Rank Distance Codes for ISI channels - Semantic Scholar

Princeton University, New Jersey. Email: calderbank@math.princeton.edu .... examples and conclude with a discussion in Section V. II. PROBLEM STATEMENT ...

Rank Distance Codes for ISI channels S. Dusad

School of Comp. & Comm. Sc. EPFL, Lausanne, CH 1015 Email: [email protected]

S N. Diggavi

A R. Calderbank

School of Comp. & Comm. Sc. EPFL, Lausanne, CH 1015 Email: [email protected]

Dept. of Elec. Engr and Math. Princeton University, New Jersey. Email: [email protected]

Abstract— Designs for transmit alphabet constrained spacetime codes naturally lead to questions about the design of rank distance codes. Recently, diversity embedded multi-level space-time codes for flat fading channels have been designed by using sets of binary matrices with rank distance guarantees over the binary field and mapping them onto QAM and PSK constellations. In this paper we give the design of diversity embedded space-time codes for fading Inter-Symbol Interference (ISI) channels with provable rank distance guarantees. In the process of doing so we also get a (asymptotic) characterization of the rate-diversity trade-off for multiple antenna fading ISI channels when there is a fixed transmit alphabet constraint. The key idea is to construct and analyze properties of binary matrices with the particular structure induced by ISI channels.

1 Throughout

2 In fact in [5] we have examples to show that some designs that will achieve the rate-diversity trade-off for flat-fading channels would fail to do so in the case of ISI channels. Though designs for ISI channels will still be optimal if we have flat-fading channels.



  y[0] . . . y[T − 1] = H0 | {z } | Y

x[0] x[1] . . . x[T − ν − 1] 0 ... 0 0 x[0] x[1] . . . x[T − ν − 1] 0 0   . . . Hν  .. .. . . {z } ... . . . . . H 0 ... 0 x[0] x[1] . . . x[T − ν − 1] | {z X

this is the construction of binary codes for ISI channels with rank-distance guarantees for the particular structure imposed by the ISI. This is done in Section IV. Finally, we give examples and conclude with a discussion in Section V. II. P ROBLEM S TATEMENT AND

CODE DESIGN CRITERIA

A. Channel Model Our focus in this paper is on the quasi-static frequency selective (ISI) channel with (ν + 1) taps where we transmit information coded over Mt transmit antennas and have Mr antennas at the receiver. Furthermore, we make the commonly used assumption that the transmitter has no channel state information, whereas the receiver is able to perfectly track the channel. The code is designed over a large enough block size T ≥ Tthr transmission symbols, where Tthr is specified in the constructions given in Section III. The received vector at time n after demodulation and sampling can be written as, y[n] = H0 x[n] + H1 x[n − 1] + . . . + Hν x[n − ν] + w[n] (1) where, y ∈ C Mr ×1 , Hl ∈ CMr ×Mt represents the matrix ISI channel, x[n] ∈ C Mt ×1 is the space-time coded transmission sequence at time n with transmit power constraint P and w ∈ C Mt ×1 is assumed to be additive white (temporally and spatially) Gaussian noise with variance σ 2 . The matrix Hl consist of fading coefficients hij which are i.i.d. CN (0, 1) and fixed for the duration of the block length (T ). Consider a transmission scheme in which we transmit over a period T − ν and send (fixed) known symbols3 for the last ν transmissions. For the period of communication we can equivalently write the received data as in (2) given at the top of the page, i.e., Y = HX + W

(3)

where Y ∈ CMr ×T , H ∈ CMr ×(ν+1)Mt , X ∈ C(ν+1)Mt ×T , W ∈ CMr ×T . Notice that the structure in (2) is different from the flat-fading case, since the channel imposes a Toeplitz structure on the equivalent space-time codewords X given in (2)-(3). This structure makes the design of space-time codes different than in the flat-fading case. For reference, the spacetime codeword is completely determined by the matrix X(1) given by,   X(1) = x[0] x[1] . . . x[T − ν − 1] 0 . . . 0 (4) 3 Taken

without loss of generality to be 0.

   +W (2)  }

B. Diversity-embedded code design criteria

A scheme with diversity order d has an error probability at high SNR behaving as P¯e (SNR) ≈ SNR−d [10]. More formally, we require that the average error probability P¯e (SNR) as a function of SNR that behaves as log(P¯e (SNR)) lim = −d. (5) SNR→∞ log(SNR) The fact that the diversity order of a space-time code is determined by the rank of the codeword difference matrix is well known [10]. Therefore, for flat-fading channels, it has been shown that the diversity order achieved by a spacetime code is determined by the codeword difference matrix of space-time codewords C1 , C2 ∈ C Mt ×T . d = Mr min rank(C1 − C2 ) . C1 6=C2

(6)

Clearly the analysis in [10] can be easily extended to fading ISI channels, and we can write d = Mr min rank(X1 − X2 ) , X1 6=X2

(7)

where X1 , X2 ∈ C(ν+1)Mt ×T are matrices with structure given in (2). It is easy to see from the structure of X in (2) that the rank of the matrix X is at most (ν + 1) times the rank of the matrix X(1) (see (4)), i.e., rank(X) ≤ (ν + 1)rank(X(1) )

(9)

The codebook structure proposed in [2] takes two information streams and outputs the transmitted sequence {x(k)}. The objective is to ensure that each information stream gets the designed rate and diversity levels. Let A denote the message set from the first information stream and B denote that from the second information stream. Then analogous to (5), we can write the diversity order for the messages as, log P¯e (B) log P¯e (A) , Db = lim . (10) Da = lim SNR→∞ log(SNR) SNR→∞ log(SNR) Given messages a ∈ A, b ∈ B, we denote the diversity embedded space-time codeword with structure given in (2) as Xa,b . Clearly we can translate the code design criterion from (7) to diversity embedded codes for ISI channels as, min min rank(Xa1 ,b1 − Xa2 ,b2 )) ≥ Da /Mr (11) a1 6=a2 ∈A b1 ,b2 ∈B

k[0] k[1] . . . k[T − ν − 1] 0 ... 0  0 k[0] k[1] . . . k[T − ν − 1] 0 0  Γ (B) =  .. .. . .  ... . . . . . 0 ... 0 k[0] k[1] . . . k[T − ν − 1] In an identical manner, we can show for the message set B, we need the following to hold. min min rank(Xa1 ,b1 − Xa2 ,b2 )) ≥ Db /Mr . (12) b1 6=b2 ∈B a1 ,a2 ∈A As one can easily see, these are simple generalizations of the diversity-embedded code design criteria developed in [2] to the fading ISI case. C. Rate and Diversity For a given diversity order, it is natural to ask for upper bounds on achievable rate. For a flat Rayleigh fading channel, this has been examined in [10] where the following result was obtained. Theorem 1: ( [10], [8]) If we use a constellation of size |A| and the diversity order of the system is qMr , then the rate R that can be achieved is bounded as R ≤ (Mt − q + 1)

(13)

in symbols per transmission, i.e., the rate is R log2 |A| bits per transmission. Just as Theorem 1 shows the tension between achieving highrate and high-diversity for fixed transmit alphabet constraint for flat fading channel, there exists a tension between achievable rate and diversity for frequency selective channels as well4 . One of the questions addressed in this paper is the characterization of this tension. Note that due to the zero padding structure for ISI channels, the effective rate Ref f is going to be smaller than the rate of space-time code. Since we do not utilize ν transmissions over a block of T transmissions for each of the antennas we can only hope for a rate R asymptotically in transmission block size T . III. D IVERSITY

EMBEDDED CODES FOR

ISI

CHANNELS

In this section we use the idea of multi-level diversity embedded codes [3], [1], [4] and the structure imposed by the ISI on the space time code as in (2) to motivate construction and analysis of a class of binary matrices as follows. Consider t ×T the matrix B ∈ FM , with the following structure, 2   B= k[0] k[1] . . . k[T − ν − 1] 0 . . . 0 , (14) where k[n] ∈ F2Mt ×1 , n = 0, . . . , T − ν − 1. We define a (ν+1)Mt ×T t ×T mapping Γ : FM → F2 by Eq. (15). 2 t ×T Definition 2: Define Kν,d ⊂ {B : B ∈ FM } to be the 2 set of binary matrices of the form given in (14) if for some fixed Tthr they satisfy the following properties for T ≥ Tthr . 4 It is tempting to guess that the trade-off for the fading ISI case is just a (ν + 1)−fold increase in the diversity order.

    

(15)

For any distinct pair of matrices A, B ∈ Kν,d the rank of Γ(A) − Γ(B) is at least d(ν + 1). T (Mt −d+1)−νMt . • |Kν,d | ≥ 2 We will postpone the construction of such sets of binary matrices to Section IV, where we show that we can set Tthr = Rν + (Mt − 1)(ν + 1)(2R − 1). Given a L-level binary partition of a QAM or PSK signal constellation, a diversity embedded space-time codeword is defined by an array K = {K(1) , K(2) , . . . , K(L) } determined by a sequence of binary matrices, where matrix, K(i) specifies the spacetime array at level i. A diversity embedded multi-level spacetime code is defined by the choice of the constituent sets of binary matrices Kν,d1 , . . . , Kν,dL . For i = 1, . . . , L the binary matrix K(i) ∈ Kν,di . Adapted easily from [4] we can state the formal construction guarantee for the diversity embedded code as follows. Theorem 3: Let C be a multi-level space-time code for a QAM or PSK constellation of size 2L with Mt transmit antennas that is determined by constituent sets of binary matrices Kl = Kν,dl , l = 1, . . . , L, such that d1 ≥ d2 . . . ≥ dL . For joint maximum-likelihood decoding, the input bits that select the codeword from the lth set Kl are guaranteed diversity dl (ν + 1)Mr in the complex domain. The proof of the Theorem 3 follows from the same techniques as in [4] by mapping binary matrices with desired rank guarantees to rank guarantees in complex domain. Therefore the codewords from lth layer achieve a rate Rl = 1 log |Kν,dl | and diversity order dl (ν +1)Mr . From Definition T 2 it follows that the size of Kν,dl can be made at least as large as 2T (Mt −dl +1)−νMt . Similar to [4] this construction for QAM constellations achieves the rate-diversity tuple (R1 , Mr d1 (ν + 1), . . . , RL , Mr dL (ν + 1)), with the overallPequivalent single layer code achieving rate-diversity point, ( l Rl , Mr dL (ν + 1)). In particular, we can construct a space-time code by choosing identical diversity requirements for all the layers, i.e., d1 = d2 = . . . = dL . From this we conclude that the rate diversity tradeoff for the ISI channel can be characterized as follows: Theorem 4: Consider transmission over a ν tap ISI channel with Mt transmit antennas from a QAM or PSK signal constellation A with |A| = 2L and communication over a time period T such that T ≥ Tthr . For diversity order disi = d(ν + 1)Mr , the rate diversity tradeoff is given by, ν (Mt − d + 1) − Mt ≤ Ref f ≤ (Mt − d + 1) . T The lower bound follows directly from Theorem 3 and the upper bound follows from (9). Note that the bounds in the •

above theorem are tight as T → ∞. We can also develop a diversity-embedded trellis code construction for ISI channels along the lines of the idea in Theorem 3. In particular, we can construct sets of binary matrices Pν,d which are based on binary convolutional codes and have rank guarantee of d, analogous to the sets Kν,d . However, in this case we need a larger Tthr = (2R − 1)ν + (2R − 1)(ν + 1)((Mt − 2)(2R − 1) + R) and a larger rate hit as log |Pν,d | ≥ R(T − ν − (ν + 1)(Mt − 1)(2R − 1)(2R−1 )). Using these sets we can construct optimal trellis codes for ISI channels as well [5]. IV. B INARY

RANK DISTANCE CODES

In this section we will construct binary codes Kν,d with properties given in Definition 2. We will outline the proof in two parts. In Section IV-B we will show that |Kν,d | ≥ 2T (Mt −d+1)−νMt . In Section IV-C, we outline the ideas for the rank distance guarantee. We will give the proof for the maximal rank distance case, i.e., for Kν,Mt and the details for the general result can be found in [5]. In Section IV-A we give a representation of Kν,d in terms of polynomials over F2T . A. Polynomial representation Given a rate R, we define the polynomial f (x) =

R−1 X

l

fl x2 ,

(16)

l=0

{fl }R−1 l=0

where ∈ F2T . To develop the binary matrices with t structure given in (14), we define cf ∈ FM 2T  t cf = f (1) f (ξ) . . . f (ξ (Mt −1) ) . (17) R

where ξ = α(2 −1)(ν+1) and α is a primitive element of F2T . Let f (0) (ξ i ) and f (k) (ξ i ) be the representations of f (ξ i ) and αk f (ξ i ) in the basis {α0 , α1 , . . . , αT −1 } respectively i.e., f (0) (ξ i ), f (k) (ξ i ) ∈ F1×T . We can get a matrix representation 2 Cf ∈ F2Mt ×T of cf as,  t Cf = f (0)t (1) f (0)t (ξ) . . . f (0)t (ξ (Mt −1) ) . (18)

Now, in order to get the structure required in (14), we need to set the last ν elements of each row of Cf to be zero. For this we define the set S as   j−1 ) =0 S = f : f ∈ FR 2T , Tr2T /2 θi f (ξ

∀i ∈ {T − ν, . . . , T − 1} and ∀j ∈ {1, . . . , Mt }} , (19)

where {θ0 , θ1 , . . . , θT −1 } is the trace dual basis of the basis {α0 , α1 , . . . , αT −1 }. Therefore, for all f ∈ S the structure required in (14) is satisfied. (ν+1)Mt ×1 Associate to f the codeword vector uf ∈ F2T where,  t uf = ctf αctf . . . αν ctf (20) Associate with every such codeword uf the codeword matrix (ν+1)Mt ×T Uf ∈ F2 given by the representation of each element of uf in the basis {α0 , α1 , . . . , αT −1 }.

Since f ∈ S we know that the last ν elements in Cf are 0 for all the Mt rows. Therefore we can see that f (k) (ξ i ) is a cyclic shift by k positions of f (ξ i ). Hence, for i ∈ {0, 1, . . . , ν} we can write,  t (i) Cf = f (i)t (1) f (i)t (ξ) . . . f (i)t (ξ (Mt −1) ) , (21)

For transmission over an ISI channel it can be shown from equation (2) that the effective transmitted codeword matrix for a particular f is of the form h it (ν)t Uf = Ctf C(1)t (22) . . . Cf f

Clearly we see that Kν,d = {Cf : f ∈ S, rank(Uf ) ≥ d(ν + 1)}. We will show in IV-C that indeed for R = Mt − d + 1 that Kν,d = {Cf : f ∈ S}, i.e., rank(Uf ) ≥ d(ν + 1), ∀f ∈ S, and hence |Kν,d | = |S|. B. Set cardinality Using the polynomial representation given in Section IV-A, we can give a lower bound on the rate as follows. Theorem 5: Consider T > (ν + 1)Mt then a lower bound to the cardinality of the set S is given by |S| ≥ 2RT −νMt . Proof: Let λθi ,βj be the mapping, t  7→ Tr2T /2 (θi f (βj )), λθi ,βj : fR−1 . . . f1 f0

for some βj ∈ F2T , j = 1, . . . , Mt . This is homomorphism of the F2 -vector space FR 2T into F2 . The cardinality of the set S is given by, \ |S|= ker(λθi ,βj ) i ∈ {T − ν, . . . , T − 1} & j ∈ {1, . . . , Mt } i,j

Note that the range space of λθi ,βj is the range of the trace function, i.e., {0, 1}. Noting that since T > (ν + 1)Mt and the rank of the equivalent matrix transformation of [λθT −ν ,β1 , . . . , λθT −1 ,βMt ]t at most νMt and therefore the null space is atleast of dimension RT − νMt . Therefore, we conclude that, |S| ≥ 2RT −νMt .

C. Rank distance of the codes In this section we first start with showing that if R = 1 then for all f ∈ S, rank(Uf ) ≥ Mt (ν + 1). In fact for this case Tthr = Mt (ν + 1) is enough. Theorem 6 (Maximal rank distance codes): Let f (x) = f0 x, as in (16) with R = 1 and. T ≥ Mt (ν + 1). Then for S t defined in (19), T1 log |S| ≥ 1− νM T and ∀f ∈ S, rank(Uf ) ≥ Mt (ν + 1) over the binary field. Proof: The rate is directly from Theorem 5. If O = {Uf : f ∈ S} has rank distance less than (ν + 1)Mt then there exists a vector uf 6= 0 for some f ∈ S such that the corresponding binary matrix Uf has binary rank less than (ν + 1)Mt (as the code is linear). So there exists a non-trivial binary vector space B ⊆ FT2 such that for every b ∈ B, bT Uf = 0 ⇐⇒ bT uf = 0

(23)

100

Now, we suppose that for b 6= 0, ν M t −1 X X

bi+k(ν+1) αi f (αk(ν+1) )

i=0 k=0 ν M t −1 X X

=f

i+k(ν+1)

bi+k(ν+1) α

i=0 k=0

!

=0

(25)

Thus, for every b ∈ B the element  Pν PMt −1 i+k(ν+1) is a zero of f (x). b α i+k(ν+1) i=0 k=0

But we know that {αi+k(ν+1) } are linearly independent for k ∈ {0, 1, . . . , Mt − 1} and i ∈ {0, 1, . . . , ν} as T ≥ (ν + 1)Mt . Therefore there is only one solution to the equation (25). This contradicts the fact that the null space is non-trivial since we cannot have b 6= 0 and b ∈ B. Hence all matrices in O have rank equal to Mt (ν + 1). Now we present the generalization of Theorem 6 for the case of codes with any rate, which allows us to construct the set Kν,d with Tthr = Rν + (Mt − 1)(ν + 1)(2R − 1). Theorem 7: Let R = (Mt − d + 1) and define the set S as t in (19). Then, T1 log |S| ≥ R − νM T and the rank distance is, rank(Uf ) ≥ (ν + 1)(Mt − R + 1), ∀f ∈ S over the binary field, when T ≥ Tthr . The consequence of Theorem 7 is that Kν,d = {Cf : f ∈ S} satisfies the requirements of definition 2 and therefore can be used to construct diversity embedded codes for fading ISI channels as done in Theorem 3. The proof of Theorem 7 is omitted here for space considerations. It relies on finding the structure of the null space of Uf and finding a basis for it. The details of this proof can be found in [5]. V. E XAMPLES

AND

10-1

(24)

D ISCUSSION

Example 1: Consider construction of a code for Mt = 2, ν = 1, T = 5 with rate R = 1 and hence Ref f = 35 . To design these codes, use the field extension F25 with the primitive polynomial given by x5 + x4 + x2 + x + 1 and the primitive element α. The set of codeword polynomials given by,  S = 0, α, α17 , α19 , α21 , α24 , α26 , α31 . (26)

3 t Then the 2×5 space time code has rate, R = 1− νM T = 5 and gives diversity order 4 when transmitted over the ISI channel with ν = 1. The corresponding 8 codewords X(1) as in (4) are,     0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0     1 0 1 1 0 0 0 1 1 0 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 1 1 0    0 1 1 1 0 1 1 1 1 0 . 1 1 1 1 0 1 1 0 1 0

Example 2: Consider signalling from a 4-QAM constellation for a system with Mt = 2, Mr = 1, ν = 1 and T = 9. Since A = 4 we can have 2 layers and we choose them such that R1 = 1 and R2 = 2 and the corresponding effective diversities are 4 and 2. Therefore we will have that, R1ef f =

Error Probability

bT uf =

10-2

10-3

10-4

10-5

First layer Second layer 6

8

10

12

14

16

18

20

SNR(dB)

Fig. 1. Error Performance of full diversty codes with Mt = 2, ν = 1, R1 = 53 R2 = 58 , d1 = 4 and d2 = 2.

1 − 92 , R2ef f = 2 − 29 . To design these codes, use the field F29 with the primitive polynomial given by x9 + x + 1. From the construction of these codes one notices a similarity to cyclic codes. But the fact that for a given vector in the solution space B, not all circular shifts of the vector remain in B, makes it diffirent from cyclic codes. The main contribution of this work is the construction and analysis of such binary matrices. The tools and techniques developed here could also have independent interest in designing codes in various other wireless or distributed settings. R EFERENCES [1] A. R. Calderbank, S. N. Diggavi and N. Al-Dhahir. Space-Time Signaling based on Kerdock and Delsarte-Goethals Codes. IEEE International Conference on Communications (ICC), pp 483 - 487, Paris, June 2004. [2] S. N. Diggavi, N. Al-Dhahir, and A. R. Calderbank. Diversity embedding in multiple antenna communications, advances in network information theory. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, pages 285-301, 2004 [3] S N. Diggavi, S. Dusad, A. R. Calderbank, N. Al-Dhahir, “On Diversity Embedded codes,” Proceedings of Allerton Conference on Communication, Control, and Computing, Illinois, September 2005. [4] S. N. Diggavi, A. R. Calderbank, S. Dusad and N. Al-Dhahir, Diversity embedded space-time codes, preprint, submitted to IEEE Transactions on Information Theory, 2006, available from http://licos.epfl.ch [5] S. Dusad, S. N. Diggavi and A. R. Calderbank, Embedded rank distance codes for ISI channels, preprint, submitted to IEEE Transactions on Information Theory, 2007, available from http://licos.epfl.ch [6] S. Dusad and S N. Diggavi, “On successive refinement of diversity for fading ISI channels,” Proceedings of Allerton Conference on Communication, Control, and Computing, Illinois, September 2006. [7] H. E. Gamal, A. R. Hammons, Y. Liu, M. P. Fitz, O. Y. Takeshita. On the  Design of Space-Time and Space-Frequency Codes for MIMO Frequency Selective Fading Channels. IEEE Transactions on Information Theory, 49(9):2277–2291, September 2003.  [8] H. F. Lu and P. V. Kumar A unified construction of space-time codes with optimal rate-diversity tradeoff, IEEE Transactions on Information Theory, 51(5):1709–1730, May 2005. [9] W. Su, Z. Safar, M. Olfat, R. Liu. Obtaining full-diversity spacefrequency codes from space-time codes via mapping IEEE Transactions on Signal Processing, 51(11):2905–2916, November 2003. [10] V. Tarokh, N. Seshadri, and A.R. Calderbank. Space-time codes for high data rate wireless communications: Performance criterion and code construction. IEEE Transactions on Information Theory, 44(2):744–765, March 1998. [11] D. N. C. Tse and P. Viswanath Fundamentals of Wireless Communication. Cambridge University Press, 2005