INTRODUCTION WIRELESS SYSTEMS with multiple antennas at the transmitter and receiver (multiple-inputmultiple-output, MIMO) have much larger capacity in fading channels than standard wireless systems . The appropriate use of space-time (ST) processing and ST codes allows us to achieve, or at least approach, these capacities in practical systems. When generated OFDM signal is transmitted through a number of antennas in order to achieve diversity or cap any gain (higher transmission rate) then it is known as MIMO-OFDM For frequency-selective channels, a combination of MIMO with OFDM (orthogonal frequency division multiplexing) is promising . The simplest way

to perform ST coding in a MIMO-OFDM system would be to apply the ST-codes for the frequency-flat channels to each tone separately. However, a recent paper has pointed out that this is suboptimum, as the inherent frequency diversity of the frequency-selective channel is not exploited. It was also stated that construction of codes that code across tones would be difficult. In this paper, we show by a very simple observation how we can extend ST code design rules to frequency selective channels without sacrificing performance. We will also develop further simplifications and illustrate the performance-com- trade off.BASIC OF MIMO OFDM SYSTEM OFDM converts a frequency-selective channel into a parallel collection of frequency flat sub-channels. The subcarriers have the minimum frequency separation required to maintain orthogonality of their corresponding time domain waveforms, yet the signal spectra corresponding to the different subcarriers overlap in frequency. Hence, the available bandwidth is used very efficiently. If knowledge of the channel is available at the transmitter, then the OFDM transmitter can adapt its signalling strategy to match the channel. Due to the fact that OFDM uses a large collection of narrowly spaced sub-channels, these adaptive strategies

can approach the ideal water pouring capacity of a frequency-selective channel. In practice this is achieved by using adaptive bit loading techniques, where different sized signal constellations are transmitted on the subcarriers.

OFDM is a block modulation scheme where a block of information symbols is transmitted in parallel on subcarriers. The time duration of an OFDM symbol is times larger than that of a single-carrier system. An OFDM modulator can be implemented as an inverse discrete Fourier transform (IDFT) on a block of information symbols followed by an analog-to-digital converter (ADC). To mitigate the effects of inter symbol interference (ISI) caused by channel time spread, each block of IDFT coefficients is typically

preceded by a cyclic prefix (CP) or a guard interval consisting of samples, such that the length of the CP is at least equal to the channel length. Under this condition, a linear convolution of the transmitted sequence and the channel is converted to a circular convolution. As a result, the effects of the ISI are easily and completely eliminated. Moreover, the approach enables the receiver to use fast signal processing

transforms such as a fast Fourier transform (FFT) for OFDM implementation . Similar techniques can be employed in single-carrier systems as well, by preceding each transmitted data block of length by a CP of length , while using frequency- domain equalization at the receiver.

Fig. 1.1 Q _ L MIMO-OFDM system, where Q and L are the numbers of inputs and outputs, respectively.

Multiple antennas can be used at the transmitter and

receiver, an arrangement called a multiple-input multiple-output (MIMO) system. A MIMO system takes advantage of the spatial diversity that is obtained by spatially separated antennas in a dense multipath scattering environment. MIMO systems may be implemented in a number of different ways to obtain either a diversity gain to combat signal fading or to obtain a capacity gain. 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, spacetime block codes (STBC) [4], [5] and spacetime

trellis codes (STTC) [6]. The second class uses a layered approach to increase capacity. One popular example of such a system is V-BLAST suggested by Foschini et al. [7] where full spatial diversity is usually not achieved. Finally, the third type exploits the knowledge of channel at the transmitter. It decomposes the channel coefficient matrix using singular value decomposition (SVD) and uses these decomposed unitary matrices as pre- and post-filters at the transmitter and the receiver to achieve near capacity [8].

OFDM has been adopted in the IEEE802.11a LAN and

IEEE802.16a LAN/MAN standards. OFDM is also being considered in IEEE802.20a, a standard in the making for maintaining high-bandwidth connections to users moving at speeds up to 60 mph. The IEEE802.11a LAN standard operates at raw data rates up to 54 Mb/s (channel conditions permitting) with a 20-MHz channel spacing, thus yielding a bandwidth efficiency of 2.7 b/s/Hz. 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 Mb/s in a typical bandwidth of 6 MHz, translating into a bandwidth efficiency of 0.7 to 3.82 bits/s/Hz. Recent developments in MIMO techniques promise a significant boost in performance for OFDM systems. Broadband MIMO-OFDM systems with bandwidth efficiencies on the order of 10 b/s/Hz are feasible for LAN/MAN environments. The physical (PHY) layer techniques described in this paper are intended to approach 10b/s/Hz bandwidth efficiency. This paper discuss several PHY layer aspects broadband MIMO-OFDM systems. Section II describes the basic. MIMO-OFDM system model. All MIMO-OFDM receivers must perform time synchronization, frequency offset estimation, and correction and parameter estimation. This is generally carried out using a preamble consisting of one or more training sequences. Once the acquisition phase is over, receiver goes into the tracking mode. Section III provides an overview of the signal acquisition process and investigates sampling frequency offset estimation and correction in Section IV. The issue of channel estimation is treated in Section V. Section VI considers spacetime coding techniques for MIMO-OFDM, while Section VII discusses coding approaches. Adaptive analog beam forming approaches

can be used to provide the best possible MIMO link.

Section VIII discusses various strategies for beam forming. Section IX very briefly considers medium access control issues. Section X discusses a software radio implementation for MIMO-OFDM. Finally, Section XI wraps up with some open issues concluding remarks.

Fig. 1.2. Frame structure for the Q _ L OFDM system

CHAPTER 2

SPACE TIME CODING Space-time codes (STC) provide transmits diversity for the Multi-Input Multi-Output fading channel. There are two main types of STCs namely space-time block codes (STBC) and space-time trellis codes (STTC). Space time block codes operate on a block of input symbols, producing a matrix output whose columns represent time and rows represent antennas. Their main feature is the provision of full diversity with a very simple decoding scheme. On the other hand, Space-time trellis codes operate on one symbol at a time, producing a sequence of vector symbols whose length represents antennas. Like traditional TCM (Trellis Coded Modulation) for a single- antenna channel, Space-time trellis codes provide coding gain. Since they also provide full diversity gain, their key advantage over space-time block codes is the provision of coding gain [3]. Their disadvantage is that they are extremely hard to design and generally require high complexity encoders and decoders. An STBC is defined by a p x n transmission matrix G, whose entries are linear combinations of x1,xk and their conjugates x, and whose columns are pair wise orthogonal. When p = n and {xi } are real, G is a linear processing orthogonal design which satisfies the condition that GT G = D, where D is the diagonal matrix with the (i,i)^th diagonal element of the form . Without loss of generality, the first row of G contains entries with positive signs. If not, one can always negate certain columns of G to arrive at a positive row. We assume that transmission at the base-band employs a signal constellation A with 2b elements. At the first time slot, nb bits arrive at the encoder and select constellation signals c1,, cn. Setting xi = ci for i = 1., n in G yields a matrix C whose entries are linear combinations of the ci and their conjugates. While G contains the in determinates x1,, xn, C contains specific c constellation symbols (or linear combinations of them), which are transmitted from the n antennas as follows: At time t, the entries of row t of C are simultaneously transmitted from the n antennas, with the ith antenna sending the ith entry of the row. So each row of C gives the symbols sent at a certain time, while each column of C gives the symbols sent by a certain antenna.

SPACE FREQUENCY CODING In this section we design a fixed space-frequency codes providing high diversity gain and manageable decoding complexity. Choosing any unitary matrix as yields the same mutual information, but it has significant impact on error performance. It can be seen from the fact that without spreading source symbols across space and frequency (i.e. = I), the diversity gain is only NR which is significantly less than the maximum diversity gain min(NNR;NPNTNR) [1], [2]. In order to maintain a manageable level of complexity for maximum-likelihood decoding, we divide the NNT source symbols into G = N=Q groups of NTQ symbols each. Then we code source symbols in each group across space and frequency applying threaded algebraic space-time (TAST) block codes since they possess the best or close to best error performance among space-time block codes [12]. As an example, a modified TAST code for the NT = Q = 4 case is shown below [9] where [z1(n) z2(n) z3(n) z4(n)]T = s(n), n = 1; 2; 3; 4, is the rotated symbol vector, and is a unit-norm scaling parameter for ensuring full-diversity of the TAST code. The unitary matrix ensures that each source symbol interacts with every channel coefficient even when the power allocation matrix DW(n) or ^D(n) mutes some spatial streams or rows (see [9] for details on ). It is convenient to write (4) in a vector form as where G generator matrix for TAST codes incorporating the rotation matrix and scaling parameter . Finally, the coded symbols are fed to the permutation matrix , whose role is to place symbol vectors belonging to the same group far apart in the subcarrier domain as shown in, so that each symbol experiences independent sub-channels instead of highly correlated ones.SPACE TIME FREQUENCY CODING Insignal processing,timefrequency analysisis a body of techniques and methods used for characterizing and manipulating signals whose statistics vary in time, such astransientsignals. It is a generalization and refinement ofFourier analysis, for the case when the signal frequency characteristics are varying with time. Since many signals of interest such as speech, music, images, and medical signals have changing frequency characteristics, timefrequency analysis has broad scope of applications. Whereas the technique of theFourier transformcan be extended to obtain the frequency spectrum of any slowly growinglocally integrablesignal, this approach requires a complete description of the signal's behaviour over all time. Indeed, one can think of points in the (spectral) frequency domain as smearing together information from across the entire time domain. While mathematically elegant, such a technique is not appropriate for analyzing a signal with indeterminate future behaviour. For instance, one must presuppose some degree of indeterminate future behaviour in any telecommunications systems to achieve non-zero entropy (if one already knows what the other person will say one cannot learn anything).

To harness the power of a frequency representation without the need of a complete characterization in the time domain, one first obtains a timefrequency distribution of the signal, which represents the signal in both the time and frequency domains simultaneously. In such a representation the frequency domain will only reflect the behavior of a temporally localized version of the signal. This enables one to talk sensibly about signals whose component frequencies vary in time.

For instance rather than usingtempered distributionsto globally transform the following function into the frequency domain one could instead use these methods to describe it as a signal with a time varying frequency.

Once such a representation has been generated other techniques in timefrequency analysis may then be applied to the signal in order to extract information from the signal, to separate the signal from noise or interfering signals, etc.MODELING OF OFDM SYSTEM

Orthogonal frequency-division Multiplexing

(OFDM) is a method of encoding digital data on multiple carrier frequencies. OFDM has developed into a popular scheme wideband communication whetherwirelessor over copperwires, used in applications such as digital television and audio broadcasting,DSL broadband internet access, wireless networks, and4Gmobile communications. OFDM is essentially identical tocoded OFDM(COFDM) anddiscrete multi-tone modulation(DMT), and is afrequency-division multiplexing(FDM) scheme used as a digital multi-carriermodulationmethod. A large number of closely spacedorthogonalsub-carrier signalsare used to carrydata. The data is divided into several parallel data streams or channels, one for each sub-carrier. Each sub-carrier is modulated with a conventional modulation scheme (such asquadrature amplitude modulationorphase-shift keying) at a lowsymbol rate, maintaining total data rates similar to conventionalsingle-carriermodulation schemes in the same bandwidth.

The primary advantage of OFDM over single-carrier schemes is its ability to cope with severechannelconditions (for example,attenuationof high frequencies in a long copper wire, narrowbandinterferenceand frequency selectivefading due tomultipath) without complex equalization filters. Channelequalizationis simplified because OFDM may be viewed as using many slowly modulated narrowbandsignals rather than one rapidly modulatedwidebandsignal. The low symbol rate makes the use of aguard intervalbetween symbols affordable, making it possible to eliminateintersymbol interference(ISI) and utilize echoes and time-spreading (that shows up asghostingon analogue TV) to achieve adiversity gain, i.e.asignal-to-noise ratioimprovement. This mechanism also facilitates the design ofsingle frequency networks(SFNs), where several adjacent transmitters send the same signal simultaneously at the same frequency, as the signals from multiple distant transmitters may be combined constructively, rather than interfering as would typically occur in a traditional single-carrier system.

(Figure-2.1)ADVANTAGES & DISADVANTAGES OF OFDMAdvantages:- The advantages using OFDM are listed below.

Makes efficient use of the spectrum by allowing overlap.

By dividing the channel into narrowband flat fading sub channels, OFDM is more resistant to frequency selective fading than single carrier systems.

OFDM is an efficient way to deal with multipath; for a given delay spread, the implementation complexity is significantly lower than that of a single-carrier system with an equalizer.

Eliminates ISI and ICI through use of a cyclic prefix.

Using adequate channel coding and interleaving one can recover symbols lost due to the frequency selectivity of the channel.

Channel equalization becomes simpler than by using adaptive equalization techniques with single carrier systems.

It is possible to use maximum likelihood decoding with reasonable complexity.

OFDM is computationally efficient by using FFT techniques to implement the modulation and demodulation functions.

It is less sensitive to sample timing offsets than single carrier systems are.

Provides good protection against co-channel interference and impulsive parasitic noise.

Disadvantages:-

The disadvantages of OFDM are as follows:

OFDM has a relatively large peak-to-average-power ratio, which tends to reduce the power efficiency of the radio frequency (RF) amplifier.

The OFDM signal has a noise like amplitude with a very large dynamic range; therefore it requires RF power amplifiers with a high peak to average power ratio.

It is more sensitive to carrier frequency offset and drift than single carrier systems are due toleakage of the DFT.

Adding a guard period lowers the symbol rate and hence lowers the overall spectral efficiency of the system.

APPLICATIONS OF OFDM DAB - OFDM forms the basis for the Digital Audio Broadcasting (DAB) standard in the European market.

ADSL - OFDM forms the basis for the global ADSL (asymmetric digital subscriber line) standard.

Wireless Local Area Networks - development is ongoing for wireless point-to-point and point-to-multipoint configurations using OFDM technology.

In a supplement to the IEEE 802.11 standard, the IEEE 802.11 working group published IEEE 802.11a, which outlines the use of OFDM in the 5GHz band.

CONCLUSION We have investigated STF codes for MIMO-OFDM. Starting from the premise that coding across the tones must be done in a systematic way, we have pointed out the basic mathematical analogy between antennas (or spatial eigen modes) and tones, and explained how this similarity allows to reuse the concepts of ST coding for space-time-frequency (STF) coding required for OFDM. We then proposed a reduced-complexity scheme that codes only across tones that are separated by about one coherence bandwidth. A logical next step would be to use real-world codes on that scheme and investigate performance with full- and reduced-complexity schemes. In this paper we proposed a joint detection-estimation scheme for MIMO-OFDM systems. The scheme consists of linked iterations of a turbo-decoder and an efficient channel estimator. The joint-detector estimator demonstrates good performance over time varying channels even at relatively high Doppler frequencies. It is shown by numerical simulation that for a 4T4R configuration with a data rate of 4 Mb/s over a 1.25 MHz channel and over a channel with Doppler as high as 200 Hz, 1% PER performance is achieved at an SNR of 16 dB. When the number of receive antenna is increased to 6, the same performance can be achieved at an SNR of only 6 dB. While there is moderate complexity increase due to turbo decoding, the proposed scheme has the advantage of avoiding high complexity interference suppression techniques. Meanwhile, the moderate complexity increase is justified by significant performance improvement over previously proposed schemes.REFERENCES [1] J. H. Winters, On the capacity of radio communications systems with diversity in Rayleigh fading environments, IEEE J. Select. Areas Commun., vol. 5, pp. 871878, June 1987.

[2] G. J. Foschini and M. J. Gans, On limits of wireless communications in fading environments when using multiple antennas, Wireless Pers. Commun., vol. 6, pp. 311335, 1998.

[3] G. J. Foschini, Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas, Bell Labs Tech. J., no. Autumn, pp. 4159, 1996.

[4] V. Tarokh, N. Seshadri, and A. R. Calderbank, Space-time codes for high data rate wireless communication: Performance criterion and code construction, IEEE Trans. Inform. Theory, vol. 44, pp. 744765, 1998.

[5] Y. Li, N. Seshadri, and S. Ariyavisitakul, Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels, IEEE J. Select. Areas Commun., vol. 17, pp. 461471, 1999.

[6] H. Boelcskei, D. Gesbert, and A. Paulraj, On the capacity of wireless systems employing OFDM-based spatial multiplexing, IEEE Trans. Commun., vol. 50, pp. 225234, 2002.

[7] H. Boelcskei and A. J. Paulraj, Space-frequency coded broadband OFDM systems, in Proc. IEEE Wireless Commun. Network Conf., 2000, pp. 16.

Proc. VTC98, vol. 3, 1998, pp. 22322236.

[8] A. F. Molisch, M. Steinbauer, M. Toeltsch, E. Bonek, and R. Thoma, Capacity of MIMO systems based on measured wireless channels, IEEE J. Select. Areas Commun., vol. 20, pp. 561569, 2002.

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