Publications

Grant-free protocols exploiting compressed sensing multi-user detection (MUD) are appealing for solving the random access problem in massive Internet of Things (IoT) networks with sporadic device activity. Such protocols would greatly benefit from prior deterministic knowledge of the sparsity level, i.e., the instantaneous number of simultaneously active devices <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> . Aiming at this, herein we introduce a framework relying on coordinated pilot transmissions (CPTs) for detecting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> . Specifically, the proposed CPT mechanism includes a downlink (DL) phase for channel state information acquisition that resolves fading uncertainty in the uplink (UL) transmission phase using shared UL pilot symbols for channel compensation. We propose a signal sparsity level detector and analytically assess its accuracy when network channels are subject to Rayleigh fading. We show that the variance of the estimator increases with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> , and its distribution approximates that of the sum of a Student's <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> and Gaussian random variable. The numerical results evince the need for carefully configuring the duration of the DL and UL phases. Indeed, we show that relatively short DL phases are preferable in highly sparse networks given the total CPT duration is fixed. Finally, we discuss and exemplify with some early results the potential of the proposed CPT framework for MUD, and highlight relevant research directions.

This paper analyzes the effective capacity (EC) of delay constrained machine type communication (MTC) networks operating in the finite blocklength (FB) regime. First, we derive a closed-form mathematical approximation for the EC in Rayleigh block fading channels. We characterize the optimum error probability to maximize the concave EC function and study the effect of SINR variations for different delay constraints. Our analysis reveals that SINR variations have less impact on EC for strict delay constrained networks. We present an exemplary scenario for massive MTC access to analyze the interference effect proposing three methods to restore the EC for a certain node which are power control, graceful degradation of delay constraint and joint compensation. Joint compensation combines both power control and graceful degradation of delay constraint, where we perform maximization of an objective function whose parameters are determined according to delay and SINR priorities. Our results show that networks with stringent delay constraints favor power controlled compensation and compensation is generally performed at higher costs for shorter packets.

This paper studies content caching in cloud-aided wireless networks where small cell base stations with limited storage are connected to the cloud via limited capacity fronthaul links. By formulating a utility (inverse of service delay) maximization problem, we propose a cache update algorithm based on spatio-temporal traffic demands. To account for the large number of contents, we propose a content clustering algorithm to group similar contents. Subsequently, with the aid of regret learning at small cell base stations and the cloud, each base station caches contents based on the learned content popularity subject to its storage constraints. The performance of the proposed caching algorithm is evaluated for sparse and dense environments while investigating the tradeoff between global and local class popularity. Simulation results show 15% and 40% gains in the proposed method compared to various baselines.

This paper addresses the establishment of secure communication links between smart-meters (Alice) and an aggregator (Bob) in the presence of an eavesdropper (Eve). The proposed scenario assumes: (i) MIMOME wiretap channel; (ii) transmit antenna selection at the Alice; (iii) no channel state information at the transmitter; (iv) fixed Wyner codes; and (v) guarantee of secure throughput by both quality of service and secrecy outage constraints. We propose a simple protocol to enhance security via transmit antenna selection, and then assess its performance in closed-form by means of secrecy outage and successful transmission probabilities. We assume these probabilities are our constraints and then maximize the secure throughput, establishing a security-reliability trade-off for the proposed scenario. Our numerical results illustrate the effect of this trade-off on the secure throughput as well as on the number of antennas at Alice, Bob and Eve. Interestingly, a small sacrifice in reliability allows secrecy enhancement in terms of secure bps/Hz. We apply this idea in our smart grid application to exemplify that, although Eve may acquire some samples of the average power demand of a household, it is not enough to properly reconstruct such curve.

Unmanned aerial vehicle (UAV) base stations (BSs) are reliable and efficient alternative to full fill the coverage and capacity requirements when the backbone network fails to provide such requirements due to disasters. In this paper, we consider optimal UAV-deployment problem in 3D space for a mmWave network. The objective is to deploy multiple aerial BSs simultaneously to completely serve the ground users. We develop a novel algorithm to find the feasible positions for a set of UAV-BSs from a predefined set of locations, subject to a signal-to-interference-plus-noise ratio (SINR) constraint of every associated user, UAV-BS's limited hovering altitude constraint and restricted operating zone constraint. We cast this 3D positioning problem as an l_0 minimization problem. This is a combinatorial, NP-hard problem. We approximate the l_0 minimization problem as non-combinatorial l_1-norm problem. Therefore, we provide a suboptimal algorithm to find a set of feasible locations for the UAV-BSs to operate. The analysis shows that the proposed algorithm achieves a set of the location to deploy multiple UVA-BSs simultaneously while satisfying the constraints.

In this paper, we develop novel two-tier interference management strategies that enable macrocell users (MUEs) to improve their performance, with the help of open-access femtocells. To this end, we propose a rate-splitting technique using which the MUEs optimize their uplink transmissions by dividing their signals into two types: a coarse message that is intended for direct transmission to the macrocell base station and a fine message that is decoded by a neighboring femtocell and subsequently relayed over a heterogeneous (wireless/wired) backhaul. For deploying the proposed technique, we formulate a non-cooperative game between the MUEs in which each MUE can decide on its relaying femtocell while maximizing a utility function that captures both the achieved throughput and the expected backhaul delay. Simulation results show that the proposed approach yields up to 125% rate improvement and up to 2 times delay reduction with wired backhaul and, 150% rate improvement and up to 10 times delay reduction with wireless backhaul, relative to classical interference management approaches, with no cross-tier cooperation.

Some of the key issues on the physical layer of the FRAMES FMA2 wideband CDMA concept are discussed. The main emphasis has been in describing the physical channel structures. As the FMA2 has been designed to support variable and high data rate transmission, also the service multiplexing and the random access issues are discussed.

Integrated access and backhaul (IAB) networks consist of IAB-donor, IAB-nodes, and user-equipments (UEs). Both IAB-donor and IAB-node provide access to UEs while IAB-donor and IAB-nodes exchange UEs data via wireless in-band backhaul using the same frequency-time resources shared with access links. Multi-antenna beamformer techniques can be used to mitigate the complicated cross-link interference scenarios arising from IAB systems. In this paper, an iterative beamformer design with the weighted queue minimization (WQM) objective is proposed for the time-division-duplexed (TDD) based IAB system. In the considered TDD based IAB model, in a given timeslot, IAB-nodes and IAB-donor are assumed to be different uplink (UL)/downlink (DL) transmission modes to mitigate conventional half-duplex loss. Also, the beamformer design is carried out over two timeslots, considering both UL and DL transmission at each node. Specifically, user-specific UL/DL queues are introduced at the IAB-nodes to guarantee the BS to/from UE data delivery. The proposed beamformer solution is based on the iterative evaluation of Karush-Kuhn-Tucker (KKT) conditions of the optimization problem, which can practically be implemented in both centralized and decentralized manner. The numerical examples illustrate the superior system performance of the proposed method in comparison to the conventional half-duplex relaying system.

This paper assesses the communication between smart-meters and aggregators as secondary users that transmit their data over the primary uplink channel. We assume: directional antennas are employed in the meter-aggregator link, secondary users transmit with limited power, meters' transmissions are randomized to avoid packet collisions, and an outage constraint for the secondary links' to guarantee their robustness. Therefore, the interference caused by the secondary users in the primary can be neglected. Conversely, the secondary users still experience interference from mobile users of the primary network, whose positions and traffic activity are unknown. Our goal is to study sampling-communication strategies for transmitting the average power demand of a household so the signal can be reconstructed by the aggregator with low deviation. Our results indicate that an event-based scheme based on energy packets can outperform the usual time-based, periodic, sampling.Paper published at ENERGYCON 2016Link: http://ieeexplore.ieee.org/document/7514023/

The wired charging and the need for battery replacements are critical barriers to unlimited, scalable, and sustainable mobile connectivity, motivating the interest in radio frequency (RF) wireless power transfer (WPT) technology. However, the inherently low end-to-end power transfer efficiency (PTE) and health/safety-related apprehensions about the technology are critical obstacles. Indeed, RF-WPT implementation and operation require efficient and cautious strategies and protocols, especially when targeting high-power charging, which constitutes the scope of this work. Herein, we overview the main factors affecting the end-to-end PTE of RF-WPT systems and their multiplicative effect and interdependencies. Moreover, we discuss key electromagnetic field (EMF) exposure metrics, safety limits, and approaches for efficient and EMF-aware deployment and operation. Quantitatively, we show that near-field RF charging may significantly reduce EMF exposure, and thus must be promoted. We also present our vision of a cyber-physical system for efficient and safe wireless charging, specify key components and their interrelation, and illustrate numerically the PTE attained by two modern low-power multi-antenna architectures in a simple setup. Throughout the paper, we highlight the need for high end-to-end PTE architectures and charging protocols transparently complying with EMF exposure regulations and outline relevant challenges and research directions. This work expands the vision and understanding of modern RF-WPT technology and constitutes a step towards making the technology attractive for worldwide commercial exploitation.

In this work we study the coexistence in the same Radio Access Network (RAN) of two generic services present in the Fifth Generation (5G) of wireless communication systems: enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC). eMBB services are requested for applications that demand extremely high data rates and moderate requirements on latency and reliability, whereas mMTC enables applications for connecting a massive number of low-power and low-complexity devices. The coexistence of both services is enabled by means of network slicing and Non-Orthogonal Multiple Access (NOMA) with Successive Interference Cancellation (SIC) decoding. Under the orthogonal slicing, the radio resources are exclusively allocated to each service, while in the non-orthogonal slicing the traffics from both services overlap in the same radio resources. We evaluate the uplink performance of both services in a scenario with a multi-antenna Base Station (BS). Our simulation results show that the performance gains obtained through multiple receive antennas are more accentuated for the non-orthogonal slicing than for the orthogonal allocation of resources, such that the non-orthogonal slicing outperforms its orthogonal counterpart in terms of achievable data rates or number of connected devices as the number of receive antennas increases.

Reconfigurable intelligent surfaces (RISs) have been introduced as arrays of nearly passive elements with software-tunable electromagnetic properties to dynamically manipulate the reflection/transmission of radio signals. Research works in this area are focused on two applications, namely {\it user-assist} RIS aiming at tuning the RIS to enhance the quality-of-service (QoS) of target users, and the {\it malicious} RIS aiming for an attacker to degrade the QoS at victim receivers through generating {\it intended} destructive interference. While both user-assist and malicious RIS applications have been explored extensively, the impact of RIS deployments on imposing {\it unintended} interference on various wireless user-equipments (EUs) remains underexplored. This paper investigates the challenges of integrating RISs into multi-carrier, multi-user, and multi-operator networks. We discuss how RIS deployments intended to benefit specific users can negatively impact other users served at various carrier frequencies through different network operators. While not an ideal solution, we discuss how ultra-narrowband metasurfaces can be incorporated into the manufacturing of RISs to mitigate some challenges of RIS deployment in wireless networks. We also present a simulation scenario to illuminate some practical challenges associated with the deployment of RISs in shared public environments.

In dynamic time-division-duplexing networks, the available resources per cell can be freely allocated to either uplink (UL) or downlink (DL) depending on the instantaneous traffic demand. Hence, complicated UL-DL and DL-UL interference scenarios arise due to simultaneous UL and DL data transmission in adjacent cells. In this paper, decentralized iterative beamformer designs are obtained for several traffic aware network optimization objectives such that only minimal information exchange is required among the coordinated base stations (BS) and user equipment (UE). Bi-directional forward-backward training via spatially precoded over-the-air pilot signaling is used to facilitate coordinated beamforming. This allows BSs and UEs to iteratively optimize their respective transmitters/receivers based on only locally measured reverse link pilot measurements. Novel bi-directional beamformer training strategies and methods for direct estimation (DE) of the stream specific beamformers are developed for each intermediate beamformer update in a limited and noisy pilot environment. The proposed signaling and DE schemes allow for non-orthogonal and overlapping pilots, which considerably reduces the resource coordination effort. Also, the decontamination ability of the proposed strategies are analyzed with limited pilot resources. The numerical examples illustrate the superior system performance of the proposed training and estimation framework in comparison to both the traditional stream-specific channel estimation method and an uncoordinated system.

In this paper, we address the deployment of base stations (BSs) in a one-dimensional network in which the users are randomly this http URL order to take into account the users' distribution to optimally place the BSs we optimize the uplink MMSE sum rate. Moreover, given a massive number of antennas at the BSs we propose a novel random matrix theory-based technique so as to obtain tight approximations for the MMSE sum rate in the uplink. We investigate a cooperative (CP) scenario where the BSs jointly decode the messages and a non-cooperative (NCP) scheme in which the BS can only decode its own users. Our results show that the CP strategy considerably outperforms the NCP case. Moreover, we show that there exists a trade off in the BS deployment regarding the position of each BS. Thus, through location games we can optimize the position of each BS in order to maximize the system performance.