27,005 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
Liu Kang
No full textLiu Kang: Essays on Art and Culture is a testament to the inexorable passion of an artist who knew no boundaries. This collection of essays, which Liu Kang wrote over 44 years, offers an insight into the artist’s myriad interests as well as his contributions as a first generation Nanyang artist and art educator. Translated into English for this volume, Liu Kang’s essays are accompanied by commentaries and photographs of the artist-author and his subjects
sj-pdf-1-jcb-10.1177_0271678X231201088 - Supplemental material for More severe initial manifestations and worse short-term functional outcome of intracerebral hemorrhage in the plateau than in the plain
No full textSupplemental material, sj-pdf-1-jcb-10.1177_0271678X231201088 for More severe initial manifestations and worse short-term functional outcome of intracerebral hemorrhage in the plateau than in the plain by Xiaoyin Wang, Haochen Sun, Xian Wang, Jing Lan, Yong Guo, Weiguo Liu, Lili Cui and Xunming Ji in Journal of Cerebral Blood Flow & Metabolism</p
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
Phoebus 10: A Journal of Art History
No full texttableOfContents: Homage to the Past: The Art of Yin Xiaofeng by Ralph Gabbard and Liu Liu.. pages 5-1
Cultural exploitation in chinese politics: Reinterpreting liu sanjie
No full textLiu Sanjie is a typical Chinese legendary figure, adapted from folk custom and transformed during many historical and political stages. By comparing the musical film Liu Sanjie with the landscape performing art Impression Liu Sanjie, this paper explores how Liu Sanjie is reconstructed in the Impression to be in accord with contemporary demands (shidaixing). In the film, made during the 1960s, Liu Sanjie was promoted as a heroine fighting against the privileged classes, but in the Impression, her class struggle has been erased and only a harmonious and abstract legend remains. Her ethnicity is promoted by Han elites as not exclusive Zhuang, but shared equally with Han, Miao and Dong ethnicity in an imagined community to propagate a sense of ethnic harmony and unified Chineseness. Her transformation from a realistic character, full of a rebelling spirit, to an abstract and disembodied ‘sense of harmony’, is a complete reinterpretation of a Chinese historical legend. Utilizing a term from Wang Ban (1997), ‘the sublime figure of history’, which refers to an ideology aestheticized by the party state for securing its governance, this paper refers to the bold artistic treatment of Liu Sanjie for cultural exploitation as ‘Liu Sanjie’s sublime’. The paper explores the evolutionary progress of Liu Sanjie from class revolution to art revolution in response to political requirements. The author is a stage-trained performing artist, specialized in both Western opera and Chinese classical and folk singing and dance. He is also a critic and art consultant in the Chinese landscape performing arts industry. These professional roles have allowed privileged access to the top people in this industry
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
Test Make Sense?
No full textCorresponding author Changyu Liu should be listed as the first corresponding author.No Full Tex
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