In the low earth orbit (LEO) satellite Internet of Things (IoT), the IoT devices can be distributed in remote areas (e.g., desert, ocean, and forest) for some special applications, where they are unable to have direct terrestrial network accesses and can only be covered by satellite. But in the LEO satellite scenario, it has to face the effects of high doppler and fast doppler rate, due to satellite movement at high speed and utilization of higher frequency bands. So fine frequency synchronization and frequency track are necessary to be done, even for LORA. In this paper, we propose a folded chirp-rate shift keying (FCrSK) modulation with strong immunity to doppler effect. Its data information is mapped to different folded chirp-rate waveforms, which have an advantage of the consistency of bandwidth and symbol length among chirp-rates compared to traditional chirp-rate shift keying system. Moreover, we analyze the optimal non-coherent receiver in case of the doppler channel, whose optimal decision depends on signal energy focusability in frequency spectrum after dechirp, so frequency peak average rate adopts as a decision criterion. Furthermore, system parameters' setting is discussed so as to maximize system modulation efficiency with the minimum interference among chirp-rate. Finally, some simulations are provided to analyze robustness to doppler shift and doppler-rate in the system. And based on universal software radio platform (USRP), some experimental measurements are made to validate its feasibility under the realistic wireless environment and doppler environment.

Compared with terrestrial network, the air-ground integrated network consisting of unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) offers the advantages of large coverage, high capacity, and seamless connection. Therefore, the air-ground integrated network can provide effective communication services for the Internet of remote things (IoRT). In order to reduce the end-to-end (e2e) packet delay and avoid network congestion of the two-hop network, we investigate a joint packet scheduling, resource allocation, and UAV trajectory design problem, with the objective of minimizing the average packet queue delay from HAP to IoRT devices in the air-ground integrated network. This problem is non-convex and difficult to solve by the traditional methods. In order to solve this problem, we reformulate it into a Markov decision process (MDP) firstly. And then, considering there are continuous and discrete hybrid action spaces in the MDP, we separate the primal action spaces into two sub-action spaces, and utilize the basic idea of multi-agent deep deterministic policy gradient (MADDPG) and multi-agent double deep Q network (MADDQN) methods to solve them, respectively. After that, in order to improve the stability, convergence rate and learning efficiency, we introduce the basic idea of adaptive prioritized experience replay, and propose a hybrid MADDPG-adaptive prioritized experience replay (MADDPG-APER) algorithm. Simulation results show that the proposed algorithm can reduce the average packet queue delay compared with other benchmark algorithms.

Although automotive radar system has tended to be practical at present, it still need setup an extra device to achieve communication for vehicle. Considering the system complexity and equipment costs, the research on automotive radar and communication integration has a very good application prospect. Nowadays, there have been some research work of radar and communication integration based on the traditional LFMCW, but not involving triangle LFMCW, FSK and MFSK waveform used typically for automotive radar, and its realization of dual function mainly depends on frequency diversity, meaning that system need to occupy more spectrum resource. In this paper, a method on radar and communication integration with shared spectrum is proposed by using FMCW, which combines between Chirp-BOK modulation and FSK radar. In the system, communication information can be demodulated by decision on chirp polar and target information can be extracted from phases of chirp. Moreover, to reduce the complexity of the receiver, matching filter method is used commonly to complete implementation of dual function in the system. However, it’s necessary to note that doppler effect make phases of matching output change, which has an impact on target detection based on FSK. Therefore, the relationship of phases between doppler and matching output will be analysed firstly. Then the system’s operating principle, derivation of algorithm, the limitation of system parameters selection and simulation results are described in this work.

The noncoherent single-input-multiple-output (SIMO) system experiences severe cross-product mutual interference with a limited number of receive antennas, severely limiting the number of simultaneous users that can be served in the uplink. In Rician channel, a multiuser energy difference detection (EDD) scheme is introduced for dual-frequency complementary amplitude signals. It eliminates cross-product interference, significantly reducing the number of antennas required by the base station (BS), while extending the constellation space for energy detection (ED). Based on this extended constellation space and channel gain information, two optimized multiuser constellations are obtained by maximizing the minimum joint constellation distance and directly equalizing the receive constellation space. Additionally, the impacts of user mobility are analyzed, and a simple strategy is designed to maintain performance by updating the moving user’s path gain. Simulation results show that the proposed scheme can handle more simultaneous users with the same scale antennas, and it also exhibits strong robustness to user mobility at the same time.

The noncoherent multiple-input multiple-output (MIMO) system is inevitably subject to cross-product interference among different spatial channels, arising the problem on solution of nonlinear model. In this letter, we propose a common difference processing framework based on dual-frequency complementary (DFC) waveform, aiming to generate an opposite cross-product term to eliminate interference. Thus, it can turn the nonlinear MIMO model into a linear one to facilitate the use of linear detectors. In our framework, two linear models are derived for MIMO with energy detection (ED) and differential detection (DD). Finally, through the complexity analysis of detectors and performance simulation results, the proposed framework is demonstrated to be a proper candidate for low-complexity implementation of noncoherent spatial multiplexing.

For user terminals, energy-detection based multiple-input-multiple-output (MIMO) has the advantages of low complexity and anti-Doppler capability. However, the energy detector makes it difficult to linearly separate the signals in spatial multiplexing. In this letter, the linearized model is introduced in $2\times 2$ M-ary frequency-shifted keying (MFSK) V-BLAST system with energy detection. To avoid rank deficiency of the model, we propose nonlinear virtual antennas (NVA) to increases both the dimension and receive diversity gain of MIMO system. Then the full-rank condition is discussed. In multiple-antenna systems, NVA is further generalized by spatial index modulation with dual antennas selection. By simulations, linear detectors are verified to be feasible for non-coherent spatial-multiplexing MIMO, and its diversity gain is obviously increased by the proposed NVA.