space. Various channel estimation techniques are employed in order to judge the physical effects of the medium present. In this project, we analyze and implement various estimation techniques for MIMO OFDM Systems such as Least Squares (LS), Minimum Mean Square Error (MMSE), Constant Modulus Algorithm (CMA) and linear Precoding. These techniques are therefore compared to effectively estimate the channel in MIMO OFDM Systems Keyword: Multiple Signal, MIMO, Channel, I. INTRODUCTION The major challenges in feature wireless communication system design are increased spectral efficiency and improved link reliability. The wireless channel constitutes a hostile propagation medium, which suffers from interference from other users. Diversity provides the receiver with several replicas of the transmitted signal and is therefore a powerful means to combat fading and interference and there by improve link reliability. Common forms of diversity are time diversity (due to Doppler spread) and frequency diversity (due to delay spread). This design is motivated by the growing demand for broadband Internet access. The challenge for wireless broadband access lies in providing a com-parable quality of service (QoS) for similar cost as competing wireline technologies. The target frequency band for this system is 2 5 GHz due to favorable propagation characteristics and low radio frequency (RF) equipment cost. The broad-band channel is typically non-los channel and includes impairments such as time-selective fading and frequency-selective fading. This article describes the physical layer design of a fourth-generation (4G) wireless broadband system that is, motivated from technical requirements of the broadband cellular channel, and from practical requirements of hardware and RF. The key objectives of the system are to provide good coverage in a nonline-of-sight (LOS) environment (>90 percent of the users within a cell), reliable transmission (>99.9 percent reliability), high peak data rates (>1 Mb/s), and high spectrum efficiency (>4 b/s/hz/sector). V.Ramakrishna Assistant Professor,Department of ECE S.R Engineering College, Warangal Telangana, India These system requirements can be met by the combination of two powerful technologies in the physical layer design: multi-input and multi-output (MIMO) antennas and orthogonal frequency division multiplexing (OFDM) modulation. Henceforth, the system is referred to as Airburst. Multiple antennas at the transmitter and receiver provide diversity in a fading environment. By employing multiple antennas, multiple spatial channels are created, and it is unlikely all the channels will fade simultaneously. In addition, the two base transceiver station (BTS) antennas are used to double the data rate for users with certain channel characteristics by transmitting independent data streams from the two antennas. This technique, known as spatial multiplexing, can significantly increase system capacity [1, 2]. At the receiver, multiple antennas are used to separate spatial multiplexing streams and for interference mitigation, which makes aggressive frequency reuse a reality. Generally, there are three categories of MIMO techniques. The first aims to improve the power efficiency by maximizing spatial diversity. Such techniques include delay diversity, space time block codes (STBC) and space time trellis codes (STTC). The second class uses a layered approach to increase capacity. One popular example of such a system is V- BLAST suggested by Foschini where full spatial diversity is usually not achieved. Finally, the third type exploits the knowledge of channel at the transmitter. The IEEE802.11a LAN standard operates at raw data rates up to 54 Mbps (channel conditions permitting) with a20-mhz channel spacing, thus yielding a bandwidth efficiency of 2.7 bps phz. The actual throughput is highly dependent on the medium access control (MAC) protocol. Likewise, IEEE802.16a operates in many modes depending on channel conditions with a data rate ranging from 4.20 to 22.91 Mbps in a typical bandwidth of 6 MHz, translating into a bandwidth efficiency of 0.7 to 3.82 bpsphz. Recent developments in MIMO techniques promise a significant boost in performance for OFDM systems. OFDM has been adopted in several wireless standards such as digital audio broadcasting (DAB), digital video broadcasting (DVB-T), the IEEE 802.11a [1] local area network (LAN) standard and the IEEE 802.16a [2] metropolitan area network (MAN) standard. OFDM is also being pursued for dedicated short-range communications (DSRC) for road side to vehicle communications and as a potential 143 www.ijraonline.com. SR Engineering College, Warangal
candidate for fourth-generation (4G) mobile wireless systems OFDM converts a frequency-selective channel into a parallel collection of frequency flat sub channels. II. GENERAL MIMO SYSTEM As a key building block of next-generation wireless communication systems, MIMOs are capable of supporting significantly higher data rates than the Universal Mobile Telecommunications System (UMTS) and the High Speed Downlink Packet Access (HSDPA) based 3G networks. As indicated by the terminology, a MIMO system employs multiple transmitter and receiver antennas for delivering parallel data streams, as illustrated in Figure 1.1. Since the information is transmitted through different paths, a MIMO system is capable of exploiting transmitter and receiver diversity, hence maintaining reliable communications. Furthermore, with the advent of multiple antennas, it becomes possible to jointly process/combine the multi-antenna signals and thus improves the system's integrity and/or throughput. Briefly, compared to Single-Input Single-Output (SISO) systems, the two most significant advantages of MIMO systems are: A significant increase of both the system's capacity and spectral efficiency. The capacity of a wireless link increases linearly with the minimum of the number of transmitter or the receiver antennas. systems provide more spatial freedoms or spatial multiplexing, so that different information can be transmitted simultaneously over multiple antennas, thereby boosting the sys-tem throughput. 2. Diversity: The signal transmission over broad-band wireless channels always suffers from attenuation due to the detrimental effect of multipath fading, and this can severely degrade the reception performance. In MIMO systems, the same information can be transmitted from multiple transmit antennas and received at multiple receive antennas simultaneously. Since the fading for each link between a pair of transmit and receive antennas can usually be considered to be independent, the probability that the information is detected accurately is increased. 3. Diversity-Multiplexing Trade off: Earlier research on MIMO systems has focused either on extracting the maximal diversity gain or the maximal spatial multiplexing gain of a channel. This has led to either a diversity-oriented or multiplexing-oriented design approach. First, all transmit antennas are partitioned into G groups, and data is encoded over these G blocks, each of which fades independently. Within the gth (g = 1,, G) group, the signals to be sent are associated with a data rate R g. Then, at the receiver group detection should be used and two approaches were proposed, namely, group zero forcing (GZF) and group successive interference cancellation (GSIC). In the first approach, a ZF decorrelator is used to separate the various groups of data and then maximum likelihood (ML) detection is applied to detect each group of data. In the GSIC approach, each group is detected using ML after canceling the interference of the already detected groups in previous stages. In a framework for constructing optimal coding/decoding schemes, which was referred to as lattice Space-Time (LAST) coding/decoding, was also proposed for achieving the optimal diversity-multiplexing trade-off. Fig.1. Schematic of the generic MIMO system employing M transmitter antennas and N receiver antennas. MIMO wireless communication refers to the transmissions over wireless links formed by multiple antennas equipped at both the transmitter and receiver. The key advantages of employing multiple antennas lay in the more reliable performance obtained through diversity and the achievable higher data rate through spatial multiplexing. 1. Spatial multiplexing: It is widely recognized that the capacity of a MIMO system is much higher than a single-antenna system. For a rich scattering environment, in a MIMO system with M t transmit antennas and M r receive antennas, the capacity will grow proportionally with min (M t, M r ). MIMO Fig. 2. A simplified block diagram of MIMO-OFDM system, where S =[s 1, s 2,, s Ns ] denotes a block of Ns data symbols. 144 www.ijraonline.com. SR Engineering College, Warangal
III. MIMO-OFDM SYSTEM MODEL Orthogonal Frequency Division Multiplexing (OFDM) is essentially a discrete implementation of multicarrier modulation, which divides the transmitted bit stream into many different substreams and sends them over many different sub channels. Thus, inter symbol interference (ISI) on each sub channel is very small. For this reason, OFDM is widely used in many high data rate wireless systems. Figure 1a shows a simplified block diagram of an N-tone OFDM system. First, the incoming bits are mapped to data symbols according to some modulation scheme such as QPSK or QAM. Then the serial data stream is converted into a number of parallel blocks, and each of them has length-n. Then, each block of symbols (including pilot symbols, which are used for channel estimation or synchronization) will be forwarded to the IFFT and transformed into an OFDM signal. After that, the OFDM signal will be appended with a cyclic prefix by copying the last N cp samples to the top of the current OFDM block. By choosing the length of the cyclic prefix larger than the maximum path delay of the channel, ISI can be eliminated. Afterward, the OFDM blocks will be converted to serial signals and sent out. At the receiver, assuming a perfect timing and carrier frequency synchronization, the received signals will be first converted to parallel signals and then the cyclic prefix will be removed. MIMOs can potentially be combined with any modulation or multiple access technique, recent research suggests that the implementation of MIMO aided OFDM is more efficient, as a benefit of the straightforward matrix algebra invoked for processing the MIMO OFDM signals. The combination of MIMO and OFDM has the potential of meeting this stringent requirement since MIMO can boost the capacity and the diversity and OFDM can mitigate the detrimental effects due to multipath fading. A general MIMO- OFDM system is shown in Fig. 1b, where M t transmit antennas, M r receive antennas, and N-tone OFDM are used. First, the incoming bit stream is mapped into a number of data symbols via some modulation type such as QAM. Then a block of N s data symbols S= [s 1, s 2,, s Ns ] are encoded into a codeword matrix C of size NT M t, which will then be sent through M t antennas in T OFDM blocks, each block consisting of N sub channels. Specifically, c 1 j, c 2 j,, c j T will be transmitted from the jth transmit antenna in OFDM blocks1, 2,, T, respectively, where c n j denotes a vector of length- N, for all j=1,2,, M t and n=1,2,,t. After appending the cyclic prefix on each OFDM block, c n j will be transmitted from the jth transmit antenna in the nth OFDM block. IV. MIMO-OFDM DESIGN CONSTRAINTS The key channel characteristics that influence the broadband wireless system design such as channel dispersion, K-factor, Doppler shift, cross-polarization discrimination, antenna correlation, and condition number. 1. Channel Dispersion An important channel characteristic that influences a system performance is channel dispersion due to reflections from close in and far away objects. The dispersion is often quantified by the rms delay spread, which increases with distance, and changes with environment, antenna beam width, and antenna height typical values are in the 0.1 5 µs range. 2. K-Factor The fading signal magnitude follows a Rice distribution, which can be characterized by two parameters: the power P c of constant channel components and the power P s from scatter channel components. The ratio of these two (P c /P s ) is called the Ricean K-factor. The worst case fading occurs when P c = 0 and the distribution is regarded as Rayleigh distribution (K = 0). The K-factor is an important parameter in system design since it relates to the probability of a fade of certain depth. Both fixed and mobile communications systems have to be designed for the most severe fading conditions for reliable operation. 3. Doppler Shift Spread in the frequency domain relative to a variation in the time domain is often related to movement of the transmitter or the receiver. Variation in the time domain of the received signal caused by the motion of the transmitter or receiver is called the Doppler shift. Based on the mobility of the transmitter, the received signal undergoes fast or slow fading. In fast fading, where the transmitter or receiver is mobile, the coherence time is smaller than the symbol period. 4.Cross-Polarization Discrimination The crosspolarization discrimination (XPD) is defined as the ratio of the co-polarized average received power P ll to the cross-polarized average received power, P.XPD quantifies the separation between two transmission channels that use different polarization orientations. The larger the XPD, the less energy is coupled 145 www.ijraonline.com. SR Engineering College, Warangal
between the cross-polarized channels. The XPD values were found to decrease with increasing distance. 5. Antenna Correlation Antenna correlation plays a very important role in single-input multi-output (SIMO), multi-input single-output (MISO), and MIMO systems. If the complex correlation coefficient is high (e.g., greater than 0.7), diversity and multiplexing gains can be significantly reduced (or completely diminished in the case of correlation of 1). Generally, it was found that the complex correlation coefficients are low, in the 0.1 0.5 range for properly selected base station and receiver antenna configurations. V. MIMO-OFDM ARCHITECTURE The combined application of multi-antenna technology and OFDM modulation (MIMO-OFDM) yields a unique physical layer capable of meeting the requirements of a second-generation non-los system. 1. Transmit Diversity Many transmit diversity schemes have been proposed in the literature offering different complexity vs. performance trade-offs. We chose delay diversity for downlink transmission due to its simple implementation, good performance, and no feedback requirement. In this scheme, the signal sent from the second antenna is a delayed copy of the signal at the first antenna. The delay introduced at the transmitter results in frequency selectivity in the received channel response. With proper coding and interleaving, space-frequency diversity gain is achieved without requiring any channel knowledge at the transmitter. 2. Spatial Multiplexing It is possible to transmit two separately encoded data streams from the two base station antennas. A high-rate signal is multiplexed into a set of lower-rate streams, each of which is encoded, modulated, and trans-mitted at a different antenna, while using the same time and frequency slot. Each of the three receiving antennas receives a linear combination of the two transmitted messages that have been filtered by different channel impulse responses. The receiver separates the two signals using a spatial equalizer, and demodulates, decodes, and de multiplexes them to yield the original signal. 3. Receive Diversity and Interference Cancellation There is some natural interference suppression with MRC since it matches the spatial signature of the desired signal, not that of the interferer, which therefore gets attenuated. In this case, it is desirable to use the minimum mean square error (MMSE) algorithm that minimizes the mean square error between each desired signal and its estimate, thereby maximizing signal-to-interference-plus-noise ratio (SINR). If the interferer is friendly (e.g., a spatial multiplexing user), the interferer s spatial signature is available to be used in the MMSE weights. For unfriendly interference from neighboring cells, second-order statistics (covariance matrices) are used to capture the spatial structure of interference. 4. Soft Decoding Both MRC and MMSE algorithms yield soft signal estimates that are input to a soft decoder. The soft decisions are weighted by the estimated SINR on a tone-by-tone basis to give more weight to good tones and less weight to bad tones. Soft-decision decoding combined with SINR weighting provides significant performance gains (3 4 db) in frequency selective channels. 5. Channel Estimation The purpose of channel estimation is to identify the channel between each pair of transmit and receive antennas. The training tones transmitted from each antenna are orthogonal with respect to each other so that the channel from each transmit antenna can be identified uniquely. The training tones are spaced in frequency, with spacing less than the channel s frequency coherence so that the channel can be interpolated between training tones. 6. Synchronization Both uplink and downlink transmission are preceded by a synchronization (sync) slot for timing phase, timing frequency, and frequency offset estimation. The slot is structured such that data and training are trans-mitted over even numbered tones, and odd tones are set to zero. This introduces a repetitive pat-tern in the time-domain signal, which allows estimation of the above parameters. Once synchronization is obtained, fine timing estimates can be computed from the training tones. 7. Adaptive Modulation and Coding The Airburst system maximizes system capacity by optimizing the link parameters available to each user. The QAM levels vary from 4 to 64, and the coding consists of a punctured convolutional code combined with a Reed- Solomon code. There are six combinations of QAM and coding levels, referred to as coding modes. The coding modes 1 6 correspond to data rates of 1.1 6.8 Mb/s obtained over a 2 MHz channel. For the downlink, the above rates are doubled when spatial multiplexing is used. Each coding mode has a different set point, which is defined to be the average SINR required to obtain a specified pre-arq packet error rate, typically in the 0.1 5 percent range. 146 www.ijraonline.com. SR Engineering College, Warangal
pp. 1451 58. [5] G. J. Foschini, Layered Space-Time Architecture for Wireless Communication in a Fading Environment When Using Multielement Antennas, Bell Labs Tech. J., vol. 1, no. 2, 1996, pp. 41 59. [6] L. Zheng and D. N. C. Tse, Diversity and Multiplexing: a Fundamental Trade-Off in Multiple-Antenna Chan-nels, IEEE Trans. Info. Theory, vol. 49, May 2003, pp. 1073 96. [7] V. Tarokh et al., Combined Array Processing and Space- Time Coding, IEEE Trans. Info. Theory, vol. 45, May 1999, pp. 1121 28. [8] S. Sfar, L. Dai, and K. B. Letaief, Optimal Diversity-Multiplexing Trade-Off with Group Detection for MIMO Systems, IEEE Trans. Commun., vol. 53, July 2005, pp. 1178 90. Fig. 3. Sample BER vs. SNR for several SUI models Figure 3 shows a BER comparison of results for a subset of channel models and transmission modes: 1. SUI-3 channel: uplink coding mode 2 2. SUI-4 channel: downlink transmit Diversity coding mode 3 3. SUI-6 channel: downlink spatial multiplexing coding mode 3 [9] H. E. Gamal, G. Caire, and M. O. Damen, Lattice Cod-ing and Decoding Achieve the Optimal Diversity-Multi-plexing Trade-Off of MIMO Channels, IEEE Trans. Info. Theory, vol. 50, June 2004, pp. 968 85. [10] B. Lu and X. Wang, Space-Time Code Design in OFDM Systems, Proc. IEEE Global Commun. Conf., Nov. 2000, pp. 1000 04. VI. CONCLUSION In this article the results show dramatic increase in capacity, coverage, and reliability over SISO, MISO, or SIMO communication systems. It was shown that orthogonal ST-coded OFDM has a simple implementation that can provide a minimal decoding complexity, but cannot achieve multipath diversity nor high rate. On the other hand, it was shown that SF-coded OFDM with signal space diversity technique can achieve the maximum diversity and full rate over multipath fading channels, at the expense of a high decoding complexity. For block-fading channels, we have demonstrated that STF-coded OFDM can achieve full rate along with full diversity in space, time, and frequency. However and similar to SF coding, a joint detection is needed in STF-coded OFDM, and this results in high decoding complexity. As one of the promising multiple. References [1] H. Bölcskei, MIMO-OFDM Wireless Systems: Basics, Perspectives and Challenges, IEEE Wireless Commun., vol. 13, Aug. 2006, pp. 31 37. [2] R. D. Murch and K. B. Letaief, Antenna Systems for Broadband Wireless Access, IEEE Commun. Mag., vol. 40Apr. 2002, pp. 76 83,. [3] S. N. Diggavi et al., Great Expectations: the Value of Spatial Diversity in Wireless Networks, Proc. IEEE, vol. 92, no. 2, Feb. 2004, pp. 219 70. [4] S. M. Alamouti, A Simple Transmit Diversity Technique for Wireless Communication, IEEE JSAC, vol. 16, Oct. 1998, 147 www.ijraonline.com. SR Engineering College, Warangal