Characterization and linearization of RF power amplifiers (PAs) are key issues of fifth-generation wireless communication systems, especially when high peak-to-average ratio waveforms are introduced. Recently, deep learning methods have achieved great success in numerous domains including wireless physical-layer. However, there has been limited work in using deep learning for PAs behavioral modeling and linearization. In this paper, we make a bridge between memory effects of the nonlinear PAs and memory of bidirectional long short-term memory (BiLSTM) neural networks. We then build a BiLSTM-based behavioral modeling architecture and its accompanying digital predistortion (DPD) model by reconciling a non-causality concern. Next, an additional model is proposed in this study to mitigate uncertainty of the tested PA when transforming phases. The experimental results demonstrate the effectiveness of the proposed scheme, in which the adequately trained networks are capable of characterizing the PA, and the artificial intelligence-based DPD shows promising linearization performance when considering the tested PAs inherent unpredictability.

Integrated sensing and communication (ISAC) is an up-and-coming technique for future 6G networks. However, the communication message carried by the detection waveform will face the risk of being eavesdropped, which leads to the challenge of wireless security for ISAC networks. In this paper, we leverage non-orthogonal multiple access (NOMA) to support more users for the ISAC network, with the precoding well designed to guarantee the security. Specifically, we formulate a joint precoding optimization problem to maximize the sum secrecy rate for multiple users via artificial jamming, where the superimposed signal for NOMA users can be concurrently employed for the target detection. Since the optimization problem is non-convex, it is transformed into a convex one based on successive convex approximation (SCA), where the Taylor’s approximation and second-order cone (SOC) constraint are further applied. Then, we propose an iterative algorithm, through which the original optimization problem can be solved effectively. Simulation results show that the proposed secure NOMA-ISAC scheme can guarantee the secure transmission while ensuring the sensing performance.

In this correspondence, covert air-to-ground communication is investigated to hide the wireless transmission from unmanned aerial vehicle (UAV). The warden's total detection error probability with limited observations is first analyzed. Considering the location uncertainty of the warden, a robust resource allocation and UAV trajectory optimization problem with worst-case covertness constraint is then formulated to maximize the average covert rate. To solve this optimization problem, we propose a block coordinate descent method based iterative algorithm to optimize the time slot allocation, power allocation and trajectory alternately. Numerical results demonstrate the effectiveness of the proposed algorithm in covert communication for UAVs.

High-precision positioning scheme, an important part of indoor navigation, can be implemented using an ultra-wideband (UWB) based ranging system. Recently, solutions for precise positioning in dense multipath and non-line-of-sight (NLOS) conditions have attracted a lot of attentions. On the other hand, it is expected that waveform division multiple access (WDMA) technology for multiuser UWB positioning applications will be indispensable in the near future. In this regard, a WDMA-UWB-based positioning scheme is investigated in this paper, to enhance the positioning accuracy in multiuser applications. In accordance with practical requirements of indoor positioning, we propose a new indoor positioning scheme, termed as WDMA-UWB-based indoor positioning (WUB-IP). This scheme adopts WDMA for multiple access, and utilizes an entropy-based approach for the time of arrival estimation. Moreover, a transfer learning approach is used to mitigate the ranging error in NLOS conditions, in order to improve the positioning accuracy in NLOS conditions. System-level simulations demonstrate that the proposed scheme enhances the performance of indoor positioning for multiuser applications.

In this letter, we consider employing a non-orthogonal multiple access (NOMA) unmanned aerial vehicle (UAV) as relay to ferry data from a remote base station to multiple ground users. Our objective is to minimize the power consumption of UAV by jointly optimizing the location and transmission power of UAV. The formulated problem is non-convex with highly coupled binary discrete constraints, which is intractable to solve. Firstly, it is transformed into a more tractable form, where the logarithmic constraints are transformed into polynomial form, and the discrete constraints are transformed into continuous form. Then the non-convex equality constraints are handled by penalty method, while the non-convex inequality constraints are handled by successive convex approximation technique. Based on these measures, we propose a double-loop iterative algorithm to obtain the solution approximately. Numerical results show the effectiveness of our proposed algorithm.