In this paper, we develop a new dynamic utility for wireless access virtualization (WAV) optimization embodying highly-dimensional time-varying multi-criteria metrics (i.e., CAPEX and OPEX costs, QoS or QoE, multi-tier and/or multi-RAT HetNets, etc.) that gauge the best deployment and viability scenarios of cloud (C)- and fog (F)-RANs within legacy networks. Exploiting the powerful tool of graph theory, we devise a progressive greyfield WAV strategy that optimizes our dynamic utility through an efficient combination of C- and F-RANs. This strategy is able to readjust very quickly to any changes in existing or new constraints as they evolve or occur in time, respectively. The resulting optimized hybrid RAN deployment outperforms both the greenfield and the pre-planed greyfield “turnkey” WAV strategies.
Lately, Internet-based solutions, brought by the Internet of Things (IoT) and cloud computation and storage technologies, have been driving revolutionary approaches in innumerable domains, including the sensitive domain of healthcare. Indicatively, real-time diagnosis of medical issues, telemedicine, remote monitoring of patients, as well as computer-assisted smart transportation in case of emergencies, are anticipated as Systems-of-Systems (SoS) that can execute several applications of different criticality, thus necessitating mission-critical and non-critical peripheral components. Therefore, managing the criticality of a specific component, application or service in such an environment, is of fundamental importance. In this respect, this paper discusses an approach to identify and model criticalities of healthcare IoT systems, as a first step to effectively manage them in system implementation and deployment. To do so, it explains the mixed-criticality characteristics of such systems, describing two principal use cases, that stem from the combination of novel technologies with classic health care practices, namely (a) a remote elderly monitoring platform, as well as (b) a smart ambulance system. The main cricalities anticipated in such systems are described, as well as open areas for future research are also identified.
In activity recognition applications, rich sensor data may be obviously desirable for performance. However, using much sensor data can lead to undesirable glut. In this paper, we investigate the effect of excessive use of sensor data on the performance of activity recognition. Particularly, we study the effect of using analog temperature sensors data on the accuracy of an HMM-based recognition approach. The performance is comparatively evaluated using confusion matrices before and after including additional temperature sensors.
An indoor positioning system (IPS) is a technology employed to locate objects and people within a building scenario using signal processing or other sensory information. Ultra Wide Band (UWB) is a versatile wireless technology that can be employed as an IPS and has shown very good performances. UWB can be used in many scenarios and its effectiveness in through wall detection along with its excellent resolution for person localization is one of the best applications of Impulse Radio (IR) UWB. The main objective of this work is to propose a concept for intelligent radar systems employing UWB augmented by machine learning approaches to not only localize but understand the location of a person or target within a building. Although suitably developed UWB is excellent for obtaining localizing data it does not automatically understand what that location effectively means or where it is thus further methods are required to create meaningful data for end user appreciation. Learning from the huge amount of UWB signal data through Multi Class Support Vector Machine (MC-SVM) architecture enables a truly evolving scheme to both localize targets and identify them in a useful way. Statistical analysis of the experimental results supports the proposed algorithm.
An effective solution for reducing complexity and power consumption of massive MIMO systems is hybrid beam-forming (HB) using analog precoding at RF and digital precoding at the baseband. Researchers have studied HB, assuming optimal antenna implementation, ideal environment, and full rank effective channel. However, the rank of the effective channel matrix could be deficient due to implementation limitations and other factors, which would degrade the performance of the system. The objective of this study is to maintain a high rank effective channel with independent beams sending data streams to the receiver. When the weights of the RF beamformer are properly selected, the structure of the multipath propagation channel will be exploited by the transmitted beams, which maximize the system capacity. This paper presents a new set of solutions for HB that performs nearly as a fully digital beamformer in terms of MIMO multiplexing gains.
In this paper, we tackle the problem of joint time and frequency synchronization for decode-and-forward (DF) systems. We devise a new maximum likelihood (ML) algorithm for the joint estimation of these synchronization parameters based on the importance sampling (IS) technique. Unlike the traditional iterative estimation techniques, the proposed IS-based ML approach enjoys guaranteed global optimality. Moreover, it transforms the original multidimensional optimization problem into multiple two-dimensional ones, thereby entailing low computational burden. Simulation results show the advantage of the IS-based ML estimator over state-of-the-art estimation techniques.
In this paper, 4 types of SiGe active downconversion mixers are designed, simulated and compared with each other for non-portable S-Band applications. All mixers have single-ended RF inputs and they are designed in the same 0.35μm BiCMOS process with 5 V supply voltages. These mixers are compared in terms of conversion gain, IIP3, P1dB, DSB noise figure, and power dissipation at 2-4 GHz RF frequency. In addition to this, an LO amplifier and an IF buffer are designed to provide higher conversion gain, lower noise figure and output matching. These circuits are common for all mixers; thus, they have the same effect on all. Figure of merits of mixers are calculated and their pros and cons are explained.
Localization networks based on orthogonal frequency-division multiplexing (OFDM) ranging systems provide desirable location-awareness with commodity WiFi chipsets in harsh propagation conditions. Understanding the fundamental limits of OFDM ranging can guide the design and operation of these systems. Existing studies on these fundamental limits generally do not account for the processing impairments such as the estimation error of the phase offset and packet detection delay, yet ignoring processing impairments may lead to inaccurate evaluation of OFDM ranging accuracy. To address such inconsistency, we develop a framework to determine the fundamental limit of OFDM ranging, accounting for both channel parameters and processing impairments. In particular, we derive the equivalent Fisher information (EFI) for the propagation delay of the first path and quantify the effects of the number of multipaths, the number of channels, and processing impairments on the ranging accuracy through case studies. Numerical results verify the insights gained from the theoretical analysis.
Wireless sensor networks are gaining ground due to their low cost. Among them, solutions exist, that use the UWB (Ultra-Wideband) physical layer, which is ideal for localization by the nature of the technology. The accuracy of one of the most commonly used time measurement-based method depends on the accuracy of the timestamps provided by the devices. In this article, the authors propose a time measurement and UWB-based calibration method, which provides more accurate receiver and transmitter delays than commonly used methods. Using this approach, the calibrated devices utilized for positioning will provide more accurate time of arrival (ToA) - commonly called time of flight (ToF) - timestamps, which results in a significantly more accurate position calculation. First, the mathematical description of this calibration method is introduced followed by an enhanced version of that, then the system calibration algorithm is disclosed. Finally, a closed formula is presented which shows the error propagation of the presented methods.
One of the major challenges with the increase in wind power generation is the uncertain nature of wind speed. So far the uncertainty about wind speed has been presented through probability distributions. Also the existing models that consider the uncertainty of the wind speed primarily view the distributions of the wind speed over a wind farm as being homogeneous. However, the uncertainty about these wind speed models has not yet been considered. In this paper the Bayesian approach to taking into account the uncertainty inherent in the wind speed model has been presented. The proposed Bayesian predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines' locations in a wind farm. More specifically, instead of using a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior and predictive inferences under different reasonable choices of prior distribution in sensitivity analysis have been presented.
The practical necessity of object tracking is increasing in various application areas. The need for accurate positioning is growing as well; therefore more and more attempts are published aiming to improve the accuracy of positioning. This paper also presents a method to increase the accuracy even more of the previously published PE-ranging (passive extended double-sided two-way ranging) method. The hereby presented method utilizes the message sequence of PE-ranging but applies an alternative calculation of the position estimation to decrease the error originating from time measurement compared to the traditional calculation method. The presented positioning algorithm provides more accurate time measurement; therefore, the accuracy of positioning increases, and opposed to other methods, it does not require that the response times of the devices is close to equal. Moreover, the method allows using less accurate clocks in the tags, and more accurate clocks in the anchors of the positioning infrastructure.
Cloud Radio Access Network (C-RAN) architecture claims to reduce capital costs and facilitate the implementation of multi-site coordination mechanisms. This paper studies the delay constraints imposed by the Common Public Radio Interface (CPRI) protocol in ring-star topologies used by mobile operators. Simulations demonstrate that centralised implementations are feasible via functional split in the baseband processing chain. We derive theoretical expressions for propagation and queueing delay, assuming a G/G/1 queueing model. Then, we examine the properties of the fronthaul traffic flows and their behaviour when they are mixed. We show that the theoretical queueing delay estimations are an upper bound on the simulation output and accurate under certain conditions. Based on our results, we further propose a packetisation strategy of the fronthaul traffic which helps reduce the worst case aggregated queueing delay by 30%. Also, the benefits of a bidirectional ring topology are shown, achieving a worst average queueing delay 10 times lower than that of unidirectional topologies.
This paper proposes a planar diplexer using hybrid substrate integrated waveguide (SIW) and coplanar waveguide (CPW). On the basis of hybrid SIW-CPW structure which possesses high quality factor, adjustable transmission zeros, and compact size simultaneously, further employing microstrip T-junction which also possesses high performance and compact size, a demo diplexer is designed, fabricated, and measured with low insertion loss, high selectivity, high isolation, and compact size.
In this paper, a generic wake-up radio (WUR) based medium access control (MAC) protocol (GWR-MAC) has been evaluated from the energy consumption point of view. GWR-MAC protocol's goal is to achieve energy efficiency by avoiding idle listening since the sensor nodes are awakened only when there is a need for communication. A successful wake-up signal (WUS) transmission and reception is needed to awaken the WUR enabled node(s) from the sleep mode which must be taken into account in the energy consumption evaluation. Success of wake-up process depends on the physical layer detection of the signal, and also on the collision probability of the wake-up signals if there can be multiple nodes that are transmitting the WUS approximately at the same time. The success probability of GWR-MAC wake-up process is analyzed in this work taking into account physical and MAC layer aspects when assuming that wake-up signals are transmitted using Aloha channel access method. WUS success probability derivation is incorporated to energy consumption model and results are obtained to compare the total energy consumption of WUR based network and duty-cycling based network. The results show that the WUR based networks can improve energy efficiency in comparison to conventional duty cycling approach when taking into account also the error probability of the wake-up process. Results show also that in which conditions the duty-cycling approach should be preferred instead of WUR approach.
The Bessel-Fourier model is one of the state-of-the-art memoryless power amplifier (PA) behavioural methods that enables fast convergence while accurately captures both amplitude-to-amplitude modulation (AM/AM) and amplitude to-phase modulation (AM/PM) distortions. This paper presents original propositions for extrapolating the power sweep measurements in order to capture operating scenarios that reach beyond the device characterization. By means of numerical and experimental results we demonstrate that our proposition enables estimating the nonlinear behaviour of the PA at the highest power levels of its AM/AM and AM/PM measurements. Additional original elements of the paper include first reported studies relating to the convergence of the method as well as the first report of experimental verification of the technique for scenarios more complex than two-tone tests.
This paper presents a computationally efficient energy detection based spectrum sensing with multiple receive antennas. In traditional spectrum sensing with multiple receive antennas (soft decision case), a statistic for the signal detection is computed at each receive antenna, and these statistic are combined for the signal detection. Therefore, the computational complexity of spectrum sensing increases as the number of receive antenna. In the presented technique, received signals are combined (added/subtracted) firstly, and the statistic for the signal detection is computed from the combined signal. Because only one statistic computation is required regardless of the number of receive antenna, the computational complexity of the spectrum sensing can be reduced. In order to prevent the cancellation of each received signal at the combining due to the phase uncertainty, the presented technique employs the binary phase rotator which can take ±1. By choosing an appropriate rotator for each received signal, we attempt to maintain the magnitude of the combined signals. Furthermore, we present the spectrum sensing schemes to obtain a suboptimum phase rotator. Some numerical examples are provided to valid the effectiveness of the presented technique, and these results show that the presented technique is effective for the signal detection of narrowband modulation signals.
Network navigation is a promising paradigm enabling location-awareness in wireless networks. In a wireless navigation network, agents estimate their locations based on inter- and intra-node measurements. Due to the scarcity of wireless resources, it is critical to design scheduling algorithms that adaptively determine with whom and when an agent should perform inter-node measurements, in order to achieve both high navigation accuracy and efficient channel usage. This paper develops a framework for the design of distributed scheduling algorithms for asynchronous wireless navigation networks, in which the algorithm parameters are optimized based on the time evolution of agents' localization errors. The results lead to high accuracy, efficient, and flexible network navigation.
In this paper, an intelligent power management strategy is proposed for hybrid DC microgrid, including wind turbine, fuel cell and battery energy storage. The considered wind turbine has a permanent magnet synchronous generator (PMSG). In the considered structure, wind turbine operates as the main energy source while the fuel cell and battery bank are both auxiliary power sources. The main control objective is to supply the load power continuously while keeping all power sources in normal conditions. Hence, the fuel cell and battery bank are managed such that the system operates in normal condition and fuel cell will not generate excessive power. The proposed control scheme is based on the fuzzy algorithm. All simulations in variant operational modes are performed by MATLAB/Simulink and results show the effectiveness of the proposed control strategy.
Cognitive radio is one of the most promising technologies in wireless communications. Spectrum sensing is the technique of detection of unused frequencies in order to achieve the efficient use of bandwidth. 5G is the new mobile generation which can be realized by 2020. GFDM is the waveform candidate for 5G physical layer, GFDM has tail biting cyclic prefix which reduces the out of band radiation. Spectrum sensing is the first step for Cognitive radio, it is the process to identify the vacant spectrum band. Cyclostationary sensing is one of the traditional spectrum sensing technique. It is known with the best performance detection in low SNR. This depends on identifying the signal from the surrounding noise due to the repetitive feature of signal caused by modulation technique or cycle prefix. In this paper, we discuss exploring the cyclostationary feature of GFDM using different time smoothing algorithms and different values of SNR, comparing the execution time of the used algorithms and finally detect the effect of roll off factor on the probability of detection of the signal. Our results show that SSCA is time efficient algorithm when calculating the spectral correlation function of GFDM, it achieves the optimal detection of the signal in low SNR. Moreover, the performance detection is getting better while increasing the roll-off factor of pulse shaping filter used in GFDM.
This paper presents the design of a compact Antipodal Vivaldi Antenna (AVA) with good performance for ultra-wideband (UWB) applications. The miniaturized AVA — 40 × 40 × 0.8 mm, its balanced feed structure and its mechanical ground support are described in detail. The simulated results show that the proposed AVA covers a bandwidth ranging from 4.73 to more than 22 GHz with a peak gain above 5 dBi for the highest frequencies. A prototype was built and its return loss was measured. The designed antenna is utilized in a direction finding (DF) system based on the amplitude-comparison monopulse technique. The calibration curves and the corresponding residual errors of the proposed system are derived. An RMS residual error lower than 2.5° is obtained over a range of 40°.
