Publications

The fifth-generation (5G) of mobile communication supported by millimetre-wave (mmWave) technology and higher base station (BS) densification facilitate to enhance user equipment (UE) positioning. Therefore, 5G cellular system is designed with many positioning measurements and special positioning reference signals with a multitude of configurations for a variety of use cases, expecting stringent positioning accuracies. One of the major factors that the accuracy of a particular position estimate depends on is the geometry of the nodes in the system, which could be measured with the geometric dilution of precision (GDOP). Hence in this paper, we investigate the time difference of arrival (TDOA) measurements based UE positioning accuracy improvement, exploiting the geometric distribution of BSs in mixed LOS and NLOS environment. We propose a BS selection algorithm for UE positioning based on the GDOP of the BSs participating in the positioning process. Simulations are conducted for indoor and outdoor scenarios that use antenna arrays with beam-based mmWave NR communication. Results demonstrate that the proposed BS selection can achieve higher positioning accuracy with fewer radio resources compared to the other BS selection methods.

Effective Capacity (EC) indicates the maximum communication rate subject to a certain delay constraint while effective energy efficiency (EEE) denotes the ratio between EC and power consumption. In this paper, we analyze the EEE of ultra-reliable networks operating in the finite blocklength regime. We obtain a closed form approximation for the EEE in Rayleigh block fading channels as a function of power, error probability, and delay. We show the optimum power allocation strategy for maximizing the EEE in finite blocklength transmission which reveals that Shannon's model underestimates the optimum power when compared to the exact finite blocklength model. Further-more, we characterize the buffer constrained EEE maximization problem for different power consumption models. The results show that accounting for empty buffer probability (EBP) and extending the maximum delay tolerance jointly enhance the EC and EEE.

Wireless energy transfer (WET) is a green enabler of low-power Internet of Things (IoT). Therein, traditional optimization schemes relying on full channel state information (CSI) are often too costly to implement due to excessive energy consumption and high processing complexity. This letter proposes a simple, yet effective, energy beamforming scheme that allows a multi-antenna power beacon (PB) to fairly power a set of IoT devices by only relying on the first-order statistics of the channels. In addition to low complexity, the proposed scheme performs favorably as compared to benchmarking schemes and its performance improves as the number of PB's antennas increases. Finally, it is shown that further performance improvement can be achieved through proper angular rotations of the PB.

Radio frequency (RF) wireless energy transfer (WET) is a key technology that may allow seamlessly powering future massive low-energy Internet of Things (IoT) networks. To enable efficient massive WET, channel state information (CSI)-limited/free multi-antenna transmit schemes have been recently proposed in the literature. The idea is to reduce/null the energy costs to be paid by energy harvesting (EH) IoT nodes from participating in large-scale time/power-consuming CSI training, but still enable some transmit spatial gains. In this paper, we take another step forward by proposing a novel CSI-free rotary antenna beamforming (RAB) WET scheme that outperforms all state-of-the-art CSI-free schemes in a scenario where a power beacon (PB) equipped with a uniform linear array (ULA) powers a large set of surrounding EH IoT devices. RAB uses a properly designed CSI-free beamformer combined with a continuous or periodic rotation of the ULA at the PB to provide average EH gains that scale as $0.85\sqrt{M}$, where $M$ is the number of PB's antenna elements. Moreover, a rotation-specific power control mechanism was proposed to i) fairly optimize the WET process if devices' positioning information is available, and/or ii) to avoid hazards to human health in terms of specific absorption rate (SAR). We show that RAB performance even approaches quickly (or surpasses, for scenarios with sufficiently large number of EH devices, or when using the proposed power control) the performance of a traditional full-CSI based transmit scheme, and it is also less sensitive to SAR constraints. Finally, we discuss important practicalities related to RAB such as its robustness against non line-of-sight conditions compared to other CSI-free WET schemes, and its generalizability to scenarios where the PB uses other than a ULA topology.

This letter investigates the secrecy performance of multiple-input single-output (MISO) wiretap channels when the eavesdropper channel is correlated with the main one. Assuming that the transmitter employs a transmit antenna selection (TAS) scheme, we derive a tight closed-form expression for the secrecy outage probability and analytically evaluate the secrecy performance. An asymptotic outage expression is also derived, from which a diversity analysis is carried out. We prove that correlation between channels works in favor at medium to high signal-to-noise ratio (SNR), however it degrades the performance at low SNR.

Recent advances on wireless energy transfer (WET) make it a promising solution for powering future Internet-of-Things (IoT) devices enabled by the upcoming sixth-generation (6G) era. The main architectures, challenges and techniques for efficient and scalable wireless powering are overviewed in this article. Candidates enablers, such as energy beamforming (EB), distributed antenna systems (DASs), advances on devices' hardware and programmable medium, new spectrum opportunities, resource scheduling, and distributed ledger technology are outlined. Special emphasis is placed on discussing the suitability of channel state information (CSI)-limited/free strategies when powering simultaneously a massive number of devices. The benefits from combining DAS and EB, and from using average CSI whenever available, are numerically illustrated. The pros and cons of the state-of-the-art CSI-free WET techniques in ultralow power setups are thoroughly revised, and some possible future enhancements are outlined. Finally, key research directions toward realizing WET-enabled massive IoT networks in the 6G era are identified and discussed in detail.

In this paper, we investigate how highly dense deployments of small cells perform when full-duplex nodes communicate under composite fading channels. With regard to an arbitrary user of interest, we first identify the impact of the extra interference from co-channel users and how that compares to the intrinsic self-interference. The performance of full-duplex networks is compared to a traditional half-duplex benchmark scenario where the only source of down-link inference is originated by simultaneous transmissions of surrounding base stations. A mathematical framework based on stochastic geometry is used to carry out our investigations. Here, the system performance is assessed by considering various network configurations and distinct fading channel conditions, namely log-normal shadowing and Nakagami-m fading. Using the aforesaid framework, we approximate the aggregate interference at the user of interest and then derive closed-form expressions for the corresponding signal to interference ratio and outage probability. Results show that users in full-duplex mode achieve higher rates even though they are subject to higher interference. It is also shown that the extent of these gains depends on the self-interference cancellation value that can be achieved and how that compares to the inference from other users.

We consider the weighted sum-rate maximization problem in downlink Orthogonal Frequency Division Multiple Access (OFDMA) systems. Motivated by the increasing popularity of OFDMA in future wireless technologies, a low complexity suboptimal resource allocation algorithm is obtained for joint optimization of multiuser subcarrier assignment and power allocation. The algorithm is based on an approximated primal decomposition-based method, which is inspired from exact primal decomposition techniques. The original nonconvex optimization problem is divided into two subproblems which can be solved independently. Numerical results are provided to compare the performance of the proposed algorithm to Lagrange relaxation based suboptimal methods as well as to optimal exhaustive search-based method. Despite its reduced computational complexity, the proposed algorithm provides close-to-optimal performance.

We propose and evaluate an iterative localization mechanism employing Bayesian inference to estimate the position of a target using received signal strength measurements. The probability density functions of the target's coordinates are estimated through a Bayesian network. Herein, we consider an iterative procedure whereby our predictor (posterior distribution) is updated in a sequential order whenever new measurements are made available. The performance of the mechanism is assessed in terms of the respective root mean square error and kernel density estimation of the target coordinates. Our numerical results showed the proposed iterative mechanism achieves increasingly better estimation of the target node position each updating round of the Bayesian network with new input measurements.

We analyze the performance of the type-I automatic repeat request (ARQ) protocol with ultra-reliability constraints. First, we show that achieving a very low packet outage probability by using an open loop setup is a difficult task. Thus, we introduce the ARQ protocol as a solution for achieving the required low outage probabilities for ultra reliable communication. For this protocol, we present an optimal power allocation scheme that would allow us to reach any outage probability target in the finite block-length regime. We formulate the power allocation problem as minimization of the average transmitted power under a given outage probability and maximum transmit power constraint. By utilizing the Karush-Kuhn-Tucker (KKT) conditions, we solve the optimal power allocation problem and provide a closed form solution. Next, we analyze the effect of implementing the ARQ protocol on the throughput. We show that by using the proposed power allocation scheme we can minimize the loss of throughput that is caused from the retransmissions. Furthermore, we analyze the effect of the feedback delay length in our scheme.

No abstract is provided for this article.

In this paper we investigate the performance of a cooperative network in the presence of an eavesdropper (Eve). Alice and Bob communicate with the help of a relay, which can operate either in half-duplex (HD) or full-duplex (FD) mode. We account for the self interference at the relay when operating under FD mode. Our analysis focus in the case that the CSI of Eve is not available at Alice. Thus, we derive closed-form expressions for secrecy outage probability. Our results allow us to compare the performance of FD and HD cooperative scenarios under secrecy constraints and, despite the additional interference at the relay, show the advantages of FD relaying over HD. In addition, we also show that a cooperative network is more vulnerable if Eve is closer to Alice than to the relay.

A deep learning (DL)-based power control algorithm that solves the max-min user fairness problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. Max-min rate optimization problem in a cell-free massive MIMO uplink setup is formulated, where user power allocations are optimized in order to maximize the minimum user rate. Instead of modeling the problem using mathematical optimization theory, and solving it with iterative algorithms, our proposed solution approach is using DL. Specifically, we model a deep neural network (DNN) and train it in an unsupervised manner to learn the optimum user power allocations which maximize the minimum user rate. This novel unsupervised learning-based approach does not require optimal power allocations to be known during model training as in previously used supervised learning techniques, hence it has a simpler and flexible model training stage. Numerical results show that the proposed DNN achieves a performance-complexity trade-off with around 400 times faster implementation and comparable performance to the optimization-based algorithm. An online learning stage is also introduced, which results in near-optimal performance with 4-6 times faster processing.