Miquel Payaró

Robust Power Allocation Designs for Multiuser and Multiantenna Downlink Communication Systems through Convex Optimization

In this paper, we study the design of the transmitter in the downlink of a multiuser and multiantenna wireless communications system, considering the realistic scenario where only an imperfect estimate of the actual channel is available at both communication ends. Precisely, the actual channel is assumed to be inside an uncertainty region around the channel estimate, which models the imperfections of the channel knowledge that may arise from, e.g., estimation Gaussian errors, quantization effects, or combinations of both sources of errors. In this context, our objective is to design a robust power allocation among the information symbols that are to be sent to the users such that the total transmitted power is minimized, while maintaining the necessary quality of service to obtain reliable communication links between the base station and the users for any possible realization of the actual channel inside the uncertainty region. This robust power allocation is obtained as the solution to a convex optimization problem, which, in general, can be numerically solved in a very efficient way, and even for a particular case of the uncertainty region, a quasi-closed form solution can be found. Finally, the goodness of the robust proposed transmission scheme is presented through numerical results. Robust designs, imperfect CSI, multiantenna systems, broadcast channel, convex optimization.

On optimal precoding in linear vector Gaussian channels with arbitrary input distribution

The design of the precoder the maximizes the mutual information in linear vector Gaussian channels with an arbitrary input distribution is studied. Precisely, the precoder optimal left singular vectors and singular values are derived. The characterization of the right singular vectors is left, in general, as an open problem whose computational complexity is then studied in three cases: Gaussian signaling, low SNR, and high SNR. For the Gaussian signaling case and the low SNR regime, the dependence of the mutual information on the right singular vectors vanishes, making the optimal precoder design problem easy to solve. In the high SNR regime, however, the dependence on the right singular vectors cannot be avoided and we show the difficulty of computing the optimal precoder through an NPhardness analysis.

Hessian and Concavity of Mutual Information, Differential Entropy, and Entropy Power in Linear Vector Gaussian Channels

Within the framework of linear vector Gaussian channels with arbitrary signaling, the Jacobian of the minimum mean square error and Fisher information matrices with respect to arbitrary parameters of the system are calculated in this paper. Capitalizing on prior research where the minimum mean square error and Fisher information matrices were linked to information-theoretic quantities through differentiation, the Hessian of the mutual information and the entropy are derived. These expressions are then used to assess the concavity properties of mutual information and entropy under different channel conditions and also to derive a multivariate version of an entropy power inequality due to Costa.

Performance comparison between FBMC and OFDM in MIMO systems under channel uncertainty

In this paper we present a performance comparison between two different multi-carrier transmission techniques: OFDM, based on the FFT and cyclic prefix (CP) addition; and FBMC (FilterBank MultiCarrier), which, as indicated by its name, is based on a filterbank architecture. For both schemes, we propose a joint beamforming design to be applied in multi-input-multi-output (MIMO) systems, which requires channel state information (CSI) at both communication ends. If perfect CSI is assumed, FBMC presents a higher energy-efficiency since it does not require a CP, differently to OFDM. However, in the imperfect CSI case, the opposite occurs: while in OFDM the presence of errors in the CSI does not cause intersymbol interference (ISI) and inter-carrier interference (ICI), in the FBMC case we show, both analytically and through simulation results, that imperfections in the CSI imply both ISI and ICI, which leads to an energy-efficiency loss. To mitigate this loss, we propose a novel robust receive beamforming strategy. In the simulations section, the performance of FBMC with robust beamforming is shown to outperform OFDM even in imperfect CSI conditions. The characterization of the tradeoff between energy-efficiency and robustness against channel uncertainty, both with robust and non-robust strategies, is, therefore, the objective of the present paper and also the motivation for future robust designs for FBMC multiantenna systems.

Energy-Efficient Transmission for Wireless Energy Harvesting Nodes

Energy harvesting is increasingly gaining importance as a means to charge battery powered devices such as sensor nodes. Efficient transmission strategies must be developed for Wireless Energy Harvesting Nodes (WEHNs) that take into account both the availability of energy and data in the node. We consider a scenario where data and energy packets arrive to the node where the time instants and amounts of the packets are known (offline approach). In this paper, the best data transmission strategy is found for a finite battery capacity WEHN that has to fulfill some Quality of Service (QoS) constraints, as well as the energy and data causality constraints. As a result of our analysis, we can state that losing energy due to overflows of the battery is inefficient unless there is no more data to transmit and that the problem may not have a feasible solution. Finally, an algorithm that computes the data transmission curve minimizing the total transmission time that satisfies the aforementioned constraints has been developed.

Order reduction of wideband digital predistorters using principal component analysis

This paper presents how to apply order reduction in wide-band digital predistortion (DPD) linearizers using the principal component analysis (PCA) technique. This method is tested in a wireless backhauling transmitter where four 28 MHz adjacent subcarrier transmission of M-QAM signals are considered. The DPD has to counteract not only the PA nonlinear behavior, but also its dynamics. This may results critical when considering wideband signals since the number of coefficients required to model memory effects can grow dramatically. By applying the PCA technique, the number of essential parameters can be significantly reduced. In addition, a strategy to minimize the computational cost of finding the optimal coefficients is also presented. A test-bed for evaluating the DPD linearization performance of the RF subsystem when PCA is applied was deployed and experimental results are presented in this paper.

Throughput Maximization for a Wireless Energy Harvesting Node Considering the Circuitry Power Consumption

The autonomy of wireless nodes is dropping year after year as the battery capacity increase is not able to follow the increasing energy consumption of the nodes, which is produced due to the high demand of data traffic and transmitter processing complexity of the users. Energy harvesting and energy efficient communications, e.g., energy efficient network topologies, are promising solutions to overcome this problem. In this context, this paper studies the power allocation and slot selection strategies that maximize the total throughput of a wireless energy harvesting node by taking into account both the transmission power and circuitry power consumption spent along the radio frequency chain of the transmitter.

On MMSE Crossing Properties and Implications in Parallel Vector Gaussian Channels

The scalar additive Gaussian noise channel has the “single crossing point” property between the minimum mean square error (MMSE) in the estimation of the input given the channel output, assuming a Gaussian input to the channel, and the MMSE assuming an arbitrary input. This paper extends the result to the parallel vector additive Gaussian channel in three phases. The channel matrix is the identity matrix, and we limit the Gaussian input to a vector of Gaussian i.i.d. elements. The “single crossing point” property is with respect to the signal-to-noise ratio (as in the scalar case). The channel matrix is arbitrary, and the Gaussian input is limited to an independent Gaussian input. A “single crossing point” property is derived for each diagonal element of the MMSE matrix. The Gaussian input is allowed to be an arbitrary Gaussian random vector. A “single crossing point” property is derived for each eigenvalue of the difference matrix between the two MMSE matrices. These three extensions are then translated to new information theoretic properties on the mutual information, using the I-MMSE relationship, a fundamental relationship between estimation theory and information theory revealed by Guo and coworkers. The results of the last phase are also translated to a new property of Fisher information. Finally, the applicability of all three extensions on information theoretic problems is demonstrated through a proof of a special case of Shannons vector entropy power inequality, a converse proof of the capacity region of the parallel degraded broadcast channel (BC) under an input per-antenna power constraint and under an input covariance constraint, and a converse proof of the capacity region of the compound parallel degraded BC under an input covariance constraint.

Achievable rates for generalized spatial Tomlinson-Harashima precoding in MIMO systems

In this paper, we study spatial Tomlinson-Harashima precoding (STHP). We propose a generalization of the original structure of STHP which enables different transmission power per antenna. The main advantage of the new scheme is that it has a scalable structure, because, despite the variable power per transmitter (even if some transmitters are switched off), the precoding structure and its properties are preserved. Next, we formulate the maximum achievable rates problem for GSTHP and solve it numerically. In addition, we show that the problem can be quasi-optimally solved in a closed form in a limit case, or if some approximations are done. The solution is found to be an antenna-selection algorithm with uniform power allocation among the active transmitters. We finally present some simulation results and conclusions.

The CTTC 5G End-to-End Experimental Platform : Integrating Heterogeneous Wireless\/Optical Networks, Distributed Cloud, and IoT Devices

The Internet of Things (IoT) will facilitate a wide variety of applications in different domains, such as smart cities, smart grids, industrial automation (Industry 4.0), smart driving, assistance of the elderly, and home automation. Billions of heterogeneous smart devices with different application requirements will be connected to the networks and will generate huge aggregated volumes of data that will be processed in distributed cloud infrastructures. On the other hand, there is also a general trend to deploy functions as software (SW) instances in cloud infrastructures [e.g., network function virtualization (NFV) or mobile edge computing (MEC)]. Thus, the next generation of mobile networks, the fifth-generation (5G), will need not only to develop new radio interfaces or waveforms to cope with the expected traffic growth but also to integrate heterogeneous networks from end to end (E2E) with distributed cloud resources to deliver E2E IoT and mobile services. This article presents the E2E 5G platform that is being developed by the Centre Tecnol?gic de Telecomunicacions de Catalunya (CTTC), the first known platform capable of reproducing such an ambitious scenario.