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• W3025692718 abstract "An IoT (Internet of things) system supports a massive number of IoT devices wirelessly. We show how to use Cell-Free Massive MIMO (multiple-input and multiple-output) to provide a scalable and energy efficient IoT system. We employ optimal linear estimation with random pilots to acquire CSI (channel state information) for MIMO precoding and decoding. In the uplink, we employ optimal linear decoder and utilize RM (random matrix) theory to obtain two accurate SINR (signal-to-interference plus noise ratio) approximations involving only large-scale fading coefficients. We derive several max-min type power control algorithms based on both exact SINR expression and RM approximations. These algorithms incorporate power and rate weighting functions and can achieve an assigned target rate with high energy efficiency. For the downlink, we employ maximum ratio precoding. To avoid solving a time-consuming quasi-concave problem that requires repeat tests for feasibility of a SOCP (second-order cone programming) problem, we develop a neural network (NN) aided power control algorithm that results in 30 times reduction in computation time. A scalable sub-optimal power control algorithm incorporating NN is also obtained for large systems." @default.
• W3025692718 created "2020-05-21" @default.
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• W3025692718 date "2020-05-14" @default.
• W3025692718 modified "2023-09-25" @default.
• W3025692718 title "A Scalable and Energy Efficient IoT System." @default.
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