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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 3, NO. 6, DECEMBER 2009

Low ML-Decoding Complexity, Large Coding Gain, Full-Rate, Full-Diversity STBCs for 2 2 and 4 2 MIMO Systems K. Pavan Srinath and B. Sundar Rajan, Senior Member, IEEE

Abstract—This paper deals with low maximum-likelihood (ML)-decoding complexity, full-rate and full-diversity space-time block codes (STBCs), which also offer large coding gain, for the 2 transmit antenna, 2 receive antenna (2 2) and the 4 transmit 2) MIMO systems. Presently, antenna, 2 receive antenna (4 the best known STBC for the 2 2 system is the Golden code and that for the 4 2 system is the DjABBA code. Following the approach by Biglieri, Hong, and Viterbo, a new STBC is 2 system. This code matches presented in this paper for the 2 the Golden code in performance and ML-decoding complexity for square QAM constellations while it has lower ML-decoding complexity with the same performance for non-rectangular QAM constellations. This code is also shown to be information-lossless and diversity-multiplexing gain (DMG) tradeoff optimal. This design procedure is then extended to the 4 2 system and a code, which outperforms the DjABBA code for QAM constellations with lower ML-decoding complexity, is presented. So far, the Golden code has been reported to have an ML-decoding complexity of the 4 for square QAM of size order of . In this paper, a scheme is presented. that reduces its ML-decoding complexity to 2 Index Terms—Coding gain, full-rate space-time block codes (STBCs), low maximum-likelihood (ML)-decoding complexity, sphere decoding.

I. INTRODUCTION AND BACKGROUND

M

ULTIPLE-INPUT multiple-output (MIMO) transmission has attracted a lot of interest in the last decade, chiefly because of the enhanced capacity it provides compared with that provided by the single-input, single-output (SISO) system. The Alamouti code [1] for two transmit antennas, due to its orthogonality property, allows a low-complexity ML-decoder. This scheme led to the development of the generalized complex orthogonal designs (CODs) [2]. These designs are famous for the simplified ML-decoding that they provide. They allow all the symbols to be decoupled from one another and

Manuscript received September 03, 2008; revised January 09, 2009. Current version published January 13, 2010. This work was supported in part by the DRDO-IISc Program on Advanced Research in Mathematical Engineering through a grant to B. Sundar Rajan. Different parts of the content of this paper have appeared in the IEEE Globecom 2008, New Orleans, LA, and the IEEE International Conference on Communications (ICC), Dresden, Germany, 2009. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Robert Calderbank. The authors are with the Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore 560012, India (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSTSP.2009.2038209

hence, are said to be single-symbol decodable. Another bright aspect about these codes is that they have full transmit diversity for arbitrary complex constellations. However, the limiting factor of these designs is the low code rate (refer to Section II for a definition of code rate) that they support. At the other extreme are the well-known codes from division algebra, first introduced in [3]. The well known perfect codes [4] have also been evolved from division algebra with large coding gains. These codes have full transmit diversity and have the advantage of a very high symbol rate, equal to that of the VBLAST scheme, which, incidentally, does not have full transmit diversity. Unfortunately, the codes from division algebra including perfect codes have a very high ML-decoding complexity (refer to Section II for a definition of ML-decoding complexity), making their use prohibitive in practice. The class of single-symbol decodable codes also includes the codes constructed using coordinate interleaving, called coordinate interleaved orthogonal designs (CIODs) [5], and the Clifford–Unitary Weight single-symbol decodable designs (CUWSSD) [6]. These designs allow a symbol rate higher than that of the orthogonal designs, although not as much as that provided by the codes from division algebra. The disadvantage with these codes when compared with the orthogonal designs is that they have full transmit diversity for only specific complex constellations. The Golden code [7], developed from division algebra, is a full-rate (see Section II for the definition of full-rate codes), full-diversity 2 2 code for integer lattice constellations, but has been known to have a high ML-decoding complexity, of , where is the size of the constellation used the order of (it is shown in Section VII that this can be reduced signifiwhen the constellation employed is a square cantly1 to QAM). The Golden code has been shown to be equivalent to the code presented in [10]. With a view of reducing the ML-decoding complexity,2 two new full-rate, full-diversity codes for QAM constellations have been proposed for the 2 2 MIMO system. The first code was independently discovered by Hottinen, Tirkkonen, and Wichman [12] and by Paredes, Gershman, and Alkhansari [13], which we call the HTW-PGA code and the second, which we call the Sezginer–Sari code, was reported in [14] by Sezginer and Sari. Both these codes enable simplified ML-decoding (see Section II for a definition of simplified 1This

has also been shown independently in [8]. Also see [9]. an essentially ML-decoding algorithm has been given to decode the Golden code with a complexity of only [11]. 2Recently,

1932-4553/$26.00 © 2009 IEEE

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SRINATH AND RAJAN: LOW ML-DECODING COMPLEXITY, LARGE CODING GAIN, FULL-RATE, FULL-DIVERSITY STBCs

ML-decoding), achieving a complexity of the order of in general, and for square QAM (shown in Section VII). These codes have a slightly lower coding gain than the Golden code and hence show a slight loss in performance compared to the Golden code. These codes sacrifice the coding gain for simplified ML-decoding complexity. For four transmit antennas, the popular codes are the quasi-orthogonal designs, first introduced in [15] and the CIOD for four transmit antenna [5], both of which are rate one codes. The CIOD is known to be single symbol decodable and the minimum decoding complexity Quasi-Orthogonal design (MDCQOD) [16] is also single symbol decodable, but when two or more receive antennas are employed, these codes cannot be considered to be full-rate. The perfect code for four transmit antennas has a high rate of four complex symbols per channel use but its use in practice is hampered by its high ML-decoding complexity, even with the use of sphere decoding [17]. For a 4 2 MIMO system, the best performing code has been the DjABBA code [12], which beats even the punctured perfect code for four transmit antennas in performance [18], [19]. This code was designed for performance alone and has a high ML-decoding com, as shown in Section VII. The first plexity, of the order of attempt at reducing the ML-decoding complexity for a 4 2 system while maintaining full-rate was made by Biglieri, Hong, and Viterbo [18]. The full-rate code that they have proposed, which we call the BHV code, has an ML-decoding complexity for general constellations, (though this has of the order of in [18]), but does not have full-diverbeen reported to be sity. However, the code matches the DjABBA code in the low signal-to-noise ratio (SNR) scenario and betters the punctured perfect code in codeword error performance (CER). Subsequent work on obtaining high-rate STBCs by multiplexing orthogonal designs has been made in [20]. The contributions of this paper are as follows. • We propose a new full-rate, full-diversity STBC for the 2 2 MIMO system. This code has an ML-decoding comin general, as compared to plexity of the order of for the Golden code. For square QAM, the ML-decoding , the same complexity of our code is of the order of as that of the Golden code. • Our code also matches the Golden code in coding gain for QAM constellations and is shown to have the non-vanishing determinant (NVD) property for QAM constellations and hence, is DMG optimal. We also show that our code is information-lossless. • We propose a new full-rate, full-diversity STBC for 4 2 MIMO systems, having ML-decoding complexity of the order of for arbitrary complex constellations, and of the order of for square QAM constellations, whereas the corresponding complexity for the DjABBA and , respectively. It also has a higher code are coding gain than the DjABBA code for 4- and 16-QAM constellations and hence, a better CER performance. • We state the conditions that allow simplified ML-decoding and show that for square QAM constellations, the ML-decoding complexity of the Golden code can be reduced to .

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The remaining content of the paper is organized as follows. In Section II, we give the system model and the code design criteria. In Section III, we present our code for the 2 2 MIMO system and show that it is information-lossless. We also show that it is not equivalent to the Golden code. In Section IV, we show that our code has the NVD property and DMG optimality. In Section V, we present our code for the 4 2 MIMO system. Section VI deals with the low complexity ML-decoding of these codes. In Section VII, we analyze the ML-decoding complexity for the Golden code, the HTW-PGA code, the DjABBA code and the BHV code. The simulations results constitute Section VIII. Concluding remarks are made in Section IX. Notations: Throughout, bold, lowercase letters are used to denote vectors and bold, and uppercase letters are used to de, , and note matrices. Let be a complex matrix. Then denote the transpose, Hermitian and determinant of , denote the respectively. For a complex variable , , and real and imaginary part of , respectively. Also, represents and the sets of all integers, all real and complex numbers are denoted by , , and , respectively. The Frobenius norm and , respecand the trace operations are denoted by tively. The operation of stacking the columns of one below . The Kronecker product is dethe other is denoted by and denote the identity matrix noted by , while and the null matrix, respectively. The inner product of two vec. For a complex random varitors and is denoted by denotes that has a complex normal able , distribution with mean 0 and variance . For any real number , denotes the operation that rounds off to the nearest integer, i.e.,

if otherwise. For a complex variable , the as follows:

operator acting on

The can similarly be applied to any matrix by , , placing each entry resulting in a matrix denoted by . Given a complex vector as

and

is defined as

It follows that

.

is defined

by re, ,

is defined

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 3, NO. 6, DECEMBER 2009

II. SYSTEM MODEL We consider Rayleigh quasi-static flat-fading MIMO channel with full channel state information (CSI) at the receiver but not MIMO transmission, we have at the transmitter. For

(1) is the codeword matrix, transmitted over where channel uses, is a complex white Gaussian noise matrix with independent and identically distributed (i.i.d.) enand is the channel matrix with tries the entries assumed to be i.i.d circularly symmetric Gaussian . is the received marandom variables trix. Definition 1: (Code rate): If there are independent complex information symbols in the codeword which are transmitted over channel uses, then, the code rate is defined to be complex symbols per channel use. For instance, for the Alamand . So, its code rate is 1 complex outi code, symbol per channel use. Definition 2: (Full-rate code): An STBC is said to be fullcomplex symbols per rate if it transmits at the rate of channel use, where . So, the Alamouti code can be considered to be full-rate for the 2 1 MIMO system alone, while the Golden code is full-rate . for Considering ML-decoding, the decoding metric that is to be minimized over all possible values of codewords is given by (2) Definition 3: (Decoding complexity): The ML decoding complexity is a measure of the maximum number of symbols that need to be jointly decoded in minimizing the ML decoding metric. This number can be in the worst scenario, being the total number of information symbols in the code. Such a code is said to have a high ML-decoding complexity, of the order of , where is the size of the signal constellation. If the code , the has an ML-decoding complexity of order less than code is said to admit simplified ML-decoding. For some codes, all the symbols can be independently decoded. Such codes are said to be single-symbol decodable, an example being the CODs. Definition 4: (Generator matrix): For any STBC that encodes information symbols, the generator matrix is defined by the following equation [18]:

where is the codeword matrix, is the information symbol vector. A codeword matrix of an STBC can be expressed in terms of weight matrices (linear dispersion matrices) as follows:

Here, , are the complex weight matrices for the STBC. It follows that

It is well known [23], that an analysis of the PEP leads to the following design criteria. 1) Rank criterion: To achieve maximum diversity, the must have nonzero codeword difference matrix and the full-rank for all possible codeword pairs . If full-rank is not achievable, then, diversity gain is , where is the minimum the diversity gain is given by rank of the codeword difference matrix over all possible codeword pairs. 2) Determinant criterion: For a full-ranked STBC, the min, defined as imum determinant

should be maximized. The coding gain is given by , with being the number of transmit antennas. If the STBC is non full-diversity and is the minimum rank of the codeword difference matrix over all possible codeword pairs, then, the coding gain is given by

where , , are the nonzero eigenvalues of the matrix . It should be noted that for high SNR values at each receive antenna, the dominant parameter is the diversity gain which defines the slope of the CER curve. This implies that it is important to first ensure full-diversity of the STBC and then try to maximize the coding gain. III. PROPOSED STBC FOR 2 2 MIMO AND INFORMATION-LOSSLESSNESS In this section, we present our STBC [24] for the 2 2 MIMO system. The design is based on the CIODs, which were studied in [5] in connection with a general class of single-symbol decodable codes which includes complex orthogonal designs as a proper subclass. Specifically, for 2 transmit antennas, the CIOD is as follows. Definition 5: The CIOD for 2 transmit antennas [5] is (3) , , 2 are the information symbols and where and are the in-phase (real) and the quadrature (imaginary) components of , respectively. Notice that in order to make the above STBC full-rank, the signal constellation from which are chosen should be such that the real part the symbols (imaginary part, resp.) of any signal point in is not equal to the real part (imaginary part, resp.) of any other signal point in [5]. So if QAM constellations are chosen, they have to be rotated. The optimum angle of rotation has been found in [5] to

SRINATH AND RAJAN: LOW ML-DECODING COMPLEXITY, LARGE CODING GAIN, FULL-RATE, FULL-DIVERSITY STBCs

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be radians and this maximizes the diversity and coding gain. We denote this angle by . The proposed 2 2 STBC is given by

natural to ask if our code is equivalent to the Dayal–Varanasi code and hence, the Golden code. We show that our code is not equivalent to the Golden code and hence is a novel design. Let

(5)

(7)

where • the four symbols , , and , where is a radians rotated version of an integer QAM signal set, denoted by , which is a finite subset of the integer lattice, , i.e, , , 2, 3, 4. and is a permutation matrix designed to make the STBC •

where denotes the codeword matrix of the proposed code , and , are full and . Expanding (7) as ranked matrices with follows:

.

full-rate and is given by

• The choice of in the above expression should be such that the diversity and coding gain are maximized. We choose to be and show in the next section that this angle maximizes the coding gain. Explicitly, our codeword matrix is

where from [7], ,

, , and ,

, , 4. Hence,

(6) with

and

, . The minimum determinant for our code when the symbols are chosen from the regular QAM constellations (one in which the difference between any two signal points is a multiple of 2) is 3.2, the same as that for the Golden code (this is proved in the next section). The generator matrix for our STBC (as defined in Definition 4), corresponding to the information vector consisting of symbols , is as shown at the bottom of the page. It is easy to see that this generator matrix is orthonormal. In [25], it was shown that a sufficient condition for an STBC to be information-lossless is that its generator matrix should be unitary. Hence, our STBC has the information-losslessness property.

(8) Since (8) is true for all values of

, we must have

A. Non-Equivalence With Golden and Dayal–Varanasi Codes The Golden code has been known to be equivalent to the code proposed by Dayal and Varanasi [10] in the following sense. If we denote and to be the codeword matrices of the Golden code and the Dayal–Varanasi code, resp., then, , where and are unitary diagonal matrices. Since the optimum angle that Dayal and Varanasi have used in , the same as for our code, it would be [10] is

(9) Since (9) is also true for all values of have

, we must (10) (11)

(4)

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 3, NO. 6, DECEMBER 2009

Solving (10) and (11), we get , . Since and are full ranked, we must have either , or , . Proceeding in a similar manner and equating the (2,2)th entry of the left-hand side (L.H.S) and the , right-hand side (R.H.S) of (7), we arrive at either , or , . Hence, and must both be either diagonal matrices or anti-diagonal matrices. Without loss of generality, we assume that they are diagonal matrices. in (8), we get Putting

For the determinant of

to be 0, we must have

The above can be written as (14)

Hence, we arrive at the following inconsistent set of equations:

Hence, (7) is not true. So, our code is not equivalent to the Golden code and the Dayal–Varanasi code and is a new STBC. IV. NVD PROPERTY AND DMG OPTIMALITY 2 CODE OF THE 2 In this section, we show that the proposed code has the NVD property [7], which, in conjunction with full-rateness, means that our code is DMG tradeoff optimal [21]. We also show that in (5) maximizes the coding gain. the angle Theorem 1: The minimum determinant of the proposed 2 2 code, given by (6), when the symbols are chosen from is 1/5. Proof: The determinant of the codeword matrix can be written as

(12) Using

Since subset of

and

, , defining , and , we get

in (12), we get

, with

, , , with

,

,

,a , , and

, and clearly where . It has been shown in [10] that (14) holds only when , i.e., only when . This means that the determinant of the codeword difference matrix is 0 only when the codeword difference matrix is itself the zero matrix. So, for any distinct pair of codewords, the codeword difference matrix is always full-rank for any constella. Also, the minimum value of the tion which is a subset of modulus of the R.H.S of (13) can be seen to be 4. This occurs or . The occurrence for of any other combination of , , , and that results in a lower value of the modulus of the R.H.S of (13) can be ruled take only values from out after noting that , , , and . For, e.g., is one such combination, but it is easy to see mathematically that such a combination , . So, , cannot occur for meaning that the minimum determinant for the code is 1/5. In particular, when the constellation chosen is the regular QAM constellation, the difference between any two signal points is a multiple of 2. Hence, for such constellations, , where and are distinct codewords. The minimum determinant is consequently 16/5 and hence the proposed code has the NVD property [7]. Now, from [21], where it was shown that full-rate codes which satisfy the NVD property achieve the optimal DMG tradeoff, our proposed STBC is DMG tradeoff optimal. As a byproduct of Theorem 1, we arrive at the following lemma. for in (5) maximizes the Lemma 1: The choice of 2 code for QAM constellacoding gain of the proposed 2 tions. Proof: Consider the CIOD whose codeword has the structure shown in (3). The set of codeword difference matrices of the CIOD is a subset of the set of the codeword difference matrices of the proposed 2 2 code, whose codeword structure is given in (6). It is to be noted that the minimum determinant and hence the coding gain of a code depends on the codeword difference , we arrive at matrices of the code. In (13), if we let the expression for the determinant of a codeword matrix of the CIOD. So, for the CIOD, whose codeword matrix is denoted by , we have (15)

Since

, we get (13)

and , with and taking where . It is evident that the minimum of the modulus values from . So, the of the R.H.S of (15) is 4, which occurs for

SRINATH AND RAJAN: LOW ML-DECODING COMPLEXITY, LARGE CODING GAIN, FULL-RATE, FULL-DIVERSITY STBCs

minimum of the absolute value of the determinant of a codeword matrix of the CIOD when the symbols take values from (not all taking zero values) is . When the symbols take values from the regular QAM constellation, the minimum of the absolute value of determinant of a nonzero codeword difand hence, the minimum determinant ference matrix is for the CIOD is 16/5. As a result, and noting that the set of the codeword difference matrices of the CIOD is a subset of the set of the codeword difference matrices of the proposed 2 2 code for any random value of in (5), we can conclude that for any choice of in (5), the minimum determinant of the resulting code cannot exceed 16/5. We have already shown that the minimum determinant for our 2 2 code is 16/5, when the symbols take values from the regular QAM. This shows that the choice for in (5) indeed maximizes the coding gain. of

V. PROPOSED STBC FOR THE 4

2 MIMO SYSTEM

In this section, we present our STBC for the 4 2 MIMO system [26] following the same approach that we took to design the 2 2 code. The design is based on the CIOD for four antennas, whose structure is as defined below. Definition 6: CIOD for four transmit antennas [5] is as follows:

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and being a permutation matrix designed with to make the STBC full-rate and given by

The choice of is to maximize the diversity and coding gain. . This value of provides the Here again, we take to be largest coding gain achievable for this family of codes. This is so because the minimum determinant for the CIOD as defined in (16) [which can also be obtained by letting the variables , , , and be zeros in (17)] is 10.24 [16] for unnormalized QAM constellations. The value of the minimum determinant for our 4 2 code, obtained for unnormalized 4-QAM and 16-QAM constellations is 10.24, which was checked by exhausmaximizes the tive search. This shows that the choice of coding gain. The resulting code matrix is shown at the bottom of this page. This code is full-rate only for the 4 2 MIMO system, unlike the perfect space time code [4], which is full-rate . Since the generator matrix for our code is non-unifor tary, we cannot claim that our STBC for the 4 2 MIMO system is information-lossless. VI. LOW COMPLEXITY ML-DECODING 2 AND 4 2 CODES OF THE 2 In this section, we show how our codes admit simplified ML-decoding. The information symbols are assumed to take values from QAM constellations. In the general setting, it can be shown that (1) can be written as

(16) are the information symbols as defined in where , the previous section. Here again, the symbols are chosen from a rotated version of the regular QAM constellation, with being the angle of rotation. The proposed STBC is obtained as follows. Our 4 4 code , encodes eight symbols matrix, denoted by drawn from a QAM constellation, denoted by . As before, we denote the rotated version of by . Let , , so that the symbols are drawn from the constellation . The codeword matrix is defined as

(17)

where

with 4, so that

is given by

being the generator matrix as in Definition , and

with , drawn from , which is the regular QAM constellation. Using this equivalent model, the ML decoding metric can be written as

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 3, NO. 6, DECEMBER 2009

On performing the decomposition of , we get , where is an orthonormal matrix (note that when ) and is for full-rateness, an upper triangular matrix. The ML decoding metric now can be written as

With these identities, we proceed as follows:

where

This indicates that the real matrix is a skew-symmetric matrix and hence its diagonal elements are , where , zeros. Let are the columns of . Then, . Therefore,

. If , where , are column vectors, then and have the general form obtained by Gram–Schmidt process as shown as follows:

where

,

Applying the

operator and using (19), we get

are column vectors, and

Since .. .

.. .

.. .

where

, , Lemma 2: Let

,

..

.

is real and skew-symmetric,

. So,

.. .

(21) ,

(22)

, where ,

(23)

. ,

. Then, . Proof: From the definition of the trace operation, we have

This proves the result. Theorem 2: For an STBC with independent complex symbols and weight matrices , , if, for any and , , , , then, the th and the th columns of the equivalent channel matrix are orthogonal. Proof: We note that the following identities hold for maand vectors trices (19) (20)

where (21) follows from Lemma 2 and (22) follows from (20). Now,

.. .

.. .

.. .

..

.

.. .

..

.

.. .

.. .

From the above structure, it is readily seen that for any , , , if , then the th and the th columns of are orthogonal. This follows from (23).

(18)

SRINATH AND RAJAN: LOW ML-DECODING COMPLEXITY, LARGE CODING GAIN, FULL-RATE, FULL-DIVERSITY STBCs

Now, let us consider the proposed STBC for 2 2 MIMO system. Here, , . It can be verified that the following holds true for if m is odd. if m is even. (24) To

be

precise,

(24)

holds for Therefore,

. from The. orem 2, Using the above results in the definition of the -matrix, it can easily be shown that . The structure of the -matrix for our 2 2 code, given by (18), is shown at the bottom of the previous page. The structure of the -matrix enables one to achieve simand plified ML-decoding. This is because once the symbols are given, and can be decoded independently. In the ML-decoding metric, it can be observed that the real and imagare entangled with one another but inary parts of symbol when are independent of the real and imaginary parts of and are conditionally given. So, the number of metric comand hence, putations required is at most the ML-decoding complexity is of the order of . When the constellation employed is a square QAM so that the real and the imaginary parts of each symbol can be decoded independently, the ML-decoding complexity can be further reduced as follows. Let denote the decoded information vector. Assuming that sphere decoding (SD) is employed (sphere decoding can be employed for constellations like square or rectangular QAM and not for any arbitrary constellation which is a finite subset of ), the following strategy is employed. 1) A four-dimensional real SD is done to make searches for such pairs for an the symbols and , and there are M-QAM constellation. 2) For every possibility for and , is decoded in parallel with , and there are possibilities for each of them. Following this, and are decoded using hard-limiting, as follows:

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where

where, for simplicity, we have denoted the th entry of the -matrix by , as shown in the equation at the bottom of the page. So, the ML-decoding complexity of our code for square QAM . If, however, the QAM constellais of the order of tion used is not a square QAM, and cannot be represented as the Cartesian product of two PAM constellations (like the energy efficient 32-QAM constellation, which is obtained by removing the four corner points of a 36-QAM constellation), then the method described above cannot be employed. So, in such a , because scenario, the ML-decoding complexity becomes one requires to decode wholly the complex symbols and , when and are given. Now, let us consider the proposed STBC for 4 2 MIMO system. For this case, , . It can be verified that . Hence, the condition in (24) holds true for from Theorem 2, for we have if m is odd. if m is even. Using the above result, it can be easily verified that for , if m is odd. if m is even. For simplicity, let us define the

-matrix as follows:

where , , and , then, can be seen to have the structure given by (25), shown at the bottom of the page. The structure of the matrix allows our code to achieve simplified ML-decoding as follows. Having fixed the symbols , , , and , the symbols , , , and can be decoded independently. In the decoding metric, it can be observed that the real and imaginary parts of symbol are entangled with one another but are independent of the real and imaginary parts of , , and when , , , and are conditionally

(25)

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given. Similarly, , and are decoupled from one another although their own real and imaginary parts are coupled with one another. So, in general, the ML-decoding complexity of our . That is due to the fact that jointly code is of the order of and followed by independecoding the symbols , , dently decoding , , , and in parallel requires a total metric computations. However, when of square QAM is employed, the ML-decoding complexity can be denote further reduced as follows. Let the decoded information vector. Assuming the use of a sphere decoder: 1) An eight-dimensional real SD is done to search for the symbols , , , and . 2) Next, , , , and are decoded in parallel. , , and are decoded using Following this, hard-limiting as follows:

TABLE I DECODING STRATEGIES FOR THE GOLDEN CODE AND THE HTW-PGA CODE

TABLE II COMPARISON OF THE ML-DECODING COMPLEXITIES OF SOME WELL-KNOWN 2 2 STBCS FOR QAM

2

TABLE III DECODING STRATEGIES FOR THE DjABBA CODE AND THE BHV CODE

where

TABLE IV COMPARISON OF THE MINIMUM DETERMINANTS AND THE ML-DECODING COMPLEXITIES OF 4 2 STBCS FOR QAM CONSTELLATIONS

2

where denotes the th entry of the -matrix. So, in all, searches only. Hence, we need to make a maximum of for square QAM constellations, the ML-decoding complexity of our code is of the order of .

VII. ML-DECODING COMPLEXITY COMPARISON OF OUR CODES WITH KNOWN STBCS The ML-decoding complexity of our 2 2 code was shown in the previous section to be of the order of . This was due solely to the behavior of the weight matrices which resulted in the -matrix structure as in (18) for our 2 2 code. Given on the next page are the -matrix structures of the Golden code and the HTW-PGA code (‘ ’ denotes a possible non-zero entry). The

codes presented in [10], [22] and [25] have their -matrix structures similar to that of the Golden code. The Sezginer-Sari code has its -matrix structure similar to that of the HTW-PGA code. Table I gives a brief outline of the ML-decoding strategies for these codes. Tables II gives a comparison of the ML-decoding 2 STBCs. In the table, the complexities of well-known 2 order of the ML-decoding complexity is given for both square M-QAM and non-rectangular QAM. The ML-decoding complexity of our 4 2 code was shown for general constellations, and to be of the order of for square QAM constellations. This simplified complexity was facilitated by the structure of the -matrix, a part of which had the structure as in (25). The structures of the -matrix for the

SRINATH AND RAJAN: LOW ML-DECODING COMPLEXITY, LARGE CODING GAIN, FULL-RATE, FULL-DIVERSITY STBCs

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DjABBA code and the BHV code are as shown in the third and fourth matrix structures below:

Fig. 1. CER performance of 2

2 2 codes for 4-QAM and 16-QAM.

TABLE V COMPARISON OF THE MINIMUM DETERMINANTS OF SOME WELL-KNOWN 2 2 STBCS

2

The decoding strategies for the two codes are given in Table III. Table IV gives a comparison of the codes for the 4 2 MIMO system. VIII. SIMULATION RESULTS In all the simulation scenarios in this section, we consider quasi-static Rayleigh flat fading channels and the plots are shown for the codeword error rate (CER) as a function of the SNR at each receive antenna. A. 2

2 MIMO

Fig. 1 shows the CER performances of our 2 2 code, the Golden code and the HTW-PGA code for 4-QAM and 16-QAM. We see that the CER curve for our 2 2 code is indistinguishable from that of the Golden code and this is due to the identical coding gains of the two codes. The HTW-PGA code has a slightly worse performance because of its lower coding gain. Table V gives a comparison of the minimum determinants of some well-known 2 2 codes. It is to be noted that in obtaining the minimum determinants for these codes, we have ensured that

the average energy per codeword is uniform across all codes, but the average energy per constellation has been allowed to increase with constellation size, or in other words, the average constellation energies have not been normalized to unity. B. 4

2 MIMO

Fig. 2 shows the CER performance plots for our 4 2 code, the well known DjABBA code [12] and the BHV code [18] 2 code outperforms both for 4-QAM and 16-QAM. Our 4 the DjABBA code and the BHV code at high SNR, and the DjABBA code in turn outperforms the BHV code. This can be attributed to the superior coding gain of our 4 2 code. The bad performance of the BHV code at a high SNR is due mainly to the fact that it does not have full-diversity. Table IV gives a comparison of the minimum determinants of the above three codes. The minimum determinants of our 4 2 code for 4-QAM and 16-QAM has been calculated using exhaustive search and the constellation energy has not been normalized to unity. However, it has been ensured that the average energy per codeword has been maintained uniform for all the three codes. The DjABBA code that we have used for our simulations is the one that has been optimized for performance, and proposed in [12, Ch 9]. It

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 3, NO. 6, DECEMBER 2009

would the STBC designed for transmit antennas, , while the rate would be . be of the order of While there is an increase in code rate, there is also a substantial increase in ML-decoding complexity, making the approach for code design using this technique for higher number of transmit antennas questionable. The following questions still remain unanswered. • For a 2 2 MIMO system, what is the minimum ML-decoding complexity achievable for a full-rate, full-diversity STBC? Is it possible to have a full-rate, full-diversity code for with an ML-decoding complexity of the order of all constellations. • Multi-group decodable codes [27] offer simplified ML-decoding complexity. For a given transmit antenna, what is the maximum rate that a multi-group decodable code can have? For the 4 2 MIMO case, is it possible to have a full-rate, full-diversity, two-group decodable STBC, so that ? the ML-decoding complexity is of the order of Fig. 2. CER performance of 4

2 2 codes for 4-QAM and 16-QAM.

can be seen that our code has a coding gain twice that of the DjABBA’s. IX. CONCLUDING REMARKS In this paper, we have seen that it is possible to have full-rate codes with simplified ML-decoding complexity without having to sacrifice performance. We presented two codes, one each for the 2 2 and the 4 2 MIMO system, both of which have lower ML-decoding complexity for general QAM constellations than the best known codes for such systems. Moreover, our 4 2 code outperforms the best DjABBA code while our 2 2 code matches the Golden code in performance. We also saw that the weight matrices play a decisive role in defining the ML-decoding complexity of an STBC and went on to show that some existing codes also offer simplified ML-decoding for square QAM constellations, something which was not known hitherto. 2 Noting the similarity between the constructions of the 2 code and the 4 2 code, it is natural to see if the design procedure can be extended to transmit antennas, . However, there are two main issues to be concerned about. 1) For our 2 2 code, we showed analytically that the minimum determinant for regular QAM constellations is 3.2. 2 code, we have checked that the However, for our 4 minimum determinant for 4-/16-QAM is 10.24 through exhaustive computer search. We could not do the same for higher constellation sizes, because such a search would run transmit anfor weeks! The rate of a square CIOD for , so that this STBC has independent tennas is information symbols. If we were to extend our approach to transmit antennas, , the code would have symbols and finding out the minimum determinant for 4-QAM itself would be time consuming. 2) The ML-decoding complexity for our 2 2 code is of the and that for our 4 2 code is , for genorder of eral constellations. So, the ML-decoding complexity for

ACKNOWLEDGMENT The authors would like to thank Dr. S. Vummintala, Beceem Communications, for the useful discussion on the ML-decoding complexity issues. They would also like to thank the Associate Editor, Robert Calderbank, for carrying forward the review process. They would also like to thank the anonymous reviewers for their useful comments and for motivating them to establish the non-equivalence between their code for the 2 2 MIMO system and the Golden code. REFERENCES [1] S. M. Alamouti, “A simple transmit diversity technique for wireless communications,” IEEE J. Sel. Areas Commun., vol. 16, no. 8, pp. 1451–1458, Oct. 1998. [2] 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, Jul. 1999. [3] B. A. Sethuraman, B. S. Rajan, and V. Shashidhar, “Full-diversity, high-rate space-time block codes from division algebras,” IEEE Trans. Inf. Theory, vol. 49, no. 10, pp. 2596–2616, Oct. 2003. [4] F. Oggier, G. Rekaya, J. C. Belfiore, and E. Viterbo, “Perfect space time block codes,” IEEE Trans. Inf. Theory, vol. 52, no. 9, pp. 3885–3902, Sep. 2006. [5] M. Z. A. Khan and B. S. Rajan, “Single symbol maximum likelihood decodable linear STBCs,” IEEE Trans. Inf. Theory, vol. 52, no. 5, pp. 2062–2091, May 2006. [6] S. Karmakar and B. S. Rajan, “Minimum-decoding complexity, maximum-rate space-time block codes from Clifford algebras,” in Proc. IEEE ISIT, Seattle, WA, Jul. 9–14, 2006, pp. 788–792. [7] J. C. Belfiore, G. Rekaya, and E. Viterbo, “The Golden code: A 2 2 full rate space-time code with non-vanishing determinants,” IEEE Trans. Inf. Theory, vol. 51, no. 4, pp. 1432–1436, Apr. 2005. [8] M. O. Sinnokrot and J. Barry, “Fast maximum-likelihood decoding of the Golden code,” available online at arXiv, arXiv:0811.2201v1 [cs.IT], Nov. 13, 2008. [9] K. P. Srinath and B. S. Rajan, “Low ML-Decoding Complexity, Large Coding Gain, Full-Rate, Full-Diversity STBCs for 2 2 and 4 2 MIMO Systems,” available online at arXiv, arXiv:0809.0635v1 [cs.IT], Sep. 3, 2008. [10] P. Dayal and M. K. Varanasi, “An optimal two transmit antenna spacetime code and its stacked extensions,” IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4348–4355, Dec. 2005. [11] S. Sirianunpiboon, A. R. Calderbank, and S. D. Howard, “Fast essentially maximum likelihood decoding of the Golden code,” IEEE Trans. Inf. Theory, 2010, submitted for publication. [12] A. Hottinen, O. Tirkkonen, and R. Wichman, Multi-Antenna Transceiver Techniques for 3G and Beyond. Chichester, U.K.: Wiley, 2003.

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[13] J. Paredes, A. B. Gershman, and M. Gharavi-Alkhansari, “A new full-rate full-diversity space-time block code with nonvanishing determinants and simplified maximum-likelihood decoding,” IEEE Trans. Signal Process., vol. 56, no. 6, pp. 2461–2469, Jun. 2008. [14] S. Sezginer and H. Sari, “Full-rate full-diversity 2 2 space-time codes of reduced decoder complexity,” IEEE Commun. Lett., vol. 11, no. 12, pp. 973–975, Dec. 2007. [15] H. Jafarkhani, “A quasi-orthogonal space-time block code,” in Proc. IEEE WCNC, 2000, vol. 1, pp. 42–45. [16] C. Yuen, Y. L. Guan, and T. T. Tjhung, “Quasi-orthogonal STBC with minimum decoding complexity,” IEEE Trans. Wireless Commun., vol. 4, no. 12, pp. 2089–2094, Dec.. 2005. [17] E. Viterbo and J. Boutros, “Universal lattice code decoder for fading channels,” IEEE Trans. Inf. Theory, vol. 45, no. 5, pp. 1639–1642, Jul. 1999. [18] E. Biglieri, Y. Hong, and E. Viterbo, “On fast-decodable space-time block codes,” IEEE Trans. Inf. Theory, vol. 55, no. 2, pp. 524–530, Feb. 2009. [19] A. Hottinen, Y. Hong, E. Viterbo, C. Mehlfuhrer, and C. F. Mecklenbrauker, “A comparison of high rate algebraic and non-orthogonal STBCs,” in Proc. ITG/IEEE Workshop Smart Antennas WSA 2007, Vienna, Austria, Feb. 2007. [20] S. Sirianunpiboon, Y. Wu, A. R. Calderbank, and S. D. Howard, “Fast optimal decoding of multiplexed orthogonal designs,” IEEE Trans. Inf. Theory, 2010, submitted for publication. [21] P. Elia, K. R. Kumar, S. A. Pawar, P. V. Kumar, and H. Lu, “Explicit construction of space-time block codes: Achieving the diversity-multiplexing gain tradeoff,” IEEE Trans. Inf. Theory, vol. 52, no. 9, pp. 3869–3884, Sep. 2006. [22] H. Yao and G. W. Wornell, “Achieving the full MIMO diversity-multiplexing frontier with rotation-based space-time codes,” in Proc. Allerton Conf. Comm. Control and Comput., Monticello, IL, Oct. 2003. [23] V. Tarokh, N. Seshadri, and A. R. Calderbank, “Space time codes for high date rate wireless communication: Performance criterion and code construction,” IEEE Trans. Inf. Theory, vol. 44, no. 2, pp. 744–765, Mar. 1998. [24] K. P. Srinath and B. S. Rajan, “A low-complexity, full-rate, full-diversity 2 2 STBC with Golden code’s coding gain,” in Proc. IEEE Globecom 2008, New Orleans, LA, Nov. 30–Dec. 4 . [25] J.-K. Zhang, J. Liu, and K. M. Wong, “Trace-orthonormal full-diversity cyclotomic space time codes,” IEEE Trans. Signal Process., vol. 55, no. 2, pp. 618–630, Feb. 2007. [26] K. P. Srinath and B. S. Rajan, “A low ML-decoding complexity, high coding-gain, full-rate, full-diversity STBC for 4 2 MIMO system,” in Proc. IEEE ICC 2009, Dresden, Germany, Jun. 14–18, 2009, pp. 1–5.

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[27] G. S. Rajan and B. S. Rajan, “Multi-group ML decodable collocated and distributed space time block codes,” IEEE Trans. Inf. Theory, available online at arXiv, arXiv:0712.2384v1 [cs.IT], accepted for publication. K. Pavan Srinath received the B.Eng. degree in electronics and communication from B. M. Sreenivasiah College of Engineering, Bangalore, India, and the M.Eng. degree in telecommunication from the Indian Institute of Science, Bangalore, in 2005 and 2008, respectively. He is currently working towards the Ph.D. degree in the Department of Electrical Communication Engineering, Indian Institute of Science. From September 2005 to June 2006, he was with Robert Bosch (India), Ltd., Bangalore. His primary research interests include wireless communication, space–time coding, and coding for wireless relay networks.

B. Sundar Rajan (S’84–M’91–SM’98) was born in Tamil Nadu, India. He received the B.Sc. degree in mathematics from Madras University, Madras, India, the B.Tech. degree in electronics from Madras Institute of Technology, Madras, and the M.Tech. and Ph.D. degrees in electrical engineering from the Indian Institute of Technology, Kanpur, in 1979, 1982, 1984, and 1989, respectively. He was a faculty member with the Department of Electrical Engineering, Indian Institute of Technology, Delhi, from 1990 to 1997. Since 1998, he has been a Professor in the Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore. His primary research interests include space–time coding for MIMO channels, distributed space–time coding and cooperative communication, coding for multiple-access, relay channels, and network coding with emphasis on algebraic techniques. Dr. Rajan is an Associate Editor of the IEEE TRANSACTIONS ON INFORMATION THEORY, an Editor of the IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, and an Editorial Board Member of the International Journal of Information and Coding Theory. He served as Technical Program Co-Chair of the IEEE Information Theory Workshop (ITW’02), held in Bangalore, in 2002. He is a Fellow of Indian National Academy of Engineering and recipient of the IETE Pune Center’s S.V.C Aiya Award for Telecom Education in 2004. Also, Dr. Rajan is a Member of the American Mathematical Society.

Low ML-Decoding Complexity, Large Coding Gain, Full ... - IEEE Xplore

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