Integrated sensing and communication (ISAC) is poised to redefine the landscape of wireless networks by seamlessly combining data transmission and environmental sensing. However, ISAC systems remain susceptible to eavesdropping, especially under uncertainty in eavesdroppers’ channel state information, which can lead to secrecy outages. On the other hand, diverse and complex sensing performance requirements further complicate resource optimization, often requiring custom solutions for each scenario. To this end, this paper introduces a unified optimization framework that holistically addresses both the worst-case user secrecy rate and the sum secrecy rate across multiple users. Besides putting the two commonly used objectives into a single but flexible objective function, the framework accurately controls secrecy outage probabilities while accommodating a broad spectrum of sensing constraints. To solve such a general problem, we integrate the sensing requirements into the objective function through an auxiliary variable. This enables efficient alternating optimization and the proposed approach is theoretically guaranteed to converge to at least a stationary point of the original problem. Extensive simulation results show that the proposed framework consistently achieves higher optimized secrecy rates under various sensing constraints compared to existing methods. These results underscore the proposed unified framework’s superiority and versatility in secure ISAC systems.

This paper addresses the challenge of large model (LM)-embedded wireless network for handling the trade-off problem of model accuracy and network latency. To guarantee a high-quality of users’ service, the network latency should be minimized while maintaining an acceptable inference accuracy. To meet this requirement, LM quantization is proposed to reduce the latency. However, the excessive quantization may destroy the accuracy of LM inference. To this end, a promising fluid antenna (FA) technology is investigated for enhancing the transmission capacity, leading to a lower network latency in the LM-embedded multiple-input multiple-output (MIMO) network. To design the FA-assisted LM-embedded network with the lower latency and higher accuracy requirements, the latency and peak signal to-noise ratio (PSNR) are considered in the objective function. Then, an efficient optimization algorithm is proposed under the block coordinate descent framework. Simulation results are provided to show the convergence behavior of the proposed algorithm, and the performance gains from the proposed FA-assisted LM-embedded network over the other benchmark networks in terms of network latency and PSNR.

Reconfigurable intelligent surface (RIS) has the capability to significantly enhance physical layer security by reconfiguring the propagation in wireless communications. However, due to the cascaded channel brought by the RIS and the hostile nature of potential eavesdroppers, acquiring perfect channel state information (CSI) of the eavesdroppers is challenging. Worse still, if the eavesdroppers are equipped with multiple antennas and there exists spatial correlation at the RIS due to closely spaced RIS elements, the random channel matrices are complicatedly coupled with the phase shift and other wireless resources in the outage probabilistic constraint, making their optimizations intractable. To date, there has been no systematic and feasible approach to address such a challenge. To fill this gap, this paper for the first time reveals an analytical transformation for handling the intractable outage probabilistic constraint. It is theoretically established that when the maximum tolerable outage probability is smaller than a threshold around 0.4, which generally holds in practice, the proposed transformation is exact and suffers no performance loss. As an illustrative example of the developed constraint transformation, the secure energy efficiency maximization is selected as the objecitve function and the resultant resource optimization is handled by the alternating maximization framework. Numerical results are presented to show the rapid convergence behavior of the proposed algorithm and unveil that the proposed probabilistic constraint transformation has superiority over the Bernstein-Type Inequality approximation. Compared with several baseline schemes (e.g., random phase-shift, fixed phase-shift, RIS ignoring CSI uncertainty, and secure transmission without RIS), the proposed scheme significantly boosts the performance, underscoring the significance of appropriately managing the probabilistic constraint outage and optimizing RIS phase shifts for secure transmission against multi-antenna eavesdroppers.

To combine the orthogonal frequency division multiplexing (OFDM) modulation with non-orthogonal multiple access (NOMA) technique, it is required to ensure the successful successive interference cancellation procedure. Because of the fast Fourier transform (FFT) and inverse FFT employed at the OFDM transceivers, the central user may have weak subcarriers and the cell-edge user may have strong subcarriers. In this paper, we propose that for the central or cell-edge user, signals over the weak (strong) subcarriers are jointly encoded and decoded. Our objective is to maximize the weighted sum average achievable rates at both the central and cell-edge users through power allocation optimization. To solve the optimization problem, we propose a constrained convex-concave procedure (CCCP) based locally optimal solution, a Lagrangian dual transformation based locally optimal solution, and a prime decomposition based near-optimal solution. The prime decomposition based algorithm, which solves the problem based on water-filling, has extremely low computational complexity. It is shown through simulation results that the proposed scheme outperforms the conventional OFDM-NOMA scheme. Furthermore, it is found that the results obtained by the CCCP based solution, Lagrangian dual transformation based solution, and prime decomposition based solution match one another.

In mobile edge computing (MEC) systems, the wireless channel condition is a critical factor affecting both the communication power consumption and computation rate of the offloading tasks. This paper exploits the idea of cooperative transmission and employing reconfigurable intelligent surface (RIS) in MEC to improve the channel condition and maximize computation efficiency (CE). The resulting problem couples various wireless resources in both uplink and downlink, which calls for the joint design of the user association, receive/downlink beamforming vectors, transmit power of users, task partition strategies for local computing and offloading, and uplink/downlink phase shifts at the RIS. To tackle the challenges brought by the combinatorial optimization problem, the group sparsity structure of the beamforming vectors determined by user association is exploited. Furthermore, while the CE does not explicitly depend on the downlink phase shifts, instead of simply finding a feasible solution, we exploit the hidden relationship between them and convert this relationship into an explicit form for optimization. Then the resulting problem is solved via the alternating maximization framework, and the nonconvexity of each subproblem is handled individually. Simulation results show that cooperative transmission and RIS deployment can significantly improve the CE and demonstrate the importance of optimizing the downlink phase shifts with an explicit form.

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the physical layer security by reconfiguring the wireless propagation environment. However, due to the hostile nature of potential eavesdroppers and the cascaded channel brought by the RIS, acquiring perfect channel state information (CSI) of the eavesdroppers is challenging. Worse still, if the eavesdroppers are equipped with multiple antennas, the design of the optimal phase-shift, power allocation, and secure transmission data rate are intractable due to the couplings of random channel matrices in the outage probability. To overcome these challenges, this paper for the first time reveals an analytical transformation for handling the outage probabilistic constraint in the secure energy efficiency maximization problem due to multi-antenna eavesdropper. The resultant problem is readily handled under the alternating maximization framework. Simulation results unveil that the proposed probabilistic constraint transformation and the associated optimization algorithm provide superior secure energy efficiency over the baseline schemes of random phase-shift, fixed phase-shift, RIS ignoring CSI uncertainty, and secure transmission without RIS.

In this paper, simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) is investigated in the multi-user mobile edge computing (MEC) system to improve the computation rate. Compared with traditional RIS-aided MEC, STAR-RIS extends the service coverage from half-space to full-space and provides new flexibility for improving the computation rate for end users. However, the STAR-RIS-aided MEC system design is a challenging problem due to the non-smooth and non-convex binary amplitude coefficients with coupled phase shifters. To fill this gap, this paper formulates a computation rate maximization problem via the joint design of the STAR-RIS phase shifts, reflection and transmission amplitude coefficients, the receive beamforming vectors, and energy partition strategies for local computing and offloading. To tackle the discontinuity caused by binary variables, we propose an efficient smoothing-based method to decrease convergence error, in contrast to the conventional penalty-based method, which brings many undesired stationary points and local optima. Furthermore, a fast iterative algorithm is proposed to obtain a stationary point for the joint optimization problem, with each subproblem solved by a low-complexity algorithm, making the proposed design scalable to a massive number of users and STAR-RIS elements. Simulation results validate the strength of the proposed smoothing-based method and show that the proposed fast iterative algorithm achieves a higher computation rate than the conventional method while saving the computation time by at least an order of magnitude. Moreover, the resultant STAR-RIS-aided MEC system significantly improves the computation rate compared to other baseline schemes with conventional reflect-only/transmit-only RIS.

Reconfigurable intelligent surface (RIS) has the revolutionary ability to customize the radio propagation environment for enhancing the secure transmission performance. However, due to the passive nature of eavesdroppers and the cascaded channel brought by the RIS, the channel state information (CSI) is imperfectly obtained at the base station, leading to uncertain CSI. Under channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for secure transmission is currently unknown due to the difficulty of handling the probabilistic constraint with coupled variables. To fill this gap, this paper investigates the energy efficient secure transmission while incorporating the probabilistic constraint. By transforming the probabilistic constraint and decoupling the variables, the secure energy-efficiency maximization problem can be solved via alternatively executing the concave-convex procedure and penalty-based method. Simulation results show that the proposed RIS-aided secure transmission scheme significantly improves the energy-efficiency compared to baseline schemes of random phase-shift, fixed phase-shift, and RIS ignoring CSI uncertainty.

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the network secure transmission performance by reconfiguring the wireless propagation environment. However, due to the passive nature of eavesdroppers and the cascaded channel brought by the RIS, the eavesdroppers’ channel state information is imperfect at the base station. Under channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for massive antennas and reflecting elements secure transmission are challenging to solve due to the outage probabilistic constraint with coupled variables. To fill this gap, this paper formulates a problem of energy-efficient secure transmission design with the probabilistic outage constraint. By leveraging the exponential distribution property of the received signal power, the stochastic resource allocation is equivalently transformed into a deterministic one, and the secure energy efficiency maximization problem can be iteratively solved via low complexity first-order algorithms under the alternating maximization (AM) framework. However, due to the nonsmooth problem, the convergence of the objective function value and nature of the converged solution under AM iteration are uncertain. Therefore, the convergence properties with respect to the objective function value and sequence of solutions are further established. Simulation results corroborate the convergence results of the first-order algorithms and show that the proposed algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant RIS aided secure transmission significantly improves the energy efficiency compared to baseline schemes of random phase-shift, fixed phase-shift, and RIS ignoring CSI uncertainty.

Intelligent reflecting surface (IRS) has the potential to significantly enhance the network secure transmission performance by reconfiguring the wireless propagation environment. However, due to the passive nature of the eavesdropper and the cascaded channel brought by the IRS, the eavesdropper’s channel state information is imperfectly obtained at the base station. Under the channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for secure transmission is currently unknown due to the difficulty of handling the probabilistic constraint with coupled variables. To fill this gap, this paper formulates a secrecy rate maximization problem while incorporating the probabilistic constraint. By transforming the probabilistic constraint and decoupling variables, the secrecy rate maximization problem can be solved via alternatively executing difference-of-convex programming and semidefinite relaxation method. The simulation results validate the strength of this newly established transmission scheme when compared to baseline schemes of random phase-shift, fixed phase-shift, and IRS ignoring CSI uncertainty.

Non-orthogonal multiple access (NOMA) is a promising solution for secure transmission under massive access. However, in addition to the uncertain channel state information (CSI) of the eavesdroppers due to their passive nature, the CSI of the legitimate users may also be imperfect at the base station due to the limited feedback. Under both channel uncertainties, the optimal power allocation and transmission rate design for a secure NOMA scheme is currently not known due to the difficulty of handling the probabilistic constraints. This article fills this gap by proposing novel transformation of the probabilistic constraints and variable decoupling so that the security guaranteed sum-rate maximization problem can be solved by alternatively executing branch-and-bound method and difference of convex programming. To scale the solution to a truly massive access scenario, a first-order algorithm with very low complexity is further proposed. Simulation results show that the proposed first-order algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant transmission scheme significantly improves the security guaranteed sum-rate compared to the orthogonal multiple access transmission and NOMA ignoring CSI uncertainty.

In multiple access systems, physical layer security is degraded since more attacking targets are available for the eavesdropper. Fortunately, it has been recently demonstrated that non-orthogonal multiple access (NOMA) could improve secure transmission performance. However, it is still unknown how to design a transmission scheme for maximizing the sum rate when the channel state information is imperfectly known at the transmitter. To fill this gap, we formulate a maximization problem of sum rate while incorporating versatile metrics such as outage probability, quality of service, and transmit power. By leveraging the first-order and log-concavity properties of the Marcum Q-function, the maximum sum rate of the secure NOMA transmission scheme is efficiently obtained. Simulation results validate the strength of this newly established scheme when compared with conventional orthogonal multiple access scheme.

In a wiretap channel, due to the passive nature of eavesdropper and the inevitable errors during channel estimation or feedback, the channel state information is usually imperfectly known at the transmitter. While probabilistic constrained secure transmission provides an elegant formulation to tackle these uncertainties, current works mostly focus on the fixed-rate secure transmission design. To exploit the dynamic channel state information for performance enhancement, this paper investigates a variable-rate transmission scheme with adjustable rate and power, under the outage probabilistic constraints and upper bounding rate constraint. By leveraging the first-order and log-concavity properties of the Marcum Q-function, closed-form optimal secure transmission design is obtained. Furthermore, the optimality of the proposed method empowers us to concisely quantify the performance gain brought by rate variation. Numerical results show that the proposed scheme achieves significantly lower average outage probability and higher throughput than the fixed-rate scheme no matter with or without upper bound rate limitation.