Joint Frequency Offset and Channel Estimation for NB-IoT Systems

Zhang, Pinchang; He, Jun; Dong, Jiankuo; Sha, Letian

doi:10.33969/j-nana.2022.020201

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…For example, an earlier work proposed an approximate maximum-likelihood estimator for frequency offset and channel parameters [25], [26]. A joint estimation of frequency offset and channel is proposed in [27], where the frequency offset and channel are stacked into one vector to be estimated. A KF-based algorithm is proposed to jointly estimate carrier frequency offset (CFO) and timevarying channel in a MIMO system without RIS [15].…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter Based Channel Tracking for RIS-Assisted Multi-User Networks

Yu,

Zheng,

Shojaeifard

et al. 2024

IEEE Trans. Wireless Commun.

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In this paper, we investigate channel estimation in a reconfigurable intelligent surface (RIS) assisted multi-user network while considering the mobility of users. Based on a time-varying channel model, we utilize the Kalman filter (KF) that is able to exploit temporal correlation to track cascaded channels. In order to maintain a relatively low pilot overhead, we present a multiple sub-phases based transmission protocol where the number of pilot sequences in each sub-phase is less than the number of users, i.e., pilot contamination exists. For the sake of practicality, we directly utilize the discrete Fourier transform matrix as the RIS phase shift matrix during the training process. We analyze normalized mean square error and provide some asymptotic results. A more practical scenario with hardware impairments (HWI) at the transceiver and the RIS is considered. Since HWI is also part of the measurement matrix and is unknown to the base station, we propose a joint estimation of the channel and HWI. Under this joint estimation framework, the underlying state space model becomes nonlinear. We develop an extended KF (EKF) algorithm to tackle the nonlinearity through which the model can be linearized. Numerical results show that the proposed algorithms outperform benchmarks under various scenarios.

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Section: Introductionmentioning

confidence: 99%

Kalman Filter Based Channel Tracking for RIS-Assisted Multi-User Networks

Yu,

Zheng,

Shojaeifard

et al. 2024

IEEE Trans. Wireless Commun.

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“…One of the fundamental procedures of downlink signal processing is channel estimation, which provides information about the distortion of the received signal, caused mainly by multipath fading, carrier frequency offset and phase noise [4][5][6][7]. This information is further utilized in the channel equalization step, which compensates for channel impairments to provide phase-amplitude alignment of a complex constellation for further demodulation and decoding.…”

Section: Introductionmentioning

confidence: 99%

A Simplistic Downlink Channel Estimation Method for NB-IoT

Magiera

2023

Applied Sciences

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This paper presents a downlink channel estimation method intended for a Narrowband Internet of Things (NB-IoT) access link. Due to its low computational complexity, this method is well suited for energy-efficient IoT devices, still providing acceptable reception quality in terms of signal-to-noise (SNR) performance. This paper describes the physical layer of NB-IoT within the scope of channel estimation, and also reviews existing channel estimation methods for OFDM signals. The proposed method, based on linear interpolation of channel coefficients, is described as a three-step procedure. Next, indicators of channel quality assessment, which may be determined without prior knowledge about the transmitted signal, are defined. Two variants of channel estimation, differing in the frequency domain processing, are evaluated to assess the significance of frequency selectivity in an NB-IoT downlink. The chosen method is compared with another method implemented in MATLAB LTE ToolboxTM. An analysis of the computation time is conducted, subsequently demonstrating the definite advantage of the proposed method.

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Joint Frequency Offset and Channel Estimation for NB-IoT Systems

Cited by 2 publications

References 9 publications

Kalman Filter Based Channel Tracking for RIS-Assisted Multi-User Networks

Kalman Filter Based Channel Tracking for RIS-Assisted Multi-User Networks

A Simplistic Downlink Channel Estimation Method for NB-IoT

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