this paper presents the design and performance analysis of linear and non-linear channel prediction algorithms used in 4G communication systems. The linear prediction algorithms are based in Autoregressive (AR) model and Kalman filter; the non-linear prediction algorithms are based on neural network (NN) in a time delay and recurrent (RNN) configuration. The design and validation of the algorithms were made using a MIMO-OFDM system described using SystemC. Performance metrics such as latency and Mean Square Error (MSE) are used for comparison. Results indicate that even though latency increases in the system, with both linear and non-linear prediction, non-linear algorithms show lower MSE when trained properly. Configuration parameters of the algorithms are key to find a relationship between latency and MSE.
This paper presents the design process and description at the system level of a communication system based on Multiple Input-Multiple Output and Orthogonal Frequency Division Multiplexing (MIMO-OFDM). These are used in fourth generation (4G) systems, due to the performance improvement when facing rapidly changing wireless environments. Hardware/software partitioning is taken under consideration for the design, with developed criteria for measuring system performance. The design and validation of the system is made using SystemC language. This is part of a research work, carried out in order to study and establish an appropriate methodology for Hardware/Software co-design.
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