In this paper, we propose a novel scheme termed layered orthogonal frequency division multiplexing with index modulation (L-OFDM-IM) to increase the spectral efficiency (SE) of OFDM-IM systems. In L-OFDM-IM, all subcarriers are first divided into multiple layers, each determining the active subcarriers and their modulated symbols. The index modulation (IM) bits are carried on the indices of the active subcarriers of all layers, which are overlapped and distinguishable with different signal constellations so that the number of the IM bits is larger than that in traditional OFDM-IM. A low-complexity detection is proposed to alleviate the high burden of the optimal maximum-likelihood detection at the receiver side. A closed-form upper bound on the bit error rate, the achievable rate, and diversity order are derived to characterize the performance of L-OFDM-IM. To enhance the diversity performance of L-OFDM-IM, we further propose coordinate interleaving L-OFDM-IM (CI-L-OFDM-IM), which interleaves the real and imaginary parts of the modulated symbols over two different subchannels. Computer simulations verify the theoretical analysis, and results show that L-OFDM-IM outperforms the conventional OFDM-IM scheme. Moreover, it is also confirmed that CI-L-OFDM-IM obtains an additional diversity order in comparison with L-OFDM-IM.

The development of Internet of Vehicles (IoVs) introduces more intrusions to the vulnerabilities of in-vehicle network. It is important to detect in-vehicle network anomaly for the purpose of driving safety. The previous studies either focus on one-dimension anomaly behavior of in-vehicle network or need to look into the semantics of in-vehicle network, which is neither effective nor efficient for vehicle pilot. In this paper, we propose a literal multi-dimensional anomaly detection approach using the distributed Long-Short-Term-Memory (LSTM) framework for in-vehicle network, especially the Control Area Network (CAN). The proposed approach only needs the literal binary CAN message instead of revealing the semantics of CAN message. To enhance the accuracy and efficiency of detection, it detects anomaly from both time and data dimension simultaneously by exploiting multi-task LSTM neural network on mobile edge. The extensive evaluation results show that the proposed anomaly detection achieves a satisfying accuracy of 90%. The detection speed is as fast as 0.61 milliseconds on mobile edge.

Quadrature spatial modulation (QSM) is recently proposed to increase the spectral efficiency (SE) of SM, which extends the transmitted symbols into in-phase and quadrature domains. In this paper, we propose a generalized QSM (GQSM) scheme to further increase the SE of QSM by activating more than one transmit antenna in in-phase or quadrature domain. A low-complexity detection scheme for GQSM is provided to mitigate the detection burden of the optimal maximum-likelihood (ML) detection method. An upper bounded bit error rate is analyzed to discover the system performance of GQSM. Moreover, by collaborating with the non-orthogonal multiple access (NOMA) technique, we investigate the practical application of GQSM to cooperative vehicular networks and propose the cooperative GQSM with OMA (C-OMA-GQSM) and cooperative GQSM with NOMA (C-NOMA-GQSM) schemes. Computer simulation results verify the reliability of the proposed low-complexity detection as well as the theoretical analysis, and show that GQSM outperforms QSM in the entire SNR region. The superior BER performance of the proposed C-NOMA-GQSM scheme make it a promising modulation candidate for next generation vehicular networks.

In this paper, we propose a robust secure communication scheme for a two-Unmanned Aerial Vehicle (UAV) scenario with the assistance of Intelligent Reflecting Surface (IRS), in which one legitimate user and one eavesdropper are considered. In the proposed scheme, the information is transmitted from one UAV named information UAV to the user via the assistance of the IRS, while the jamming information is transmitted by the other UAV named jamming UAV equipped with multi-antenna to interfere with the eavesdropper and safeguard the communication between information UAV and legitimate receiver. For the considered communication system, we assume that the Channel State Information (CSI) of the eavesdropping channels are imperfect. In this imperfect CSI case, we formulate a robust non-convex optimization problem. By jointly optimizing UAV transmit power, trajectory, and IRS phase shifters, the suboptimal average secrecy rate of the proposed scheme can be obtained. Specifically, Lagrangian dual transform and quaduratic transform are introduced to convert the objective function into more tractable form; S-Procedure is used for handling CSI uncertainty; and a two-stage penalty function algorithm is applied to obtain rank-one phase shifters solution. Numerical simulation results validate some interesting insights: (1) The IRS can bring strong robustness to the two-UAV security communication system; (2) Deploying jamming UAV in the IRS-UAV security communication system can greatly improve security performance, especially employing more antennas at the jamming UAV.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) transmits additional information bits through the indices of active subcarriers. In this paper, we explore the potential of an improvement of OFDM-IM by reusing inactive subcarriers to transmit signals for a secondary system. We consider a cognitive radio network assisted by OFDM-IM, where the primary user (PU) transmits its signal to the primary receiver, and the secondary user (SU) senses the idle spectrum and transmits its own signal to the secondary receiver via those idle subcarriers. By either combining the signal transmitted from SU or not, we propose two different detection strategies with different trade-offs between the computational complexity and system performance. An asymptotically tight upper bound on the bit error rate is derived to evaluate the error performance for PU and SU.