{"id":316,"date":"2020-05-13T12:54:20","date_gmt":"2020-05-13T12:54:20","guid":{"rendered":"https:\/\/researcherstore.com\/product\/massive-mimo-channel-prediction-using-neural-networks-codes\/"},"modified":"2022-01-12T04:01:24","modified_gmt":"2022-01-12T01:01:24","slug":"massive-mimo-channel-prediction-using-neural-networks-codes","status":"publish","type":"product","link":"https:\/\/researcherstore.com\/product\/massive-mimo-channel-prediction-using-neural-networks-codes\/","title":{"rendered":"Python codes for an LSTM neural network system used to predict channel state information."},"content":{"rendered":"

Python codes for all the figures in the manuscript titled “Massive MIMO Channel Prediction Using Neural Networks” Massive MIMO Channel Prediction Using Recurrent Neural Networks \u00b7 Issue 1 (pubpub.org)<\/a><\/strong><\/p>\n

========= WORK SUMMARY ========<\/strong><\/p>\n

Massive MIMO has been classified as one of the high potential wireless communication<\/strong>
\ntechnologies due to its unique abilities such as high user capacity, increased spectral density, and <\/strong>diversity among others. These properties are of great importance for the current 5G-IoT era and future <\/strong>telecommunication networks. Outdated channel state information (CSI) caused by multipath fading is <\/strong>a major problem facing massive MIMO systems. Outdated CSI occurs when the information obtained <\/strong>about the channel, i.e. the constellation size, coding rate, transmit power, precoding codeword, time<\/strong>
\nand frequency resource block, transmit antennas, and relays changes before it can be used. In this <\/strong>work, we employ neural network models to predict instantaneous CSI. We start by defining prediction <\/strong>parameters from signal transmission equations, then we design a network model for prediction, and <\/strong>finally, we compare the performance results.<\/strong><\/p>\n