Constant envelope orthogonal frequency division multiplexing (CE-OFDM) is a multi-carrier waveform with 0 dB peak-to-average power ratio (PAPR). This property enables the exploitation of ...multi-carrier waveforms with non-linear power amplifiers, avoiding the undesirable clipping effects. However, the existing channel estimation techniques designed for OFDM cannot be reused, since the use of a phase modulator makes CE-OFDM a non-linear waveform. Previous works assumed that the channel estimation process relies on the transmission of preambles, and the data symbols are equalized using a frequency domain equalizer (FDE). To avoid the overhead induced by preambles, a phase-domain injected training (PIT) is proposed, where the pilot sequence is embedded in the phase dimension of the data symbols. This novel approach does not waste time and/or frequency resources as in preamble-based schemes. Moreover, it does not require additional power for the training. The received symbols are averaged with a dual procedure, and owing to the particular structure of CE-OFDM, the channel estimates are recovered. Also, the analytical expression of the channel estimation mean squared error (MSE) is derived. Finally, several numerical results illustrate the performance of the proposal, showing that the MSE, bit error rate (BER) and achievable rate are improved, as compared to the existing works.
Cognitive radio is considered a relevant communication paradigm to deal with the increasing demands in modern communications systems. Adaptive schemes are required to recognize channel conditions and ...to properly adjust main transmission parameters to improve the quality of communications. In this direction, blind algorithms to recover constellation, from phase-modulated signals, represent a means to implement cognitive capabilities to allow automatic modulation recognition (AMR) on receivers. Commonly, the most popular approaches for blind constellation recovery are based on a two-step scheme. The first step uses to equalize channel effects and reduce inter-symbol interference (ISI). The second step carries out constellation recovery utilizing phase locked loop (PLL) systems like the Costas Loop, then to classify the incoming signal. This work proposes a novel single-step blind adaptive filter solution, inspired by an adaptive interference canceler, for joint equalization and constellation symbol recovery from received phase shift keying (PSK) waveforms. Furthermore, we propose new coefficients update mechanisms based on the constant amplitude of PSK signals. The proposed solution exhibits reduced computational complexity compared to the state of the art and a reduced time of convergence. Additionally, the proposed scheme does not require a training sequence to operate properly. The obtained results clearly show that the proposed scheme significantly improves accuracy regarding phase symbol estimation and ISI reduction.
Pilot-pouring superimposed training (PPST) is a novel channel estimation technique specially designed for cyclic block filtered multi-tone (CB-FMT), where the pilot symbols are poured into the ...subcarriers taking advantage of the power left unused by the data symbols. Hence, since this technique is based on superimposed training (ST) principles, the data rate is not reduced, unlike the pilot symbol assisted modulation (PSAM). Besides, it exploits a weighted average at the receiver side that is capable of minimizing the mean squared error (MSE) of the channel estimation, and then enhancing the performance of the system. However, the existing proposal on PPST is limited to the minimization of the MSE to improve channel estimation for a given power allocation factor, without solving the joint optimization of channel estimation and data detection procedures. With this aim, this work addresses the whole problem to reach the best performance for both tasks, thus taking into account also the power allocation factor in the opt where the pilot symbols are poured into the subcarriers taking advantage of the power left unused by the data symbols. Hence, since this technique is based on superimposed training (ST) principles, the data rate is not reduced, unlike the pilot symbol assisted modulation (PSAM). Besides, it exploits a weighted average at the receiver side that is capable of minimizing the mean squared error (MSE) of the channel estimation, and then enhancing the performance of the system. However, the existing proposal on PPST is limited to the minimization of the MSE to improve channel estimation for a given power allocation factor, without solving the joint optimization of channel estimation and data detection procedures. With this aim, this work addresses the whole problem to reach the best performance for both tasks, thus taking into account also the power allocation factor in the optimization process, where the spectral efficiency must be maximized through the signal-to-interference plus noise ratio (SINR). Two optimization approaches are proposed, where the first one, referred as pilot-pouring optimization (PPO), is focused on performance at the expense of a high complexity, while the second one, denoted as low-complexity PPO (LPPO), is able to trade-off between performance and execution time. Numerical results are provided in order to show the validity of our proposal, where the different optimization problems are compared in terms of SINR and execution time.
The Spanish Quality Assurance Program applied to the process of donation after brain death entails an internal stage consisting of a continuous clinical chart review of deaths in critical care units ...(CCUs) performed by transplant coordinators and periodical external audits to selected centers. This paper describes the methodology and provides the most relevant results of this program, with information analyzed from 206,345 CCU deaths. According to the internal audit, 2.3% of hospital deaths and 12.4% of CCU deaths in Spain yield potential donors (clinical criteria consistent with brain death). Out of the potential donors, 54.6% become actual donors, 26% are lost due to medical unsuitability, 13.3% due to refusals to donation, 3.1% due to maintenance problems and 3% due to other reasons. Although the national pool of potential donors after brain death has progressively decreased from 65.2 per million population (pmp) in 2001 to 49 pmp in 2010, the number of actual donors after brain death has remained at about 30 pmp. External audits reveal that the number of actual donors could be 21.6% higher if all potential donors were identified and preventable losses avoided. We encourage other countries to develop similar comprehensive approaches to deceased donation performance.
The Spanish Quality Assurance Program applied to the process of donation after brain death represents a systematic, continuous and sustainable approach for a comprehensive assessment of this process, allowing for the estimation of the potential of donation, the analysis of performance and the identification of areas for improvement.
Abstract
Improvements in cost and speed of next generation sequencing (NGS) have provided a new pathway for delivering disease diagnosis, molecular typing, and detection of antimicrobial resistance ...(AMR). Numerous published methods and protocols exist, but a lack of harmonisation has hampered meaningful comparisons between results produced by different methods/protocols vital for global genomic diagnostics and surveillance. As an exemplar, this study evaluated the sensitivity and specificity of five well-established in-silico AMR detection software where the genotype results produced from running a panel of 436
Escherichia coli
were compared to their AMR phenotypes, with the latter used as gold-standard. The pipelines exploited previously known genotype–phenotype associations. No significant differences in software performance were observed. As a consequence, efforts to harmonise AMR predictions from sequence data should focus on: (1) establishing universal minimum to assess performance thresholds (e.g. a control isolate panel, minimum sensitivity/specificity thresholds); (2) standardising AMR gene identifiers in reference databases and gene nomenclature; (3) producing consistent genotype/phenotype correlations. The study also revealed limitations of in-silico technology on detecting resistance to certain antimicrobials due to lack of specific fine-tuning options in bioinformatics tool or a lack of representation of resistance mechanisms in reference databases. Lastly, we noted user friendliness of tools was also an important consideration. Therefore, our recommendations are timely for widespread standardisation of bioinformatics for genomic diagnostics and surveillance globally.
Nowadays, Cognitive Radio Sensor Networks (CRSN) arise as an emergent technology to deal with the spectrum scarcity issue and the focus is on devising novel energy-efficient solutions. In static ...CRSN, where nodes have spatial fixed positions, several reported solutions are implemented via sensor selection strategies to reduce consumed energy during cooperative spectrum sensing. However, energy-efficient solutions for dynamic CRSN, where nodes are able to change their spatial positions due to their movement, are nearly reported despite today's growing applications of mobile networks. This paper investigates a novel framework to optimally predict energy consumption in cooperative spectrum sensing tasks, considering node mobility patterns suitable to model dynamic CRSN. A solution based on the Kataoka criterion is presented, that allows to minimize the consumed energy. It accurately estimates -with a given probability-the spent energy on the network, then to derive an optimal energy-efficient solution. An algorithm of reduced-complexity is also implemented to determine the total number of active nodes improving the exhaustive search method. Proper performance of the proposed strategy is illustrated by extensive simulation results for pico-cells and femto-cells in dynamic scenarios.
Superimposed training (ST) is a semiblind channel estimation technique, proposed for orthogonal frequency division multiplexing (OFDM), where training sequences are added to data symbols, avoiding ...the use of dedicated pilot-subcarriers, and increasing the available bandwidth compared with pilot symbol assisted modulation (PSAM). Filter bank multicarrier offset quadrature amplitude modulation (FBMC-OQAM) is a promising waveform technique considered to replace the OFDM, which takes advantage of well-designed filters to avoid the use of cyclic prefix and reduce the out-band-emissions. In this paper, we provide the expressions of the average channel capacity of the FBMC-OQAM combined with either PSAM or ST schemes, considering imperfect channel estimation and the presence of the pilot sequences. In order to compute the capacity expression of our proposal, ST-FBMC-OQAM, we analyze the channel estimation error and its variance. The average channel capacity is deduced considering the noise, data interference from ST, and the intrinsic self-interference of the FBMC-OQAM. Additionally, to maximize the average channel capacity, the optimal value of data power allocation is also obtained. The simulation results confirm the validity of the capacity analysis and demonstrate the superiority of the ST-FBMC-OQAM over existing proposals.
Network densification is an emerging technology to achieve the key performance indicators expected in future wireless systems. However, several practical challenges as the appearance of interference ...channels need to be addressed. Interference alignment (IA) is a powerful technique to manage multiple concurrent transmissions in ultra-dense networks (UDNs). The fully digital (FD) IA has been widely studied. However, the research on IA with hybrid digital-analog beamforming (HB) for massive multiple-input multiple-output (mMIMO) scenarios, is limited. The reported works on HB-IA are designed assuming ideal phase shifters (PSs) without quantized phase constraints. Therefore, their performance severely degrades in practical systems with low-resolution PSs. Although quantized phase restrictions were considered in some HB schemes, they were limited to scenarios with a single transmitter, either single-user or multi-user. Thus, they fail when applied to UDNs since interference from neighboring cells is not considered in the design. Then, a new HB-IA is proposed for UDNs based on an iterative partial construction (IPC) procedure with the novelty of awareness of PSs quantization. The IPC algorithm relies on algebraic properties to iteratively compute the analog and digital matrices, via a partial submatrix composition, such that the Euclidean distance to the FD-IA solution is reduced. An additional digital block, based on an IA algorithm, is also applied to cancel the residual interference. Numerical results reveal that this proposal, referred as HB IPC-IA, significantly outperforms reported works achieving a near FD-IA performance. The obtained results provide practical insights for HB design in UDNs.
Network densification is one of the most promising solutions to address the high data rate demands in 5G and beyond (B5G) wireless networks while ensuring an overall adequate quality of service. In ...this scenario, most users experience significant interference levels from neighbouring mobile stations (MSs) and access points (APs) making the use of advanced interference management techniques mandatory. Clustered interference alignment (IA) has been widely proposed to manage the interference in densely deployed scenarios with a large number of users. Nonetheless, the setups considered in previous works are still far from the densification levels envisaged for 5G/B5G networks that are considered in this paper. Moreover, prior designs of clustered-IA systems relied on oversimplified channel models and/or enforced single-stream transmission. In this paper, we explore an ultradense deployment of small cells (SCs) to provide coverage in 5G/B5G wireless networks. A novel cluster design based on a size-restricted k-means algorithm to divide the SCs into different clusters is proposed taking into account path loss and shadowing effects, thus providing a more realistic solution than those available in the current literature. Unlike previous works, this clustering method can also cater for spatial multiplexing scenarios. Also, several design parameters such as the number of transmit antennas, multiplexed data streams, and deployed APs are analyzed in order to identify trade-offs between performance and complexity. The relationship between density of network elements per area unit and performance is investigated, thus allowing to illustrate that there is an optimal coverage area value over which the network resources should be distributed. Moreover, it is shown that the spectral-efficiency degradation due to the intercluster interference in ultradense networks (UDNs) points to the need of designing an interference management algorithm that accounts for both intracluster and intercluster interferences. Simulation results provide key insights for the deployment of small cells in interference-limited dense scenarios.
Optimal Sensing Policy for Energy Harvesting Cognitive Radio Systems Villalonga, David Alejandro Urquiza; Gomez, Jorge Torres; Garcia, M. Julia Fernandez-Getino
IEEE transactions on wireless communications,
2020-June, 2020-6-00, 20200601, Letnik:
19, Številka:
6
Journal Article
Recenzirano
Odprti dostop
Energy harvesting (EH) emerges as a novel technology to promote green energy policies. Based on Cognitive Radio (CR) paradigm, nodes are designed to operate with harvested energy from radio frequency ...signals. CR-EH systems state several strategies based on sensing and access policies to maximize throughput and protect primary users from interference, simultaneously. However, reported solutions do not consider to maximize detection performance to detect spectrum holes which represent a major drawback whenever available energy is not efficiently used. In this concern, this paper addresses optimal sensing policies based on energy harvesting schemes to maximize probability of detection of available spectrum. These novel policies may be incorporated to previous reported solutions to maximize performance. Optimal processing scheduling schemes are proposed for offline and online scenarios based on convex optimization theory, Dynamic Programming (DP) algorithm and heuristic solutions (Constant Power and Greedy policies). Performance of proposed policies are validated by simulations for common detection techniques such as Matched Filter (MF), Quadrature Matched Filter (QMF) and Energy Detector (ED). As a result, it is shown that the best detection scheme theoretically addressed by MF, does not always perform better than the poorest detection scheme, given by the ED, in an energy harvesting scenario.
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