Integrated sensing and communication is a key feature in next-generation wireless networks, enabling joint data transmission and environmental radar sensing on shared spectrum. In multi-user scenarios, simultaneous transmissions cause mutual interference on overlapping frequencies, leading to spurious target detections and degraded sensing accuracy. This paper proposes an interference detection and exploitation algorithm for sensing using spectrally interleaved orthogonal frequency division multiplexing. A statistically rigorous procedure is introduced to detect interference while controlling the familywise error rate. We propose an algorithm that estimates the angle by exploiting interference, while estimating the delay by avoiding the interference. Numerical experiments demonstrate that the proposed method reliably detects interference, and that the delay and angle estimation error approaches the Cramér–Rao lower bound.

Frequency-shifting reconfigurable intelligent surfaces (FS-RISs) represent a novel operation mode relying upon metamaterials capable of shifting the carrier frequency of transmitted signals. While the technology is still not mature for wider deployment, previous work has revealed its significant potential for improvements in wireless communication. This work expands the application domain of FS-RIS towards localization within cellular networks operating at sub-6 GHz. The FS-RIS upconverts signals transmitted by the base station to the millimetre-wave spectrum, redirecting them to the user and enabling precise position estimation through narrow, highly directive beams. To eliminate the overhead associated with traditional reference symbols, a superimposed training (ST) scheme is employed. This novel approach achieves accurate localization using only a single FS-RIS, similar to existing solutions based on multiple base stations or traditional RISs. The derived Cramér-Rao lower bound (CRLB) demonstrates that the proposed ST scheme surpasses the performance of conventional regular pilot approaches in localization accuracy while maintaining the data rate. Simulation results further validate the proposed method, showcasing significant performance gains regarding bit-error rate (BER) and position estimation error. These findings underscore the transformative potential of FS-RISs in enhancing spectrum utilization, optimizing interference management, and advancing localization applications in next-generation networks.

Frequency-shifting reconfigurable intelligent surfaces (FS-RISs) can be developed using a new generation of meta-materials that shift the original frequency spectrum of sent signals, it is also known as frequency conversion. Their application in wireless communications and localization remains in the early stages of development. This work proposes the FSRIS to provide localization services in an existing cellular network operated at sub- 6 GHz. It can up-convert the signal transmitted by the base station (BS) to the millimetre Wave and forward it to the user, allowing this to estimate its position thanks to the use of narrow directive beams. The proposed method significantly outperforms the existing solution based on multiple BSs in terms of the Cramér-Rao lower bound, which is analytically derived. An efficient data-aided beam-sweeping is provided to reduce the beam-training delay at FS-RIS without sacrificing the data rate. This work introduces a novel approach to utilizing FS-RIS in wireless systems, with the potential to reshape how we view spectrum and interference management, paving the way for more efficient communication and localization applications.

We propose an efficient solution to the state estimation problem in multi-scan multi-sensor multiple extended target sensing scenarios. We first model the measurement process by a doubly inhomogeneous-generalized shot noise Cox process and then estimate the parameters using a jump Markov chain Monte Carlo sampling technique. The proposed approach scales linearly in the number of measurements and can take spatial properties of the sensors into account, herein, sensor noise covariance, detection probability, and resolution. Numerical experiments using radar measurement data suggest that the algorithm offers improvements in high clutter scenarios with closely spaced targets over state-of-the-art clustering techniques used in existing multiple extended target tracking algorithms.

Wireless Sensor Networks (WSNs) play a pivotal role in diverse domains such as environmental sensing, industrial monitoring, and entertainment. This paper focuses on modeling the connectivity of the link considering the quality of service (QoS) criterion. This is a baseline for considering the probability density function (PDF) of the node’s distance from the base station (BS) to form a multi-hop wireless sensor networks (WSNs) structure. We delve into modeling the connectivity behavior in multi-hop networks, addressing scenarios involving non-homogeneous Poison Point Process (NHPPP) and homogeneous Poison Point Process (HPPP). Furthermore, we introduce closed-form expressions with reduced computational complexity. This contribution establishes a comprehensive framework for understanding and enhancing the performance of multi-hop wireless sensor networks.

Location information is often used as a proxy to guarantee the performance of a wireless communication link. However, localization errors can result in a significant mismatch with the guarantees, particularly detrimental to users operating the ultra-reliable low-latency communication (URLLC) regime. This paper unveils the fundamental statistical relations between location estimation uncertainty and wireless link reliability, specifically in the context of rate selection for ultra-reliable communication. We start with a simple one-dimensional narrowband Rayleigh fading scenario and build towards a two-dimensional scenario in a rich scattering environment. The wireless link reliability is characterized by the meta-probability, the probability with respect to localization error of exceeding the outage capacity, and by removing other sources of errors in the system, we show that reliability is sensitive to localization errors. The ϵ-outage coherence radius is defined and shown to provide valuable insight into the problem of location-based rate selection. However, it is generally challenging to guarantee reliability without accurate knowledge of the propagation environment. Finally, several rate-selection schemes are proposed, showcasing the problem’s dynamics and revealing that properly accounting for the localization error is critical to ensure good performance in terms of reliability and achievable throughput.

Intelligent metasurfaces are one of the favorite technologies for integrating sixth-generation (6G) networks, especially the reconfigurable intelligent surface (RIS), which has been extensively researched in various applications. Although many applications and studies of electromagnetic manipulation under the linear RIS topology were performed, applying coding sequences to the element switching enables the space-frequency scattering feature, referred to as Space-Time-Coding metasurface (STCM) topology. This type of topology causes impairments to the established communication methods by generating undesirable interference both in frequency and space, which is worsened when using wideband signals. Nevertheless, it can potentially bring forward useful features for sensing and localization. This work exploits STCM sensing capabilities in target detection, localization, and classification using narrowband downlink pilot signals at the base station (BS). The results of this novel approach reveal the ability to retrieve scattering points (SP) localization within the sub-centimeter and sub-decimeter accuracy depending on the SPs positions in space. We also analyze the associated detection and classification probabilities, which show reliable performance for both in the whole analyzed environment, especially when using the STCM information. We conclude that this method presents a promising approach for future integrated sensing and communications (ISAC) protocols by providing a tool to perform sensing and localization services using legacy communication signals.