arXiv subjects

Pedro H. J. Nardelli

Publications and source records attributed to Pedro H. J. Nardelli.

At least 37 records · Page 2Linked to original sources

arXiv2022

Stochastic Geometry Analysis of Spectrum Sharing Among Multiple Seller and Buyer Mobile Operators

Sharing the licensed frequency spectrum among multiple mobile network operators (MNOs) is a promising approach to improve licensed spectrum utilization. In this paper, we model and analyze a non-orthogonal spectrum sharing system consisting of multiple seller and multiple buyer MNOs where buyer MNOs lease several licensed sub-bands from different seller MNOs. All base stations (BSs) owned by a buyer MNO can also utilize various licensed sub-bands simultaneously, which are also used by other buyer MNOs. To reduce the interference that a buyer MNO imposes on one seller MNO sharing its licensed sub-band, this buyer MNO has a limitation on the maximum interference caused to the corresponding seller MNO's users. We assume each MNO owns its BSs and users whose locations are modeled as two independent homogeneous Poisson point processes. Applying stochastic geometry, we derive expressions for the downlink signal-to-interference-plus-noise ratio coverage probability and the average rate of both seller and buyer networks. The numerical results validate our analysis with simulation and illustrate the effect of the maximum interference threshold on the total sum-rate of the network.

cs.IT↗

arXiv2022

Rate-Splitting Multiple Access and its Interplay with Intelligent Reflecting Surfaces

Rate-splitting multiple access (RSMA) has recently appeared as a powerful technique for improving the downlink performance of multiple-input multiple-output (MIMO) systems. By flexibly managing interference, RSMA can deliver high spectral and energy efficiency, as well as robustness to imperfect channel state information (CSI). In another development, an intelligent reflecting surface (IRS) has emerged as a method to control the wireless environment through software-configurable, near-passive, sub-wavelength reflecting elements. This article presents the potential of synergy between IRS and RSMA. Three important improvements achievable by IRS-RSMA schemes are identified, supported by insightful numerical examples, and mapped to beyond-5G use cases, along with future research directions.

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arXiv2022

Blockchain Function Virtualization: A New Approach for Mobile Networks Beyond 5G

Many of the key enabling technologies of the fifth-generation (5G), such as network slicing, spectrum sharing, and federated learning, rely on a centralized authority. This may lead to pitfalls in terms of security or single point of failure. Distributed ledger technology, specifically blockchain, is currently employed by different applications related to the Internet of Things (IoT) and 5G to address the drawbacks of centralized systems. For this reason, mobile blockchain networks (MBNs) have recently attracted a great deal of attention. To add a transaction to the blockchain in MBNs, mobile or IoT users must perform various tasks like encryption, decryption, and mining. These tasks require energy and processing power, which impose limitations on mobile and IoT users' performance because they are usually battery powered and have a low processing power. One possible solution is to perform the tasks virtually on commodity servers provided by mobile edge computing (MEC) or cloud computing. To do so, all tasks needed to add a transaction to the blockchain can be treated as virtual blockchain functions that can be executed on commodity servers. We introduce a blockchain virtualization framework called blockchain function virtualization (BFV), through which all blockchain functions can be performed virtually by MEC or cloud computing. Furthermore, we describe applications of the BFV framework and resource allocation challenges brought by the BFV framework in mobile networks. In addition, to illustrate the advantages of BFV, we define an optimization problem to simultaneously minimize the energy consumption cost and maximize miners' rewards. Finally, simulation results show the performance of the proposed framework in terms of total energy consumption, transaction confirmation rate, and miners' average profit.

cs.NI↗

arXiv2021

Swish-Driven GoogleNet for Intelligent Analog Beam Selection in Terahertz Beamspace MIMO

In this paper, we propose an intelligent analog beam selection strategy in a terahertz (THz) band beamspace multiple-input multiple-output (MIMO) system. First inspired by transfer learning, we fine-tune the pre-trained off-the-shelf GoogleNet classifier, to learn analog beam selection as a multi-class mapping problem. Simulation results show 83% accuracy for the analog beam selection, which subsequently results in 12% spectral efficiency (SE) gain, upon the existing counterparts. Towards a more accurate classifier, we replace the conventional rectified linear unit (ReLU) activation function of the GoogleNet with the recently proposed Swish and retrain the fine-tuned GoogleNet to learn analog beam selection. It is numerically indicated that the fine-tuned Swish-driven GoogleNet achieves 86% accuracy, as well as 18% improvement in achievable SE, upon the similar schemes. Eventually, a strong ensembled classifier is developed to learn analog beam selection by sequentially training multiple fine-tuned Swish-driven GoogleNet classifiers. According to the simulations, the strong ensembled model is 90% accurate and yields 27% gain in achievable SE, in comparison with prior methods.

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arXiv2021

Home Energy Management Systems: Operation and Resilience of Heuristics against Cyberattacks

Internet of Things (IoT) and advanced communication technologies have demonstrated great potential to manage residential energy resources by enabling demand-side management (DSM). Home energy management systems (HEMSs) can automatically control electricity production and usage inside homes using DSM techniques. These HEMSs will wirelessly collect information from hardware installed in the power system and in homes with the objective to intelligently and efficiently optimize electricity usage and minimize costs. However, HEMSs can be vulnerable to cyberattacks that target the electricity pricing model. The cyberattacker manipulates the pricing information collected by a customer's HEMS to misguide its algorithms toward non-optimal solutions. The customer's electricity bill increases, and additional peaks are created without being detected by the system operator. This article introduces demand-response (DR)-based DSM in HEMSs and discusses DR optimization using heuristic algorithms. Moreover, it discusses the possibilities and impacts of cyberattacks, their effectiveness, and the degree of resilience of heuristic algorithms against cyberattacks. This article also opens research questions and shows prospective directions.

cs.CR↗

arXiv2021

Distributed Joint Power and Rate Control for NOMA/OFDMA in 5G and Beyond

In this paper, we study the problem of minimizing the uplink aggregate transmit power subject to the users' minimum data rate and peak power constraint on each sub-channel for multi-cell wireless networks. To address this problem, a distributed sub-optimal joint power and rate control algorithm called JPRC is proposed, which is applicable to both non-orthogonal frequency-division multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA) schemes. Employing JPRC, each user updates its transmit power using only local information. Simulation results illustrate that the JPRC algorithm can reach a performance close to that obtained by the optimal solution via exhaustive search, with the NOMA scheme achieving a 59\% improvement on the aggregate transmit power over the OFDMA counterpart. It is also shown that the JPRC algorithm can outperform existing distributed power control algorithms.

cs.IT↗

arXiv2021

Packetized Energy Management Controller for Residential Consumers

In this paper, we investigate the management of energy storage control and load scheduling in scenarios considering a grid-connected photovoltaic (PV) system using packetized energy management. The aim is to reduce an average aggregated system cost through the proposed \textit{packetized energy management controller} considering household energy consumption, procurement price, load scheduling delays, PV self-sufficiency via generated renewable energy and battery degradation. The proposed approach solves the joint optimization problem using established heuristics, namely genetic algorithm (GA), binary particle swarm optimization (BPSO), and differential evolution (DE). Additionally, the performances of heuristic algorithms are also compared in terms of the effectiveness of load scheduling with delay constraints, packetized energy transactions, and battery degradation cost. Case studies have been provided to demonstrate and extensively evaluate the algorithms. The numerical results show that the proposed packetized energy management controller can considerably reduce the aggregated average system cost up to 4.7\%, 5.14\%, and 1.35\% by GA, BPSO, and DE, respectively while meeting the packetized energy demand and scheduling delays requirements.

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arXiv2021

IRS-Assisted Massive MIMO-NOMA Networks with Polarization Diversity

In this paper, the appealing features of a dual-polarized intelligent reflecting surface (IRS) are exploited to improve the performance of dual-polarized massive multiple-input multiple-output (MIMO) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC). By considering the downlink of a multi-cluster scenario, the IRSs assist the base station (BS) to multiplex subsets of users in the polarization domain. Our novel strategy alleviates the impact of imperfect SIC and enables users to exploit polarization diversity with near-zero inter-subset interference. Our results show that when the IRSs are large enough, the proposed scheme always outperforms conventional massive MIMO-NOMA and MIMO-OMA systems even if SIC error propagation is present. It is also confirmed that dual-polarized IRSs can make cross-polar transmissions beneficial to the users, allowing them to improve their performance through polarization diversity.

eess.SP↗

arXiv2021

IRS-Assisted Massive MIMO-NOMA Networks: Exploiting Wave Polarization

A dual-polarized intelligent reflecting surface (IRS) can contribute to a better multiplexing of interfering wireless users. In this paper, we use this feature to improve the performance of dual-polarized massive multiple-input multiple-output (MIMO) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC). By considering the downlink of a multi-cluster scenario, the IRSs assist the base station (BS) to multiplex subsets of users in the polarization domain. Our novel strategy alleviates the impact of imperfect SIC and enables users to exploit polarization diversity with near-zero inter-subset interference. To this end, the IRSs are optimized to mitigate transmissions originated at the BS from the interfering polarization. The formulated optimization is transformed into quadratic constrained quadratic sub-problems, which makes it possible to obtain the optimal solution via interior-points methods. We also derive analytically a closed-form expression for the users' ergodic rates by considering large numbers of reflecting elements. This is followed by representative simulation examples and comprehensive discussions. The results show that when the IRSs are large enough, the proposed scheme always outperforms conventional massive MIMO-NOMA and MIMO-OMA systems even if SIC error propagation is present. It is also confirmed that dual-polarized IRSs can make cross-polar transmissions beneficial to the users, allowing them to improve their performance through diversity.

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arXiv2021

Containing Future Epidemics with Trustworthy Federated Systems for Ubiquitous Warning and Response

In this paper, we propose a global digital platform to avoid and combat epidemics by providing relevant real-time information to support selective lockdowns. It leverages the pervasiveness of wireless connectivity while being trustworthy and secure. The proposed system is conceptualized to be decentralized yet federated, based on ubiquitous public systems and active citizen participation. Its foundations lie on the principle of informational self-determination. We argue that only in this way it can become a trustworthy and legitimate public good infrastructure for citizens by balancing the asymmetry of the different hierarchical levels within the federated organization while providing highly effective detection and guiding mitigation measures towards graceful lockdown of the society. To exemplify the proposed system, we choose the remote patient monitoring as use case. In which, the integration of distributed ledger technologies with narrowband IoT technology is evaluated considering different number of endorsed peers. An experimental proof of concept setup is used to evaluate the performance of this integration, in which the end-to-end latency is slightly increased when a new endorsed element is added. However, the system reliability, privacy, and interoperability are guaranteed. In this sense, we expect active participation of empowered citizens to supplement the more usual top-down management of epidemics.

cs.DC↗

arXiv2021

Incorporating Wireless Communication Parameters into the E-Model Algorithm

Telecommunication service providers have to guarantee acceptable speech quality during a phone call to avoid a negative impact on the users' quality of experience. Currently, there are different speech quality assessment methods. ITU-T Recommendation G.107 describes the E-model algorithm, which is a computational model developed for network planning purposes focused on narrowband (NB) networks. Later, ITU-T Recommendations G.107.1 and G.107.2 were developed for wideband (WB) and fullband (FB) networks. These algorithms use different impairment factors, each one related to different speech communication steps. However, the NB, WB, and FB E-model algorithms do not consider wireless techniques used in these networks, such as Multiple-Input-Multiple-Output (MIMO) systems, which are used to improve the communication system robustness in the presence of different types of wireless channel degradation. In this context, the main objective of this study is to propose a general methodology to incorporate wireless network parameters into the NB and WB E-model algorithms. To accomplish this goal, MIMO and wireless channel parameters are incorporated into the E-model algorithms, specifically into the $I_{e,eff}$ and $I_{e,eff,WB}$ impairment factors. For performance validation, subjective tests were carried out, and the proposed methodology reached a Pearson correlation coefficient (PCC) and a root mean square error (RMSE) of $0.9732$ and $0.2351$, respectively. It is noteworthy that our proposed methodology does not affect the rest of the E-model input parameters, and it intends to be useful for wireless network planning in speech communication services.

eess.AS↗

arXiv2021

Virtual Microgrid Management via Software-defined Energy Network for Electricity Sharing

Digitalization has led to radical changes in the distribution of goods across various sectors. The tendency is to move from traditional buyer-seller markets to subscription-based on-demand "smart" matching platforms enabled by pervasive ICTs. The driving force behind this lies in the fact that assets, which were scarce in the past, are readily abundant, approaching a regime of zero marginal costs. This is also becoming a reality in electrified energy systems due to the substantial growth of distributed renewable energy sources such as solar and wind; the increasing number of small-scale storage units such as batteries and heat pumps; and the availability of flexible loads that enable demand-side management. In this context, this article proposes a system architecture based on a logical (cyber) association of spatially distributed (physical) elements as an approach to build a virtual microgrid operated as a software-defined energy network (SDEN) that is enabled by packetized energy management. The proposed cyber-physical system presumes that electrical energy is shared among its members and that the energy sharing is enabled in the cyber domain by handshakes inspired by resource allocation methods utilized in computer networks, wireless communications, and peer-to-peer Internet applications (e.g., BitTorrent). The proposal has twofold benefits: (i) reducing the complexity of current market-based solutions by removing unnecessary and costly mediations and (ii) guaranteeing energy access to all virtual microgrid members according to their individual needs. This article concludes that the proposed solution generally complies with the existing regulations but has highly disruptive potential to organize a dominantly electrified energy system in the mid- to long-term, being a technical counterpart to the recently developed social-oriented microgrid proposals.

eess.SY↗

arXiv2021

Energy and Cost Efficient Resource Allocation for Blockchain-Enabled NFV

Network function virtualization (NFV) is a promising technology to make 5G networks flexible and agile. NFV decreases operators' OPEX and CAPEX by decoupling the physical hardware from the functions they perform. In NFV, users' service request can be viewed as a service function chain (SFC) consisting of several virtual network functions (VNFs) which are connected through virtual links. Resource allocation in NFV is done through a centralized authority called NFV Orchestrator (NFVO). This centralized authority suffers from some drawbacks such as single point of failure and security. Blockchain (BC) technology is able to address these problems by decentralizing resource allocation. The drawbacks of NFVO in NFV architecture and the exceptional BC characteristics to address these problems motivate us to focus on NFV resource allocation to users' SFCs without the need for an NFVO based on BC technology. To this end, we assume there are two types of users: users who send SFC requests (SFC requesting users) and users who perform mining process (miner users). For SFC requesting users, we formulate NFV resource allocation (NFV-RA) problem as a multi-objective problem to minimize the energy consumption and utilized resource cost, simultaneously.

cs.NI↗

arXiv2020

What Role Do Intelligent Reflecting Surfaces Play in Multi-Antenna Non-Orthogonal Multiple Access?

Massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) are two key techniques for enabling massive connectivity in future wireless networks. A massive MIMO-NOMA system can deliver remarkable spectral improvements and low communication latency. Nevertheless, the uncontrollable stochastic behavior of the wireless channels can still degrade its performance. In this context, intelligent reflecting surface (IRS) has arisen as a promising technology for smartly overcoming the harmful effects of the wireless environment. The disruptive IRS concept of controlling the propagation channels via software can provide attractive performance gains to the communication networks, including higher data rates, improved user fairness, and, possibly, higher energy efficiency. In this article, in contrast to the existing literature, we demonstrate the main roles of IRSs in MIMO-NOMA systems. Specifically, we identify and perform a comprehensive discussion of the main performance gains that can be achieved in IRS-assisted massive MIMO-NOMA (IRS-NOMA) networks. We outline exciting futuristic use case scenarios for IRS-NOMA and expose the main related challenges and future research directions. Furthermore, throughout the article, we support our in-depth discussions with representative numerical results.

eess.SP↗

arXiv2020

Review of the State-of-the-Art on Adaptive Protection for Microgrids based on Communications

The dominance of distributed energy resources in microgrids and the associated weather dependency require flexible protection. They include devices capable of adapting their protective settings as a reaction to (potential) changes in system state. Communication technologies have a key role in this system since the reactions of the adaptive devices shall be coordinated. This coordination imposes strict requirements: communications must be available and ultra-reliable with bounded latency in the order of milliseconds. This paper reviews the state-of-the-art in the field and provides a thorough analysis of the main related communication technologies and optimization techniques. We also present our perspective on the future of communication deployments in microgrids, indicating the viability of 5G wireless systems and multi-connectivity to enable adaptive protection.

eess.SY↗

arXiv2020

Review of the State-of-the-art on Bio-signal-based Brain-controlled Vehicles

Brain-controlled vehicle (BCV) is an already established technology usually designed for disabled patients. This review focuses on the most relevant topics on the brain controlling vehicles, especially considering terrestrial BCV (e.g., mobile car, car simulators, real car, graphical and gaming cars) and aerial BCV, also named BCAV (e.g., real quadcopter, drone, fixed wings, graphical helicopter and aircraft) controlled using bio-signals such as electroencephalogram (EEG), electrooculogram and electromyogram. For instance, EEG-based algorithms detect patterns from motor imaginary cortex area of the brain for intention detection, patterns like event related desynchronization/event related synchronization, state visually evoked potentials, P300, and generated local evoked potential patterns. We have identified that the reported best performing approaches employ machine learning and artificial intelligence optimization methods, namely support vector machine, neural network, linear discriminant analysis, k-nearest neighbor, k-means, water drop optimization and chaotic tug of war optimization optimization. We considered the following metrics to analyze the efficiency of the different methods: type and combination of bio-signals, time response, and accuracy values with the statistical analysis. The present work provides an extensive literature review of the key findings of previous ten years, indicating the future perspectives in the field.

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arXiv2020

Reconfigurable Intelligent Surface-Aided Grant-Free Access for Uplink URLLC

Reconfigurable intelligent surfaces (RISs) have been recently considered as one of the emerging technologies for future communication systems by leveraging the tuning capabilities of their reflecting elements. In this paper, we investigate the potential of an RIS-based architecture for uplink sensor data transmission in an ultra-reliable low-latency communication (URLLC) context. In particular, we propose an RIS-aided grant-free access scheme for an industrial control scenario, aiming to exploit diversity and achieve improved reliability performance. We consider two different resource allocation schemes for the uplink transmissions, i.e., dedicated and shared slot assignment, and three different receiver types, namely the zero-forcing, the minimum mean squared error (MMSE), and the MMSE-successive interference cancellation receivers. Our extensive numerical evaluation in terms of outage probability demonstrates the gains of our approach in terms of reliability, resource efficiency, and capacity and for different configurations of the RIS properties. An RIS-aided grant-free access scheme combined with advanced receivers is shown to be a well-suited option for uplink URLLC.

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arXiv2020

Three-layer Approach to Detect Anomalies in Industrial Environments based on Machine Learning

This paper introduces a general approach to design a tailored solution to detect rare events in different industrial applications based on Internet of Things (IoT) networks and machine learning algorithms. We propose a general framework based on three layers (physical, data and decision) that defines the possible designing options so that the rare events/anomalies can be detected ultra-reliably. This general framework is then applied in a well-known benchmark scenario, namely Tennessee Eastman Process. We then analyze this benchmark under three threads related to data processes: acquisition, fusion and analytics. Our numerical results indicate that: (i) event-driven data acquisition can significantly decrease the number of samples while filtering measurement noise, (ii) mutual information data fusion method can significantly decrease the variable spaces and (iii) quantitative association rule mining method for data analytics is effective for the rare event detection, identification and diagnosis. These results indicates the benefits of an integrated solution that jointly considers the different levels of data processing following the proposed general three layer framework, including details of the communication network and computing platform to be employed.

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