K. Kusume, "Linear Space-Time Precoding for OFMA Systems Based on Long-Term CSI," in Proc. IEEE Int. Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2004), pp. 949-953, (Barcelona, Spain), September 2004. This paper was invited for International Journal of Wireless Information Networks.

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Katsutoshi Kusume http://kusume.googlepages.com/

LINEAR SPACE-TIME PRECODING FOR OFDM SYSTEMS BASED ON LONG-TERM CSI Katsutoshi Kusume Wireless Solution Laboratory, DoCoMo Communications Laboratories Europe GmbH Landsherger Strasse 312, 80687 Munich, Germany Email:[email protected]

Abstract - This paper addresses severe performance degradation for OFDM systems in case of long channel delays exceeding a guard interval. Such long delays can be seen as interference source on one hand and also as valuable frequency diversity source on the other hand. Instead of simply suppressing such long delays, e.g. by beamforming at transmitter, the newly proposed precoding scheme attempts to find the optimum trade-off between the interference suppression and the diversity gain for the long delays. The optimization problem is formulated exploiting only longterm channel state information instead of instantaneous one and it is solved based on minimum mean square error criterion with a constraint on the transmit power. Computer simulation results show that the proposed scheme outperforms conventional beamforming scheme. I . INTRODUCTION

Broadband signals to achieve high data rates are expected

in future wireless communication systems. Multicamer systems, such as orthogonal f'equency division multiplexing (OFDM) and multicarrier code division multiple access (MC-CDMA), were proposed as promising schemes for future mobile communications [l], [2]. The multicamer systems handle the severe frequency selectivity resulting from the broadband signals in a simple and elegant way that is to apply Fourier transform and cyclic prefur (CP) as the guard interval (GI). However, considering various propagation scenarios in indoor and outdoor environments, when the maximum channel delay exceeds the GI, then severe performance degradation shall be observed. To combat the performance degradation due to the delays exceeding the GI, several proposals have been made, e.g. in [3], [4], [5], [6], [7], these proposals are equalizer variants performed at the receiver. Using these schemes loses the simplicity of multicamer systems and results in complex receiver. That is not suitable for mobile terminals. Recently a linear precoding scheme for OFDM systems based on instantaneous channel state information (CSI) has been proposed in [8]. Although this scheme is effective for scenarios where the CSI is known prior to the transmission, yet the knowledge of the instantaneous CSI at the transmitter is too demanding in some systems, e.g. in frequency division duplex (FDD) systems. The instantaneous CSI estimated from the uplink cannot be directly exploited for the downlink

0-7803-8523-3/04/$20.00 02004 IEEE.




Y [nl


Hol HI

NM N Transmitter Channel


Nc N, OFDM Demodulator (fixed)

Fig. 1 Generalized system model. The transmit filter T is subject to the optimization while the receiver remains unchanged, i.e. standard OFDM demodulation FGn.

due to the camer frequency difference. It should be also noted that the channel estimation error and time varying channel will have strong impact on the performance of the precoding scheme. In FDD systems several transmit processing schemes based on long-term CSI have been proposed, e.g. in [9], [IO] for DS-CDMA systems and in [ I l l for GSM. The longterm CSI (Path delays and directions of departure) reflect the geometrical characteristics ofthe communication channel and are independent of carrier frequency (e.g. [12]). This paper proposes new space-time precoding scheme for OFDM systems only based on the long-term CSI. The short-term and long-term CSI are explained in Section I1 in the context of the system model description. In Section 111 the beamforming scheme is discussed as a conventional approach suppressing the long channel delays. And its disadvantage is discussed that motivates the finding of the new space-time precoding filter. The proposed space-time precoding scheme is derived in Section 1V.The performance is evaluated by computer simulations which are shown in Section V. This paper is concluded in Section VI. 11. S Y S T E M M O D E L This section introduces the discrete linear baseband system model which is illustrated in Fig. 1. Note that channel (de)coding and (de)interleaving are not shown in this figure. After the channel coding and interleaving, information bits are modulated to complex valued baseband symbols which are arranged in the vector

~ [ n=][SI["] ,..., ~ , v ~ [EnC]N]' ,~



Table 1 Basic Simulation Parameters,

ofthe n-th OFDM symbol where iV, and are the number of subcamers and the transpose of a matrix, respectively. The modulated signals are transformed by the filter T and are transmitted to the communication channel. Thus, the transmitted signal is expressed as

y[n] = T s [ n ]t



where M IS the number of antenna elements at the transmitter and N = N, + Ns is the size of the OFDM symbol including the GI of size N g . Note that the main focus of this paper IS to design the transmit filter T rather than the signal processing at the receiver in order to keep the simple structure of the mobile terminals. In case of the standard OFDM system with single antenna (Ad = 1) at the transmitter, the filter T is simply the OFDM modulation expressed by ToFDM



Modulation Number of subcarriers Fmme size Channel Channel coding Number of antennas at Tn


where FHand GI are the inverse Fourier transform and the CP insertion matrix, respectively. The N,-point Fourier transform matrix F is defined as

h', = 64 22 symbols quasi static. exponential decay 112 convolutional. memoly 4

s[n].Therefore, the received signal is written as follows (cf. 1131) = HoyInI + H l y [ n- 11 + VbI: (9)


where ~ ( n E] C N is a noise vector in the time domain. The channel matrices in (6) and (8) reduce to simple convolution matrices in case of single antenna at the transmitter because al = l , W , for M = 1. Throughout this paper, the standard OFDM demodulation is performed at the receiver with single antenna that is to remove the GI and to perform Fourier transform to recover the subcamer signals. This is expressed by

d[n] = FGR?'[~]: where[~]l,,isthe(l~m)-thentryofamatrixandl5 Z.m 5 N,. The CP insertion matrix GI is written as (cf. [13])

where 1~~ and B are an identity matrix of size N, and the last Ng rows of l ~ respectively. ~ , In case of multiple antenna elements deployed at the transmitter (M > l), the filter T must include signal processing in the spatial domain. The signal processing in space is discussed in Section 111 and 1V. The channel convolution matrix of block Toeplitz structure for the OFDM symbol at time n can be expressed as


where '8' and al t C" denote the Kronecker product and the array steering vector (e.g. [12]), respectively. The order L channel taps, hl, 1 = 0,. . . , L, are assumed to be uncorrelated complex Gaussian random variables. The delay operator r N is introduced and is defined as

Similar to (6), the channel matrix for the OFDM symbol at time n - 1 can be expressed as


The channel matrix in (8) is defined in order to model the intersymbol interference (ISI) from the symbol s [ n - 11 to


where the GI removal matrix is defined as (cf. [13])



O N ~ ~ 1~~ N ~] E

IwN , x N .

(1 1)

The signal d[n] is further deinterleaved and channel decoded subsequently. Ill. CONVENTIONAL BEAMFORMING The beamforming approach at receiver to suppress the long delays exceeding the GI has been proposed in [14]. This approach may be applied at the transmitter based on the long-term CSI. One of the conventional beamforming schemes based on the long-term CSI is look direction beamforming [ 15l.h order to concentrate the transmit power toward the paths within the GI, a simple beamforming vector may be calculated as (also cf. Fig. 5)

where (e)* denotes complex conjugation and & is the number of paths chosen for the beamforming. Note that the paths with shortest delays should always be chosen for the beamforming in order to suppress the interference caused by the long delays. The transmit filter in this case is expressed as TBF= ( 18~W ) T ~ FEDCNArxNc M . (13) Computer simulations are conducted in order to clarify the influence of the different choice of Nb on the performance. Basic simulation parameters are summarized in Table I . The GI size is set to N g = 0 as an extreme case to clearly see


.. .. Fig. 3 System model for designing the precoding filter T . unknown to transmitter-



i I Ii. i Fig. 2 BER performance of conventional look direction beamforming suppressing channel delays with different values of Nb. The number of channel taps is 4 ( L = 3) with exponential decay of 0.5 dB. the impact of the choice of Nb. The number of channel taps is set to 4 ( L = 3) with the exponential decay of 0.5 dB. The BER performance against EbjNo is plotted in Fig. 2 for both uncoded and coded case. In the uncoded case, it can be seen that the smaller value of Nb performs better because the transmit power is concentrated on the path which does not cause interference. However, the picture becomes different with the channel coding. For high SNR, the same tendency as the uncoded case is observed, but for low SNR the larger value of Nb is of advantage due to the more diversity picked by the channel coding although more interferences are observed. This observation shows the trade-off between the diversity gain and interference suppression for the long delays that motivates the finding of the new precoding scheme explained in Section IV. IV. PROPOSED SPACE-TIME PRECODING

The proposed precoding filter is designed to find an optimum trade-off between the diversity gain and the interference suppression for the long channel delays exceeding the GI. Let us start with an analysis on the diversity in relation to the multipath in the ideal case of no interference assuming the GI to he sufficiently long. Then, the pair of the OFDM modulation and demodulation transforms the multipath into independent parallel sub-channels. The estimated signal can he written as i[n]= As[.] ?[.I, (14)


where ?in]is a noise vector in frequency domain and A is a diagonal matrix whose diagonal element represents channel

optimize Fig. 4 Linearly transformed system model for designing the precoding filter T (cf. Fig. 3).

frequency response of each suhcarrier. The diagonal matrix A can be further expressed as L

A = d i a g ( A ~ ..., , AN,) = X h t d ,



where n = d i a g ( l : w ,...,U"-').


The diagonal elements in (16) are Fourier coefficients defined in (4).The frequency diversity resulting from the multipath is naturally expressed in (15). The diagonal matrix A is a superposition of the diagonal matrices nt weighted by the channel tap hl for each path, and in other words, the orthogonality among subcarriers is kept when that is true for every path. Since it is assumed that the channel tap hl is unknown at the transmitter, the hest strategy for the transmitter is to make use of all the available paths to get full diversity while keeping the orthogonality in every path regardless of the value of hl. The effects of hl can he easily compensated at the receiver by means of simple pilot aided channel estimation as long as the orthogonality is kept. Now consider the system model illustrated in Fig. 3 to design the precoding filter. For the brevity, noise is not shown in the figure. Ideally, the precoding filter T should be designed to optimize the signal i[n]. However, the channel tap is assumed to be unknown at the transmitter, thus the linearly transformed equivalent system model in Fig. 4 is considered for the filter design. The OFDM demodulation FGn is linearly shifted to every path of the multipath channel. The filter is designed to optimize all the signals before the channel taps instead of B[n]in Fig. 4.


We set up conditions to he satisfied by the filter T which are twofold; to avoid intercarrier interference (ICI) and 1st. The condition to avoid the IC1 on the I-th path can be written as


GR ( r lN8 ai T s [ n ]= FHni s[n],


= [*Ll.l>




' I



*c :l

= [*:cl,,

> '.' > *.:CI.LI (18)

Note that Olcl and Wlcl are the conditions for the estimated signal and the desired signal to avoid ICI, respectively. The condition to avoid the 1% on the l-th path reads as

J ~ (r;-L,T G ~ B a:) T + ] "




pop,is found to he





The scalar y appeared in (25) and (26) is calculated as

for 0 5 I 5 L to get full diversity from all the available paths. Those conditions are collected in matrices as *&I

p-1 =




where the optimum weighting factor


= o ( l - N e ) x Ns[n] ,

p = N,(L

+ 1)+ -21( L - N g ) ( L- Ns + 1).


Comparing to the conventional beamforming in (13), the new filter in (25) spreads the signal in space and time simultaneously. The resulting guard interval is no longer cyclic extension, but it is rather optimized taking into account the long-term CSI. Fig. 5 illustrates the difference between the conventional beamforming approach and the proposed new filter.




for the path delays exceeding the GI (Ng+ 1 5 1 5 L). And the matrix Ji is introduced in order to cut all z e E @ a g*lacements the equation. It is defined as I

The matrices %SI and *,SI are also defined similar to aIcI and *IC] by stacking QISI,, and * l s l . ~in (19) for Ns 1 5 1 5 L as follows

in time



in space





IFFT With these developments, the estimated signal a[n]only based on the long-term CSI is now formulated as follows

ajn] =

[ E;;;]



T s [ n ] i'[n]= *Ts[n] i'jn], (22)

where the white noise q'[n]of variance U: is also taken into account. Correspondingly the desired signal is written as

Using these definitions, the filter T is calculated by means of some optimization methods. Here, the minimum mean square error is chosen for the optimization. The optimization problem reads as

with the constraint on the transmit power ET.. The solution can be given by

Fig. 5 From conventional heamforming to space-time processing. In contrast to cyclic prefix insertion in time domain and beamforming in spatial domain, the proposed space-time processing spreads signal in space and time simultaneously.

V. SIMULATION RESULTS Computer simulations have been performed to compare the performance of the proposed space-time precoding filter with that of the conventional heamforming. The basic simulation parameters can be found in Table I . The number of channel taps is set to 7 ( L = 6) with the exponential decay of 0.5 dB. The transmitter is equipped with A4 = 4 antennas while the receiver is equipped with only single antenna. Two extreme cases of the GI are examined; no IS1 where sufficiently large GI (Ng = 16 ) is used and the strong IS1 where no GI is used (Ng= 0). The coded BER performance is illustrated in Fig. 6. In case of no IS1 ( N E = 16), there is no big performance difference


Fig. 6 BER performance of the proposed space-time precoding and the conventional beamforming. The number of channel taps is 7 ( L = 6) with exponential decay of O S dB. between the two approaches. However, for Ng = 0, where the strong IS1 and IC1 are present, then the performance difference becomes apparent. As discussed in Section 111, the beamforming approach shows the crossover points for the different choices of Nb due to the trade-off between the interference suppression and the diversity gain. It can be seen that the proposed precoding filter clearly outperforms the conventional heamforming with any choice of Nb. VI. CONCLUSION It has been pointed out that the conventional beamforming suppressing the long delays is not optimal in OFDM systems due to the trade-off between the diversity gain and the interference suppression if channel coding is applied. The newly proposed space-time precoding filter finds the optimum trade-off between the diversity gain and the interference suppression. The proposed scheme may be applied in various scenarios because it does not depend on the short-term CSI. REFERENCES [l] J. A. C. Bingham, “Multicamer Modulation for Data

Transmission: An Idea Whose Time Has Come,” IEEE Communications Magazine, vol. 28, no. 5, pp. 5-14, May 1990. [2] S. Abeta, H. Atarashi, and M. Sawahashi, “Forward link Capacity of Coherent DS-CDMA and MC-CDMA Broadband Packet Wireless Access in a Multi-cell Environment,” in Proc. IEEE Vehicular Technology Conference (VTC’2OOO-FaII),Boston, USA, September 2000. [3] N. Al-Dhahir and J. M. Cioffi, “Optimum Finite-Length Equalization for Multicamer Transceivers,” IEEE Transactions on Communications, vol. 44, no. 1, pp. 5 6 4 4 , January 1996.

[4] Y. Sun, “Bandwidth-Efficient Wireless OFDM,” IEEE Journal of Selected Areas in Commnnications, vol. 19, no. I I , pp. 2267-2278, November 2001. [SI H. Schmidt and K. D. Kammeyer, “Impulse tmncation for wireless OFDM systems,” in 5fh International OFDM-Workshop, Hamburg, Germany, September 2000. [6] P. J. W. Melsa, R. C. Younce, and C. E. Rohrs, “Impulse Response Shortening for Discrete Multitone Transceivers,” IEEE Transacfions on Communications, vol. 44, no. 12, pp. 1662-1672, December 1996. [7] S. Trautmann, T. Karp, and N. J . Fliege, “Frequency Domain Equalization of DMTiOFDM Systems with Insufficient Guard Interval,” in Proc. IEEE Int. Conference on Communications (ICC 2OO2), Apr/May 2002, vol. 3, pp. 1646-1650. [SI K. Kusume, “Linear Space-Time Precoding for OFDM Systems based on Channel State Information,” in Proc. IEEE Inf. Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2OO3), Beijing, China, September 2003, vol. I , pp. 231-235. [9] G. Montalbano, I. Ghauri, and D. T. M. Slock, “SpatioTemporal Array Processing for FDDKDMNSDMA Downlink Transmission,” in Proc. IEEE Vehicular Technology Conference (VTC’99fall), Amsterdam, The Netherlands, September 1999, vol. 3, pp. 1910-1914. M. Joham, K. Kusume, W. Utschick, and J . A. Nossek, “Transmit Matched Filter and Transmit Wiener Filter for the Downlink of FDD DS-CDMA Systems,” in Proc. IEEE Int. Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2OO2), Lisbon, Portugal, September 2002, vol. 5, pp. 2312-2316. P. Forster, L. FBty, and M. Le Bot, “Spatio-Temporal Filters for Downlink Processing in FDD Systems,” in Proc. IEEE Int. Conference on Acoustics, Speech, and Signal Processing. (ICASSP’OO),June 2000, vol. 5, pp. 3017-3020. L. Bigler, H. P. Lin, S. S. Jeng, and G . Xu, “Experimental Direction of Arrival and Spatial Signature Measurements at 900 MHz for Smart Antenna Systems,” in Proc. IEEE Vehicular Technology Conference (VTC’95), Chicago, USA, July 1995, vol. 1, pp. 55-58. Z. Wang and G . B. Giannakis, “Wireless Multicamer Communications,” IEEE Signal Processing Magazine, vol. 17, no. 3, pp. 2 9 4 8 , May 2000. M. Budsabathon, Y. Hara, and S. Hara, “On a Pre-FFT OFDM Adaptive Antenna Array for Delayed Signal Suppression:’ in Pmc. the 7-th International OFDMWorkhop, Hamburg, Germany, September 2002. L. C. Godara, “Application of Antenna Arrays to Mobile Communications, Part 11: Beam-Forming and Direction-of-Amval Considerations,” Proceedings of the IEEE, vol. 85, no. 8, pp. 1195-1245, August 1997.


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