Francisco Cano-Broncano

Power Distribution for SVD-Aided MIMO Transmission with Receiver-Side Antennas Correlation

Multiple input multiple output (MIMO) systems have influenced nearly any mobile communication standard. However, in many cases the theoretically possible potential can’t be reached since of the proximity of transmitter and receiver side antenna arrays. The phenomena called correlation affects the channel capacity and the link BER (biterror rate). Therefore in this paper receiver-side antennas correlation and its modelling are investigated. Together with the appropriate signal processing (e. g. singular values decomposition), the effect of receiver-side antennas correlation is studied. Our results show that under the effect of correlation not necessarily all layers might be used for the data transmission since the weighting of the stronger layer within the MIMO system becomes even more stronger. Simulation results are shown to underline these effects and the benefits of using appropriate power distribution strategies.

Resource allocation in GMD and SVD-based MIMO system

Singular-value decomposition (SVD)-based multiple-input multiple output (MIMO) systems, where the whole MIMO channel is decomposed into a number of unequally weighted single-input single-output (SISO) channels, have attracted a lot of attention in the wireless community. The unequal weighting of the SISO channels has led to intensive research on bit- and power allocation even in MIMO channel situation with poor scattering conditions identified as the antennas correlation effect. In this situation, the unequal weighting of the SISO channels becomes even much stronger. In comparison to the SVD-assisted MIMO transmission, geometric mean decomposition (GMD)-based MIMO systems are able to compensate the drawback of weighted SISO channels when using SVD, where the decomposition result is nearly independent of the antennas correlation effect. The remaining interferences after the GMD-based signal processing can be easily removed by using dirty paper precoding as demonstrated in this work. Our results show that GMD-based MIMO transmission has the potential to significantly simplify the bit and power loading processes and outperforms the SVD-based MIMO transmission as long as the same QAM-constellation size is used on all equally-weighted SISO channels.

Bit- and Power Allocation in GMD and SVD-Based MIMO Systems

The singular value decomposition (SVD) is a popular technique used in multiple-input multiple-output (MIMO) systems to remove inter-antennas interferences in order to achieve the best performance. As a result, the MIMO channel is decomposed into a number of independent singular-input singular-output (SISO) channels with different weightings. In order to improve the performance, bit- and power-allocation strategies are required due to the unequal weighting coefficients. In contrast, the geometric mean decomposition (GMD) decomposes the MIMO channel into a number of equally weighted SISO channels with remaining inter-antenna interference which can be removed by using dirty paper precoding at the transmit side. Having equally weighted layers, the computational complexity required to implement bit- and power-allocation strategies decreases and GMD-based MIMO systems seem to be an appropriate solution. This paper analyses and compares the performance of SVD- and GMD-based MIMO systems affected by antennas correlation where QAM constellations are transmitted along the transmit antennas, demonstrating that the GMD-based one is more robust against antennas correlation. Furthermore, optimal and suboptimal bit- and power-allocation strategies are compared. This investigation demonstrates that the suboptimal solution provides a performance close to that offered by the optimal one but with a reduced computational cost.

Transmitter-Side Antennas Correlation in SVD-assisted MIMO Systems

MIMO techniques allow increasing wireless channel performance by decreasing the BER and increasing the channel throughput and in consequence are included in current mobile communication standards. MIMO techniques are based on benefiting the existence of multipath in wireless communications and the application of appropriate signal processing techniques. The singular value decomposition (SVD) is a popular signal processing technique which, based on the perfect channel state information (PCSI) knowledge at both the transmitter and receiver sides, removes inter-antenna interferences and improves channel performance. Nevertheless, the proximity of the multiple antennas at each front-end produces the so called antennas correlation effect due to the similarity of the various physical paths. In consequence, antennas correlation drops the MIMO channel performance. This investigation focuses on the analysis of a MIMO channel under transmitter-side antennas correlation conditions. First, antennas correlation is analyzed and characterized by the correlation coefficients. The analysis describes the relation between antennas correlation and the appearance of predominant layers which significantly affect the channel performance. Then, based on the SVD, pre- and post-processing is applied to remove inter-antenna interferences. Finally, bit- and power allocation strategies are applied to reach the best performance. The resulting BER reveals that antennas correlation effect diminishes the channel performance and that not necessarily all MIMO layers must be activated to obtain the best performance.

Downlink Multiuser MIMO-OFDM Systems

Multiple input multiple output (MIMO) techniques for wireless communication systems have attracted in the last years huge research activity due to the possibility of improving the link performance by increasing the channel capacity and decreasing the bit-error rate (BER). Due to the strongly increasing demand in high-data rate transmission systems, frequency non-selective MIMO links have reached a state of maturity and frequency selective MIMO links are in the focus of interest. In this field, the combination of MIMO transmission and OFDM (orthogonal frequency division multiplexing) can be considered as an essential part of fulfilling the requirements of future generations of wireless systems. However, single-user scenarios have reached a state of maturity. By contrast multiple users’ scenarios require substantial further research, where in comparison to ZF (zero-forcing) multiuser transmission techniques, the individual user’s channel characteristics are taken into consideration in this contribution. Furthermore, the use of multiple antennas both at the transmit and the receive front-ends introduces a correlation effect between the antennas due to their proximity producing interference. In consequence, the BER increases and the channel capacity decreases. The goal of the present contribution is to analyze the system performance under different spatial antennas distributions for Multiuser MIMO-OFDM systems in correlated and non-correlated fading channels. The performed joint optimization of the number of activated MIMO layers and the number of transmitted bits per subcarrier along with the appropriate allocation of the transmit power shows that not necessarily all user-specific MIMO layers per subcarrier have to be activated in order to minimize the overall BER under the constraint of a given fixed data throughput.