KNN-Based ML Model for the Symbol Prediction in TCM Trellis Coded Modulation TCM Decoder

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Neeta N. Thune
Archana B. Kanwade
Mohini Sardey
Sarika A. Panwar
G.S. Gawande


Machine Learning is a booming technology today. In a machine learning set of training, data is to be provided to the model for training and that model predicts the output. Machine Learning models are trained using a computer program known as ML algorithms.The new machine learning-based Transition Metric Unit (TMU) of 4D- 8PSK Trellis coded Modulation TCM Decoder is presented in this work. The classic Viterbi decoder's branch metric unit, or TMU, takes on a complex structure. Trellis coded Modulation (TCM) is a combination of 8 PSK modulations and Error Correcting Code (ECC). TMU is one of the complex units of the TCM decoder, which is essentially a Viterbi decoder. Similar to how the first Branch metric is determined in the straightforward Viterbi decoder, the TCM decoder performs this BM computation via the TMU unit. The TMU becomes challenging and uses more dynamic power as a result of the enormous constraint length and the vast number of encoder states.In the proposed algorithm innovative KNN (K nearest neighbours) based ML model is developed. It is a supervised learning model in which input and output both are provided to the model, training data also called the labels, when a new set of data will come the model will give output based on its previous set experience and data.Here we are using this ML model for the symbol prediction at the receiver end of the TCM decoder based on the previous learning. Using the proposed innovation, the paper perceives the optimization of the TCM Decoder which will further reduce the H/W requirements and low latency which results in less power consumption.

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Thune, N. N. ., Kanwade, A. B. ., Sardey, M. ., Panwar, S. A. ., & Gawande, G. . (2023). KNN-Based ML Model for the Symbol Prediction in TCM Trellis Coded Modulation TCM Decoder. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9s), 623–627.


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