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.Item A robust transmission with enhancement of 5G PHY using FBMC and AMC for machine-to-machine communication node(KeAi Communications Co., 2023) Sharma, V.; Arya, R.K.; Kumar, S.Advancement of 5G new radios has enabled more robust communication for the Machine-to-Machine (M2M) communication node, using filter bank multicarrier (FBMC). This paper focuses on robust transmission over random fluctuations of the channel and also enhances the battery life for the massive machine type communication (mMTC) node. Filter bank multicarrier and Adaptive Modulation Coding (AMC) have been utilized together to enhance the performance of the 5G (NR) PHY layer. A frame-to-frame implementation is used to diminish the impact of fading using AMC, while efficient utilization of spectrum is achieved using FBMC. The selection of the AMC profile is obtained through the analysis of uplink packets using the Distance Statistics (DS). The FBMC is incorporated with 5G PHY in place of OFDM to achieve the optimum utilization of spectrum and also obtain a significant reduction in peak to average power ratio (PAPR) for robust transmission, which saves 10% of the battery requirement. On the basis of channel state information, distance statistics were employed to optimize the AMC. The optimum selection of AMC with FBMC will reduce the bit error rate (BER) against multipath fading and ensure the better utilization of available spectrum to attain the optimum utilization of the power amplifier. © 2023 The Authors