Reciprocity Calibration for Massive MIMO: Proposal, Modeling and Validation

This paper presents a mutual coupling based calibration method for time-division-duplex massive MIMO systems, which enables downlink precoding based on uplink channel estimates. The entire calibration procedure is carried out solely at the base station (BS) side by sounding all BS antenna pairs. An Expectation-Maximization (EM) algorithm is derived, which processes the measured channels in order to estimate calibration coefficients. The EM algorithm outperforms current state-of-the-art narrow-band calibration schemes in a mean squared error (MSE) and sum-rate capacity sense. Like its predecessors, the EM algorithm is general in the sense that it is not only suitable to calibrate a co-located massive MIMO BS, but also very suitable for calibrating multiple BSs in distributed MIMO systems. The proposed method is validated with experimental evidence obtained from a massive MIMO testbed. In addition, we address the estimated narrow-band calibration coefficients as a stochastic process across frequency, and study the subspace of this process based on measurement data. With the insights of this study, we propose an estimator which exploits the structure of the process in order to reduce the calibration error across frequency. A model for the calibration error is also proposed based on the asymptotic properties of the estimator, and is validated with measurement results.Comment: Submitted to IEEE Transactions on Wireless Communications, 21/Feb/201

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Caracterização não-linear de agregados de antenas para aplicações 5G

The present mobile scenario demands are stretching the existing telecom infrastructure to the limit. New technologies centred around antenna arrays and spatial multiplexing have been proposed to overcome the challenges imposed by these demands. This work overviews the mobile scenario, scrutinizing demands, presented solutions, challenges and the industry’s perspective of the Fifth Generation of mobile communications. From a careful analysis, the 5G’s most critical radio frequency hardware issues are detailed, and a long-term approach to address them is presented. On the short-term the work focuses on antenna characterization, because antennas are a central part of future wireless communications. Initially, basic antenna concepts are presented, then emphasis is given to microstrip antennas, going through all the steps of designing, optimizing and measuring a rectangular microstrip antenna and an eight element linear antenna array for 5.67GHz. Array features such as scanning and source synthesis are also explored. Finally, the impact of signal nonlinear distortion on the antenna array pattern is studied, aiming to expand state-of-the-art knowledge on how signal nonlinear distortion can limit spatial multiplexing. A theoretical model of the phenomenon is proposed and validated both by electromagnetic simulation and measurements.As crescentes exigências das redes móveis estão a levar a infraestrutura de telecomunicações ao seu limite. Novas tecnologias centradas em agregados de antenas e multiplexagem espacial têm sido propostas para ultrapassar os desafios impostos por tais exigências. Este trabalho apresenta uma visão abrangente das redes móveis atuais, escrutinando as suas exigências, as soluções apresentadas, os desafios adjacentes, bem como a opinião da indústria. Os problemas mais crı́ticos do hardware de radio frequência para a quinta geração de redes móveis são apurados a partir de uma análise detalhada do cenário das redes sem fios, sendo apresentado um plano a longo prazo para abordar estas problemáticas. A curto prazo o trabalho foca-se em caracterização de antenas, visto que as antenas são um ponto central nas comunicações sem fios do futuro. Inicialmente são apresentados conceitos básicos sobre antenas, dando-se de seguida ênfase às antenas microstrip, sendo apresentado todo o processo de sı́ntese, otimização e caracterização de uma antena microstrip retangular e de um agregado de antenas linear de oito elementos com frequência de operação 5.67GHz. Neste âmbito, algumas propriedades dos agregados, como o varrimento angular do feixe eletromagnético e técnicas de sı́ntese de fonte eletromagnética, são também exploradas. Finalmente, apresenta-se um estudo sobre o impacto que a distorção não linear de sinal pode ter no diagrama de radiação do agregado de antenas. O objetivo é expandir os conhecimentos do estado-da-arte acerca das limitações que a distorção não linear pode impor na multiplexagem espacial. Neste sentido, um modelo teórico descritivo deste fenómeno é proposto e validado por simulação eletromagnética e por medições experimentais.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Location-Based Beamforming and Physical Layer Security in Rician Wiretap Channels

We propose a new location-based beamforming (LBB) scheme for wiretap channels, where a multi-antenna source communicates with a single-antenna legitimate receiver in the presence of a multi-antenna eavesdropper. We assume that all channels are in a Rician fading environment, the channel state information from the legitimate receiver is perfectly known at the source, and that the only information on the eavesdropper available at the source is her location. We first describe how the optimal beamforming vector that minimizes the secrecy outage probability of the system is obtained, illustrating its dependence on the eavesdropper's location. We then derive an easy-to-compute expression for the secrecy outage probability when our proposed LBB scheme is adopted. We also consider the positive impact a friendly jammer can have on our beamforming solution, showing how the path to optimality remains the same. Finally, we investigate the impact of location uncertainty on the secrecy outage probability, showing how our solution can still allow for secrecy even when the source only has a noisy estimate of the eavesdropper's location. Our work demonstrates how a multi-antenna array, operating in the most general channel conditions and most likely system set-up, can be configured rapidly in the field so as to deliver an optimal physical layer security solution.Comment: 11 pages, 8 figures. Accepted for publication in IEEE Transactions on Wireless Communications. arXiv admin note: substantial text overlap with arXiv:1510.0856