Beamforming in MISO Systems: Empirical Results and EVM-based Analysis

We present an analytical, simulation, and experimental-based study of beamforming Multiple Input Single Output (MISO) systems. We analyze the performance of beamforming MISO systems taking into account implementation complexity and effects of imperfect channel estimate, delayed feedback, real Radio Frequency (RF) hardware, and imperfect timing synchronization. Our results show that efficient implementation of codebook-based beamforming MISO systems with good performance is feasible in the presence of channel and implementation-induced imperfections. As part of our study we develop a framework for Average Error Vector Magnitude Squared (AEVMS)-based analysis of beamforming MISO systems which facilitates comparison of analytical, simulation, and experimental results on the same scale. In addition, AEVMS allows fair comparison of experimental results obtained from different wireless testbeds. We derive novel expressions for the AEVMS of beamforming MISO systems and show how the AEVMS relates to important system characteristics like the diversity gain, coding gain, and error floor.Comment: Submitted to IEEE Transactions on Wireless Communications, November 200

MIMO Transmission with Residual Transmit-RF Impairments

Physical transceiver implementations for multiple-input multiple-output (MIMO) wireless communication systems suffer from transmit-RF (Tx-RF) impairments. In this paper, we study the effect on channel capacity and error-rate performance of residual Tx-RF impairments that defy proper compensation. In particular, we demonstrate that such residual distortions severely degrade the performance of (near-)optimum MIMO detection algorithms. To mitigate this performance loss, we propose an efficient algorithm, which is based on an i.i.d. Gaussian model for the distortion caused by these impairments. In order to validate this model, we provide measurement results based on a 4-stream Tx-RF chain implementation for MIMO orthogonal frequency-division multiplexing (OFDM).Comment: to be presented at the International ITG Workshop on Smart Antennas - WSA 201

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions