Mark F. Flanagan

Integrated sensing and communication (ISAC) has emerged as a promising technology for sixth-generation (6G) communication networks. At the same time, ensuring the privacy of targets in ISAC is important in contexts where a malicious sensor is present. In this paper, we investigate a reconfigurable intelligent surface (RIS)-assisted ISAC system designed to protect a sensing region against an adversarial detector (AD), where the base station (BS) has imperfect knowledge of the AD’s location. The RIS consists of both reflecting and absorptive elements (the latter serving as sensing elements), which can be adaptively reconfigured to meet system requirements. Specifically, the system is designed to maximize the jamming power from the BS to the AD by jointly optimizing the transmit beamformer at the BS, the RIS phase-shift matrix, the receive beamformer at the RIS, and the allocation between reflecting and absorptive elements at the RIS while ensuring a minimum sensing signal-to-interference-plus-noise ratio (SINR) at sample points within the sensing region, as well as a minimum communication SINR for each user. To address this challenging optimization problem, we propose an alternating optimization framework combined with a successive convex approximation method tailored for each subproblem. Our results show that the proposed system model offers significant protection of the sensing area compared to the case where the target privacy is not considered. Simulations also confirm that the proposed adaptive RIS partitioning outperforms the fixed RIS partitioning approach.

Stacked intelligent metasurfaces (SIMs) have recently emerged as a promising metasurface-based physical-layer paradigm for wireless communications, enabling wave-domain signal processing through multiple cascaded metasurface layers. However, conventional SIM designs rely on rigid planar layers with fixed interlayer spacing, which constrain the propagation geometry and can lead to performance saturation as the number of layers increases. This paper investigates the potential of introducing structural flexibility into SIM-enabled communication systems. Specifically, we consider two flexible SIM architectures: distance-adaptive SIM (DSIM), where interlayer distances are optimized, and stacked flexible intelligent metasurface (SFIM), where each metasurface layer is fully morphable. We jointly design the meta-atom positions and responses together with the transmit beamformer to maximize the system sum rate under per-user rate, quantization, morphing, and interlayer distance constraints. An alternating optimization framework combining gradient projection, penalty-based method, and successive convex approximation is developed to address the resulting non-convex problems. Perturbation analysis reveals that the flexibility gains of both DSIM and SFIM scale approximately linearly with the morphing range, with SFIM exhibiting a faster growth rate. Simulation results demonstrate that flexible SIM designs mitigate performance saturation with increasing layers and achieve significant transmit power savings compared to rigid SIMs.

Carrier mode shift keying (CMSK) is a recently proposed waveform-domain index modulation scheme that improves the spectral efficiency (SE) of hybrid carrier (HC) communication systems. This innovative scheme simultaneously transmits information bits mapped to the traditional constellation symbols as well as information bits mapped to the index of the weighted-type fractional Fourier transform (WFRFT) parameter. In this paper, we propose an enhanced version of CMSK (E-CMSK) that introduces a subblock precoding module at the transmitter side. Compared with CMSK, E-CMSK conveys index information through subblock-level parameter mapping, thereby further improving the SE. Furthermore, E-CMSK provides enhanced system flexibility, enabling trade-offs among the SE, reliability and detection complexity through subblock size adjustment. To address the prohibitive complexity of the maximum likelihood (ML) detector, a low-complexity sequential detector based on linear frequency domain equalization (FDE) is developed, which reduces the computational complexity from exponential to linear. Furthermore, to compensate for the performance loss inherent in linear equalization, an enhanced iterative block decision feedback equalization (IB-DFE) detector is proposed. This iterative scheme effectively suppresses residual interference, enabling the system to approach the global ML performance. An analytical expression for the average overall bit error rate (BER) is derived to provide theoretical performance insights. While this expression reveals the impact of key parameters like Euclidean distance, its complexity makes it intractable for direct optimization. Therefore, we directly analyze the Euclidean distance distribution of the E-CMSK constellation as an intuitive yet effective tool for system design. This analysis guides the design of a Gray-coded bit-parameter mapping scheme and an equally spaced WFRFT parameter selection strategy, which aims to improve the overall BER performance. Simulation results demonstrate that the proposed E-CMSK scheme outperforms the conventional HC system in multi-path fading channels, particularly at high SNRs.

This paper proposes a novel low-complexity syndrome-based linear programming (SB-LP) decoding algorithm for decoding quantum low-density parity-check (LDPC) codes. Under the code-capacity model, the SB-LP decoder can be used as a standalone decoder; however, it is particularly powerful when used as a post-processing step following syndrome-based min-sum (SB-MS) decoding. In the latter case, the proposed decoder is shown to be capable of significantly reducing the error floor of the SB-MS decoder for both flooded and layered SB-MS scheduling. Also, an early stopping criterion is introduced to decide when to activate the SB-LP algorithm, avoiding executing a predefined maximum number of iterations for the SB-MS decoder. Simulation results show, for some example hypergraph and generalized bicycle (GB) codes, that the proposed decoder can lower the error floor by one to three orders of magnitude compared to SB-MS for the same total number of decoding iterations. Furthermore, for the class of GB codes, it is shown that as the minimum distance of the code increases, the logical error rate provided by the proposed decoder also improves, indicating that the solution is scalable. Under the circuit-level noise model, it is shown that while the SB-LP decoder does not fully replace the need for ordered statistics decoding (OSD) when flooded SB-MS is used as a preliminary step, it reduces the number of calls to the OSD post-processor, which directly impacts the overall latency. Additionally, the method offers a syndrome-matching decoder and improves the accuracy of the logical error rate for bivariate bicycle codes of distances six to eighteen, particularly at low error rates, when compared to the BP+OSD benchmark.

The emergence of technologies requiring high data rates and precise sensing information, such as autonomous vehicles and Internet of things devices, has driven the growing popularity of integrated sensing and communication (ISAC). ISAC provides an efficient framework where communication and sensing occur simultaneously or in coordination, and features two possible integration levels. First, radio-communications coexistence (RCC), which involves separate hardware, waveforms, and signal processing for communication and radar systems, but in a shared and coordinated spectrum use. Second, dual-function radar-communications (DFRC), which uses the same hardware, waveform, and other key resources for both functions. At the architectural level, intelligent metasurfaces are a key enabler for sixth-generation (6G) wireless communication due to their energy-efficient control of the propagation environment. The ability of metasurfaces to enhance both communication and sensing has led to numerous studies on metasurface-assisted ISAC performance gains. Additionally, some ISAC frameworks address limitations of reconfigurable intelligent surfaces (RIS) in communications, making their integration mutually beneficial. This survey reviews metasurface-assisted ISAC literature, outlining challenges and opportunities. We begin with background on ISAC and metasurfaces fundamentals. The core sections summarize state-of-the-art studies, focusing on metasurfaces as separate entities between transmitter and receiver, i.e., as RISs, emphasizing RCC and DFRC operations. We also explore holographic ISAC. Each category includes challenges, opportunities, and future research directions.

Integrated sensing and communication (ISAC) has been recognized as one of the key technologies for future wireless networks, which potentially need to operate in multiple frequency bands to satisfy ever-increasing demands for both communication and sensing services. Motivated by this, we consider the sum sensing rate (SR) optimization for a cooperative ISAC system with linear precoding, where each base station (BS) works in a different frequency band. With this aim, we propose an optimization algorithm based on the semi-definite rank relaxation that introduces covariance matrices as optimization variables, and we apply the inner approximation (IA) method to deal with the nonconvexity of the resulting problem. Simulation results show that the proposed algorithm increases the SR by approximately 25% and 40% compared to the case of equal power distribution in a cooperative ISAC system with two and three BSs, respectively. Additionally, the algorithm converges in only a few iterations, while its most beneficial implementation scenario is in the low power regime.

Integrated sensing and communication (ISAC) has been identified as a promising technology for the sixth generation (6G) of communication networks. Target privacy in ISAC is essential to ensure that only legitimate sensors can detect the target while keeping it hidden from malicious ones. In this paper, we consider a downlink reconfigurable intelligent surface (RIS)-assisted ISAC system capable of protecting a sensing region against an adversarial detector. The RIS consists of both reflecting and sensing elements, adaptively changing the element assignment based on system needs. To achieve this, we minimize the maximum sensing signal-to-interference-plus-noise-ratio (SINR) at the adversarial detector within sample points in the sensing region, by optimizing the transmit beamformer at the base station, the RIS phase shift matrix, the received beamformer at the RIS, and the division between reflecting and absorptive elements at the RIS, where the latter function as sensing elements. At the same time, the system is designed to maintain a minimum sensing SINR at each monitored location, as well as minimum communication SINR for each user. To solve this challenging optimization problem, we develop an alternating optimization approach combined with a successive convex approximation based method tailored for each subproblem. Our results show that the proposed approach achieves a 25 dB reduction in the maximum sensing SINR at the adversarial detector compared to scenarios without sensing area protection. Also, the optimal RIS element assignment can further improve sensing protection by 3 dB over RISs with fixed element configuration.

In this paper, we provide expressions for the secrecy outage probability (SOP) for suboptimal and optimal opportunistic scheduling schemes in a reconfigurable intelligent surface (RIS) aided single antenna system with multiple eavesdroppers in approximate closed form. A suboptimal scheduling (SS) scheme is analyzed, which is used when the channel state information (CSI) of the eavesdropping links is unavailable, and the optimal scheduling (OS) scheme is also analyzed, which is used when the global CSI is available. For each scheme, we provide a simplified expression for the SOP in the high signal-to-noise ratio (SNR) regime to demonstrate its behavior as a function of the key system parameters. At high SNR, the SOP saturates to a constant level which decreases exponentially with the number of RIS elements in the SS scheme and with the product of the number of RIS elements and the number of users in the OS scheme. We also show that the derived SOP of the SS scheme can directly provide the SOP for the best antenna-user pair scheduling scheme in a multiple antenna system. We compare the performance of the opportunistic user scheduling schemes with that of a non-orthogonal multiple access (NOMA) based scheduling scheme which chooses a pair of users in each time slot for scheduling and we show that the opportunistic schemes outperform the NOMA-based scheme. We also derive a closed-form expression for the SOP of a decode-and-forward (DF) relay-aided scheduling scheme in order to compare it with that of the RIS-aided system. It is found that the RIS-aided system outperforms the relay-aided systems when the number of RIS elements is sufficiently large. An increased number of RIS elements is required to outperform the relay-aided system at higher operating frequencies.