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Abstract
This paper presents a new idea of perfect signal reconstruction in multivariable wireless communications systems including a different number of transmitting and receiving antennas. The proposed approach is based on the polynomial matrix S -inverse associated with Smith factorization. Crucially, the above mentioned inverse implements the so-called degrees of freedom. It has been confirmed by simulation study that the degrees of freedom allow to minimalize the negative impact of the propagation environment in terms of increasing the robustness of whole signal reconstruction process. Now, the parasitic drawbacks in form of dynamic ISI and ICI effects can be eliminated in framework described by polynomial calculus. Therefore, the new method guarantees not only reducing the financial impact but, more importantly, provides potentially the lower consumption energy systems than other classical ones. In order to show the potential of new approach, the simulation studies were performed by author's simulator based on well-known OFDM technique.
Highlights
As we all know the environment in which we live and move has a huge impact on our behavior
This paper presents the results of simulation studies using a new signal reconstruction method dedicated to propagation environments described by nonsquare polynomial matrices
Because fo the new method is based on a polynomial approach, the section presents alternative tools that are equivalent to those commonly used in the classical reconstruction processes, where the propagation environments are described by parametric matrices [1719]
Summary
As we all know the environment in which we live and move has a huge impact on our behavior. A recently introduced new method of signal recovery is presented and confirmed by simulation examples here. Notwithstanding, the basic method of independent separate between channels is the usage, for instance, the commonly practiced tool in form of SVD factorization [3,4,5,6,7] All of those methods suffer from the parasitic effect of the environment in the form of noise. This paper presents the results of simulation studies using a new signal reconstruction method dedicated to propagation environments described by nonsquare polynomial matrices. As is shown by simulation studies, the new idea can restrain the negative influence of the propagation environment on the process of signal reconstruction/recovery.
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