On the Reduction of Transmission Complexity in MIMO-WCDMA Frequency-Selective Fading Orientations via Eigenvalue Analysis

Abstract

In this paper, a novel transmission strategy for Mutliple Input Multiple Output Wideband Code Division Multiple Access (MIMO-WCDMA) orientations operating in frequency-selective fading environments is investigated, in terms of overall algorithmic complexity reduction. To this end, Principal Component Analysis (PCA) is employed on the received data matrix, in order to define the significant terms that are taken into account during transmission matrix formulation. According to the presented results, feedback information of only the primary eigenvector of the corresponding covariance matrix of the received data matrix is required, in order to maintain the mean Bit Error Rate (BER) at acceptable levels. In particular, a complexity reduction of up to 10% can be achieved, when comparing BER values derived by the selection of all components of the received covariance matrix during transmission matrix formulation, and the corresponding BER when selecting half of the components. This reduction is maintained to 10%, when considering a realistic four-element antenna design; however, in this case mean BER inaccuracy is further reduced to 1%.

Non-Orthogonal Multiple Access in Multiuser MIMO Configurations via Code Reuse and Principal Component Analysis

“…As it was shown in [21], PCA can lead to transmission complexity reduction in MIMO-CDMA wireless configurations. In this context, only the primary eigenvector of the corresponding correlation matrix C k of received data is sent back to the transmitter, which formulates a modified matrix C k…”

“…In this work, a code reusage scheme is proposed and evaluated which separates active users into groups according to their channel quality and interuser interference. To this end, the received data correlation matrix is constructed at the transmitter, using only the primary eigenvector and eigenvalue of the corresponding correlation matrix as the input via feedback, deducted via principal component analysis (PCA) at MSs [21]. Hence, as it is shown in the presented results, our approach can achieve significant code assignment gain (CAG) with minimum computational and feedback complexity as well as average bit error rate (BER) deterioration.…”

“…is the received symbol and ρ, ρ are the corresponding partial cross-correlations [21]. Hence, in a multiuser (MU) MIMO configuration, the equivalent signal to interference ratio (SIR) can be expressed as (neglecting intersymbol interference) [25]…”

“…In Equations ( 5) and ( 6), S is the total number of samples and w is the number of equivalent eigenvectors (denoted as V) and eigenvalues in the diagonal matrix Σ for correlation matrix reconstruction (in our case w equals 1 in order to reduce overall feedback burden [21]). Notation a:b indicates all elements from a to b with a unity step.…”