A dual-scale deployment (DSD) framework is proposed for pinching antenna systems (PASS), under which four implementation protocols are developed. In the proposed framework, coarse-scale deployment moves the pinching antenna (PA) over a wide range along the waveguide, while fine-scale deployment adjusts the PA with high precision within a local region. By jointly optimizing these two scales, the DSD framework fully exploits the flexibility of PA deployment while maintaining low computational complexity. Based on this framework, we establish a practical power-consumption model and derive closed-form expressions for the energy efficiency of PASS. An energy-efficiency maximization problem is then formulated to jointly optimize transmit precoding, PA radiation power, and dual-scale PA deployment. To solve this non-convex and highly coupled problem, a low-complexity penalty-based alternating optimization algorithm is proposed. Simulation results validate the accuracy of the theoretical analysis and the convergence of the proposed algorithm. The proposed DSD framework delivers about 70% higher energy efficiency than the conventional cell-free architecture and nearly atwofoldimprovement over MIMO systems.

Deep learning-based automatic modulation recognition (AMR) is essential for enabling access authorization and spectrum management for interconnected devices and sensors in Internet of Things (IoT) systems. However, the open collection of data and the use of third-party training resources may introduce security vulnerabilities, especially backdoor attacks. Current research allows attackers to mislead receivers into misclassifying signals as a specific modulation type, but the fixed position of the trigger restricts its use in complex wireless networks. In this work, we propose a novel spatially distributed multi-target backdoor attack (SMBA) method. This method utilizes a trigger pattern to manipulate all types of input signals into multiple target modulation types by embedding the stealthy trigger at different spatial locations within the input signals. SMBA disseminates malicious samples across multiple target types specified by attackers, making it difficult for defenders to predict the target type into which the modulation signal embedded with the trigger is classified. This work reveals new security threats to AMR and provides important insights for developing defense technologies for IoT systems.

Presents corrections to the paper, Coverage Rate Analysis for Integrated Sensing and Communication Networks.

Integrated sensing and communication (ISAC) has emerged as a promising technology to facilitate high-rate communications and super-resolution sensing, particularly operating in the millimeter wave (mmWave) band. However, the vulnerability of mmWave signals to blockages severely impairs ISAC capabilities and coverage. To tackle this, an efficient and low-cost solution is to deploy distributed reconfigurable intelligent surfaces (RISs) to construct virtual links between the base stations (BSs) and users in a controllable fashion. In this paper, we model the generalized RIS-assisted mmWave ISAC networks considering the blockage effect, and examine the beneficial impact of RISs on the coverage rate utilizing stochastic geometry. Based on the proposed beam patterns and user association policies, we derive the conditional coverage probability and ergodic rate of communication and sensing dual functions for two association cases, as well as the marginal coverage rate using the distance-dependent thinning method. Taking into account the coupling effect of ISAC dual functions within the same network topology, we further calculate the joint coverage probability of ISAC performance. Simulation results verify the accuracy of derived theoretical formulations, and illustrate the impact of the RIS aperture, blockage, BS and RIS densities on ISAC coverage rates, which provide valuable guidelines for the practical network deployment. Specifically, our results indicate the superiority of the RIS deployment with the density of 40 km${}^{-2}$BSs, and that the joint coverage rate of ISAC performance exhibits potential growth from 62% to 97% with the deployment of RISs.

In this paper, the energy efficiency maximization problem in space-air-ground integrated network (SAGIN)-enabled probabilistic semantic communication (PSC) is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSC technique to compress the transmitted data, while the GTs can automatically recover the original data. In the considered PSC system, shared probability graphs serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Therefore, it is important to study the trade-off between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, semantic compression ratio, and UAV location constraints. To solve this non-convex problem, we propose an alternating algorithm. Numerical results show the effectiveness of the proposed algorithm.

In this paper, we propose an uplink multi-modal probabilistic semantic communication (PSCom) system that considers both communication and computation. In the considered PSCom model, the users and base station share the common knowledge, which is characterized by probability graph. Based on the shared probability graph, the original large-size semantic data is compressed into the small-size semantic information, which will introduce additional computation costs for semantic compression. Besides, semantic information recovered by the base station will also bring additional computation costs. Although this method incurs additional computation costs, it effectively reduces communication energy consumption. Based on the considered model, an optimization problem is formulated to minimize the total communication and computation energy consumption, considering latency, power budget, bandwidth, and semantic compression ratio constraints. To solve this mixed integer optimization problem, we first adopt a greedy algorithm for the semantic compression level selection of each model, thereby transforming the original problem into a continuous variable optimization problem. Then, derive the optimal solution of the communication power. Finally, the Lagrange multiplier method is used to determine the optimal bandwidth, which can result in a closed-form optimal solution. Simulation results validate the effectiveness of the proposed algorithm.

Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functionalities coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage rate of sensing and communication performance under resource constraints. Further, we present theoretical results for the coverage rate of unified ISAC performance, taking into account the coupling effects of dual functions in coexistence networks. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from 1 km-2to 10 km-2can boost the ISAC coverage rate from 1.4% to 39.8%.

In this paper, the problem of joint transmission and computation resource allocation for probabilistic semantic communication (PSC) network with rate splitting multiple access (RSMA) is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data, which is represented by substantial knowledge graphs, to multiple users. Due to limited communication resource, the BS needs to utilize semantic communication techniques to compress the large-sized data. In this paper, the semantic communication is enabled by shared probability graphs between the BS and users. The process of semantic compression requires computation power at the BS, which has an impact on limited power budget. Therefore, it is necessary to balance the power between transmission and computation. Based on the probability graph, the semantic rate related to semantic compression ratio is first theoretically formulated. Then, the problem is formulated as an optimization problem with the aim of maximizing the sum semantic rate of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is accordingly proposed to obtain a suboptimal solution. Numerical results validate the effectiveness of the proposed scheme.

Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functions coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage and ergodic rate of sensing and communication performance under resource constraints. Then, we shed light on the fundamental limits of ISAC networks by presenting theoretical results for the coverage rate of the unified performance, taking into account the coupling effects of dual functions in coexistence networks. Further, we obtain the analytical formulations for evaluating the ergodic sensing rate constrained by the maximum communication rate, and the ergodic communication rate constrained by the maximum sensing rate. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from$1~\text {km}^{-2}$to$10~\text {km}^{-2}$can boost the ISAC coverage rate from 1.4% to 39.8%. Further, results also reveal that with the increase of the constrained sensing rate, the ergodic communication rate improves significantly, but the reverse is not obvious.

An integrated sensing and communication (ISAC) system assisted by distributed passive intelligent reflecting surfaces (IRSs) is proposed in this paper, which enables simultaneous signal demodulation and location sensing, without channel state information (CSI). A detailed workflow of the proposed IRS-based ISAC system is designed, including transmission protocol, joint location sensing and demodulation, as well as beamforming optimization. Specifically, each coherent block consists of two stages, and each stage is divided into two time blocks, where the signal demodulation, channel estimation, and location sensing are conducted by the proposed integrated localization and demodulation (I-LAD) algorithm, simultaneously. Simulation results show that the signal demodulation performance of the proposed I-LAD algorithm is close to that of the benchmark scheme assuming perfect CSI. At the same time, the channel estimation performance is comparable to that of the parallel factor decomposition (PAPRFAC) method or the compressive sensing-based channel estimation (CS-EST) method. In addition, the proposed location sensing scheme almost achieves the Cramér-Rao lower bound (CRLB) for distributed IRS-assisted localization-only systems, confirming the effectiveness of the proposed I-LAD algorithm.

This article considers a novel intelligent reflecting surface (IRS)-assisted cell-free symbiotic radio (SR) system, where an IRS is employed to facilitate the transmission from all the base stations (BSs) to the primary receivers (PRs), i.e., the primary transmission, and simultaneously, to send messages to the Internet of Things (IoT) devices, i.e., the secondary transmission. We aim to minimize the bit error rate (BER) of the IRS symbols by jointly optimizing the transmit beamformers at all the BSs and the IRS phase shifts. To solve this nonconvex problem, we propose a plenty-based algorithm by using the multidimensional complex quadratic transform (MCQT) and the consensus alternative direction method of multipliers (ADMMs) methods, and obtain a high-performance solution. Besides, the convergence of the proposed algorithm is analyzed. Simulation results demonstrate the effectiveness of the proposed algorithm and show that the proposed algorithm outperforms two baselines under different setups.

Reconfigurable intelligent surface (RIS) can be employed in a cell-free system to create favorable propagation conditions from base stations (BSs) to users via configurable elements. However, prior works on RIS-aided cell-free system designs mainly rely on the instantaneous channel state information (CSI), which may incur substantial overhead due to extremely high dimensions of estimated channels. To mitigate this issue, a low-complexity algorithm via the two-timescale transmission protocol is proposed in this paper, where the joint beamforming at BSs and RISs is facilitated via alternating optimization framework to maximize the average weighted sum-rate. Specifically, the passive beamformers at RISs are optimized through the statistical CSI, and the transmit beamformers at BSs are based on the instantaneous CSI of effective channels. In this manner, a closed-form expression for the achievable weighted sum-rate is derived, which enables the evaluation of the impact of key parameters on system performance. To gain more insights, a special case without line-of-sight (LoS) components is further investigated, where a power gain on the order of$\mathcal {O}(M)$is achieved, with$M$being the BS antennas number. Numerical results validate the tightness of our derived analytical expression and show the fast convergence of the proposed algorithm. Findings illustrate that the performance of the proposed algorithm with two-timescale CSI is comparable to that with instantaneous CSI in low or moderate SNR regime. The impact of key system parameters such as the number of RIS elements, CSI settings and Rician factor is also evaluated. Moreover, the remarkable advantages from the adoption of the cell-free paradigm and the deployment of RISs are demonstrated intuitively.

Reconfigurable intelligent surface (RIS) is a promising solution to build a programmable wireless environment with reconfigurable passive elements, which can achieve high spectral and energy efficiency. In this paper, we investigate the ergodic capacity of RIS-assisted multi-user multiple-input single-output (MISO) wireless systems in both uplink and downlink scenarios. Unlike most of prior works, where instantaneous channel state information (CSI) is assumed, we consider the realistic scenario with only statistical CSI. For both scenarios, we first present an analytical expressions for the ergodic sum capacity of the system. Based on which, the joint power control (or transmit beamforming) and phase shift design problem maximizing the ergodic sum capacity is formulated. Capitalizing on the alternating direction method of multipliers (ADMM), fractional programming (FP) and alternating optimization (AO) methods, efficient suboptimal solutions are obtained for the non-convex design problems. Simulation results are presented to validate the accuracy of the analytical ergodic sum capacity expressions and evaluate the impact of key system parameters such as CSI, Rician$K$factor, number of RIS elements, and RIS location on the ergodic capacity performance. The findings suggest that the proposed statistical CSI design achieves decent performance compared with the instantaneous CSI based design. Moreover, a signal hot spot can be created when placing the RIS close to the users.