FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2912931692> ?p ?o ?g. }

• W2912931692 abstract "This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not replace, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communications networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is presented. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely-used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for network design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches. For each application, the merits of the proposed approaches will be demonstrated by a numerical analysis in which the implementation and training of the artificial neural network used to solve the problem is discussed. Finally, concluding remarks describe those that in our opinion are the major directions for future research in this field." @default.
• W2912931692 created "2019-02-21" @default.
• W2912931692 creator A5050480768 @default.
• W2912931692 creator A5056145687 @default.
• W2912931692 creator A5081612175 @default.
• W2912931692 date "2019-02-05" @default.
• W2912931692 modified "2023-09-25" @default.
• W2912931692 title "Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?" @default.
• W2912931692 cites W104184427 @default.
• W2912931692 cites W1498436455 @default.
• W2912931692 cites W1527571715 @default.
• W2912931692 cites W1533861849 @default.
• W2912931692 cites W1535041666 @default.
• W2912931692 cites W1565327149 @default.
• W2912931692 cites W1568229137 @default.
• W2912931692 cites W1663973292 @default.
• W2912931692 cites W1665214252 @default.
• W2912931692 cites W1731081199 @default.
• W2912931692 cites W1831449718 @default.
• W2912931692 cites W1836465849 @default.
• W2912931692 cites W1906224694 @default.
• W2912931692 cites W196761320 @default.
• W2912931692 cites W1971673042 @default.
• W2912931692 cites W1981494355 @default.
• W2912931692 cites W1984649364 @default.
• W2912931692 cites W1987200260 @default.
• W2912931692 cites W1988720110 @default.
• W2912931692 cites W1995875735 @default.
• W2912931692 cites W2018036523 @default.
• W2912931692 cites W2018932238 @default.
• W2912931692 cites W2025198378 @default.
• W2912931692 cites W2029538739 @default.
• W2912931692 cites W2040340473 @default.
• W2912931692 cites W2054692642 @default.
• W2912931692 cites W2065105631 @default.
• W2912931692 cites W2072128103 @default.
• W2912931692 cites W2075728230 @default.
• W2912931692 cites W2076063813 @default.
• W2912931692 cites W2077151136 @default.
• W2912931692 cites W2077723394 @default.
• W2912931692 cites W2078626246 @default.
• W2912931692 cites W2087070363 @default.
• W2912931692 cites W2095705004 @default.
• W2912931692 cites W2099471712 @default.
• W2912931692 cites W2100303002 @default.
• W2912931692 cites W2110667918 @default.
• W2912931692 cites W2118761136 @default.
• W2912931692 cites W2118833474 @default.
• W2912931692 cites W2119717200 @default.
• W2912931692 cites W2121607567 @default.
• W2912931692 cites W2121863487 @default.
• W2912931692 cites W2122053769 @default.
• W2912931692 cites W2125918842 @default.
• W2912931692 cites W2131672785 @default.
• W2912931692 cites W2144000019 @default.
• W2912931692 cites W2146400449 @default.
• W2912931692 cites W2146502635 @default.
• W2912931692 cites W2150010995 @default.
• W2912931692 cites W2150166076 @default.
• W2912931692 cites W2150341604 @default.
• W2912931692 cites W2151854870 @default.
• W2912931692 cites W2154952480 @default.
• W2912931692 cites W2156387975 @default.
• W2912931692 cites W2159291411 @default.
• W2912931692 cites W2160160578 @default.
• W2912931692 cites W2161388792 @default.
• W2912931692 cites W2163605009 @default.
• W2912931692 cites W2165150801 @default.
• W2912931692 cites W2165561479 @default.
• W2912931692 cites W2166116275 @default.
• W2912931692 cites W2176412452 @default.
• W2912931692 cites W2182401448 @default.
• W2912931692 cites W2187603890 @default.
• W2912931692 cites W2195693430 @default.
• W2912931692 cites W2211148590 @default.
• W2912931692 cites W2256578114 @default.
• W2912931692 cites W2320046825 @default.
• W2912931692 cites W2337472511 @default.
• W2912931692 cites W2368600038 @default.
• W2912931692 cites W2535838896 @default.
• W2912931692 cites W2541884796 @default.
• W2912931692 cites W2558299392 @default.
• W2912931692 cites W2561109897 @default.
• W2912931692 cites W2562947506 @default.
• W2912931692 cites W2576906596 @default.
• W2912931692 cites W2580175322 @default.
• W2912931692 cites W2602923095 @default.
• W2912931692 cites W2616867685 @default.
• W2912931692 cites W2618746189 @default.
• W2912931692 cites W2623357190 @default.
• W2912931692 cites W2624684564 @default.
• W2912931692 cites W2624813472 @default.
• W2912931692 cites W2734408173 @default.
• W2912931692 cites W2735793369 @default.
• W2912931692 cites W2738463471 @default.
• W2912931692 cites W2738538347 @default.
• W2912931692 cites W2751811815 @default.
• W2912931692 cites W2759213147 @default.
• W2912931692 cites W2764155154 @default.
• W2912931692 cites W2766190981 @default.

• next