The amicable-kronecker construction of quaternion orthogonal designs

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1 University of Wollongong Research Online Faculty of Informatics - Papers (Archive) Faculty of Engineering and Information Sciences 2011 The amicable-kronecker construction of quaternion orthogonal designs Jennifer Seberry University of Wollongong, jennie@uow.edu.au Sarah S. Adams Franklin W.Olin College of Engineering, USA Publication Details Seberry, J and Adams, SS, The amicable-kronecker construction of quaternion orthogonal designs, Australasian J Combinatorics, 50, 2011, Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW Library: research-pubs@uow.edu.au

2 The amicable-kronecker construction of quaternion orthogonal designs Abstract Recently, quaternion orthogonal designs (QODs) were introduced as a mathematical construct with the potential for applications in wireless communications. The poten- tial applications require new methods for constructing QODs, as most of the known methods of construction do not produce QODs with the exact properties required for implementation in wireless systems. This paper uses real amicable orthogonal designs and the Kronecker product to construct new families of QODs. The proposed Amicable- Kronecker Construction can be applied to build quaternion orthogonal designs of a variety of sizes and types. Although it has not yet been simulated whether the result- ing designs are useful for applications, their properties look promising for the desired implementations. Furthermore, the construction itself is interesting because it uses a simple family of real amicable orthogonal designs and the Kronecker product as build- ing blocks, opening the door for future construction algorithms using other families of amicable designs and other matrix products. Keywords era2015 Disciplines Physical Sciences and Mathematics Publication Details Seberry, J and Adams, SS, The amicable-kronecker construction of quaternion orthogonal designs, Australasian J Combinatorics, 50, 2011, This journal article is available at Research Online:

3 The Amicable-Kronecker Construction of Quaternion Orthogonal Designs January 12, 2011 Jennifer Seberry a and Sarah Spence Adams b a: J. Seberry is a Professor in the Centre for Computer and Information Security Research, School of Information Technology and Computer Science, University of Wollongong, NSW 2522, Australia, jennie@uow.edu.au. b: Corresponding author. S. S. Adams is an Associate Professor of Mathematics and Electrical and Computer Engineering, Franklin W. Olin College of Engineering, Olin Way, Needham, MA Ph: Fax: sarah.adams@olin.edu. Supported in part by National Security Agency Grant H Abstract Recently, quaternion orthogonal designs (QODs) were introduced as a mathematical construct with the potential for applications in wireless communications. The potential applications require new methods for constructing QODs, as most of the known methods of construction do not produce QODs with the exact properties required for implementation in wireless systems. This paper uses real amicable orthogonal designs and the Kronecker product to construct new families of QODs. The proposed Amicable- Kronecker Construction can be applied to build quaternion orthogonal designs of a variety of sizes and types. Although it has not yet been simulated whether the resulting designs are useful for applications, their properties look promising for the desired implementations. Furthermore, the construction itself is interesting because it uses a simple family of real amicable orthogonal designs and the Kronecker product as building blocks, opening the door for future construction algorithms using other families of amicable designs and other matrix products. EDICS: SPC-STCD Key words: complex orthogonal designs, real amicable orthogonal designs, quaternions, quaternion orthogonal designs, space-time block codes. MSC: Primary 05B20, Secondary 94C30, 94A05, 94A11, 62K10 1 Introduction and Preliminaries This section motivates the results in this paper and provides the necessary definitions and background. 1.1 Motivation Orthogonal designs over the quaternion domain have been recently proposed as potential building blocks for future applications in signals processing [7, 13, 1], while orthogonal 1

4 designs with quaternion elements have also been used recently in current applications [3]. The study of the recently proposed designs over the quaternion domain was motivated by the successful implementation of complex orthogonal designs as space-time block codes, which effectively combine space and time diversities [2, 15, 11]. The goal of the proposed future applications was to further improve performance through the additional combination of polarization diversity [4, 5, 6, 12, 19]. Since it has been shown that polarization states can be modeled by means of quaternion representations, [10], it was natural to investigate orthogonal designs over the quaternion domain. Subsequent results [17] showed that only certain orthogonal designs over the quaternion domain will be useful in practical wireless systems. Thus, it has become increasingly important to develop additional construction techniques to generate designs more suitable for applications. In this paper, we give the first example of using real amicable orthogonal designs and a matrix product construction to build designs over the quaternion domain, though other products have been used with other types of orthogonal matrices in the past [15, 8]. The resulting quaternion designs look promising, though further simulations will be necessary to determine if they are applicable in practical systems. More importantly, we expect that the proposed idea of utilizing amicable families with matrix products will lead to improved construction techniques in the future. 1.2 Real Amicable Designs The original definition for orthogonal designs proposed by Geramita, Geramita and Seberry Wallis concerned only square matrices with real commuting variables or zero entries [8, 9]: Definition 1. An orthogonal design, OD, of order n and type (s 1, s 2,..., s u ) in commuting real variables x 1, x 2,..., x u, denoted OD(n; s 1, s 2,..., s u ), is an n n matrix A with entries in the set {0, ±x 1, ±x 2,..., ±x u } satisfying AA T u s h x 2 h I n, where I n is the identity matrix of order n. The definition can be broadened to include r n matrices A where u A T A s h x 2 h I n. Such rectangular orthogonal designs are denoted OD(r; n; s 1, s 2,..., s u ), and their columns are formally orthogonal. Definition 2. Two square orthogonal designs A and B are said to be amicable if A T B B T A and AB T BA T. If A and B are r n orthogonal designs and A T B B T A, we will also say they are amicable. Example 3. Let w h, for h 1, 2,..., 2u, be real variables. Define wh W h w 0 w h. h 0 1 Then, W T l W m (w l w m )(I 2 ) W T mw l for 1 l, m 2u. Hence, for any l m, W h and W l are amicable. These matrices will be used in the case of e 1 in the proof of Theorem 14. 2

5 Example 4. Let w h, for h 1, 2,..., 2u, be real variables. Define wh w W h h 1 1 w w h w h. h 1 1 Then, Wl T W m (w l w m )(2I 2 ) WmW T l for 1 l, m 2u. Hence, for any l m, W h and W l are amicable. These matrices will be used in the case of e 2 in the proof of Theorem 14. w0 w Example 5. If w 0 and w 1 are real variables, then the orthogonal design W 1 1 w 1 w 0 is amicable with all W h introduced in Example 3 and amicable with all W h introduced in Example 4. This will be used in the proof of Theorem Orthogonal Designs over the Quaternions We recall that the non-commutative quaternions Q {±1, ±i, ± j, ± k} satisfy i 2 j 2 k 2 ijk 1. A quaternion variable a a 1 + a 2 i + a 3 j + a 4 k, where a 1, a 2, a 3, a 4 are real variables has a quaternion conjugate defined by a Q a 1 a 2 i a 3 j a 4 k. It follows that a Q a aa Q a 2 is real. Given a matrix A (a l,m ), where a l,m are quaternion variables, its quaternion transpose is A Q (a Q m,l ). We now review the definitions of quaternion orthogonal designs (QODs) originally introduced in [13], along with some new notation to help distinguish among QODs defined over real, complex, and quaternion variables (QOD-R, QOD-C, and QOD-Q, resp.). Definition 6. A quaternion orthogonal design on commuting real variables (QOD-R) x 1, x 2,..., x u of type (s 1, s 2,..., s u ) is an r n matrix A with entries from {0, ±q 1 x 1, ±q 2 x 2,..., ±q u x u }, q h Q, that satisfies A Q A ( u s hx 2 h) In. This design is denoted by A QOD-R(r, n; s 1, s 2,..., s u ). Similarly, a quaternion orthogonal design on commuting complex variables (QOD-C) z 1, z 2,..., z u is an r n matrix A with entries from the set {0, ±z 1, ±z 1, ±z 2, ±z 2,..., ±z u, ±z u} including possible multiplications on the left and/or right by quaternion elements q Q, that satisfies A Q A ( u s h z h 2 )I n. This design is denoted by A QOD-C(r, n; s 1, s 2,..., s u ). Finally, we define a quaternion orthogonal design on non-commuting quaternion variables (QOD-Q) a 1, a 2,..., a u as an r n matrix A with entries from the set {0, ±a 1, ±a Q 1, ±a 2, ±a Q 2,..., ±a u, ±a Q u } including possible multiplications on the left and/or right by quaternion elements q Q and to satisfy A Q A ( u s h a h 2 )I n. This design is denoted by A QOD-Q(r, n; s 1, s 2,..., s u ). In all cases, the columns of a QOD-R, QOD-C, or QOD-Q are mutually orthogonal. We can generalize these definitions to allow the design entries to be real linear combinations of the permitted variables and their quaternion multipliers, in which case we say the design is with linear processing. We can write each complex variable z h, h 1, 2,..., u, that appears within an QOD-C A as z h x 2h 1 + x 2h i, where the x l, l 1, 2,..., 2u, are real variables. This allows us to write the orthogonality constraint for A as A Q A u ( s h x2h x 2h 2) I n. (1) Now, we notice that any entry qz h q of a QOD-C A (when A is without linear processing) can be written as q(x 2h 1 + x 2h i)q, where q, q are quaternion elements from Q. This 3

6 entry expands to qx 2h 1 q + qx 2h iq, and then since quaternion elements commute with real variables, we can rewrite this entry as q 2h 1 x 2h 1 +q 2h x 2h, where q 2h 1, q 2h are some quaternion elements. So, if we permit linear processing in A, so that the entries of A are linear combinations of the terms qz h q, we see that a general entry of A is actually a linear combination of terms q 2h 1 x 2h 1 + q 2h x 2h. We can write an arbitrary such linear combination as 2u α hq h x h, where the α h are appropriate real constants, the q h are quaternion elements from Q, and the x h are real variables. 2 The Amicable-Kronecker Construction Throughout, we let A be a QOD-C with linear processing on u complex variables z h x 2h 1 + x 2h i, h 1, 2,..., u. Such QOD-Cs can be obtained most easily using the constructions presented in Theorem 4 of [13] or Theorem 1 of [1] (additional constructions for QOD-Cs are also included in those references). The entries of the QOD-C A have the form 2u α hq h x h, as defined above in Section 1.3. Since each complex variable z h x 2h 1 + x 2h i, for h 1, 2,..., u, appears within every column of A, we can say that A is on real variables x 1, x 2,..., x 2u, each of which appears in every column of A. Then, it is possible to rewrite A as a sum of the real variables times their coefficient matrices C h defined so that: A x h C h. (2) A coefficient matrix will have at least one entry from Q (potentially with real scalar multipliers) in each row and each column, while other entries will be zero. Definition 7. Define the support of a matrix to be the positions of the matrix that are nonzero. Example 8. Let z 1 x 1 + ix 2 and z 2 x 3 + ix 4 be complex variables where x 1, x 2, x 3 and x [ 4 are real variables. ] Then, let A be the following QOD-C (with no linear processing), z1 j z A 2, which satisfies A z 2 z 1 j Q A ( z z 2 2 )I 2, so that s h 1 for h 1, 2. Then, we can decompose A into its coefficient matrices as follows: x1 j + x A 2 k x 3 + x 4 i x 3 + x 4 i x 1 j + x 2 k j 0 k i x 1 + x 0 j 2 + x 0 k 3 + x i 0 x 1 C 1 + x 1 C 2 + x 3 C 3 + x 4 C 4. Notice that since z 1 x 1 + ix 2, the coefficient matrices C 1 and C 2 have exactly the same support; similarly, since z 2 x 3 + ix 4, the coefficient matrices C 3 and C 4 have exactly the same support. Example 9. Let A be the following QOD-C with linear processing, where z h are complex variables, z h are the complex conjugates, and AQ A 2( z z z 3 2 )I 4 so that s h 2 4

7 for h 1, 2, 3: A z3 z 1 j z 2j z 1 + z 3 j z 2 z2 j z1 + z 3 j z 3 z 1j 2 z2 z 3 + z 1 j z1 + z 3j z 2 j z2 z 1 + z3 j z 2 j z 3 + z1 j. Then, using z h x 2h 1 + x 2h i, for h 1, 2, 3, where the x h are real variables, we can rewrite A as: (x 5 x 6 i) (x 1 x 2 i)j (x 3 + x 4 i)j (x 1 + x 2 i) + (x 5 + x 6 i)j (x 3 + x 4 i) (x A 3 x 4 i)j (x 5 x 6 i) (x 1 + x 2 i)j (x 3 x 4 i) (x 1 x 2 i) + (x 5 + x 6 i)j (x 1 x 2 i) + (x 5 x 6 i)j (x 3 + x 4 i) (x 5 + x 6 i) + (x 1 + x 2 i)j (x 3 + x 4 i)j (x 3 x 4 i) (x 1 + x 2 i) + (x 5 x 6 i)j (x 3 x 4 i)j (x 5 + x 6 i) + (x 1 x 2 i)j x 1 + x 3 + x 5 j j j j 0 j 0 1 j j 1 0 j j j j j x 2 + x 4 + x 6 k 0 i 0 0 k 0 i i 0 k 0 0 i 0 k 0 k 0 i k 0 i 0 0 i 0 k i 0 k 0 i 0 k 0 0 i 0 k k 0 i 0 0 k 0 i x 1 C 1 + x 1 C 2 + x 3 C 3 + x 4 C 4 + x 5 C 5 + x 6 C 6. Notice that for each h, since z h x 2h 1 + x 2h i, the coefficient matrices C 2h 1 and C 2h have the same supports. We now review the definition of the Kronecker product: Definition 10. Let A be an l n matrix with entries a h,m, and B be an p q matrix with entries b h,m. Then their Kronecker product, denoted A B, is the lp nq matrix with a 1,1 B a 1,n B block entries a n,1 B a n,n B w0 w Example 11. Recall from Example 5, we have W 1 1, where w w 1 w 0, w 1 are real 0 j 0 variables, and let C 1 be the first coefficient matrix from Example 8. 0 j Then, w0 C W 1 C 1 1 w 1 C 1 w 1 C 1 w 0 C 1 w 0 j 0 w 1 j 0 0 w 0 j 0 w 1 j w 1 j 0 w 0 j 0. 0 w 1 j 0 w 0 j 5

8 Our development thus far allows us to write the following theorem: Theorem 12. Let A be a QOD-C with linear processing on complex variables z h x 2h 1 + x 2h i, h 1, 2,..., u. Then, writing A in the form A x h C h, (3) where the C h are the appropriate k n coefficient matrices with entries from Q, implies that C Q 2h 1 C 2h 1 C Q 2h C 2h s h I n and C Q l C m + C Q mc l 0, l m. Proof. As discussed above in Section 1.3, any QOD-C A has entries of the form 2u α hq h x h, where the α h are constants, the q h are quaternion elements from Q, and the x h are real variables; so A can indeed be written as A 2u x hc h, where C h are the coefficient matrices. Then, we get: A Q A x h C Q h ( x h C h ) x h 2 C Q h C h + 1 l<m 2u x l x m ( C Q l C m + C Q mc l ). (4) Also, the orthogonality constraint required by the definition of a QOD-C implies that A satisfies A Q A ( u s h z h 2 )I n u ( s h x2h x 2h 2) I n. (5) Now, compare (4) and (5) and notice that the latter equation has no terms of the form x l x m. Therefore, we must have C Q 2l 1 C 2l 1 C Q 2l C 2l s l I n, and C Q l C m + C Q mc l 0, l m. Example 13. Given the coefficient matrices in Example 8, wherein all s h 1, we observe that C Q h C h I 2, h 1,..., 4 and C Q l C m + C Q mc l 0, l m. Similarly, given the coefficient matrices in Example 9, wherein all s h 2, we observe that C Q h C h 2I 4, h 1,..., 6 and C Q l C m + C Q mc l 0, l m. 6

9 We now provide the main result, which provides a construction technique that uses QOD-Cs to build QOD-Rs and certain QOD-Cs that have double the size and the equivalent of one more independent real variable in the alphabet. The resulting designs also have a predictable redundancy that may be important for applications by providing a means for error control. Theorem 14. (The Amicable-Kronecker Construction) Suppose there exists a k n QOD- C A on u complex variables, with or without linear processing. Then there exists a 2k 2n QOD-R D on 2u + 1 real variables with each variable appearing once per column and a 2k 2n QOD-R D on 2u + 1 real variables with all but two variables appearing twice per column. Proof. Let A be a QOD-C(k, n; s 1,..., s u ) with linear processing on u complex variables z h x 2h 1 + x 2h i for h 1, 2,..., u. Then, we can write A 2u x hc h, where the C h are the appropriate k n coefficient matrices and for each h 1, 2,..., u, C 2h 1 and C 2h must have the same support. w0 w Now, let w 0, w 1,..., w 2u be real variables. Let W 1 1 w2 w, W w 1 w w 2 w [ 2 ] wh 0 wh w and for h > 2, let W h in the case of e 1 and let W 0 w h h h w h w h in the case of e 2. (Recall that the pairwise amicability of these matrices is discussed in Examples 3, 4, and 5.) Then, with the Kronecker product, we will show that through cases e 1 and e 2, D W h C h (6) gives the desired orthogonal designs over the quaternion domain. First, we will show that the columns of D are formally orthogonal. To show this, we will utilize the following wellestablished facts: D1 For any matrices A, B, C, D, we have (A B) Q A Q B Q. D2 For any matrices A, B, C, D, we have (A B)(C D) (AC BD). D3 W T l W m W T mw l for all 1 l < m, which is true by amicability as shown in Examples 3-5. D4 C Q l C m + C Q mc l 0 and C Q 2h 1 C 2h 1 C Q 2h C 2h s h I n, which hold by Theorem 12. D5 W T 1 W 1 (w 2 0 +w2 1 )I 2 and W T h W h ew 2 h I 2, for h 2, 3,..., 2u, as implied by Examples

10 D Q D (W 1 C W 2u C 2u ) Q (W 1 C W 2u C 2u ) (W Q 1 CQ W Q 2u CQ 2u )(W 1 C W 2u C 2u ) (by D1) (W Q 1 CQ 1 )(W 1 C W 2u C 2u ) + (W Q 2 CQ 2 )(W 1 C W 2u C 2u ) + +(W Q 2u CQ 2u )(W 1 C W 2u C 2u ) (W Q 1 W 1 C Q 1 C 1) + + (W Q 1 W 2u C Q 1 C 2u) + (W Q 2 W 1 C Q 2 C 1) + + (W Q 2 W 2u C Q 2 C 2u) + (by distributing) +(W Q 2u W 1 C Q 2u C 1) + + (W Q 2u W 2u C Q 2u C 2u) (by D2) W Q h W h C Q h C h + ) (W Q l W m C Q l C m + WmW Q l CmC Q l (collecting terms) W T h W h C Q h C h + 1 l<m 2u 1 l<m 2u ) Wl T W m (C Q l C m + CmC Q l W T h W h C Q h C h (by D4) (w w 2 1)I 2 s 1 I n + ( (s 1 w s 1 w 2 1)I 2n + (s 1 w s 1 w h2 h2 h2 (by D2 and D3) ew 2 h I 2) s h I n (by D4 and D5) s h ew 2 h I 2n s h ew 2 h )I 2n. (by Kronecker product) This shows that D Q D is a diagonal matrix, so its columns are formally orthogonal as required. Now we will examine the entries of D. In both cases of e 1 and e 2, by the definition of W h for all h and since C 2h 1 and C 2h have the same support for all h, the entries of D will contain quaternion combinations of w 0 with w 2, quaternion combinations of w 1 and w 2, or quaternion combinations of w 2h 1 with w 2h for h 2, 3,..., u. As such, the resulting design D will be a QOD-R on 2u + 1 real variables w 0, w 1,... w 2u with linear processing. It is also possible to view D as a QOD-C on u + 1 complex variables v 1 w 0 + w 2 i, v 2 w 1 + w 2 i, v 3 w 3 + w 4 i, v 4 w 5 + w 6 i,..., v u+1 w 2u 1 + w 2u i, however v 1 and v 2 are not independent complex variables: they have different real terms but the same imaginary terms. The cases of e 1 and e 2 differ in how many times each real variable component is repeated in each column. If we begin with a design A with no linear processing and s h 1 for all h, then e 1 will give a QOD-R of type (2k, 2n; 1, 1, 2, 1, 1,..., 1) and e 2 will give a QOD-R of type (2k, 2n; 1, 1, 2, 2,..., 2). Changing a particular s h will multiply the type of the corresponding entries appropriately. In summary, given an initial QOD-C A (readily available for any number of columns using existing constructions [13, 1]), we have shown a method for applying the Kronecker 8

11 product with a simple family of real amicable orthogonal designs to build a QOD-R (or certain QOD-C s) with double the size and the equivalent of one more independent real variable, which is important for increasing the amount of information that can be conveyed by a design. The construction also allows us to provide redundancy in the real variables in each column, a potential benefit for coding applications. Example 15. Recall from Example 8 the QOD-C A of type (2, 2; 1, 1): z1 j z A 2 z 2 z 1 j [ j 0 k i x 1 + x 0 j 2 + x 0 k 3 + x i 0 x 1 C 1 + x 2 C 2 + x 3 C 3 + x 4 C 4. First, we will apply the Amicable-Kronecker Construction to A with e 1 to obtain a 4 4 QOD-R D (e1) of type (4, 4; 1, 1, 2, 1, 1) and a QOD-C D (e1) of type (4, 4; 1, 1, 1). According to (6) in the proof of Theorem 14, we consider D (e1) (W 1 C 1 ) + (W 2 w0 w C 2 ) + (W 3 C 3 ) + (W 4 C 4 ), where W 1 1 w2 w, W w 1 w 2 2, and W 0 w 2 w m 2 wm 0, for m 3, 4. Note that we have already computed W 0 w 1 C 1 in Example 11, m and we have already seen in Examples 3 and 5 that the W m, m 1, 2, 3, 4 are pairwise amicable. We have ] D (e1) (W 1 C 1 ) + (W 2 C 2 ) + (W 3 C 3 ) + (W 4 C 4 ) [ w0 C 1 w 1 C 1 w2 C + 2 w 2 C 2 w3 C + 3 (0)C 3 w4 C + 4 (0)C 4 w 1 C 1 w 0 C 1 w 2 C 2 w 2 C 2 (0)C 3 w 3 C 3 (0)C 4 w 4 C 4 w 0 j 0 w 1 j 0 w 2 k 0 w 2 k 0 0 w 0 j 0 w 1 j w 1 j 0 w 0 j w 2 k 0 w 2 k w 2 k 0 w 2 k 0 0 w 1 j 0 w 0 j 0 w 2 k 0 w 2 k + 0 w w w w w 4 i 0 0 w 4 i w 4 i 0 0 w 4 i 0 w 0 j + w 2 k w 3 + w 4 i w 1 j + w 2 k 0 w 3 + w 4 i w 0 j + w 2 k 0 w 1 j + w 2 k w 1 j + w 2 k 0 w 0 j w 2 k w 3 w 4 i 0 w 1 j + w 2 k w 3 w 4 i w 0 j w 2 k. ] This shows that D (e1) is naturally viewed as a QOD-R with linear processing on real variables w 0,..., w 5, and D (e1) is of type QOD-R(4, 4; 1, 1, 2, 1, 1). The novelty and benefit of this construction is that we have a 4 4 orthogonal structure that uses five independent real variables. In contrast, a traditional 4 4 real orthogonal design could incorporate at most four real variables. A traditional 4 4 complex orthogonal design could incorporate up to six real variables, however such designs do not have any quaternion coefficients which may be useful for modeling polarization in wireless systems. 9

12 Now, writing v 1 w 0 + w 2 i, v 2 w 1 + w 2 i and v 3 w 3 + w 4 i gives v 1 j v 3 v 2 j 0 D (e1) v 3 v 1 j 0 v 2 j v2 j 0 v 1 j v. 3 0 v2 j v 3 v1 j So, D (e1) can also be viewed as a QOD-C on the complex variables v 1, v 2, v 3 as expected by the proof of Theorem 14, however v 1 and v 2 are not independent but rather have the same imaginary component. So, by applying the Amicable-Kroncker Construction with e 1 to a 2 2 QOD-C A of type (2, 2; 1, 1), we obtained D (e1) a QOD-R(4, 4; 1, 1, 2, 1, 1) and D (e1), a QOD-C(4,4; 1,1,1) whose first two variables are dependent. Now we will apply the Amicable-Kronecker Construction to A with e 2 to obtain a 4 4 QOD-R D (e2) of type (4, 4; 1, 1, 2, 2, 2) with linear processing and a QOD-C(4, 4; 1, 1, 2). According to (6) in the proof of Theorem 2, we consider D (e2) (W 1 C 1 ) + (W 2 w0 w C 2 ) + (W 3 C 3 ) + (W 4 C 4 ), where W 1 1 wm w, and W w 1 w m m, for 0 w m w m m 2, 3, 4. We have already seen in Examples 4 and 5 that the W m, m 1, 2, 3, 4 are pairwise amicable. We have D (e2) (W 1 C 1 ) + (W 2 C 2 ) + (W 3 C 3 ) + (W 4 C 4 ) [ w0 C 1 w 1 C 1 w2 C + 2 w 2 C 2 w3 C + 3 w 3 C 3 w 1 C 1 w 0 C 1 w 2 C 2 w 2 C 2 w 3 C 3 w 3 C 3 w 0 j 0 w 1 j 0 w 2 k 0 w 2 k 0 0 w 0 j 0 w 1 j w 1 j 0 w 0 j w 2 k 0 w 2 k w 2 k 0 w 2 k 0 0 w 1 j 0 w 0 j 0 w 2 k 0 w 2 k + 0 w 3 0 w 3 w 3 0 w w 3 0 w 3 w 3 0 w w 4 i 0 w 4 i w 4 i 0 w 4 i 0 0 w 4 i 0 w 4 i w 4 i 0 w 4 i 0 w 0 j + w 2 k w 3 + w 4 i w 1 j + w 2 k w 3 + w 4 i w 3 + w 4 i w 0 j + w 2 k w 3 + w 4 i w 1 j + w 2 k w 1 j + w 2 k w 3 + w 4 i w 0 j w 2 k w 3 w 4 i w 3 + w 4 i w 1 j + w 2 k w 3 w 4 i w 0 j w 2 k. ] [ w4 C + 4 w 4 C 4 w 4 C 4 w 4 C 4 ] This shows that D (e2) is naturally viewed as a QOD-R with linear processing on real variables w 0,..., w 5, and D (e2) is of type QOD-R(4, 4; 1, 1, 2, 2, 2). This QOD-R has the same benefits of D (e1), with the additional benefit of having no zero entries, which is preferred in applications [14, 16, 18]. Also, all but two of the real variables are repeated in each column, which may be an exploitable redundancy pattern. Using D (e2), we can write v 1 w 0 + w 2 i, v 2 w 1 + w 2 i and v 3 w 3 + w 4 i to give D (e2) v 1 j v 3 v 2 j v 3 v 3 v 1 j v 3 v 2 j v2 j v 3 v1 j v 3 v 3 v2 j v 3 v1 j 10,

13 which is an QOD-C on the complex variables v 1, v 2, v 3, however v 1 and v 2 are not independent. So, by applying the Amicable-Kroncker Construction to the 2 2 QOD-C A of type (2, 2; 1, 1), we obtained D (e2) of type QOD-R(4, 4; 1, 1, 2, 2, 2) and D (e2) of type QOD- C(4, 4; 1, 1, 2), wherein the first two complex variables are not independent. Notice that we can again apply the Amicable-Kronecker Construction to the D matrices, and through reordering of variables and varying the order in which we apply the cases of e 1 and e 2, we can obtain designs of a variety of sizes and types. As an important final example, consider D (e2) from Example 15. We may add any two columns of D (e2) together, without impacting the orthogonality of the columns. Therefore, we consider the 4 2 matrix D whose two columns are, respectively, the sum of the first and second columns of D (e2) and the sum of the third and fourth columns of D (e2) : D w 3 + w 4 i + w 0 j + w 2 k w 3 + w 4 i + w 0 j + w 2 k w 3 + w 4 i w 1 j + w 2 k w 3 + w 4 i w 1 j + w 2 k w 3 + w 4 i + w 1 j + w 2 k w 3 + w 4 i + w 1 j + w 2 k w 3 w 4 i + w 0 j w 2 k. w 3 w 4 i + w 0 j w 2 k Notice that the columns of D are orthogonal and (D ) Q (D ) 4 4 w hi 2. Also, notice that the entries of D are full quaternion variables, or in other words, the entries are quaternion variables that are non-zero in every component of the quaternion variable. Therefore, we say that D is QOD-Q. In this example D, the quaternion variables in each entry are not independent, however this redundancy may be useful when implementing in a wireless system by providing some error control. QOD-Q s were initially more challenging to construct than QOD-R s and QOD-C s, due to the difficulties introduced when handling entries that are full quaternion variables [13], however a later construction was introduced that provides QOD-Qs for any number of columns (see Theorem 4 in [1]). An advantage of this example D is that it has no zero entries, which is a desirable property for applications [14, 16, 18]. Only some of the sporadic 2 2 examples demonstrated in [13] also have this property, where the two quaternion variables have components that satisfy a variety of dependencies. The more general construction in [1] necessarily gives QOD-Qs with zero entries in every column for any number of columns greater than 2. 3 Conclusions Quaternion orthogonal designs of various types (QOD-R, QOD-C, and QOD-Q) have been introduced for use in wireless communications systems [13]. Such QODs are interesting mathematical objects, but due to system requirements, only certain QODs will be useful in applications [17]. This increases the need for additional construction techniques, to widen the search for applicable QODs. In this paper, we provided a new construction technique using real amicable orthogonal designs and the Kronecker product. We were able to use small QOD-C s to build larger QOD-R s with the equivalent of one additional independent real variable. In some cases, the resulting designs have the desirable property of no zero entries and all but two real variables repeating exactly twice per column. This construction also allowed us to build QOD-C s and QOD-Q s whose variables include certain dependencies. It is an interesting open problem to determine if using alternative families of real amicable orthogonal designs can significantly improve the QODs that result from the Amicable-Kronecker Construction. 11

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