Exploring underused spectrum bands such as the mmWave band is key to solve future wireless communications necessities. This work studies the problem of channel estimation in multi-antena systems in this high-frequency band considering the few propagation parameters that are unveiled due to the sparse propagation nature. We address the channel estimation by applying atomic norm as a gridless multidimensional spectral estimation. The applied methodology is compared in different scenarios with several on-grid spectral estimation approaches and also with traditional non-parametrical methods.

We consider a cellular network, where the uplink transmissions to a base station (BS) are interferenced by other devices, a condition that may occur, e.g., in cell-free networks or when using non-orthogonal multiple access (NOMA) techniques. Assuming that the BS treats this interference as additional noise, we focus on the problem of estimating the interference correlation matrix from received signal samples. We consider a BS equipped with multiple antennas and operating in the millimeter-wave (mmWave) bands and propose techniques exploiting the fact that channels comprise only a few reflections at these frequencies. This yields a specific structure of the interference correlation matrix that can be decomposed into three matrices, two rectangular depending on the angle of arrival (AoA) of the interference and the third square with smaller dimensions. We resort to gridless approaches to estimate the AoAs and then project the least square estimate of the interference correlation matrix into a subspace with a smaller dimension, thus reducing the estimation error. Moreover, we derive two simplified estimators, still based on the gridless angle estimation that turns out to be convenient when estimating the interference over a larger number of samples.

Parametrical channel estimation enables not only the expected large spectral efficiency in MIMO systems but also unveils relevant propagation parameters and allows low complexity representation of the channel matrix that facilitates massive deployments of antennas. In this work we propose to apply atomic norm as a gridless multidimensional spectral estimation approach to address parametrical channel estimation where both transmitter AoD and receiver AoA are identified. The proposed methodology is compared with several state of the art approaches based on grid recovery.

We propose a new iterative detection and decoding (IDD) algorithm for multiple-input multiple-output (MIMO) based on expectation propagation (EP) with application to massive MIMO scenarios. Two main results are presented. We first introduce EP to iteratively improve the Gaussian approximations of both the estimation of the posterior by the MIMO detector and the soft output of the channel decoder. With this novel approach, denoted by double-EP (DEP), the convergence is very much improved with a computational complexity just two times the one of the linear minimum mean square error (LMMSE) based IDD, as illustrated by the included experiments. Besides, as in the LMMSE MIMO detector, when the number of antennas increases, the computational cost of the matrix inversion operation required by the DEP becomes unaffordable. In this work we also develop approaches of DEP where the mean and the covariance matrix of the posterior are approximated by using the Gauss-Seidel and Neumann series methods, respectively. This low-complexity DEP detector has quadratic complexity in the number of antennas, as the low-complexity LMMSE techniques. Experimental results show that the new low-complexity DEP achieves the performance of the DEP as the ratio between the number of transmitting and receiving antennas decreases.

A full multi-dimensional characterization of the angle of arrival (AoA) has immediate applications to the efficient operation of modern wireless communication systems. In this work, we develop a compressed sensing based method to extract multi-dimensional AoA information exploiting the sparse nature of the signal received by a sensor array. The proposed solution, based on the atomicl0norm, enables accurate gridless resolution of the AoA in systems with arbitrary 3D antenna arrays. Our approach allows characterizing the maximum number of distinct sources (or scatters) that can be identified for a given number of antennas and array geometry. Both noiseless and noisy measurement scenarios are addressed, deriving and evaluating the resolvability of the AoA propagation parameters through a multi-level Toeplitz matrix rank\nolimits-minimization problem. To facilitate the implementation of the proposed solution, we also present a least squares approach regularized by a convex relaxation of the rank\nolimits-minimization problem and characterize its conditions for resolvability.

Every year around 200 million people are affected by hazards of different nature. In most of these situations public protection and disaster relief personnel are usually the first responders to provide help. To provide differential relief coverage in these scenarios, novel communication and network functionalities are being demanded, relegating today’s narrowband private radio (PMR) emergency systems to the background. These are data-support, increased coverage, broadband communication, and high reliability which will be addressed by novel communication technologies such as Long Term Evolution (LTE), LTE Advanced-pro, and future 5 G. In this work we tackle two key technological solutions for future emergency communication networks such as an architecture based on relay nodes and enhanced user equipment by means of multiple-input-multiple-output (MIMO) techniques.

The use of current narrowband Private Mobile Radio (PMR) systems, such as Terrestrial Trunked Radio (TETRA) still has significant shortcomings for public safety networks. In disaster situations, for first responder emergency services to communicate more effectively, the exchange of multimedia information (e.g. high-resolution images) could really help. Due to this fact, LTE technology is postulated as the choice for public safety broadband communication systems. In this paper, we examine a proposal based on two-hop links that seeks to reach locations with low or null coverage and to achieve a physical layer improvement. Such a solution is carried out by combining some LTE features provided by the Third Generation Partnership Project (3GPP) with the use of a wearable massive MIMO solution formed by a large textile array directly implemented at the end user.

Optimum scheduling is a key objective in many communications systems where different users have to share a common resource. Typically, centralized implementations are capable of guaranteeing certain fairness. In our approach, we follow a different path modeling the scheduling process as a dynamic infinite horizon discrete-time game. This formulation allows us to include any kind of dynamics and distributed implementations. Despite, these games are very difficult to solve, we are able to show that they are in fact dynamic potential games equivalent to a non-stationary multivariate optimum control problem. The dynamic control problem is solved via an augmented Bellman equation including time as an extra state.

An experimental evaluation of Blind Interference Alignment (BIA) over a hardware platform is presented in this work. In contrast to other transmission techniques such as Linear Zero Forcing Beamforming (LZFB) or Interference Alignment (IA), BIA achieves a growth in Degrees of Freedom (DoF) without channel state information at the transmitter (CSIT). A real implementation based on Orthogonal Frequency Division Multiplexing (OFDM) and LTE parameters is implement on a testbed made up of a transmitter equipped with two antennas and two users equipped with a reconfigurable antenna each. Furthermore, a full CSIT technique such as LZFB is also implemented for comparison purposes. First, the theoretic achievable rates are obtained for both techniques. After that, the bit error rate of both schemes is evaluated regarding the achieved sum-thorughput.

Modern communications systems use efficient encoding schemes, multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the dimensions of the system grow, the design of efficient and low-complexity MIMO receivers possesses technical challenges. Symbol detection can no longer rely on conventional approaches for posterior probability computation due to complexity. Marginalization of this posterior to obtain per-antenna soft-bit probabilities to be fed to a channel decoder is computationally challenging when realistic signaling is used. In this work, we propose to use Expectation Propagation (EP) algorithm to provide an accurate low-complexity Gaussian approximation to the posterior, easily solving the posterior marginalization problem. EP soft-bit probabilities are used in an LDPC-coded MIMO system, achieving outstanding performance improvement compared to similar approaches in the literature for low-complexity LDPC MIMO decoding.

Modern communications systems use multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the number of antennas and the order of the constellation grow, the design of efficient and low-complexity MIMO receivers possesses big technical challenges. For example, symbol detection can no longer rely on maximum likelihood detection or sphere-decoding methods, as their complexity increases exponentially with the number of transmitters/receivers. In this paper, we propose a low-complexity high-accuracy MIMO symbol detector based on the Expectation Propagation (EP) algorithm. EP allows approximating iteratively at polynomial-time the posterior distribution of the transmitted symbols. We also show that our EP MIMO detector outperforms classic and state-of-the-art solutions reducing the symbol error rate at a reduced computational complexity.

A relay station (RS)-based cellular system deployment is considered, where multiple base stations (BS) cooperate in the BS-RS in-band transmission for the downlink. With the joint optimization of the resources allocated to the wireless backhaul and to the RS-MS access, it is possible to exploit the benefits of networked-MIMO along with combating the pathloss and shadowing effects, and thus obtain significantly enhanced spectral efficiency and coverage homogeneity. The problem is shown to be convex under conventional QoS objective functions. Suboptimal low complexity solutions are also proposed and evaluated.

In a Coordinated Base Station Transmission scenario for the downlink, we propose a precoding scheme based on a MMSE criterion to manage the inter-user interference. In order to allocate the transmitted power to each user's signal, we set up an iterative procedure based on a modified waterfllling distribution, which approaches the optimal performance with a viable complexity with respect to the numerical optimization. A further simplification may be introduced, giving a simple iterative waterfilling scheme, at the expense of some performance loss. The performance is illustrated obtaining the achievable rates for different antenna configurations and different kinds of power constraints. A comparison with a Block-Diagonalization approach shows the advantage of MMSE, in particular at low SNR.

This paper addresses the problem of resource allocation in a multiple access channel where several mobile terminals (MT's) are transmitting simultaneously to a single receiving base station, all of them with multiple antennas and exploiting beamforming. A multi-carrier modulation is assumed, either orthogonal frequency division multiplexing (OFDM) or filter bank multi-carrier (FBMC). The main advantage of FBMC is that the spectrum of the sub-channels is much more concentrated than for OFDM. This property is explicitly exploited in this paper in order to evaluate an asynchronous network where the transmissions from different MT's are not aligned in time. This implies that a frequency guard between groups of carriers assigned to different users has to be included, being larger for OFDM than for FBMC. The improvement in performance due to this fact is evaluated by means of the rate that can be achieved by dynamically allocating carriers to users and by calculating the power and bit loading to be applied.

In this contribution we analyze the performance of several transmission strategies in a MIMO-OFDM system deployed in a wireless metropolitan area network. With this aim, a measurement campaign has statistically characterized a typical outdoor-indoor scenario for wireless metropolitan area networks. The performance is measured in terms of spectral efficiency under several channel knowledge scenarios at the transmitter and power allocation matrices that lead to different transmission strategies. Also two alternatives (averaging in the time or frequency domains) are explored in order to provide the transmitter with the information needed to implement the transmission strategy when it is only possible to get information about the channel statistics.

We compare the performance between beamforming and spatial multiplexing showing in which downlink scenarios the higher performance of spatial multiplexing justify its complexity. We compute performance using readily measurable parameters such as angle spread (AS), antenna separation and signal to noise ratio (SKIR). Firstly, a semi-analytical approach relates these measurable parameters with parameters that theoretically characterize beamforming optimality such as the spatial correlation matrix first two eigenvalues and SNR. Secondly, the achieved spectral efficiency is given for beamforming and spatial multiplexing as a function of antenna separation, AS and SNR. Also, a "practical" region is given where beamforming achieves at least 90% of the spectral efficiency of spatial multiplexing.

This paper studies the Doppler sensitivity of the link reciprocity provided by TDD in a MIMO (multiple-input multiple-output) system. Assuming perfect channel state information at the receiver, the knowledge at the transmitter is restricted to the channel probability distribution as well as its precise value at the uplink/downlink switch instant. Doppler sensitivity is studied by means of the rate loss within the time window spanned by two consecutive switch instants

In this work, we consider the problem of detection in a sensor network with censored transmission. The motivation for this is the improvement of the energy efficiency by means of allowing just positive detection transmission. Both Bayes and NP (Neyman-Pearson) tests are developed and the performance of the NP test is studied using large deviation bounds on the error probability. We also show that these bounds using the KL divergence between the probability density functions of the observations under each hypothesis might be used as a criteria for determining the optimal exploration area or even the optimal strategy for energy efficiency. The "Spanish hat" model for the probability of detection is used to exemplify the performance of the proposed bound.