We study the physical-layer security of a cognitive radio system in the face of multiple eavesdroppers (EDs), which is composed of a secondary base station (SBS), multiple secondary users (SUs) as well as a pair of primary transmitter (PT) and primary receiver (PR), where the SUs first harvest energy from their received radio frequency signals transmitted by the PT and then communicate with the SBS relying on opportunistic scheduling. We consider two specific user scheduling schemes, namely the channel-aware user scheduling (CaUS) and energyaware user scheduling (EaUS). In the CaUS scheme, an SU having the best instantaneous SU-SBS link (spanning from SUs to SBS) will be activated to communicate with the SBS. By contrast, the EHbUS scheme takes into account both the amount of energy harvested from the PT and the instantaneous quality of the SU-SBS link. We analyze the security-reliability tradeoff (SRT) of both the CaUS and of the EaUS schemes in terms of their intercept vs outage probability. We also provide the SRT analysis of traditional round-robin user scheduling (RrUS) used as a benchmarker of the CaUS and EaUS schemes. We demonstrate that the EaUS scheme achieves the best outage and secrecy performance in the high main-to-eavesdropper ratio (MER) region, but a worse secrecy performance than the CaUS method in the low-MER region. Moreover, from a security vs reliability perspective, the CaUS outperforms both the EaUS and the RrUS in the low-MER region. Surprisingly, this also implies that although the user scheduling criterion of EaUS exploits the knowledge of both the amount of harvested power and instantaneous channel state information (CSI), it exhibits a degraded physical-layer security in the low-MER region, due to the increased harvested energy is beneficial not only for the legitimate SBS receiver, but also for the EDs.

Fingerprint localization has gained significant attention due to its cost-effective deployment, low complexity, and high efficacy. However, traditional methods, while effective for static data, often struggle in dynamic environments where data distributions and feature spaces evolve–a common occurrence in real-world scenarios. To address the challenges of robustness and adaptability in fingerprint localization for dynamic indoor environments, this article proposes DF-Loc, an end-to-end dynamic fingerprint localization system based on multisource unsupervised domain adaptation (MUDA). DF-Loc leverages historical data from multiple time scales to facilitate knowledge transfer in specific feature spaces, thereby enhancing generalization capabilities in the target domain and reducing reliance on labeled data. Extensive experiments conducted in office and classroom environments demonstrate that DF-Loc outperforms comparative methods in terms of both localization accuracy and robustness.

We study the physical-layer security of multiple source-destination (SD) pairs communicating within a wireless network in the face of an eavesdropper attacking the SD pairs. In order to protect the wireless transmission against eavesdropping, we propose a cooperation framework relying on two stages. Specifically, an SD pair is selected to access the total allocated spectrum using an appropriately designed scheme at the beginning of the first stage. The other source nodes (SNs) simultaneously transmit their data to the SN of the above-mentioned SD pair relying on orthogonal resources during the first stage. Then, the SN of the chosen SD pair transmits the data packets containing its own messages and the other SNs' messages to its dedicated destination node (DN) in the second stage. Finally, this dedicated DN will forward all the other DNs' data to the application center via the core network. We conceive a specific SD pair selection scheme, termed as the transmit antenna selection aided source-destination pair selection (TAS-SDPS). We continue by deriving the secrecy outage probability (SOP) expressions of both the TAS-SDPS conceived, as well as of the conventional round-robin source-destination pair selection (RSDPS) and of the conventional non-cooperative (Non-coop) schemes for comparison. Furthermore, we carry out the secrecy diversity gain analysis in the high main-to-eavesdropper ratio (MER) region, showing that the TAS-SDPS scheme is capable of achieving the maximum attainable secrecy diversity order. Additionally, we show that increasing the number of transmitting pairs will reduce the SOP, whilst increasing the secrecy diversity order of the TAS-SDPS scheme. It is demonstrated that the SOP of the TAS-SDPS scheme is better than that of the RSDPS and of the conventional Non-coop schemes. We also demonstrate that the secrecy diversity gain of the proposed TAS-SDPS scheme is $M$ times that of the RSDPS scheme in the high-MER region, where $M$ is the number of the SD pairs.

The combination of fingerprint positioning and 5G (the 5th Generation Mobile Communication Technology) offers broader application prospects for indoor positioning technology, but also brings challenges in real-time performance. In this paper, we propose a fingerprint positioning method based on a deep convolutional neural network (DCNN) using a classification approach in a single-base station scenario for massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation. The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences. First, the DCNN model is used to determine the sub-area to which the mobile terminal belongs, and then the weighted K-nearest neighbor (WKNN) matching algorithm is used to estimate the position within the sub-area. The positioning performance is simulated in a DeepMIMO indoor environment, showing that the classification DCNN method reduces the positioning time by 77.05% compared to the non-classification method, with only a 1.08% increase in average positioning error.

The deep integration of 5G and the Internet of Things has spurred a pressing demand for high-precision indoor positioning in scenarios such as industrial automation and smart healthcare. However, traditional fingerprinting-based positioning techniques face significant constraints, including multipath signal degradation and insufficient adaptability, especially in challenging outdoor-to-indoor scenarios that rely on signals from external base stations. Concurrently, existing deep learning methods face challenges such as high annotation costs and limited model receptive fields. To address these limitations, this paper proposes an intelligent fingerprinting positioning system specifically designed to achieve robust indoor positioning using 5G signals from a single outdoor base station. By innovating in data preprocessing, feature matrix optimization, and an attention-enhanced model, this system aims to overcome the bottlenecks of positioning in such complex environments. Experimental results based on measured data from a real-world office building validate the superior performance of the proposed positioning system, achieving a mean absolute error (MAE) of 1.5442 m, which represents a significant improvement over state-of-the-art benchmarks.