Faculty Publications
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Publications by NITK Faculty
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Item Robust transmission using channel encoding towards 5G New Radio: A telemetry approach(Elsevier Ltd, 2021) Sharma, V.; Arya, R.K.; Kumar, S.This paper presents a robust channel encoding scheme under adaptive modulation and coding for a massive machine type communication device in 5G new radio. For the very first time, mode-selection and distance statistics algorithms have been simultaneously evaluated, in which together it provides the closest approximation of efficient adaptive modulation and coding with robust transmission. The prediction of optimum adaptive modulation and coding is based on the analysis of uplink packet using distance statistics, and downlink packet using mode-selection mechanism. The performance of 5G new radio by incorporating OFDM subcarrier has been evaluated using analytical as well as simulation approach. Mode-selection algorithm has been considered to predict the environmental condition under a fading channel while the distance statistics provide feedback of the previously transmitted channel condition. The result of both the approaches provide a better bit error rate for adaptive modulation & coding profile under 1/4, 1/18, 1/16 and 1/32 cyclic prefix. © 2021Item Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices(Institute of Electrical and Electronics Engineers Inc., 2022) Sharma, V.; Arya, R.K.; Kumar, S.Efficient utilisation of adaptive modulation and coding ensures the quality transmission of information bits through the significant reduction in bit error rate (BER). Channel prediction using parametric estimation is not efficient for massive machine-type communication (mMTC) devices under the 5G New Radio (NR). In this paper, we have proposed a channel prediction scheme based on a deep learning (DL) algorithm possessed by parametric analysis. In deep learning, the pipeline methodology is used along with the image processing technique to predict the channel condition for optimal selection of the adaptive modulation and coding (AMC) profile. The deep learning-based pipelining approach utilises image restoration (IR) and image super-resolution (SR). The super-resolution method is used to de-noise the low-pixel 2-D image that is obtained from the parametric value of the beacon to predict the channel condition. The estimation results are compared with the conventional minimum mean square error (MMSE) and an approximation to the linear MMSE (ALMMSE) method, which is obtained through channel state information (CSI). The comparison results show that the parametric-enabled deep learning approach is superior, especially in poorer channel conditions. The performance of BER through parametric estimation along with the DL approach is 66% more efficient as compared to the conventional MMSE method for BPSK mapping. © 2013 IEEE.