Channel Estimation and Peak‐to‐Average Power Ratio Analysis of Narrowband Internet of Things Uplink Systems

Abstract

Narrowband Internet of Things (NB-IoT) is a cellular based promising low-power wide-area network (LPWN) technology standardized by the 3rd Generation Partnership Project (3GPP) in release-13 as a part of the future 5th Generation (5G) wireless communication systems. The main design target of NB-IoT was to enhance radio coverage by repeating signal over an additional period of time for the ultralow-end IoT devices that would be operated in extreme coverage environments. But the power efficiency of the low-cost NB-IoT user equipment (NB-IoT UE) in the uplink is the major concern. Coverage improvement from signal repetitions depends on the channel estimation quality at extremely bad radio conditions. The typical operating signal-to-noise ratio (SNR) for NB-IoT is expected to be much lower than the zero. In this paper, we have proposed two efficient narrowband demodulation reference signal (NDMRS)-assisted channel estimation algorithms based on the conventional least squares (LS) and minimum mean square error (MMSE) estimation methods. The theoretical analysis and the link-level performance of our proposed estimation methods are presented. Simulation results exhibit that the proposed methods provide better estimation precision compared to the traditional LS and MMSE methods at the low SNR situations. Furthermore, we have analyzed the raised-cosine (RC) and square-root-raised cosine (RRC) pulse shaping to reduce peak-to-average power ratio (PAPR) as an uplink transmit filter. The PAPR values are evaluated through extensive computer simulations for both single-tone and multi-tone transmissions. Our evaluation results vindicate that the RRC pulse shaping with lower PAPR values is feasible to design of practical NB-IoT uplink transmitter and increases power efficiency.

Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

“…However, the model is only efficient for linear data but not suitable for noisy or nonlinear data. In [99], two efficient narrowband demodulation reference signal (NDMRS)assisted channel estimation algorithms have been proposed. The NDMRS algorithms are based on the conventional least-squared (LS) [100], and minimum means square error (MMSE) [101] methods.…”

On Efficient DCT Type-I Based Low Complexity Channel Estimation for Uplink NB-IoT Systems

“…The CE methods for NB-IoT can be categorized in two ways, namely frequency-domain and transform-domain. Frequency-domain CE approaches, particularly the least squares (LS) [11], [25], maximum likelihood (ML) [26], and linear minimum mean square error (LMMSE) [5], [12] estimators, can be used. The LS is the most straightforward CE approach among them but it exhibits severe performance degradation due to irreducible Gaussain distribution noise.…”

“…In the literature, the research related to channel estimation is inadequate since the NB-IoT system is still in its early stage of development for 5G IoT framework. Authors in [5] reported significant coverage performance improvement of uplink NB-IoT system by exploiting modified LS and LMMSE algorithms with considerable computational costs. Note that the NB-IoT supports only phase-shift keying modulation schemes [24].…”

“…The traditional OFDM is considered as a baseband modulation for the NB-IoT downlink transmission. The FDMA is employed in NB-IoT uplink single-tone transmission, while the DFT-spreading OFDM systems also known as SC-FDMA is used for multi-tone operation owing to reduced peakto-average power ratio (PAPR) [5], [24]. Without loss of generality, one PRB (i.e., 12 subcarriers correspond to 180 kHz) is assumed to be a system bandwidth from LTE carrier for transmitting the NB-IoT signal.…”

“…A small chunk of 200 kHz bandwidth from the Global System for Mobile Communications (GSM) carriers will be employed to implement standalone mode of operation. The NB-IoT deployment scenarios [5] are depicted in Fig. 1.…”