As a foundational technology for the sixth generation networks that integrates communication, sensing, computing, and intelligence, integrated sensing and communication (ISAC) surpasses conventional isolated system designs across multiple performance dimensions. Given its superior interference management capability, non-orthogonal multiple access (NOMA) presents significant potential for integration into ISAC systems. To this end, this paper establishes a unified NOMA-ISAC framework that concurrently incorporates residual hardware impairments, channel estimation errors, and imperfect successive interference cancellation (SIC). Within this framework, we propose two distinct SIC designs tailored for different operational priorities: a communication-centric design (CCD) and a sensing-centric design (SCD), thereby introducing SIC ordering as a new dimension for managing the sensing-communication trade-off. For both proposed designs, we derive analytical expressions encompassing exact and asymptotic lower bounds of outage probabilities, ergodic communication rates for users, as well as probability of detection (PoD), probability of false alarm, and sensing sum rate for the base station. These analytical results provide a theoretical foundation for optimizing critical system parameters, such as power allocation and beamforming design. Our analysis reveals the synergistic effect of these impairments, leading to a simultaneous error floor in both communication and sensing performance. Notably, the proposed SIC ordering enables substantial performance gains in the prioritized domain: the CCD scheme improves ECRs by over 40%, whereas the SCD scheme enhances target PoD by more than 25%, under practical impairment conditions.

Integrated sensing and communication (ISAC) technology presents promising prospects for improving spectral efficiency, enabling hardware resource sharing, and facilitating novel application scenarios. Nevertheless, the mutual interference between communication and sensing remains a critical barrier to overcome. As an effective interference management solution, both non-orthogonal multiple access (NOMA) and rate splitting multiple access (RSMA) demonstrate unique advantages in interference suppression, which are expected to substantially enhance the overall performance of ISAC systems. To this end, the design options of NOMA versus RSMA for the ISAC systems are investigated in this paper. Furthermore, due to the inherent complexities in both signal propagation characteristics and receiver processing architectures, channel estimation errors (CEEs) and imperfect successive interference cancellation (ipSIC) are incorporated during system modeling. Against the above background, the communication and sensing performance of the NOMA and RSMA ISAC systems is analyzed by respectively deriving the exact and asymptotic outage probabilities (OPs) and ergodic rates (ERs) for the users, the probability of detection (PoD) for the base station and Cramér-Rao bound (CRB). The numerical results demonstrate that RSMA outperforms NOMA in terms of OPs, ERs, PoD, and CRB.

An integrated sensing and communication (ISAC) system is investigated, where the base station (BS) provides both uplink and downlink Internet of Things (IoT) services as well as target sensing services. Furthermore, nonorthogonal transmission (NO-T) is introduced for improving the spectrum efficiency. The deleterious effects of hardware impairments, channel estimation errors, and imperfect successive interference cancellation are taken into account. Both the exact and asymptotic outage probabilities (OPs) of the IoT devices as well as the Probability of successful Detection (PoD) are derived for characterizing the communication and sensing (C&S) performances. As a further development, in the presence of the sensing requirements, a communication-centric power allocation (PA) problem is formulated for maximizing the sum rate of the IoT devices. Given the nonconvexity of the problem, an alternating optimization algorithm is developed for finding a near-optimal PA. The simulation results confirm the accuracy of the analysis and demonstrate that: 1) the above nonideal factors degrade the C&S performances; 2) the NO-T ISAC system considered outperforms pure ISAC in terms of both its OP and PoD; and 3) compared to other baseline PA schemes, the proposed algorithm maximizes the sum rates while meeting the sensing requirements.

Blocking jamming and deceptive jamming pose significant threats to synthetic aperture radar (SAR) systems in practical combat scenarios, as they can heavily obscure the scene and confuse the detection of practical targets. To effectively suppress this hybrid jamming, this article proposes a novel hybrid jamming suppression method for multichannel SAR based on group iterative separation. In this method, multichannel SAR signals with hybrid jammings are suppressed by using grouped iterative separation. To enhance the operational efficiency and determine the number of signal sources, the proposed algorithm initially preprocesses the mixed signals received by the SAR. Then, the mixed signals are divided into two groups to separate the multichannel signals by eliminating the jumping parts of the jamming and iteratively solving an optimization problem. Thus, this approach enables more efficient and accurate separated signals. Finally, the target echo signal is identified by the peak-to-average power ratio in the time domain. Simulation results and experiments using measured data demonstrate that the proposed algorithm effectively separates signals with a high degree of similarity and performs well in terms of hybrid jamming suppression.

Radar forward-looking imaging is a leading topical issue in the radar research domain, which plays a significant role in many fields such as target estimation, forward-looking detection, and precision guidance. By generating a stochastic radiation field through the wavefront modulation of the transmitted signals and associating the target echo with the radiation field to achieve the forward-looking imaging of the target in the beam. This method has superior performance on sparse targets, while the performance for complex targets will degrade severely. Therefore, this paper proposes a radar forward-looking imaging method for complex targets based on sparse representation with regularized approximated K-singular value decomposition (RAK-SVD) and damped Gaussian generalized approximate message passing-based variational sparse Bayesian learning (GGAMP-VSBL) algorithm. Firstly, the imaging principle and model are introduced to realize forward-looking imaging. Secondly, a regularization parameter is added to the classical dictionary learning K-SVD algorithm to obtain the regularized AK-SVD algorithm, and it is used for complex targets to improve the accuracy of sparse representation. Thirdly, the variational Bayesian inference (VBI) methodology is applied to SBL to form the VSBL algorithm, and GGAMP is embedded into VSBL to obtain the GGAMP-VSBL algorithm to speed up image reconstruction. Meanwhile, a new variance updating formula is adopted to further reduce the computational complexity and improve the robustness of the algorithm. The experimental results show that the proposed method can effectively image complex targets and has favorable performance in different complex scenes.

This paper investigates the performance of simultaneous wireless information and power transfer (SWIPT) non-orthogonal downlink transmission integrated sensing and communication (ISAC) system. Particularly, residual hardware impairments, channel estimation errors, and imperfect successive interference cancelation are taken into account. The expressions of exact and asymptotic outage probabilities, probability of detection (PoD), and energy efficiency (EE) are derived for communication and sensing (C&S) performance evaluation. The simulation results verify the accuracy of our theoretical analysis and reveal that the introduce of SWIPT into ISAC system can improve both C&S performance in terms of fairness, PoD as well as EE.

To mitigate the overlapping of the radar and communication frequency bands caused by large-scale devices access, we propose a novel integrated sensing and communication (ISAC) system, where a micro base station (MiBS) simultaneously carries out both target sensing and cooperative communication. Concretely, the MiBS, acting as the sensing equipment, can also serve as a full-duplex decode-and-forward relay to assist end-to-end communication. Moreover, nonorthogonal downlink transmission (NO-DLT) is adopted between the macro base station and the Internet of Things devices, so that the spectrum utilization can be further improved. To facilitate the performance evaluation, both the exact and asymptotic outage probabilities, the ergodic rates associated communication, and the probability of successful sensing detection are characterized. Subsequently, a pair of problems of maximizing the receive signal-to-interference-plus-noise ratio of the sensing signal and maximizing the sum rate of communication are formulated that are solved by the classic Lagrangian method while exploiting the associated function monotonicity. Our simulation results demonstrate that: 1) The proposed ISAC NO-DLT system improves both the communication and sensing performance under the same power consumption as noncooperative NO-DLT and 2) the proposed power allocation (PA) schemes are superior to the random PA scheme.

Massive connectivity and limited energy are main challenges for the beyond 5G (B5G)-enabled massive Internet of Things (IoT) to maintain diversified Qualify of Service (QoS) of the huge number of IoT device users. Motivated by these challenges, this article studies the performance of cooperative simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) for massive IoT systems. Under the practical assumption, residual hardware impairments (RHIs) and channel estimation errors (CEEs) are taken into account. The communication between the base station (BS) and two NOMA IoT device users is realized through a direct link and the assistance of multiple relays with finite energy storage capability that can harvest energy from the BS. Aiming at improving the system performance, an optimal relay is selected among K relays by using the partial relay selection (PRS) protocol to forward the received signal to the two NOMA IoT device users, namely, the far user (FU) and near user (NU). To evaluate the system performance, exact analytical expressions for the outage probability (OP) are derived in closed form. In order to get a better understanding of the overall system performance, we further undertake diversity order analyses by deriving asymptotic expressions for the OP in the high signal-to-noise ratio (SNR) regime. In addition, we also investigate the energy efficiency (EE) of the considered system, which is a crucial performance metric in massive IoT systems so that the impact of key system parameters on the performance can be quantified. Finally, the optimal power allocation scheme to maximize the sum rate of the considered system in the high SNR regime is also designed. Numerical results have shown that: 1) hardware impairment parameter has a deleterious effect on system performance while the channel estimation parameter is always beneficial to the OP; 2) the expected performance improvements obtained by the user of PRS protocol are enhanced by increasing the number of relays; and 3) the proposed power allocation scheme can optimize the sum-rate performance of the considered system.