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

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

Hybrid Optimized LMMSE based Channel Estimation with Low Power Trellis Coded Modulation

In the wireless channel environment, the data transmission faces many safety problems and power transmission loss. This results with the negative impacts while precise channel estimation. Furthermore, the existing channel estimation (CE) model directly estimates the channel matrix; still, the accuracy and the high complexity may cause path loss. Alternate to the direct estimation in the channel matrix, the parameter estimation model is used to solve the above issues. Moreover, the training based CE including Linear Minimum Mean Square Error (LMMSE) and Least Square Error (LSE) are ubiquitous in certain wireless standards to reduce the Mean Square Error (MSE) among the estimated and original channel. Certain intelligent optimized techniques are introduced to optimize the channel. This paper intends to introduce a Trellis Coded Modulation (TCM) with hybrid optimization in LMMSE for the Optimal CE. Moth amalgamated Elephant Herding Optimization (MAEHO) algorithm is the proposed hybrid optimization. At last, the performance of the adopted model is computed over other existing schemes in terms of various measures.