This paper investigates the design of jointly supporting physical layer security (PLS) and covert communications (CCs) in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a-STAR-RIS) assisted integrated sensing and communication (ISAC) system. Due to the unified waveform design of ISAC signals, we consider a challenging scenario with two targets being suspicious attackers, where one warden target potentially detects the confidential transmission behavior of covert users and another eavesdropper target attempts to intercept the broadcasted confidential information of security users. We investigate the joint beamforming design at the base station (BS) and the a-STAR-RIS to achieve a high-quality sensing beampattern while meeting covertness and security communication requirements. (1) For the ideal scenario with perfect channel state information (CSI) and precise target locations, we propose an alternative optimization (AO) method to address the optimization problem involving highly coupled variables. Specifically, the optimal beamforming design at the BS is handled using the semi-definite relaxation (SDR) technique, while the beamforming design at the a-STAR-RIS is addressed through a penalty-based iterative algorithm. (2) A more practical case with uncertain target locations and imperfect CSI is considered to achieve a robust beamforming design, where the non-deterministic outage probability constraints are effectively transformed by employing the Bernstein-type inequality. Numerical results demonstrate the superiority of the a-STAR-RIS over the baseline cases and certify that the proposed algorithms can effectively balance the tradeoff among the sensing quality, covert and secure communication requirements. Besides, results also show that the proposed robust beamforming scheme can construct adequate sensing beampattern, even with imperfect CSI and uncertain target locations.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), as a revolutionary technique, can boost transmission security by controlling unfavorable environments for signal eavesdropping and reducing interference. Integrating unmanned aerial vehicles (UAVs) with STAR-RISs has generated considerable interest due to its enhanced deployment flexibility. However, developing secure communication capabilities using flying STAR-RIS remains an open issue. Therefore, this work investigates the secrecy energy efficiency (SEE) maximization problem for the uplink non-orthogonal multiple access (NOMA) systems, where the UAV-mounted STAR-RIS is employed against the eavesdroppers. Specifically, we consider the joint optimization of the power control, the transmission/reflection coefficients, and the UAV/STAR-RIS’s placement for static and mobile scenarios. The problems are also subject to the minimum data rate requirements and the safety flight region. To tackle the intractable problems, we first adopt the iterative-based method to solve the problem under the static scenario. After that, we invoke the fractional programming and successive convex approximation methods to get the power control scheme, the semidefinite relaxation method to get the transmission/reflection (T/R) coefficients design, and the search-based method to obtain the UAV/STAR-RIS position. Extending to the mobile scenario, we adopt the double deep Q-network (DDQN) algorithm to learn the online UAV trajectory design policy from a long-term perspective. Numerical results unveil that: 1) the proposed iterative-based joint optimization algorithm for static scenarios achieves a near-optimal solution; 2) the NOMA communications aided by the UAV-mounted STAR-RIS achieve significant SEE gain over the conventional reflection-only RIS and the fixed STAR-RIS cases; 3) the DDQN-based algorithm for mobile scenario achieves a near-optimal solution and obtains a valuable performance gain over the short-sighted greedy algorithm.

Integrated sensing and communication (ISAC) has been regarded as an emerging technique to satisfy the sensing requirements for future 6G networks. However, the confidential communication information embedded in the probing waveform could be eavesdropped by the radar targets, which leads to insecurity issues for ISAC systems. To this end, we propose a coordinated multi-point transmission (CoMP) empowered secure ISAC system. Unlike existing work focusing on a single base station (BS), multiple BSs are coordinated to improve sensing performance and communication security. Specifically, non-orthogonal multiple access (NOMA) is employed to improve spectrum efficiency and facilitate spectrum sharing between sensing and communication functions. By importing the artificial noise (AN) to disrupt eavesdropper reception, a joint radar association and beamforming design optimization problem is formulated to maximize the minimum beampattern gain, subject to the maximum power constraint and secure communication requirements. The mixed-integer non-convex optimization problem is first transformed into more tractable forms. Then, a near-optimal solution is obtained by applying an accelerated stochastic coordinate descent algorithm for radar association and the penalty-based iterative algorithm for beamforming design. Moreover, the optimization problem is further extended to more practical cases with uncertain target directions. Our numerical results show: i) the proposed AN-aided COMP-NOMA empowered ISAC system can support much higher high-quality radar sensing, while simultaneously guaranteeing secure communication; ii) the proposed scheme significantly outperforms the relevant benchmark schemes in terms of the beampattern gain; iii) the proposed joint optimization algorithm can achieve high beampattern gain, even with uncertain target directions.

In this paper, the joint task offloading and resource allocation are investigated for the semi-grant-free (SGF) non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system. Moreover, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) are deployed to improve the quality of wireless communications under the mode switching protocol. Each MU can partially or fully offload its task to the base station (BS) based on its differentiated channel conditions and computing capacity in the proposed MEC system. We formulate the joint task offloading, channel assignment, power allocation, and the RIS coefficients design problem to save energy consumption. The formulated problem is modeled from a long-term optimization perspective as a multi-agent Markov game (MG). Then, a multi-agent deep reinforcement learning (MADRL) based joint task offloading and resource allocation (JTORA) algorithm is proposed to solve the problem. The simulation results confirm that the applied SGF-NOMA scheme can significantly reduce energy consumption under a stringent latency constraint. Moreover, the effectiveness of the STAR-RIS and the proposed algorithm are confirmed.

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has been viewed as one of the promising technologies to meet massive connectivity requirements of the next-generation networks. A novel intelligent reconfigurable surface (IRS) assisted SGF NOMA transmission system is proposed, where the IRS is employed to satisfy the channel gain requirements for grant-based users (GBUs) and grant-free users (GFUs). The dynamic optimization on the sub-carrier assignment and power allocation for roaming GFUs, and the amplitude control and phase shift design for reflecting elements of the IRS, is formulated. Aiming at maximizing the long-term data rate of all GFUs, the optimization problem is first modeled as a multi-agent Markov decision problem. Then, three multi-agent deep reinforcement learning based frameworks are proposed to solve the problem under three different IRS cases, including the ideal IRS, non-ideal IRS with continuous phase shifts, and non-ideal IRS with discrete phase shifts. Specifically, for each GFU agent, a sub-carrier assignment deep Q-network (DQN) and a power allocation deep deterministic policy gradient (DDPG) are integrated to dynamically assign network resources for each GFU. For the only IRS agent, two DDPGs are integrated to dynamically assign phase shift and amplitude for each reflecting element of ideal IRS. The single DDPG for dynamically assigning continuous phase shifts, and parallel DQNs for dynamically assigning discrete phase shifts for non-ideal IRS with fixed amplitude are also proposed. Simulation results demonstrate that: 1) The network sum rates of all GFUs can achieve a significant improvement with the aid of IRS, comparing with the system without IRS. 2) The network sum rates of the NOMA assisted SGF transmissions are superior to that of OMA assisted GF transmissions.