Pre-DFT Processing for MIMO-OFDM Systems with Space ... - CiteSeerX

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 6, NO. 11, NOVEMBER 2007

Pre-DFT Processing for MIMO-OFDM Systems with Space-Time-Frequency Coding Shaohua (Steven) Li, Defeng (David) Huang, Senior Member, IEEE, K. B. Letaief, Fellow, IEEE, and Zucheng Zhou

Abstract— Subcarrier based space processing was conventionally employed in Orthogonal Frequency Division Multiplexing (OFDM) systems under Multiple-Input and Multiple-Output (MIMO) channels to achieve optimal performance. At the receiver of such systems, multiple Discrete Fourier Transform (DFT) blocks, each corresponding to one receive antenna, are required to be used. This induces considerable complexity. In this paper, we propose a pre-DFT processing scheme for the receiver of MIMO-OFDM systems with space-time-frequency coding. With the proposed scheme, the number of DFT blocks at the receiver can be any number from one to the number of receive antennas, thus enabling effective complexity and performance tradeoff. Using the pre-DFT processing scheme, the number of input signals to the space-time-frequency decoder can be reduced compared with the subcarrier based space processing. Therefore, a high dimensional MIMO system can be shrunk into an equivalently low dimension one. Due to the dimension reduction, both the complexity of the decoder and the complexity of channel estimation can be reduced. In general, the weighting coefficients calculation for the pre-DFT processing scheme should be relevant to the specific space-time-frequency code employed. In this paper, we propose a simple universal weighting coefficients calculation algorithm that can be used to achieve excellent performance for most practical space-time-frequency coding schemes. This makes the design of the pre-DFT processing scheme independent of the optimization of the space-time-frequency coding, which is desirable for multiplatform systems. Index Terms— Orthogonal frequency division multiplexing (OFDM), multiple-input and multiple-output (MIMO), Spacetime-frequency code.

I. I NTRODUCTION Or high data rate wideband wireless communications, Orthogonal Frequency Division Multiplexing (OFDM) can be used with Multiple-Input and Multiple-Output (MIMO) technology to achieve superior performance. In conventional MIMO-OFDM systems, subcarrier based space processing [1]-[9] was employed to achieve optimal performance. However, it requires multiple discrete Fourier transform/inverse

F

Manuscript received June 3, 2006; revised November 25, 2006; accepted January 28, 2007. The associate editor coordinating the review of this paper and approving it for publication is A. Nallanathan. This paper was presented in part at the 49th IEEE Global Telecommunications Conference, San Francisco, USA, November 27-December 1, 2006. S. Li is with the Networks Research Platform, Nokia Siemens Networks, Beijing, China (email: [email protected]). D. Huang is with the School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Crawley, WA 6009, Australia (email: [email protected]). K. B. Letaief is with the Center for Wireless Information Technology, Electronic and Computer Engineering Department, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong (email: [email protected]). Z. Zhou is with the Department of Electronic Engineering, Tsinghua University, Beijing, China (e mail: [email protected]). Digital Object Identifier 10.1109/TWC.2007.060321.

DFT (DFT/IDFT) blocks, each corresponding to one receive/transmit antenna. Even though DFT/IDFT can be efficiently implemented using fast Fourier transform/inverse FFT (FFT/IFFT), its complexity is still a major concern for OFDM implementation [10]. In addition, the use of multiple antennas requires the basedband signal processing components to handle multiple input signals, thus inducing considerable complexity for the decoder and the channel estimator at the receiver. To reduce the complexity of such systems, several schemes [10]-[15] were proposed in the literature. For an OFDM system with multiple transmit antennas, the schemes mentioned above [13]-[15], explicitly or implicitly, assume that the channel state information (CSI) is known at the transmitter. In mobile communications, where the channel can vary rapidly, it is difficult to maintain the CSI at the transmitter up-to-date without substantial system overhead [16]. Space-time-frequency codes [1]-[9] were proposed for OFDM systems to fully take advantage of the frequency diversity and spatial diversity presented in frequency selective fading channels without the requirement of the availability of CSI at the transmitter. For such a system, traditional subcarrier based space processing induces considerable complexity due to the reasons mentioned before. In this paper, we propose to use pre-DFT processing to reduce the receiver complexity of MIMO-OFDM systems with space-time-frequency coding. In our proposed scheme, the received signals at the receiver are first weighted and then combined before the DFT processing. Owing to the pre-DFT processing, the number of DFT blocks required at the receiver can be reduced, and a high dimensional MIMO system can be shrunk into an equivalently low dimension one. Both enable effective complexity reduction. One important issue in the proposed pre-DFT processing scheme for MIMO-OFDM systems with space-time-frequency coding is the calculation of the weighting coefficients before the DFT processing. In general, the weighting coefficients calculation are specific to the space-time-frequency coding scheme. In this paper, we propose a universal weighting coefficients calculation algorithm that can be applied in most practical space-time-frequency codes such as those proposed in [1]-[4], [6], [8] and [9]. This makes the design of the pre-DFT processing scheme independent of the optimization of the space-time-frequency coding, which is desirable for multiplatform systems. In general, the weighting coefficients before the DFT processing can be calculated assuming that the CSIs are explicitly available. In this paper, we will show that the weighting coefficients can also be obtained using the signal

c 2007 IEEE 1536-1276/07$25.00

LI et al.: PRE-DFT PROCESSING FOR MIMO-OFDM SYSTEMS WITH SPACE-TIME-FREQUENCY CODING

space method [10], [11] without the explicit knowledge of the CSIs. This helps to reduce the complexity of channel estimation required by the space-time-frequency decoding since the number of equivalent channel branches required to be estimated in the proposed scheme can be reduced from the number of receive antennas to the number of DFT blocks. This paper is organized as follows. In Section II, the proposed scheme for MIMO-OFDM systems is described. The calculation of weighting coefficients with/without explicit CSIs is introduced in Section III and IV, respectively. Some discussions about the proposed pre-DFT processing scheme are given in Section V. Simulation results are then presented in Sections VI. Finally, Section VII concludes our work. Throughout this paper, the following notations will be used. (·)∗ , (·)T , and (·)H in the superscripts denote conjugate, transpose and Hermitian transpose, respectively. Likewise, diag(x) denotes a diagonal matrix x with on its diagonal; rank(·) denotes the rank of (·) ; E(·) denotes the expectation of (·); trace(·) denotes the trace of matrix (·); and λi (·) denotes the ith largest eigenvalue of matrix (·).

4177

(m)

antenna, and zt,l denotes the additive white Gaussian noise (AWGN) component at the mth receive antenna. At the receiver, before the DFT processing, the M data streams from the output of the M receive antennas are weighted and then combined to form Q branches. After the guard interval removal, the weighted and combined signals are then applied to the DFT processors. Note that there are Q branches, and hence the number of DFT blocks required at the receiver is Q. As a result, compared to the conventional receiver structure [1]-[9], where M DFT blocks are used, the number of DFT blocks employed at the receiver can be reduced when pre-DFT processing is used. For the qth branch, the output of the DFT processor at the tth OFDM symbol period is given by (t) = vn,q

F M

(t)

ωm,q Hn(m,f) cn,f +

f =1 m=1

M

(m)

ωm,q zˆt,n

(4)

m=1

where Hn(m,f) =

L−1

(m,f ) −j2πln/N

hl

e

(5)

,

l=0

II. S YSTEM M ODEL

(m)

We investigate a MIMO-OFDM system with N subcarriers as shown in Fig. 1. In the system, there are F transmit antennas and M receive antennas. At the tth OFDM symbol period, the output of the space-time-frequency encoder is assumed to be as follows: (t)

(t)

(t)

(t)

C(t) = c0,1 , · · · , cN −1,1 , · · · , c0,F , · · · , cN −1,F t = 0, 1, · · · , T − 1

(1)

(t) cn,f

is the coded information symbol at the nth subwhere carrier of the tth OFDM symbol period transmitted from the f th transmit antenna, and T is the number of OFDM symbols in a space-time-frequency codeword. When T = 1, the spacetime-frequency code reduces to a space-frequency code. After the IDFT processing, at the tth OFDM symbol period, the lth sample at the f th transmit antenna is given by (f ) st,l

N −1 1 (t) j2πln/N = c e , N n=0 n,f

−Ng ≤ l < N, f = 1, · · · , F, t = 0, · · · , T − 1 (2) where Ng is the length of the cyclic prefix, and we assume that (Ng + 1) < N to keep high transmission efficiency. In the following, we assume that the channel does not vary over the period of one space-time-frequency codeword (i.e., the period of T OFDM symbols). Furthermore, we assume that the channel impulse responses (CIRs) decay to zero during the cyclic extension, or L ≤ (Ng + 1) < N where L is the maximum length of the CIRs. At the mth receive antenna, the lth sample at the tth OFDM symbol period is then given by (m) rt,l

=

F

(m,f ) hl

∗

(f ) st,l

+

zˆt,n =

(m)

zt,l e−j2πln/N .

(6)

l=0

and ωm,q is the weighting coefficient for the mth receive antenna at the qth branch. In order to keep the noise white and its variance at different branch the same, we assume that the weighting coefficients are normalized (i.e.,ΩH Ω = IQ , where Ω is an M × Q matrix with the (m, q)th entry given by ωm,q , and IQ is a Q × Q identify matrix). III. W EIGHTING C OEFFICIENTS C ALCULATION WITH E XPLICIT CSI In this section, we will present a way to calculate the weighting coefficients for the proposed pre-DFT processing scheme. When the ML decoder is employed, the pair-wise error probability (PEP) can be used to denote system performance, which is further determined by the pair-wise codeword distance [11]. The pair-wise codeword distance d2 (C, E|H) between a favored coded sequence T E = E(0) , E(1) , · · · , E(T −1) where

(t) (t) (t) (t) E(t) = e0,1 , · · · , eN −1,1 , · · · , e0,F , · · · , eN −1,F

(∀t ∈ [0, T − 1]) and the transmitted coded sequence T C = C(0) , C(1) , · · · , C(T −1) where C(t) (∀t ∈ [0, T − 1]) is defined in (1), is given by d2 (C, E |H ) =

(m) zt,l

N −1

Q N T −1 −1 M

M

∗ ωm,q ωm ,q

t=0 q=1 n=0 m=1 m =1

f =1

− Ng ≤ l < N, m = 1, · · · , M, t = 0, · · · , T − 1 (3) (m,f) hl

where ∗ denotes the convolution product, denotes the CIR between the f th transmit antenna and the mth receive

F

F

(t)

(t)

(t)

(t)

Hn(m,f ) (Hnm ,f )∗ (cn,f − en,f )(cn,f − en,f )∗ .

f =1 f =1

(7)

4178


di

IDFT

Add Guard Interval

IDFT

Add Guard Interval

#1

Space-time frequency encoding

#F

(a) #1

rt(,1l )

#2

r%t (,l1)

...

rt(,l ) 2

#M

rt(,l

M)

Remove Guard

vn( ,1) t

DFT

Interval

ω( 2,1)

ω(1,1)

... ω( M ,1) dˆi

Weighting Coefficients

ML

Calculation

Decoding

r%t (,l

Q)

Remove Guard

vn( ,)Q t

DFT

Interval

ω(1,Q ) ω( 2,Q ) ... ω ( M ,Q ) Weighting Coefficients Calculation

(b) Fig. 1.

Pre-DFT processing for a MIMO-OFDM system with space-time-frequency coding: (a) transmitter; (b) receiver.

According to [11], minimizing the pair-wise error probability is equivalent to maximizing the pair-wise codeword distance given by (7). A close observation of (7) indicates that the optimal weighting coefficients are related to the specific codeword pair. To make the weighting coefficients and the codeword pair independent, we average (7) over all codewords pair ensemble 1 . As a result, we get d2 (C, E |H ) =

Q M M

∗ ωm,q ωm ,q

q=1 m=1 m =1

Hn(m,f) (Hnm ,f )∗

T −1

(t)

N −1 F

F

n=0 f =1 f =1 (t)

(t)

(t)

(cn,f − en,f )(cn,f − en,f )∗

(8)

t=0

where the overbar stands for the average over all the codewords pair ensemble. In order to rewrite (8) into a matrix form, let Cn be (t) an F × T matrix with the (f, t)th entry given by cn,f , (t) En be an matrix with the (f, t)th entry given by en,f , and Hn (n = 0, · · · , N − 1) be an M ×F matrix with the (m, f )th (m,f) entry given by Hn . With these definitions, (8) can be written into (9) d2 (C, E |H ) = trace ΩT ΦΩ∗ 1 We note here that maximizing the average codeword distance is not equivalent to minimizing the average frame error rate.

where Φ=

N −1

Hn kn HH n

(10)

n=0

with H

kn = (Cn − En ) (Cn − En ) .

(11)

Let the eigenvalues of Φ be λq (q = 1, · · · , M ) with λ1 ≥ λ2 ≥ · · · ≥ λM and ω q (q = 1, · · · , Q) be the ith column of Ω. It is well known that when ωq (q = 1, · · · , Q) are the conjugate of the eigenvectors of Φ corresponding to the eigenvalues λq (q = 1, · · · , M ), the maximum of d2 (C, E |H ) is achieved and is given by [22] d2 (C, E |H )

max

=

Q

λq (Φ).

(12)

q=1

In general, to obtain Φ in (14), we need both knowledge of the CSIs and the space-time-frequency code since kn is dependent on the specific space-time-frequency code. However, since the channel information is not available at the transmitter, the space time-frequency coding scheme should not favor or bias a particular sub-carrier or a particular transmit antenna. As a result, in the following, it will be shown that for most practical space-time-frequency codes, it is reasonable to assume that Φ


is in the following form: Φ=k

N −1

Hn HH n

(13)

The (m, m )th element of R is given by ∗ ( m ) (m) ρm,m = E rt,l rt,l

n=0

where k is a constant that is independent of n. As a result, the weighting coefficients (i.e., ωq (q = 1, · · · , Q)), which are the conjugate of the eigenvectors of Φ, are independent of the specific space-time-frequency coding scheme. For the space-time-frequency codes proposed in [8] and [9], for example, as shown in Appendix A, kn can be expressed as follows: (14) kn = k1 · diag βτ (n) where k1 is a constant number independent of n, βτ (n) = [0, · · · , 0, 1, 0, · · · , 0] is an F − dimensional standard basis vector with 1 in its τ (n)th component and 0 elsewhere, and τ (n) is determined by the space-frequency coding scheme. Using (14), as shown in Appendix A, Φ can be proved to be in the form of (13) with k = k1 /F . For space-time-frequency codes where the orthogonal space time block code (STBC) [17], [18] is employed (e.g., the codes proposed in [1]-[4]) as an inner code. Using the orthogonal property of STBC, we can easily prove that

2

2

(0)

(0) (0)

(0)

kn = diag 2 cn,1 − en,1 , · · · , 2 cn,F − en,F . (15) It is reasonable to assume that the signals at the input of the inner encoder have the same distribution for different subcarriers and different transmit antennas, especially when an interleaver is employed between the outer encoder and the inner encoder. As a result, kn can be written as kn = kIF .

(16)

Therefore, (10) can also be simplified into (13) for these codes. For a general space-time-frequency code such as that proposed in [6], simulation results in Section VI will also show that excellent performance can be achieved by using the weighting coefficients calculated based on Φ given by (13). IV. W EIGHTING C OEFFICIENTS C ALCULATION W ITHOUT E XPLICIT CSI In the following, we propose a way to obtain the weighting coefficients (i.e., the eigenvectors of Φ) without explicit CSI. This is especially important for differential modulation, where the CSI is not supposed to be explicitly known at the receiver. For coherent modulation, when CSI is not explicitly required for the weighting coefficients calculation, the complexity of channel estimation2 can be reduced since the number of equivalent channel branches required to be estimated is now reduced from the number of receive antennas to the number of DFT branches. Note that the covariance matrix of the received signal vector (1)

(2)

(M)

rt,l = rt,l , rt,l , · · · , rt,l

T

can be given by

R = E rt,l rH t,l . 2 In

(17)

this case, explicit CSI knowledge is required for the ML decoder.

4179

=

F F

l

l

(m,f) hl−v

f =1 f =1 v=l−L+1 v =l−L+1 ∗ (f ) (f ) E st,v st,v + N0 δ (m

∗ (m ,f ) hl−v

− m ) .

(18)

Similar to the SIMO case proposed in [10], when a large number of subcarriers are used, it is reasonable to assume that the transmitted signals are white, that is ∗ (f ) (f ) (19) = Es δ (f − f ) δ (v − v ) E st,v st,v where Es is the average energy of the coded symbol. Hence, by substituting (19) into (18) and after some manipulations, ρm,m can be proven to be given by F N−1 m ,f ∗ Es (m,f ) ) ( ρm,m =

N

Hn

Hn

+ N0 δ m − m

f =1 n=0

(20)

where N0 is the variance of the noise. Using (13), we then have Es Φm,m + N0 δ (m − m ) (21) ρm,m = Nk where Φm,m is the (m, m )th entry of Φ. From (21), it can be easily seen that the eigenvectors of Φ are the same as those of R. As a result, we can obtain the weighting coefficients directly from R without explicit knowledge of CSI. V. C OMPLEXITY C ONSIDERATION The proposed MIMO-OFDM system consists of pre-DFT weighting and combining, weighting coefficients calculation, DFT-processing, channel estimation, and ML decoding. By weighting and combining before the DFT processing, the number of branches to be handled by the ML decoder is reduced from M to Q. As a result, compared with the subcarrier based processing [1]-[9], the complexity of ML decoding can be reduced. As for the complexity coming from the DFT processing3, the pre-DFT weighting and combining, the ratio of the number of multiplications needed between the proposed scheme and the subcarrier based scheme is as follows: Q log2 N + M QN log2 N + QM N = η= . (22) M N log2 N M log2 N From (22), it can be seen that, when log2 N >> M , η is close to Q/M . From (12), it is easy to see that the number of DFT blocks at the receiver, Q, is determined by the rank of Φ. After some manipulations, we have

where

˜ 1/2 ), M, FL). rank(Φ) ≤ min(rank(R

(23)

˜ 1/2 = R1/2 , R1/2 , · · · , R1/2 . R 0 1 L−1

(24)

3 We note here that the complexity of a DFT can be only a small fraction of the receiver complexity especially when the total number of sub-carriers is small.

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 6, NO. 11, NOVEMBER 2007 1/2

0

10

1 DFT 2 DFTs 3 DFTs 4 DFTs Optimal −1

10

BER

1/2

while Rl = Rl Rl are the receive correlation matrix as defined in [19]. From (23), we can see that Φ is singular ˜ 1/2 is not of full row rank or F L is smaller than when R M . In this case, the number of DFT blocks required can be smaller than the number of receive antennas to achieve optimal performance. On the other hand, when Φ is nonsingular, it is still possible to achieve good performance with a limit number of DFT blocks due to the small contribution of the small eigenvalue to the average pair-wise codeword distance d2 (C, E |H ).

−2

10

−3

VI. S IMULATION R ESULTS In the considered MIMO-OFDM system, the number of subcarriers in an OFDM symbol is 64 (N = 64) and the length of the guard interval is 12 (Ng = 12). In the simulations, we assume that there are four receive antennas at the receiver and two or four transmit antennas at the transmitter. Further, we assume that the channel is quasi-static and perfect channel information is available at the receiver. Without special notation, the optimal lines in the figures are obtained using ML decoders based on subcarrier space processing as the corresponding references. Further, Eb /N0 in all figures is a shorthand for Eb /N0 per receive antenna.

10

−4

10

B. Performance of space-time-frequency codes proposed in [8] and [6] with pre-DFT processing The space-time-frequeancy code proposed in [8] can achieve full diversity without any rate reduction. In our simulations, the codeword of the space-frequency code C is given by Eqn. (3.1) in [8], and QRD-M algorithm is employed as the space-time-frequency decoder [23]. The performance of the proposed scheme over a six-ray exponential decay quasistatic Rayleigh fading channel is shown in Fig. 3. In Fig.

−2

0

2

4 E /N (dB) b

6

8

10

12

0

Fig. 2. BER performance of the proposed scheme with the space-timefrequency code proposed in [1] over a two-ray equal gain Rayleigh fading channel. The optimal performance is denoted by the dash line, the performance with the weighting coefficients calculated using perfect CSI and that using the signal space method are denoted by the solid lines and dotted lines, respectively. M = 4 and F = 2. 0

10

1 DFT 2 DFTs 3 DFTs 4 DFTs Optimal

A. Performance of space-time-frequency codes proposed in [1] with pre-DFT processing

−1

BER

10

−2

10

−3

10

−4

10

−4

−2

0

2

4

6 E /N (dB) b

8

10

12

14

16

0

Fig. 3. BER performance of the proposed scheme with space-time-frequency code proposed in [8] over a six-ray exponential decay Rayleigh fading channel. M = 4 and F = 4. 0

10

1 DFT 2 DFTs 3 DFTs 4 DFTs optimal −1

10

BER

In this part, we consider the code proposed in [1], where full diversity order provided by the fading channel can be achieved with low trellis complexity. As in [1], we use the optimal rate 2/3 TCM codes [21] designed for flat fading channels. For simplicity, only the 4-state 8PSK TCM code is used and the parity check matrix is (6 4 7) in octal form. When the two-ray equal gain Rayleigh fading channel model is employed, the bit error rate (BER) performance of the proposed scheme is shown in Fig. 2. It can be observed that, with the increase of the number of DFT blocks at the receiver, better performance can be achieved. When the number of DFT blocks is increased from one to two, significant performance gain (e.g., 5.01 dB when Pe = 10−4 ) can be achieved. When the number of DFT blocks is three or four, the performance is close to optimal. When the weighting coefficients are obtained based on the signal space method as discussed in Section IV, the performance of the proposed scheme over two-ray equal gain Rayleigh fading channel is also shown in Fig. 2. In the simulations, P is set to 64. From Fig. 2, we can see that the performance of the proposed scheme using the signal space method is almost the same as that with complete CSI.

−4

−2

10

−3

10

−4

10

−4

−2

0

2

4

6 E /N (dB) b

8

10

12

14

16

0

Fig. 4. BER performance of the proposed scheme with space-time-frequency code proposed in [6] over a two-ray equal gain Rayleigh fading channel. M = 4 and F = 2.


4, the general space-time-frequency code proposed in [6] is employed with 16-state trellis and QPSK modulation [21]. It can be seen from Fig. 3 and Fig. 4 that similar results can be obtained as those in Part A irrespective for channel type and system configuration. As a result, the weighting coefficients obtained in Section III can also be applied here. VII. C ONCLUSION In this paper, a pre-DFT processing scheme was proposed for a MIMO-OFDM system with space-time-frequency coding. With the proposed scheme, system complexity and performance can be effectively traded off. A simple weighting coefficients calculation algorithm was also derived. Theoretical analysis and simulation results have shown that the algorithm can be applied for most existing practical spacetime-frequency codes. Using the proposed scheme, we have also shown that it is possible to use a very limited number of DFT blocks to achieve near optimal system performance. A PPENDIX A P ROOF OF (13) FOR T HE S PACE - FREQUENCY C ODES P ROPOSED IN [8] A ND [9] The codeword of the space-time-frequency codes proposed in [8] and [9] includes only one OFDM symbol. Therefore, they are space-frequency codes with T = 1. Based upon [8] and [9], when f = f , we have (0)

(0)

(0)

(0)

(cn,f − en,f )(cn,f − en,f )∗ = 0.

(A.1)

As a result, kn in Eqn. (11) is a diagonal matrix for any n. When f = f , we have

2

(0) (0) (0) (0) (0) (0)

(cn,f − en,f )(cn,f − en,f )∗ = cn,f − en,f . (A.2) For any n, from [8] and [9], there exists only one f that makes (0) cn,f nonzero. As a result, this f is a function of n, i.e., f = (0) τ (n). From [8] and [9], when f = τ (n), en,f is also nonzero.

2

(0)

(0) It is then reasonable to assume that cn,τ (n) − en,τ (n) have the same distribution for any n. As a result, for the spacefrequency codes proposed in [8] and [9], kn is in the form of (14). Specifically, for the space-frequency code proposed in [8], τ (n) is in the following form n n − τ (n) = ×F +1 (A.3) Γ FΓ where Γ can be any fixed integer between 1 and L, and a is the nearest integer less than or equal to a. For simplicity, here N is assumed to be an integer multiple of F Γ. As a result, the (m, m )th entry of Φ can be written as (A.4) as shown at the top of the next page. Note that N /F Γ−1

e−j2π(l−l )(F Γμ)/N

μ=0

=

N /F Γ, (l − l ) mod (N /F Γ) = 0 0, (l − l ) mod (N /F Γ) = 0,

(A.5)

where mod is the modulus function. In practical applications, N /F Γ > L. As a result, Φm,m can be given by

Φm,m

4181

∗ F Γ−1 L−1 N k1 (m,f) (m ,f ) hl hl FΓ f =1 τ =0 l=0 ∗ L−1 F N k1 (m,f ) (m ,f ) hl hl . (A.6) F

=

=

l=0 f =1

On the other hand, it can be easily proved that L−1 F F m ,f ∗ N−1 (m,f )

N

hl

( hl

)

l=0 f =1

(m ,f ) Hn(m,f ) Hn

=

n=0 f =1

∗

.

(A.7)

As a result, we have Φm,m

∗ N −1 F k1 (m,f) (m ,f ) = Hn Hn F n=0

(A.8)

f =1

and Eqn.(13) is satisfied. Similarly, we can prove that Eqn.(13) is also satisfied for the space-frequency code proposed in [9]. R EFERENCES [1] Y. Gong and K. B. Letaief, “An efficient space-frequency coded OFDM system for broadband wireless communications,” IEEE Trans. Commun., vol. 51, no. 11, pp. 2019-2029, Nov. 2003. [2] D. R. V. Jagannadha Rao, V. Shashidhar, Z. A. Khan, and B. S. Rajan, “Low complexity, full-diversity space-time-frequency block codes for MIMO-OFDM,” in Proc. IEEE Global Telecommun. Conf., vol. 1, Nov. 29- Dec. 3, 2004, pp. 204-208. [3] Z. Liu, Y. Xin, and G. B.Giannakis, “Space-time-frequency coded OFDM over frequency-selective fading channels,” IEEE Trans. Signal Processing, vol. 50, no. 10, pp. 2465-2476, Oct. 2002. [4] A. Stamoulis and N. Al-Dhahir, “Impact of space-time block codes on 802.11 network throughput,” IEEE Trans. Wireless Commun., vol. 2, no. 5, pp. 1029-1039, Sept. 2003. [5] B. Lu, X. Wang, and K. R. Narayanan, “LDPC-based space-time coded OFDM systems over correlated fading channels: performance analysis and receiver design,” IEEE Trans. Commun., vol. 50, no. 1, pp. 74-88, Jan. 2002. [6] D. Agrawal, V. Tarokh, A. Naguib, and N. Seshadri, “Space-time coded OFDM for high data-rate wireless communication over wideband channels,” in Proc. IEEE Veh. Technol. Conf., vol. 3, May 18-21, 1998, pp. 2232-2236. [7] H. Bolcskei, M. Borgmann, and A. J. Paulraj, “Space-frequency coded MIMO-OFDM with variable multiplexing-diversity tradeoff,” in Proc. IEEE Int. Conf. Commun., vol. 4, May. 11-15, 2003, pp. 2837-2841. [8] W. Su, Z. Safar, and K. J. R. Liu, “Full-rate full-diversity space-frequency codes with optimum coding advantage,” IEEE Trans. Inf. Theory, vol. 51, no. 1, pp. 229-249, Jan. 2005. [9] L. Shao and S. Roy, “Rate-one space-frequency block codes with maximum diversity gain for MIMO-OFDM,” IEEE Trans. Wireless Commun., vol. 4, no. 4, pp. 1674-1687, July 2005. [10] M. Okada and S. Komaki, “Pre-DFT combining space diversity assisted COFDM,” IEEE Trans. Veh. Technol., vol. 50, no. 2, pp. 487-496, Mar. 2001. [11] D. Huang and K. B. Letaief, “Pre-DFT processing using eigen-analysis for coded OFDM with multiple receive antennas,” IEEE Trans. Commun., vol. 52, no. 11, pp. 2019-2027, Nov. 2004 [12] S. Hara, M. Budsabathon, and Y. Hara, “A pre-FFT OFDM adaptive antenna array with eigenvector combining,” in Proc. IEEE Int. Conf. Commun., vol. 4, June 2004, pp. 2412-2416. [13] D. Huang and K. B. Letaief, “Symbol-based space diversity for coded OFDM systems,” IEEE Trans. Wireless Commun., vol. 3, no. 1, pp. 117127, Jan. 2004. [14] S. Li, D. Huang, K. B. Letaief, and Z. Zhou, “Joint time-frequency beamforming for MIMO-OFDM systems,” in Proc. IEEE Global Telecommun. Conf., Nov. 2005, pp. 3927-3931. [15] J. Yang and Y. Li, “Low complexity OFDM MIMO system based on channel correlations,” in Proc. IEEE Global Telecommun. Conf., vol. 2, Dec. 2003, pp. 591-595.

4182

Φm,m


=

k1

Γ−1 Γ−1 F N /F f =1

=

k1

k1

τ =0

Γ−1 Γ−1 L−1 L−1 F N /F

f =1

=

μ=0

(m,f)

HF Γμ+(f −1)Γ+τ

μ=0

(m,f) hl

∗ (m ,f ) HF Γμ+(f −1)Γ+τ

∗ (m ,f ) −j2π (l−l )(F Γμ+(f −1)Γ+τ )/N e h l

τ =0 l=0 l =0

F Γ−1 L−1 L−1

⎡

⎛ ⎞⎤ ∗ N /F Γ−1 m ,f ) ⎝ ⎣h(m,f ) h( e−j2π(l−l )(F Γμ+(f −1)Γ+τ )/N ⎠⎦. l l

f =1 τ =0 l=0 l =0

[16] J. C. Roh and B. D. Rao, “Multiple antenna channels with partial channel state information at the transmitter,” IEEE Trans. Wireless Commun., vol. 3, no. 2, pp. 677-688, Mar. 2004. [17] V. Tarokh, H. Jafarkhani, and A. R. Calderbank, “Space-time block codes from orthogonal designs,” IEEE Trans. Inf. Theory, vol. 45, no. 5, pp. 1456-1467, July 1999. [18] S. M. Alamouti, “A simple transmit diversity technique for wireless communications,” IEEE J. Sel. Areas Commun., vol. 16, no. 8, pp. 14511458, Oct. 1998. [19] H. Bolcskei, M. Borgmann, and A. J. Paulraj, “Impact of the propagation environment on the performance of space-frequency coded MIMOOFDM,” IEEE J. Sel. Areas Commun., vol. 21, no. 3, pp. 427-439, Apr. 2003. [20] A. Edelman, Eigenvalues and condition number of random matrics, Ph. D. Thesis, M. I. T. Press, Cambridge, MA, USA, May 1989. [21] H. Jamali and T. Le-Ngoc, Coded Modulation Techniques for Fading Channels. Boston, MA: Kluwer, 1994. [22] R. A. Horn and C. R. Johnson, Matrix Analysis. Cambridge Univ. Press, 1985. [23] W. H. Chin, “QRD based tree search data detection for MIMO communication systems,” in Proc. IEEE Veh. Technol. Conf., vol. 3, May 30June 1, 2005, pp. 1624–1627. Shaohua (Steven) Li received the B.S.E.E and M.S.E.E degrees in electronic engineering from Harbin Institute of Technology, Harbin, China, in 2000 and 2002, respectively, and the Ph.D. degree in the department of Electronic Engineering, Tsinghua University, Beijing, China, in 2006. His research interests include wireless communications, OFDM, MIMO, Beamforming, space-timefrequency processing and digital implementation of communication systems. Defeng (David) Huang (M’01-S’02-M’05-SM’07) received the B. E. E. E. and M. E. E. E. degree in electronic engineering from Tsinghua University, Beijing, China, in 1996 and 1999, respectively, and the Ph.D. degree in electrical and electronic engineering from the Hong Kong University of Science and Technology (HKUST), Kowloon, Hong Kong, in 2004. From 1998, he was an assistant teacher and later a lecturer with Tsinghua University. Currently, he is a lecturer with School of Electrical, Electronic and Computer Engineering at the University of Western Australia. His research interests include broadband wireless communications, OFDM, OFDMA, cross-layer design, multiple access protocol, and digital implementation of communication systems. Dr. Huang serves as an Editor for the IEEE Transactions on Wireless Communications. He received the Hong Kong Telecom Institute of Information Technology Postgraduate Excellence Scholarships in 2004.

(A.4)

μ=0

Khaled B. Letaief (S’85-M’86-SM’97-F’03) received the BS degree with distinction in Electrical Engineering from Purdue University at West Lafayette, Indiana, USA, in December 1984. He received the MS and Ph.D. Degrees in Electrical Engineering from Purdue University, in August 1986, and May 1990, respectively. From January 1985 and as a Graduate Instructor in the School of Electrical Engineering at Purdue University, he has taught courses in communications and electronics. From 1990 to 1993, he was a faculty member at the University of Melbourne, Australia. Since 1993, he has been with the Hong Kong University of Science and Technology where he is currently Chair Professor and Head of the Electronic and Computer Engineering Department. He is also the Director of the Hong Kong Telecom Institute of Information Technology as well as the Director of the Center for Wireless Information Technology. His current research interests include wireless and mobile networks, Broadband wireless access, OFDM, CDMA, and Beyond 3G systems. In these areas, he has published over 280 journal and conference papers and given invited talks as well as courses all over the world. Dr. Letaief served as consultants for different organizations and is currently the founding Editor-in-Chief of the IEEE Transactions on Wireless Communications. He has served on the editorial board of other prestigious journals including the IEEE Journal on Selected Areas in Communications — Wireless Series (as Editor-in-Chief) and the IEEE Transactions on Communications. He has been involved in organizing a number of major international conferences and events. These include serving as the Technical Program Chair of the 1998 IEEE Globecom Mini-Conference on Communications Theory, held in Sydney, Australia as well as the Co-Chair of the 2001 IEEE ICC Communications Theory Symposium, held in Helsinki, Finland. In 2004, he served as the Co-Chair of the IEEE Wireless Communications, Networks and Systems Symposium, held in Dallas, USA as well as the Co-Technical Program Chair of the 2004 IEEE International Conference on Communications, Circuits and Systems, held in Chengdu, China. He is the Co-Chair of the 2006 IEEE Wireless Ad Hoc and Sensor Networks Symposium, held in Istanbul, Turkey. In addition to his active research and professional activities, Professor Letaief has been a dedicated teacher committed to excellence in teaching and scholarship. He received the Mangoon Teaching Award from Purdue University in 1990; the Teaching Excellence Appreciation Award by the School of Engineering at HKUST (4 times); and the Michael G. Gale Medal for Distinguished Teaching (Highest university-wide teaching award and only one recipient/year is honored for his/her contributions). He is a Fellow of IEEE, an elected member of the IEEE Communications Society Board of Governors, and an IEEE Distinguished lecturer of the IEEE Communications Society. He also served as the Chair of the IEEE Communications Society Technical Committee on Personal Communications as well as a member of the IEEE ComSoc Technical Activity Council. Zucheng Zhou graduated from the department of Radio Electronics at Tsinghua University, Beijing, China, in 1964. Since 1964, he has been with Tsinghua University, where he is a professor of Electronic Engineering Department and Director of the CAD laboratory of the State Key Laboratory on Microwave and Digital Communications. His major research fields are the design methodology and design automation of integrated circuits and systems, design of integrated circuits for communication.