Deep Learning and Optimization in Massive MIMO explores intelligent channel estimation for millimeter wave based next-generation wireless systems. It integrates deep learning models with hybrid optimization algorithms to enhance estimation accuracy and efficiency. The book highlights advanced architectures such as DNNs, LSTMs, and Meta-heuristic–aided methods. Case studies with datasets like DeepMIMO illustrate practical implementations and performance gains. Written for researchers, engineers, and graduate students, it blends theory with application. A valuable resource for advancing 5G, 6G, and beyond in wireless communications. “Bridging deep learning intelligence with optimization to redefine channel estimation in massive MIMO for the 5G and 6G era”.
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Deep Learning and Optimization in Massive MIMO explores intelligent channel estimation for millimeter wave based next-generation wireless systems. It integrates deep learning models with hybrid optimization algorithms to enhance estimation accuracy and efficiency. The book highlights advanced architectures such as DNNs, LSTMs, and Meta-heuristic-aided methods. Case studies with datasets like DeepMIMO illustrate practical implementations and performance gains. Written for researchers, engineers, and graduate students, it blends theory with application. A valuable resource for advancing 5G, 6G, and beyond in wireless communications.'Bridging deep learning intelligence with optimization to redefine channel estimation in massive MIMO for the 5G and 6G era'. Nº de ref. del artículo: 9789999330527
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Taschenbuch. Condición: Neu. Deep Learning and Optimization in Massive MIMO | Hybrid Approaches for Advanced Channel Estimation | Nallamothu Suneetha | Taschenbuch | Englisch | 2025 | Eliva Press | EAN 9789999330527 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 134572293
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