With the maneuverability and mobility control of unmanned aerial vehicle (UAV), carrying mobile edge computing (MEC) servers on UAVs is able to effectively alleviate the explosive growth of data traffic pressure. However, UAV adopts line-of-sight transmission which has broadcasting characteristics. Malicious eavesdroppers can easily take advantage of the characteristics to eavesdrop information during the UAV edge computing. Therefore, the security of the UAV-MEC systems is a challenging problem. This article proposes a secure communication scheme for the dual-UAV-MEC system. In the proposed scheme, UAV server assists ground users in calculating the offloading tasks. In order to reduce the eavesdropping of offloading information by UAV eavesdropper, jammer sends interference signals on the ground. We aim to maximize the user's minimum secure calculation capacity by optimizing resources and trajectory of the UAV server. We first transform the optimization problem into a tractable form through mathematical methods and use successive convex approximation and block coordinate descent algorithms to solve it in an iterative manner. The final numerical results show that, compared with the benchmark schemes, the method proposed in this article effectively increases the secure calculation capacity of the system.

This paper is focused on the fusion of radar and wireless communication, called RadCom, which has been extensively studied in recent years for future intelligent transportation systems. We propose a new waveform design algorithm for reducing peak-to-average power ratio (PAPR) in OFDM-based RadCom systems. We consider a flexible and generic RadCom structure in which a number of non-contiguous sub-bands for data transmission are located within a large contiguous spectrum band for radar detection/sensing. New RadCom waveforms with low PAPR are obtained by carrying out optimization over those subcarriers which are complementary to the communication bands. As an application of the majorization-minimization (MM) optimization method, our major contribution is an $l$ -norm cyclic algorithm which is capable of efficiently reducing the maximum PAPR of RadCom waveforms. We show by numerical simulation results that significant performance enhancements can be achieved compared to OFDM RadCom waveforms from legacy approaches.

Sixth generation (6G)-enabled Internet of Things (IoT) needs sufficient spectrum resources to provide spectrum access for massive IoT’s terminals. However, traditional orthogonal multiple access restricts the full use of limited spectrum resources. In this article, a nonorthogonal multiple access (NOMA)-based hybrid spectrum access scheme is proposed for 6G-enabled cognitive IoT (CIoT), where the CIoT may access both the idle and busy spectrum via NOMA regardless of the primary user’s (PU) state. The uplink resource allocations for the CIoT are optimized in the decoding-PU-last and decoding-PU-first schemes, respectively, which seek to maximize the average total transmission rate of CIoT while ensuring the minimum transmission rates for PU and each CIoT node. Then, a clustering NOMA-based CIoT is presented to decrease the interuser interference, where the nodes in each cluster use NOMA to transmit in the allocated subchannel. The simulation results have shown the performance advantages for the NOMA-based hybrid spectrum access and better rate guarantee for the clustering NOMA-based CIoT.

Internet of Things (IoT) is considered as the future network to support machine-to-machine (M2M) communications. However, IoT network face a problem of allocating sufficient spectrum for the rapidly increasing devices. Through cooperative spectrum sharing, the unlicensed IoT devises (UIDs) help forward the signal of primary users (PUs) to exchange for dedicated spectrum to transmit their signals, which can effectively improve the spectrum utilization for the IoT network. However, UIDs are selfish and rational. They may not be willing to take participate in the cooperative spectrum sharing after considering the potential costs. It is necessary to provide effective incentives to encourage them to take part in the cooperative spectrum sharing. In this paper, we use contract theory to model the incentive mechanism in OFDM-based cognitive IoT network under a practical scenario with incomplete information, where negotiations between PUs and UIDs are modeled as a labor market. We first study the optimal contract design to maximize PUs' utility, in which the contract consists of a menu of expected signal-to-ratios (SNRs) and payments over each subcarrier. Then we propose heuristic UIDs' selection method with a finite PUs' budget. Finally, simulation results demonstrate the efficiency of our proposed contract design and UIDs selection method.

To supporting the systematic requirements of higher spectrum efficiency and user deployment density in future industrial internet-of-things (IIoT), traditional orthogonal multiple access schemes, such as orthogonal frequency division multiplex, are quite limiting. Non-orthogonal multiple access has been recognized as one of enabling technologies for IIoT in 5G network. In this paper, we have proposed an alternative non-orthogonal multiple access scheme called interleave-grid multiple access (IGMA). Depending on interleaving and grid-mapping process, IGMA is capable to provide reliable block error rate performance, marvelous user multiplexing capability, as well as robustness against inter-cell interference. Interleave-grid multiple access can apply various detection and decoding techniques at receiver sides to improve the detection performance with acceptable complexity. Both of link-level and system level results show the promising benefits of interleave-grid multiple access, in particular that nearly seven times user multiplexing gain compared with orthogonal frequency division multiple access has been observed in the system-level simulation. In addition, a hardware test-bed has been implemented to verify the interleave-grid multiple access performance and the testing results proved the strong competitiveness of interleave-grid multiple access over orthogonal frequency division multiple access in terms of block error rate performance in user overloading scenarios.

The explosive growth of Internet of Things (IoT) has mandated the security of data access. Although authentication methods can enhance network security, their vulnerability to malicious attacks may be a barrier for the wide deployments in IoT scenarios. To address the security issue, we advocate the use of physical-layer security through radio-frequency (RF) fingerprint recognition. Observing that most RF fingerprint recognition methods show a degradation of performance under low signal-to-noise ratio (SNR) environments, we present a dynamic shrinkage learning network (DSLN) to enhance security for IoT applications, particularly in the setting of low SNR. We design a novel dynamic shrinkage threshold for improving the accuracy of recognition under low-SNR environments. Additionally, we design an identity shortcut for reducing the running time of RF fingerprint recognition. In comparison with convolutional neural network (CNN), recurrent neural network (RNN), and a hybrid CNN+RNN network (CRNN), our proposed DSLN yields accuracy improvements of up to 20%. Moreover, DSLN can reduce the running time by up to 60%, indicating its great potential to a real-time IoT system, e.g., an intelligent automotive system.