Mobile Edge Computing (MEC) is a promising technology in the next generation network, which provides computing services for user equipments (UEs) to reduce the task delay and prolong the usage time of UEs. To address the deficiency of poor channel quality caused by multipath and blockages in traditional MEC networks, a multiple input single output (MISO) UAV-assisted MEC network is studied. A system energy consumption minimization problem is formulated by jointly optimizing the the UAV’s beamforming vectors, the UAV’s central processing unit (CPU) frequency, the UAV’s trajectory, the UEs’ transmission power and the UEs’ CPU frequency subject to the constraints on the task, the UAV’s trajectory, and the UEs’ computation tasks. A three-stage iterative algorithm is proposed to solve the challenging non-convex problem. The closed-form expressions for the optimal UAV CPU frequency and the transmission power of UEs are derived. Simulation results show that the proposed algorithm is superior to the benchmark schemes in terms of energy consumption, and the convergence performance is guaranteed.

Deep learning (DL)-based radio frequency fingerprint identification (RFFI), despite its state-of-the-art capability in improving the security performance of communication networks, is still vulnerable to carefully crafted and imperceptible adversarial attack. However, conventional radio frequency (RF) adversarial attacks ignore the impact of the practical fading channels between the attacker and the sensor. In this letter, a generation adversarial perturbations (GAP) problem is formulated in a DL-based RFFI network. Both the case of ideally perfect channel state information (CSI) and the practical imperfect CSI are considered. The closed-form expression of the adversarial example based on the perfect CSI is provided. In order to address the intractable non-convex robust optimization problem under the imperfect CSI, a spoofing attack algorithm is proposed by exploiting the S-procedure. Simulation results demonstrate that our proposed scheme achieves a superior spoofing ratio compared with other benchmark schemes.

This paper studies the coordinated direct and relay transmission (CDRT) in the non-orthogonal multiple access network, which consists of a base station (BS), a cell-center user (CCU), a cell edge user (CEU), and a group of half-duplex relays. During each transmission, the BS sends information to the CCU directly and to the CEU with the help from one of relays. To improve the transmission reliability, a novel dynamic detection CDRT (DD-CDRT) strategy is designed, which fully exploits the available side information to eliminate the inter-user interference. Based on the DD-CDRT strategy, we further propose a relay selection scheme which maximizes the CCU’s successful decoding probability with guaranteeing the reliable reception of the CEU. To evaluate our designs, exact and asymptotic outage probabilities are derived for each user. Results show that the achievable diversity order for the CEU is equal to the number of relays. Meanwhile, the CCU achieves a diversity order as one. Specifically, when the system sum rate is relatively small or the relay number is large enough, the outage performance of the CCU can approach the performance of the interference-free situation. Finally, numerical results are presented to verify the theoretical analysis, and to demonstrate the advantages of the DD-CDRT strategy using the proposed relay selection scheme.

Spectrum efficiency (SE) and energy efficiency (EE) are two key design objectives in future wireless communication networks. In traditional cognitive radio (CR) network, a secondary user (SU) performs spectrum sensing at the start of each time slot, which may waste energy and the transmission opportunities for the SU. In order to improve the SE and EE, a CR network using energy harvesting techniques is considered. A partially observable Markov decision process (MDP) framework and an MDP framework are used to determine the optimal spectrum sensing energy, the transmit energy and spectrum sensing interval to maximize the long-term average weighed sum of the throughput of the SU and the interference caused to the PU. Simulation results show that the proposed algorithm can significantly increase the average throughput of the SU and EE compared with the traditional spectrum scheme, at the cost of the small but tolerable interference caused to the PU.

Spectrum sensing is performed at the beginning of each time slot in traditional cognitive radio networks, which is unreasonable and needless since the presence or the absence of a primary user (PU) always lasts several time slots. A hidden Markov model is used to describe the imperfect spectrum sensing process over Rayleigh fading channels. On the basis of the sensing results, a hybrid interweave/underlay mode is exploited by the secondary user (SU) to achieve a higher throughput. To solve the tradeoff problem among the average energy consumption for spectrum sensing, the average throughput of SU and the average interference to the PU, an optimisation problem is proposed. The optimal sensing interval to determine when the next spectrum sensing should be performed is obtained by solving the optimisation problem. Numerical results are given to verify the authors’ analysis.