Integrated sensing and communications (ISAC) is envisioned as a core technology in 6G, enabling base stations to jointly support radar and communication functions. However, this dual capability exposes opportunities for exploitation by malicious adversaries, thereby threatening the integrity and reliability of 6G cellular networks. To tackle this challenge, we propose a sensor network-based framework to detect and monitor unauthorized ISAC-based transmissions originating from a rogue base station within a restricted coverage area. Our method employs a novel signal-to-interference-plus-noise ratio (SINR)-based test statistic, which is then used by a classification filter to discriminate between ISAC and non-ISAC based signalling. Since the distribution of the test statistic is mathematically intractable, we instead derive closed-form expressions for the SINR coverage probability and establish its relationship to the detection performance. We analyze the impact of different point process-based sensor distributions on coverage probability and demonstrate that it serves as a robust indicator of the presence of ISAC-based transmissions. Extensive simulations demonstrate how detection performance depends on sensor network configurations, channel conditions, and system imperfections.

Joint radar and communications (JRC) is expected to be a key enabling technology in future 6G mobile networks where base stations simultaneously perform target sensing while communicating with mobile user equipments. However, the need for countering rogue base stations (RBS) that misuse JRC capabilities for malicious purposes has been a largely overlooked issue in existing studies. A RBS can misuse JRC functionality to gain control over unsuspecting mobile users and sense unintended radar targets, thereby compromising the fundamental security and reliability of a 6G network. To tackle this issue, this paper presents a novel scheme for detecting and monitoring a RBS that illegally uses JRC functionality in a restricted radio environment. The proposed detector consists of a sensor network that measures the signal-to-interference-plus-noise ratios (SINR) of the radar and communication signals. Novel test statistics are formulated to characterize beamforming behaviour that is indicative of illegal JRC-enabled transmissions. A classification filter that operates on a block of test statistic values is designed to determine if the RBS uses illegal JRC-enabled functionality. The simulation results show that with the optimum system parameter settings, a high detection accuracy can be achieved for wide coverage areas in low SINR regimes, and changes from JRC to non-JRC based transmissions can be accurately tracked.

With the increasing frequency and intensity of natural disasters, there is a necessity for advanced technologies that can provide reliable situational awareness and communication. Conventional systems are often inadequate due to unreliable infrastructure, power grid failures, high investment costs and scalability challenges. This paper explores the potential of ad-hoc mesh joint radar and communication (JRC) networks as a scalable, resilient, energy-efficient solution for disaster management that can operate independently of conventional infrastructure. The proposed JRC network enhances disaster response by integrating target detection (such as identifying vital signs, hazardous leaks, and fires) with communication capabilities to ensure efficient information dissemination under intense clutter conditions. Key performance metrics, including data rate, Signal-to-Clutter and Noise Ratio (SCNR), probability of detection, and false alarm rate, are used to assess performance. An optimization approach is proposed to provide an energy-efficient resource allocation scheme. The results show the performance of ad-hoc mesh JRC systems, underscoring their potential to enhance disaster management efforts by addressing unique operational challenges.

In this paper, multi-tier LiFi networks are studied in terms of energy efficiency (EE) and spectral efficiency (SE), which are crucial metrics for LiFi system design. We derived a closed-form expression of the user association probability for different tiers using stochastic geometry based Poisson Voronoi Tessellation (PVT) LiFi network. The performance metrics of the network, EE and SE, are analyzed in terms of different parameters such as transmit power and Lambertian index. Performance evaluations and numerical results show that multi-tier LiFi networks have an optimum transmit power in which EE is maximized. Besides, increasing the transmit power does not increase SE after passing a threshold point. The resulting trade-off between EE and SE is presented.

This paper analyzes a new and accurate overthe-air time synchronization (TS) method for industrial LiFi networks which achieves synchronization via the exchange of timestamps between nodes. The necessity for accurate TS will become increasingly important with future networks due to its connection with industrial applications. Over-the-air TS vision is to achieve accuracy of the order of 1 µs. In this technique, timestamps are transmitted using optical signals which are used for the estimation of the time-of-arrival (TOA). To this end, the Cramer-Rao Lower Bound (CRLB) is computed as the theoretical limit on the performance and accuracy. In this way, the effects of distance, optical power, and semi-angle at half illuminance of the transmitter are investigated. Calculations and comparison show that the proposed technique can be efficiently used for TS in future wireless networks.

This paper presents a two-tier LiFi network and analyses the cross-tier handover rate between the primary and secondary cells. Based on the semiangle at half illuminance of the primary and secondary cells, we propose coverage model for the secondary cells. Using stochastic geometry, closed-form expressions are derived for the cross-tier handover rate and sojourn time in terms of the received optical signal intensity, time-to-trigger and user mobility. The analytical models are validated with simulation results.