Benefiting from the merits of low cost and high mobility, Unmanned aerial vehicle (UAV) enabled communications have recently drawn considerable attentions. In this paper, we consider the UAV-enabled physical-layer secure communications. Specifically, we consider a UAV-ground communication systems with multiple eavesdroppers whose locations are not perfectly known and are assumed to follow Gaussian distribution. An outage probability-constrained average secrecy rate maximization (ASRM) problem is formulated by jointly optimizing the trajectory and the transmit power of the UAV over a given flight duration. The ASRM problem is challenging due to the probabilistic constraints. To circumvent the difficulty, we employ a safe approximation approach and the successive convex optimization method to find a tractable solution to the ASRM problem. The effectiveness and robustness of the proposed design are demonstrated through simulations.

Recently, the intelligent reflecting surfaces (IRSs)-aided wireless transmission has drawn considerable attention. This paper investigates the use of IRS in enhancing the physical-layer security of the multiuser multiple-input single-output (MU-MISO) broadcast system, where a base station (BS) transmits a common data stream to multiple legitimate receivers in the presence of multiple eavesdroppers (Eves). The BS is assumed to have only some erroneous channel state information (CSI) of the Eves. The CSI error is modeled by a moment-based random error model, in which the BS only knows the first- and second-order statistics of the error, but not the exact distribution. Under this CSI error model, we investigate the joint robust design of the secure beamforming at the BS and the phase shift at the IRS to maximize the worst legitimate user’s SNR, while keeping the Eves’ SNR below certain threshold with high probability, evaluated with respect to any distribution fulfilling the given first and second-order statistics. This robust secure transmission design problem is a semi-infinite chance-constrained problem, which is in general intractable. We show that the considered problem admits an equivalent conic reformulation, which can be handled by alternately optimizing the beamformer at the BS and the phase shift at the IRS via semidefinite relaxation (SDR) and penalty convex-concave procedure (CCP), respectively. To further improve the secrecy performance, we also study another rank-two beamformed Alamouti transmission scheme at the BS, which can be seen as a generalization of conventional rank-one beamforming. Simulation results demonstrate that the proposed designs are robust against the error distribution, and that the inclusion of IRS is not only helpful for enhancing the security, but also useful for promoting a low-rank SDR solution.

This work considers robust beamforming for simultaneous wireless information and power transfer (SWIPT) in multiuser MISO interfering broadcast channels. The base stations (BSs) are assumed to have imperfect channel state information (CSI) of receivers, which is modeled by a moment-based random error model. Specifically, the BSs only know the first- and second-order moments of receivers' CSI errors, but not the exact distribution. Under this error model, the robust SWIPT beamforming problem is formulated as an outage probability-constrained optimization problem, in which we minimize the total transmit power at the BSs while keeping the signal-to-interference-plus-noise ratio (SINR) and energy harvesting (EH) outage probabilities, evaluated with respect to (w.r.t.) any distribution with the given first- and second-order statistics, below a given threshold. By applying recent results in distributionally robust optimization, we show that the proposed problem admits an explicit conic reformulation, which can be approximately solved by the semi-definite relaxation (SDR) technique. Simulation results demonstrate the robustness of the proposed design.

This paper considers symbol-level precoding (SLP) for multiuser multi-input single-output (MISO) downlink transmission with the aid of intelligent reflecting surface (IRS). Specifically, by assuming one-bit transmitted signals at the base station (BS), which arises from the use of low-resolution DACs in the regime of massive transmit antennas, a joint design of one-bit SLP at the BS and the phase shifts at the IRS is proposed with a goal of minimizing the worst-case symbol error probability (SEP) of the users under the PSK modulation. This joint design problem is essentially a mixed integer nonlinear program (MINLP). To tackle it, we alternately optimize the one-bit signal and the phase shifts. For the former, a dual of the relaxed one-bit SLP problem is solved by the mirror descent (MD) method with the maximum block improvement (MBI) heuristics. For the latter, the accelerated projected gradient (APG) method is employed to optimize the phases. Numerical results demonstrate that the proposed joint design can attain better SEP performance than the conventional linear precoding and one-bit SLP.