Non-Terrestrial Networks (NTNs) are widely recognized as a potential solution to achieve ubiquitous connections of Narrow Bandwidth Internet of Things (NB-IoT). In order to adopt NTNs in NB-IoT, one of the main challenges is the uplink synchronization of Narrowband Physical Random Access procedure which refers to the estimation of time of arrival (ToA) and carrier frequency offset (CFO). Due to the large propagation delay and Doppler shift in NTNs, traditional estimation methods for Terrestrial Networks (TNs) can not be applied in NTNs directly. In this context, we design a two stage ToA and CFO estimation scheme including coarse estimation and fine estimation based on abrupt change point detection (CPD) of phase series with machine learning. Our method achieves high estimation accuracy of ToA and CFO under the low signal-noise ratio (SNR) and large Doppler shift conditions and extends the estimation range without enhancing Random Access preambles.

The acquisition of the channel covariance matrix is of paramount importance to many strategies in multiple-input-multiple-output (MIMO) communications, such as the minimum mean-square error (MMSE) channel estimation. Therefore, plenty of efficient channel covariance matrix estimation schemes have been proposed in the literature. However, an abrupt change in the channel covariance matrix may happen occasionally in practice due to the change in the scattering environment and the user location. Our paper aims to adopt the classic change detection theory to detect the change in the channel covariance matrix as accurately and quickly as possible such that the new covariance matrix can be re-estimated in time. Specifically, this paper first considers the technique of on-line change detection (also known as quickest/sequential change detection), where we need to detect whether a change in the channel covariance matrix occurs at each channel coherence time interval. Next, because the complexity of detecting the change in a high-dimension covariance matrix at each coherence time interval is too high, we devise a low-complexity off-line strategy in massive MIMO systems, where change detection is merely performed at the last channel coherence time interval of a given time period. Numerical results show that our proposed on-line and off-line schemes can detect the channel covariance change with a small delay and a low false alarm rate. Therefore, our paper theoretically and numerically verifies the feasibility of detecting the channel covariance change accurately and quickly in practice.

The knowledge of channel covariance matrices is of paramount importance to the estimation of instantaneous channels and the design of beamforming vectors in multi-antenna systems. In practice, an abrupt change in channel covariance matrices may occur due to the change in the environment and the user location. Although several works have proposed efficient algorithms to estimate the channel covariance matrices after any change occurs, how to detect such a change accurately and quickly is still an open problem in the literature. In this paper, we focus on channel covariance change detection between a multi-antenna base station (BS) and a single-antenna user equipment (UE). To provide theoretical performance limit, we first propose a genie-aided change detector based on the log-likelihood ratio (LLR) test assuming the channel covariance matrix after change is known, and characterize the corresponding missed detection and false alarm probabilities. Then, this paper considers the practical case where the channel covariance matrix after change is unknown. The maximum likelihood (ML) estimation technique is used to predict the covariance matrix based on the received pilot signals over a certain number of coherence blocks, building upon which the LLR-based change detector is employed. Numerical results show that our proposed scheme can detect the change with low error probability even when the number of channel samples is small such that the estimation of the covariance matrix is not that accurate. This result verifies the possibility to detect the channel covariance change both accurately and quickly in practice.

The recently proposed orthogonal time-frequency space (OTFS) modulation scheme is able to provide significant performance gain over orthogonal frequency division multiplexing (OFDM) in high Doppler spread scenarios. Qualified channel estimation in delay-Doppler domain is the prerequisite for such good performance, but it requires a large number of guard and pilot symbols, which degrades channel capacity significantly. In this paper, a prior channel statistics based scheme is proposed to maximize the system ergodic capacity by optimizing the channel estimation overhead while ensuring the high-quality performance of the OTFS over delay-Doppler channels. We first investigate the signal-to-interference-plus-noise ratio (SINR) performance of the proposed scheme and derive the closed-form ergodic capacity of the OTFS system on its basis. And then, the capacity maximization problem is studied given the root-mean-square (RMS) delay spread rather than a request for instantaneous perfect channel state information (CSI). In addition, we reveal that the additive white Gaussian noise (AWGN) power has an impact on the solution to the optimization problem, so optimization instances are proposed to treat differently according to SNR levels. Extensive simulations are carried out to evaluate the performance of the proposed overhead reduction scheme. Numerical results demonstrate the superiority of the proposed scheme, and the effect of channel characteristics and system parameters are also revealed.

In this paper, a Contour-based Cluster Identification (CCI) algorithm is proposed to identify the clusters in Concatenated Power Delay Profiles (CPDPs). To predict the characteristics of clusters, we introduce four methods to the three-fold CCI algorithm. Our new methods increase identification accuracy with greatly decreased computational complexity from 2-D to 1-D Hough-transform searching. The accuracy of prediction methods and the efficiency of the CCI algorithm are both confirmed by simulation results.

With the evolution of FeMBMS technology and the definition of new numerologies in 3GPP from Release 14 until now, physical performance of LTE-based terrestrial broadcast has been improved, such as larger geographic area with longer CP (Cyclic prefix) of 200μs or even 300μs applied for HPHT (High Power High Tower) compared with traditional unicast with CP of 16.67μs, however as for the limitation of OFDM modulation adopted in FeMBMS in high Doppler scenarios, it has to define another kind of numerology with larger sub-carrier spacing (CP of 100μs) to be published in R16 to support the mobility speed of 250km/h. It is not possible to support large area and high mobility speed in only one numerology at the current technological regime. OTFS (Orthogonal Time Frequency Space) modulation is famous for its performance in resisting high Doppler, this paper builds the bridge between OTFS and LTE-based terrestrial broadcast, and does research on OTFS modulation under the parameters of LTE-based terrestrial broadcast, including as follows. First, briefly introduces the background and development of OTFS modulation and its advantage over other traditional modulation. Basic principle and the signal processing flow of OTFS modulation is described in section II and section III. In the fourth part, this paper gives the development requirements of 5G broadcasting, furthermore, generates the simulation about OTFS system under the numerologies of LTE-based terrestrial broadcast published in R14, compared with OFDM performance individually in CAS and MBSFN mode. Simulation results and conclusions are given in section V.