In this paper, we proposed a deep neural network (DNN) based fractional Doppler channel estimation scheme for orthogonal time frequency space (OTFS) modulation in the air-to-ground communication scenario with high-dynamic Doppler. Based on the zero-padded OTFS structure, the traditional pilot pattern with guard symbols is adopted. The received pilots in the OTFS domain are used as the inputs of the network to estimate the channel parameters which are used in the MRC algorithm to demodulate the signal. In our proposed method, it can achieve the similar performance with 14 dB boost of pilot energy comparing with the ideal channel estimation case, while the conventional method requires 30 dB higher. Both the accuracy and generalization ability of the DNN network are validated.

Machine-type communication (MTC) is the key technology to support data transfer among devices (sensors and actuators) in Internet of Things (IoT). Although cellular communication technologies are developed mainly for “human-type”communications, enabling MTC with cellular networks not only improves the connectivity, accessibility, and availability of an MTC network but also has the potential to further drive down the operation cost. However, cellular MTC, especially when applied to low-power massive IoT (mIoT), poses some unique challenges due to the low-cost and low-power nature of an mIoT device. One of the most challenging issues is providing a robust way for an mIoT device to acquire the network under a large frequency offset due to the use of low-cost crystal oscillators and under extended coverage. Although differentiation is a well-known technique for removing impairments caused by frequency offset, its “noise amplification”effect limits its applications in cellular communications due to the fact that cellular communication is typically interference limited. Matched-filter-based detection is, therefore, almost unexceptionally used. We show that the differential technique can actually benefit system acquisition in mIoT, where the use of low-cost crystals is a default. Although the existing system acquisition design in a cellular mIoT system, i.e., NB-IoT, facilitates both techniques, there still remain issues that need to be solved in order to take full advantage of the design. We provide a comprehensive analysis on the performance of two most common techniques when applied in a typical NB-IoT environment based on two factors, the geometry factor and the frequency offset factor. Finally, we derive the operating regions for matched-filter-based detection and differentiation using these two factors, in which the system acquisition performance of the two types of techniques is maximized for NB-IoT.

The high-dynamic Doppler frequency offset presents great challenges to communications in the low-Earth orbit (LEO) satellite Internet of Things (IoT) scenarios, including the high-complexity receiver synchronization algorithm, frequency tracking capability, and high-receiver sensitivity. In this article, we propose an effective communication system architecture based on chirp modulation for LEO satellite IoT with reference to LoRa system. Our approach introduces an improved chirp modulation based on low-rate optimization (LRO) with specified frequency interval and a CRC-assisted decoding scheme. Simulation results show that the proposed optimal LRO factor can improve the spectral efficiency. Furthermore, we propose a low-complexity synchronization algorithm and LRO-based frequency tracking strategy. The simulation results confirm the feasibility of the proposed communication system for LEO satellite IoT, which demonstrates robust performance under high-dynamic Doppler frequency offsets.

The Internet of Things (IoT), which is a network that enables devices in the network to exchange information, has rapidly developed in recent years. Wireless communication technologies on the IoT are the best way to solve the “last mile”problem and gain substantial attention. The low power consumption and low cost are notably important for the IoT devices that are called massive IoT (mIoT). Narrow-band communication is one of the solutions to reduce the cost and power consumption and is considered the solution to large coverage without an available spectrum. Large coverage also implies a long transmission time interval, which makes the random access process in the current LTE system unsuitable for mIoT applications. The random access process in the current LTE system is notably complex with some handshakes to exchange information between the base station and the device. We address the issue in this paper and propose a packet-based preamble to simplify the random access process to decrease battery consumption.

Hybrid analog and digital (HAD) beamforming has been recently receiving considerable deserved attention for practical implementation on the large-scale antenna systems. Compared to fully-digital beamforming, partially-connected HAD beamforming can significantly reduce the hardware cost, complexity, and power consumption. This paper aims to mitigate interference and increase robustness against direction-of-arrival (DOA) mismatch along with lowering hardware complexity, cost, and power dissipation. To achieve these goals, a novel robust HAD beamforming receiver with partially-connected structure is proposed. It is based on methods of an improved bat algorithm (I-BA) and robust adaptive beamformers (RABs) in the digital domain. Since most of the RAB methods are sensitive to the DOA mismatch and depending on the complex weights which lead to an expensive receiver, the I-BA is proposed. In the analog part, analog phase alignment by linear searching (APALS) with sufficiently fine grid points is employed to optimize the analog beamforming matrix. The performance of the proposed I-BA is assessed using MATLAB simulation and compared with BA, and particle swarm optimization (PSO) algorithms. It shows better performance in terms of convergence speed, stability, and convergence rate. Besides, the proposed hybrid I-BA-APALS approach showed better performance compared to other proposed robust hybrid techniques, i.e., diagonal loading (DL) APALS (DL-APALS) and DL spatial matched filter (SMF) APALS (DL-SMF-APALS).

The massive machine-type communications (mMTC) is one of the three generic services for 5G. With the connection-based random access (CBRA) scheme, each machine-type device (MTD) establishes a connection with the base station (BS) prior to its data transmission. Due to the explosive growth of the number of MTDs, many MTDs would establish connections with the BS, which necessitates the study on how to efficiently manage the massive connections with MTDs. To address this issue, in this article, we propose a unified utility-based analytical framework for the optimal connection management of mMTC in 5G networks, where the signaling overhead for connection establishment, access delay, and the connection resource utilization ratio is included. Specifically, we first derive key performance metrics, i.e., the mean time length of each connection and resource utilization ratio, as functions of traffic input rate and inactivity timer. By further considering the signaling overheads and access delay of each MTD, the network utility is formulated and maximized by optimally choosing the inactivity timer. We then present a detailed discussion on the effect of system parameters on the optimal inactivity timer and the corresponding maximum network utility. Finally, we extend the analytical framework to the scenario in which the CBRA scheme coexists with the packet-based random access scheme, i.e., transmitting packets in the random access channel without connection establishment. The critical threshold in terms of the traffic input rate is characterized, which sheds important light on the access scheme selection issue.