The integration of Maritime Internet of Things (M-IoT) technology and unmanned aerial/surface vehicles (UAVs/USVs) has been emerging as a promising navigational information technique in intelligent ocean systems. In this article, we consider the UAV-assisted M-IoT network where USVs offload computation-intensive maritime tasks via non-orthogonal multiple access (NOMA) to the UAV equipped with the mobile-edge computing (MEC) server subject to the UAV mobility. To improve the energy efficiency of offloading transmission and workload computation, we focus on minimizing the total energy consumption by jointly optimizing the USVs’ offloaded workload, transmit power, computation resource allocation, as well as the UAV trajectory subject to the USVs’ latency requirements. Despite the nature of mixed discrete and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a two-layered algorithm for solving it efficiently. Specifically, the top-layered algorithm is proposed to solve the problem of optimizing the UAV trajectory based on the idea of deep reinforcement learning (DRL), and the underlying algorithm is proposed to optimize the underlying multidomain resource allocation problem based on the idea of the Lagrangian multiplier method. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of NOMA-enabled computation offloading in terms of overall energy consumption.

Spectrum sensing can effectively improve the spectrum utilization. In practice, it is difficult to sense whether the spectrum is occupied or not due to the low signal energy at very low signal-to-noise ratio (SNR) (e.g., -20dB). To overcome this issue, this letter considers the correlation of the time-frequency domains, and proposes a ConvLSTM based spectrum sensing method. To be specific, we first apply the ConvLSTM network to extract the temporal and spatial features of the sensed IQ signals simultaneously, and then realize the low-SNR spectrum sensing according to the extracted features. Simulation results show that our proposed method can reduce the sensing error by about 25%, in comparison with other deep learning based spectrum sensing methods when the SNR is -20dB.

Digital twin has been emerging as a promising paradigm that connects physical entities and digital space, and continuously evolves to optimize the physical systems. In this article, we focus on studying efficient communication and computation scheme when constructing the Marine Internet of Things (M-IoT)’s digital twin with secrecy provisioning. Specifically, the digital twin model is trained based on federated learning, in which all the unmanned surface vehicles deliver the trained models with nonorthogonal multiple access (NOMA) to the high-altitude platform (HAP) for global model aggregation. Considering the potential eavesdropping on the radio signals of HAP, we utilize the chaotic sequences to spread the model information before the global model broadcasting. In this framework, we aim to minimize the total energy consumption for constructing the digital twin of M-IoT by jointly optimizing the global accuracy, the local accuracy, the HAP’s transmission power and NOMA transmission duration, subject to the secrecy provisioning and latency constraint. An effective low-complexity algorithm is proposed to tackle this joint optimization problem with the use of a layered feature. Finally, numerical results are given to validate the performance gain of the proposed scheme, in comparison with the fixed accuracy scheme, the nonspread spectrum scheme and the time division multiple access transmission scheme.

We address here the issue of jointly estimating the angle parameters of a single sinusoid with Wiener carrier phase noise and observed in additive, white, Gaussian noise (AWGN). We develop the theoretical foundation for time-domain, phase-based, joint maximum likelihood (ML) estimation of the unknown carrier frequency and the initial carrier phase, with simultaneous maximum a posteriori probability (MAP) estimation of the time-varying carrier phase noise. The derivation is based on the amplitude and phase-form of the noisy received signal model together with the use of the best, linearized, additive observation phase noise model due to AWGN. Our newly derived estimators are closed-form expressions, consisting of both the phase and the magnitude of all the received signal samples. More importantly, they all have a low-complexity, sample-by-sample iterative processing structure, which can be implemented iteratively in real-time. As a basis for comparison, the Cramer-Rao lower bound (CRLB) for the ML estimators and the Bayesian CRLB (BCRLB) for the MAP estimator are derived in the presence of carrier phase noise, and the results simply depend on the signal-to-noise ratio (SNR), the observation length and the phase noise variance. It is theoretically shown that the estimates obtained are unbiased, and the mean-square error (MSE) of the estimators attain the CRLB/BCRLB at high SNR. The MSE performance as a function of the SNR, the observation length and the phase noise variance is verified using Monte Carlo simulation, which shows a remarkable improvement in estimation accuracy in large phase noise.

Due to the limited energy and computing resources of Internet of Things (IoT) devices, the collaboration of IoT devices and edge servers is considered to handle the complex deep neural network (DNN) inference tasks. However, the heterogeneity of IoT devices and the various accuracy requirements of inference tasks make it difficult to deploy all the DNN models in edge servers. Moreover, a large-scale data transmission is engaged in collaborative inference, resulting in an increased demand on spectrum resource and energy consumption. To address these issues, in this paper, we first design an accuracy-aware multi-branch DNN inference model and quantify the relationship between branch selection and inference accuracy. Then, based on the multi-branch DNN model, we aim to minimize the energy consumption of devices by jointly optimizing the selection of DNN branches and partition layers, as well as the computing and communication resources allocation. The proposed problem is a mixed-integer nonlinear programming problem. We propose a hierarchical approach to decompose the problem, and then solve it with a proportional integral derivative based searching algorithm. Experimental results demonstrate our proposed scheme has better inference performance and can reduce the total energy consumption up to 65.3%, compared to other collaboration schemes

With the rapid boost of wireless networks, it is challenging to guarantee the secure transmission and massive connectivity in the process of data collection. This letter considers an unmanned aerial vehicle (UAV)-aided non-orthogonal multiple access (NOMA) communication network, where a UAV is deployed to collect the data from sensing devices (SDs) in the NOMA manner subject to the eavesdropping attack, and a group of auxiliary devices (ADs) are deployed to provide the cooperative jamming to the eavesdropper. We aim to maximize the total secrecy throughput by jointly optimizing SDs’ transmit power and ADs’ scheduling decisions. We first formulate this problem as a mixed-integer non-convex optimization problem, which turns out to be mathematically intractable due to the non-convexity. Then, an alternative optimization algorithm is developed to find a suboptimal solution. Specifically, the successive convex approximation approach and the cross-entropy technique are used to solve the two sub-problems decomposed from the optimization problem, respectively. Numerical results demonstrate that the proposed algorithm can achieve considerable performance compared with the benchmark schemes.