Algorithms and Bounds for Complex and Quaternionic Lattices With Application to MIMO Transmission

TL;DR

This paper extends lattice algorithms to complex and quaternionic domains, improving efficiency and quality for MIMO communication systems by generalizing classical algorithms and analyzing their performance.

Contribution

It introduces generalized lattice algorithms for complex and quaternionic numbers, with bounds and complexity analysis, applied to enhance MIMO transmission techniques.

Findings

Generalized algorithms outperform real-valued counterparts in complexity and quality.

Bounds for algorithm performance are established.

Application to MIMO shows improved transmission efficiency.

Abstract

Lattices are a popular field of study in mathematical research, but also in more practical areas like cryptology or multiple-input/multiple-output (MIMO) transmission. In mathematical theory, most often lattices over real numbers are considered. However, in communications, complex-valued processing is usually of interest. Besides, by the use of dual-polarized transmission as well as by the combination of two time slots or frequencies, four-dimensional (quaternion-valued) approaches become more and more important. Hence, to account for this fact, well-known lattice algorithms and related concepts are generalized in this work. To this end, a brief review of complex arithmetic, including the sets of Gaussian and Eisenstein integers, and an introduction to quaternion-valued numbers, including the sets of Lipschitz and Hurwitz integers, are given. On that basis, generalized variants of two important algorithms are derived: first, of the polynomial-time LLL algorithm, resulting in a reduced basis of a lattice by performing a special variant of the Euclidean algorithm defined for matrices, and second, of an algorithm to calculate the successive minima - the norms of the shortest independent vectors of a lattice - and its related lattice points. Generalized bounds for the quality of the particular results are established and the asymptotic complexities of the algorithms are assessed. These findings are extensively compared to conventional real-valued processing. It is shown that the generalized approaches outperform their real-valued counterparts in complexity and/or quality aspects. Moreover, the application of the generalized algorithms to MIMO communications is studied, particularly in the field of lattice-reduction-aided and integer-forcing equalization.