1,721,041 research outputs found
Deep learning based successive interference cancellation for the non-orthogonal downlink
No full textNon-orthogonal communications are expected to play a key role in future wireless systems. In downlink transmissions, the data symbols are broadcast from a base station to different users, which are superimposed with different power to facilitate high-integrity detection using successive interference cancellation (SIC). However, SIC requires accurate knowledge of both the channel model and channel state information (CSI), which may be difficult to acquire. We propose a deep learningaided SIC detector termed SICNet, which replaces the interference cancellation blocks of SIC by deep neural networks (DNNs). Explicitly, SICNet jointly trains its internal DNNaided blocks for inferring the soft information representing the interfering symbols in a data-driven fashion, rather than using hard-decision decoders as in classical SIC. As a result, SICNet reliably detects the superimposed symbols in the downlink of non-orthogonal systems without requiring any prior knowledge of the channel model, while being less sensitive to CSI uncertainty than its model-based counterpart. SICNet is also robust tochanges in the number of users and to their power allocation. Furthermore, SICNet learns to produce accurate soft outputs, which facilitates improved soft-input error correction decoding compared to model-based SIC. Finally, we propose an onlinetraining method for SICNet under block fading, which exploits the channel decoding for accurately recovering online data labels for retraining, hence, allowing it to smoothly track the fading envelope without requiring dedicated pilots. Our numericalresults show that SICNet approaches the performance of classical SIC under perfect CSI, while outperforming it under realistic CSI uncertainty
Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels
No full textDeep neural networks (DNNs) based digital receivers can potentially operate in complex environments. How-ever, the dynamic nature of communication channels implies that in some scenarios, DNN-based receivers should be periodically retrained in order to track temporal variations in the channel conditions. To this aim, frequent transmissions of lengthy pilot sequences are generally required, at the cost of substantial overhead. In this work we propose a DNN-aided symbol detector, Meta-ViterbiNet, that tracks channel variations with reduced overhead by integrating three complementary techniques: 1) We leverage domain knowledge to implement a model-based/data-driven equalizer, ViterbiNet, that operates with a relatively small number of trainable parameters; 2) We tailor a meta-learning procedure to the symbol detection problem, optimizing the hyperparameters of the learning algorithm to facilitate rapid online adaptation; and 3) We enable online training with short-length pilot blocks and coded data blocks. Numerical results demonstrate that Meta-ViterbiNet operates accurately in rapidly-varying channels, outperforming the previous best approach, based on ViterbiNet or conventional recurrent neural networks without meta-learning, by a margin of up to 0.6dB in bit error rate in various challenging scenarios. Index terms - Viterbi algorithm, meta-learning.</p
A Deep-NN Beamforming Approach for Dual Function Radar-Communication THz UAV
Get PDFIn this paper, we consider a scenario with one unmanned aerial vehicle (UAV) equipped with a uniform linear array (ULA), which sends combined information and sensing signals to communicate with multiple ground base stations (GBSs) and, at the same time, senses potential targets placed within an interested area on the ground. We aim to jointly design the transmit beamforming with the GBSs association to optimize communication performance while ensuring high sensing accuracy. We propose a predictive beamforming framework based on a dual deep neural network (DNN) solution to solve the formulated nonconvex optimization problem. A first DNN is trained to produce the required beamforming matrix for any point of the UAV flying area in a reduced time compared to stateof- the-art beamforming optimizers. A second DNN is trained to learn the optimal mapping from the input features, power, and effective isotropic radiated power (EIRP) constraints to the GBSs association decision. Finally, we provide an extensive simulation analysis to corroborate the proposed approach and show the benefits of EIRP, Signal-to-Noise-plus-Interference Ratio (SINR) performance and computational speed
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Rapid Optimization of Superposition Codes for Multi-Hop NOMA MANETs via Deep Unfolding
Get PDFVarious communication technologies are expected to utilize mobile ad hoc
networks (MANETs). By combining MANETs with non-orthogonal multiple access
(NOMA) communications, one can support scalable, spectrally efficient, and
flexible network topologies. To achieve these benefits of NOMA MANETs, one
should determine the transmission protocol, particularly the superposition
code. However, the latter involves lengthy optimization that has to be repeated
when the topology changes. In this work, we propose an algorithm for rapidly
optimizing superposition codes in multi-hop NOMA MANETs. To achieve reliable
tunning with few iterations, we adopt the emerging deep unfolding methodology,
leveraging data to boost reliable settings. Our superposition coding
optimization algorithm utilizes a small number of projected gradient steps
while learning its per-user hyperparameters to maximize the minimal rate over
past channels in an unsupervised manner. The learned optimizer is designed for
both settings with full channel state information, as well as when the channel
coefficients are to be estimated from pilots. We show that the combination of
principled optimization and machine learning yields a scalable optimizer, that
once trained, can be applied to different topologies. We cope with the
non-convex nature of the optimization problem by applying parallel-learned
optimization with different starting points as a form of ensemble learning. Our
numerical results demonstrate that the proposed method enables the rapid
setting of high-rate superposition codes for various channels.Comment: Under review for publication in the IEE
Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM
Get PDFDistributed optimization is a fundamental framework for collaborative inference and decision making in decentralized multi-agent systems. The operation is modeled as the joint minimization of a shared objective which typically depends on observations gathered locally by each agent. Distributed optimization algorithms, such as the common D-ADMM, tackle this task by iteratively combining local computations and message exchanges. One of the main challenges associated with distributed optimization, and particularly with D-ADMM, is that it requires a large number of communications, i.e., messages exchanged between the agents, to reach consensus. This can make D-ADMM costly in power, latency, and channel resources. In this work we propose unfolded D-ADMM, which follows the emerging deep unfolding methodology to enable D-ADMM to operate reliably with a predefined and small number of messages exchanged by each agent. Unfolded D-ADMM fully preserves the operation of D-ADMM, while leveraging data to tune the hyperparameters of each iteration of the algorithm. These hyperparameters can either be agent-specific, aiming at achieving the best performance within a fixed number of iterations over a given network, or shared among the agents, allowing to learn to distributedly optimize over different networks. For both settings, our unfolded D-ADMM operates with limited communications, while preserving the interpretability and flexibility of the original D-ADMM algorithm. We specialize unfolded D-ADMM for two representative settings: a distributed estimation task, considering a sparse recovery setup, and a distributed learning scenario, where multiple agents collaborate in learning a machine learning model. Our numerical results demonstrate that the proposed approach dramatically reduces the number of communications utilized by D-ADMM, without compromising on its performance
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