Publications

5G networks must provide a highly resilient, secure, and privacy-protected platform to support the emergence of new business and technologies expected from the so-called vertical-industry paradigm. However, as the definition and implementation of 5G networks are in progress, many security challenges arise. Thus, special emphasis will be given in the coming years to provide security and privacy for 5G and beyond networks. In this regard, physical layer security has been recognized as a potential solution to safeguard the confidentiality and privacy of communications in such stringent scenarios. In light of this, herein we provide an overview on some promising physical-layer techniques, focusing on the requirements and design challenges for machine-type communication scenarios. Key issues are discussed along with potential solutions.

This work proposes an analytical framework to study how relay selection strategies perform in half- and full-duplex deployments by combining renewal theory and stochastic geometry. Specifically, we assume that the network nodes-operating in either half- or full-duplex mode-are scattered according to a 2-D homogeneous Poisson point process to compute the relay selection cost by using a semi-Markov process. Our results show that: 1) fixed relay outperforms the reactive option in either cases; 2) the performance of both reactive and fixed relay strategies depends on the self-interference attenuation in full-duplex scenarios, evincing when they outperform the half-duplex option; and 3) the reactive relay selection suffers from selecting relays at hop basis, while the fixed relay selection benefits most from the full-duplex communication.

Wireless energy transfer (WET) is emerging as an enabling green technology for Internet-of-Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio-frequency transmitters called power beacons (PBs). In this article, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs.

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of the transmit and the receive beamformers according to different optimization criteria. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. By exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. The simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient. Only one iteration of the proposed method is enough to substantially outperform the zero forcing based solution.

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. Furthermore, 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.

Radio frequency wireless energy transfer (WET) is a promising solution for powering autonomous IoT deployments. Recent works on WET have mainly focused on extremely low-power/cost IoT applications. However, trending technologies such as energy beamforming and waveform optimization, distributed and massive antenna systems, smart reflect arrays and reconfigurable metasurfaces, flexible energy transmitters, and mobile edge computing, may broaden WET applicability, and turn it plausible for powering more energy-hungry IoT devices. In this work, we specifically leverage energy beamforming for powering multiple user equipments (UEs) with stringent energy harvesting (EH) demands in an indoor cell-free massive MIMO. Based on semi-definite programming, successive convex approximation (SCA), and maximum ratio transmission (MRT) techniques, we derive optimal and sub-optimal precoders aimed at minimizing the radio stripes' transmit power while exploiting information of the power transfer efficiency of the EH circuits at the UEs. Moreover, we propose an analytical framework to assess and control the electromagnetic field (EMF) radiation exposure in the considered indoor scenario. Numerical results show that i) the EMF radiation exposure can be more easily controlled at higher frequencies at the cost of a higher transmit power consumption, ii) training is not a very critical factor for the considered indoor system, iii) MRT/SCA-based precoders are particularly appealing when serving a small number of UEs, thus, specially suitable for implementation in a time domain multiple access (TDMA) scheduling framework, and iv) TDMA is more efficient than spatial domain multiple access (SDMA) when serving a relatively small number of UEs. Results suggest that additional boosting performance strategies are needed to increase the overall system efficiency, thus making the technology viable in practice.

Ultrareliable low-latency communication (URLLC) constitutes a key service class of the fifth generation (5G) and beyond cellular networks. Notably, designing and supporting URLLC pose a herculean task due to the fundamental need to identify and accurately characterize the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior of protocols. In general, multilayer end-to-end approaches considering all the potential delay and error sources and proper statistical tools and methodologies are inevitably required for providing strong reliability and latency guarantees. This article contributes to the body of knowledge in the latter aspect by providing a tutorial on several statistical tools and methodologies that are useful for designing and analyzing URLLC systems. Specifically, we overview the frameworks related to the following: 1) reliability theory; 2) short packet communications; 3) inequalities, distribution bounds, and tail approximations; 4) rare-events simulation; 5) queuing theory and information freshness; and 6) large-scale tools, such as stochastic geometry, clustering, compressed sensing, and mean-field (MF) games. Moreover, we often refer to prominent data-driven algorithms within the scope of the discussed tools/methodologies. Throughout this article, we briefly review the state-of-the-art works using the addressed tools and methodologies, and their link to URLLC systems. Moreover, we discuss novel application examples focused on physical and medium access control layers. Finally, key research challenges and directions are highlighted to elucidate how URLLC analysis/design research may evolve in the coming years.

RIS is one of the significant technological advancements that will mark next-generation wireless. RIS technology also opens up the possibility of new security threats, since the reflection of impinging signals can be used for malicious purposes. This article introduces the basic concept for a RIS-assisted attack that re-uses the legitimate signal towards a malicious objective. Specific attacks are identified from this base scenario, and the RIS-assisted signal cancellation attack is selected for evaluation as an attack that inherently exploits RIS capabilities. The key takeaway from the evaluation is that an effective attack requires accurate channel information, a RIS deployed in a favorable location (from the point of view of the attacker), and it disproportionately affects legitimate links that already suffer from reduced path loss. These observations motivate specific security solutions and recommendations for future work.

We consider the worst-case weighted sum-rate maximization (WSRMax) problem under imperfect channel state information (CSI) in multicell downlink multiple-input singleoutput systems. The problem is known to be NP-hard even for the perfect CSI case. We propose a solution method, based on semi-definite relaxation (SDR) and branch and bound technique, which solves globally the noncovex robust WSRMax problem with an optimality certificate. Novel bounding technique based on SDR is proposed.

Vehicles are among the fastest growing type of connected devices. Therefore, there is a need for Vehicle-to-Everything (V2X) communication i.e. passing of information from a Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) and vice versa. In this paper, the main focus is on the communication between vehicles and road side units (RSUs) commonly referred to as V2I communication in a multi-lane freeway scenario. Moreover, we analyze network related bottlenecks such as the maximum number of vehicles that can be supported when coverage is provided by the Long Term Evolution Advanced (LTE-A) network. The performance evaluation is assessed through extensive system-level simulations. Results show that new resource allocation and interference mitigation techniques are needed in order to achieve the required high reliability requirements, especially when network load is high.

The problem of spectrum sharing between two operators in a dynamic network is considered. We allow both operators to share (a fraction of) their licensed spectrum band with each other by forming a common spectrum band. The objective is to maximize the gain in profits of both operators by sharing their licensed spectrum bands rather than using them exclusively, while considering the fairness among the operators. This is modeled as a two-person bargaining problem, and cast as a stochastic optimization. To solve this problem, we propose centralized and distributed dynamic control algorithms. At each time slot, the proposed algorithms perform the following tasks: 1) determine spectrum price for the operators; 2) make flow control decisions of users data; and 3) jointly allocate spectrum band to the operators and design transmit beamformers, which is known as resource allocation (RA). Since the RA problem is NP-hard, we have to rely on sequential convex programming to approximate its solution. To derive the distributed algorithm, we use alternating direction method of multipliers for solving the RA problem. Numerically, we show that the proposed distributed algorithm achieves almost the same performance as the centralized one. Furthermore, the results show that there is a trade-off between the achieved profits of the operators and the network congestion.

Effective Capacity defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between effective capacity and power consumption. We analyze the EEE of ultra-reliable networks operating in the finite blocklength regime. We obtain a closed form approximation for the EEE in quasi-static Nakagami-$m$ (and Rayleigh as sub-case) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the QoS constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultra-reliability using one transmission consumes huge amount of power, which is not applicable for energy limited IoT devices. In this context, accounting for empty buffer probability in machine type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the non-empty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.