1,721,049 research outputs found
Intelligent adaptive communication and radar systems.
No full textThe escalating demand for faster, reliable, and energy-efficient wireless communications has steered researchers towards millimetre-wave (mm Wave) frequencies, offering immense bandwidth and high data rates. To adapt to the increasing complexity of such networks, machine learning (ML)-assisted techniques are used for efficient adaptation without complete parameter dependence knowledge. ML-assisted adaptive techniques are applied to an OFDM-CSIM system over amm Wave channel, utilising index modulation and compressed sensing for improved spectral efficiency, energy efficiency, and system design freedom. A DNN-based classifier is proposed, enhancing throughput and outperforming traditional adaptive modulations. A novel multi-layer Sparse Bayesian learning algorithm estimates channel state information with lower complexity, providing more accurate estimation and better performance than conventional methods. Then, the ML-assisted techniques are extended to joint radar and communication systems, using radar-derived side information to adjust communication beams, reducing training overhead and complexity for channel estimation. The system employs a uniform rectangular planar array with adaptive adjustment of antenna elements and array configurations via deep neural network and convolutional neural network classifiers. The simulation results show that the proposed method can achieve a satisfactory data rate that approaches the upper bound obtained by the exhaustive search scheme as well as guaranteeing the required sensing performance. In contrast to previous joint radar and communication system designs that separate these functions through different sub-antenna arrays, a more efficient approach integrating both sensing and communication tasks within a single system, called dual functional radar-communication, is introduced. An ML-assisted beamforming design for ultra-dense device-to-device mm Wave networks uses a convolutional long short-term memory-integrated graph neural network (CL-GNN) to learn historical channel characteristics and predict the beamforming matrix. Our findings show that this design meets the required sensing performance and achieves a near-optimal sum rate. The adaptable CL-GNN can be generalised for networks of varying sizes and densities
Research Data - Machine Learning Assisted Adaptive Index Modulation for mmWave Communications
No full textThis dataset supports the publication on IEEE Open Journal of the Communications Society, which title is 'Machine Learning Assisted Adaptive Index Modulation for mmWave Communications'.
This dataset contains Figure 2, 6, 7, 8, 10, 12, 13, 14, 15, 16, 17 and 18 of the aforementioned paper. Each folder is named according to its content, where the curves of each figure are stored in mat files.</span
Machine learning assisted adaptive index modulation for mmWave communications
No full textIn this paper, we propose an orthogonal frequency-division multiplexing system supported by the compressed sensing assisted index modulation, termed as (OFDM-CSIM), applied to millimeter-wave (mmWave) communications. In the OFDM-CSIM mmWave system, information is conveyed not only by the classic constellation symbols but also by the on/off status of subcarriers, where the size of constellation symbols and the number of active subcarriers can be beneficially configured for maximizing the system's throughput. We conceive a machine learning (ML) assisted adaptive OFDM-CSIM mmWave system, which simultaneously benefits from the OFDM with index modulation (IM), compressed sensing (CS) and the hybrid beamforming techniques.Specifically, a ML-assisted link adaptation scheme is designed based on the -nearest neighbors (k-NN) algorithm with the objective to maximize the system's throughput. Our studies show that the proposed ML-assisted link adaptation is capable of providing higher throughput than the conventional threshold-based link adaptation when different antenna structures are considered.Furthermore, the achievable data rates of four types of antenna arrays, including uniform linear array (ULA), uniform rectangular planar array (URPA), uniform circle planar array (UCPA) and uniform cylindrical array (UCYA), are investigated and compared over mmWave channels. The simulation results show that the UCYA achieves the highest data rate among these antenna array
Deep learning assisted adaptive index modulation for mmWave communications with channel estimation
No full textThe efficiency of link adaptation in wireless communications relies greatly on the accuracy of channel knowledge and transmission mode selection. In this paper, a novel deep learning based link adaptation framework is proposed for the orthogonal frequency-division multiplexing (OFDM) systems with compressed-sensing-assisted index modulation, termed as OFDM-CSIM, communicating over millimeter-wave (mmWave) channels. To achieve link adaptation, a novel multi-layer sparse Bayesian learning (SBL) algorithm is proposed for accurately and instantaneously providing the required channel state information. Meanwhile, a deep neural networks (DNN)-assisted adaptive modulation algorithm is proposed to choose the best possible transmission mode to maximize the achievable throughput. Simulation results show that the proposed multi-layer SBL algorithm enables more accurate channel estimation than the conventional techniques. The DNN-based adaptive modulator is capable of achieving a higher throughput than the learning-assisted solution based on the k nearest neighbor (k-NN) algorithm, and also the classic average signal-to-noise ratio (SNR)-based solutions. Moreover, analysis shows that both the multi-layer SBL algorithm and the DNN-assisted adaptive modulator achieve better performance than their respective conventional counterparts while at a significantly lower computational complexity cost
Dataset supporting the article - Deep Learning Assisted Adaptive Index Modulation for mmWave Communications with Channel Estimation
No full textThis dataset supports the publication of an article in IEEE Transactions on Vehicular Technology, 'Deep Learning Assisted Adaptive Index Modulation for mmWave Communications with Channel Estimation'.
This dataset contains Figures 6, 7, 8, 9, 10, 11 and 12 of the aforementioned paper. Each folder is named according to its content, where the curves of each figure are stored in mat files. To regenerate the results, please use Matlab.</span
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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