The Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Therefore, efficient indoor positioning technology has become a very active research topic. Bluetooth beacon positioning is one of the most widely used technologies. Because of the time-varying characteristics of the Bluetooth received signal strength indication (RSSI), traditional positioning algorithms have large ranging errors because they use fixed path loss models. In this paper, we propose an RSSI real-time correction method based on Bluetooth gateway which is used to detect the RSSI fluctuations of surrounding Bluetooth nodes and upload them to the cloud server. The terminal to be located collects the RSSIs of surrounding Bluetooth nodes, and then adjusts them by the RSSI fluctuation information stored on the server in real-time. The adjusted RSSIs can be used for calculation and achieve smaller positioning error. Moreover, it is difficult to accurately fit the RSSI distance model with the logarithmic distance loss model due to the complex electromagnetic environment in the room. Therefore, the back propagation neural network optimized by particle swarm optimization (PSO-BPNN) is used to train the RSSI distance model to reduce the positioning error. The experiment shows that the proposed method has better positioning accuracy than the traditional method.

Heterogeneous network (HetNet) with energy harvesting is a promising technique to provide perpetual power supplies and ubiquitous coverage as well as high data rate for next-generation wireless communications. In this paper, we consider a robust power allocation and power splitting (PS) problem for downlink simultaneous wireless information and power transfer (SWIPT)-enabled HetNets. The robust energy-efficiency (EE) maximization problem of femtocell users (FUs) is formulated under the outage-probability interference power constraint of macrocell user (MU), the maximum allowable transmission power of FU and the EE-based outage constraint of each FU. The originally fractional optimization problem with the probabilistic constraint is NP-hard and difficult to solve. Without knowing the distribution of uncertain parameters, a min-max probability machine approach is firstly introduced to convert the semi-infinite optimization problem into a deterministic one which is transformed into a deterministic convex one by using the Dinkelbach method and the quadratic transformation approach. An iterative power allocation and PS scheme is obtained based on convex optimization methods. Finally, the effectiveness of the proposed algorithm is demonstrated by simulation results from the perspective of EE and robustness.

Resource allocation (RA) is a key technique to guarantee the quality of service of users and maximize system capacity in cognitive non-orthogonal multiple access (NOMA) networks. However, traditional RA approaches have been proposed under perfect channel state information (CSI) which is too ideal in practical systems due to the impact of link delays, quantization errors, etc. In this paper, with imperfect CSI, a downlink robust RA algorithm is proposed for robust transmission to maximize the sum energy efficiency (EE) of secondary users (SUs) under channel uncertainties. Meanwhile it protects the minimum data rates of SUs, satisfying the maximum transmission power constraints of base stations and the maximum interference temperature constraint of each primary user (PU). The formulated problem is non-convex, thus challenging to solve. For delay-tolerant services, to deal with the intractability of the problem caused by outage probability constraints, the closed-form expressions of outage probabilities of SUs and PUs are derived under Gaussian CSI error models. For delay-sensitive services, the robust constraints with bounded uncertainty sets are transformed into convex ones. Based on successive convex approximation and the parameter transformation approach, the original problem is converted into a closed-form geometric programming problem solved by dual decomposition methods and subgradient methods. Additionally, users’ outage probabilities and the minimum required transmit power under the two modeling approaches are analyzed. Computational complexity and sensitivity analysis are provided. Simulation results show the proposed algorithm serves good robustness and EE.

Most recent object detection methods have achieved growing performance on public datasets. However, enormous efforts are needed for these methods due to the extensive annotations of ground-truth boxes. Weakly Supervised Object Detection (WSOD) methods hence have been proposed to solve this problem as only image-level annotations are required and then output bounding boxes related to the objects. In order to further elevate the weakly supervised detection methods on the extraction of reasonable features, the training of potential positive proposals, and the generation of proposals before training, we propose a new Combined Backbone and Advanced Selection Heads (CBASH) method with the proposals generated from the object semantic information. Specifically, Combined Backbone will make the unobvious object features more noticeable, Advanced Selection Heads promote more potential positive proposals to get training, and the generated object semantic proposals elevate the quality and quantity of positive proposals. The proposed method is evaluated on the challenging PASCAL VOC 2007 and 2012 benchmark datasets. Experimental results show that our proposed method can achieve improved performance on both VOC 2007 and VOC 2012 datasets and outperforms the existing state-of-the-art methods.

The rapid growth of low-Earth-orbit (LEO)satellites has enabled integrated space-air-ground networks to provide seamless connectivity to mobile users. However, these networks face challenges such as physical layer security risks from line-of-sight channels and the energy constraints of high-altitude platforms (HAPs), necessitating solutions for secure communication and energy efficiency. In this work, we address the challenges of energy efficiency and secure communication in space-air-ground networks, which are becoming critical with the increasing deployment of LEO satellites to support high-mobility users. We propose a novel downlink architecture where high-altitude platforms (HAPs) assist the LEO satellite in serving ground users. To tackle the demands of secrecy energy efficiency (SEE) in this dynamic and complex network, we formulate a non-convex optimization problem that jointly considers HAP trajectory, user-HAP association, and beamforming. The problem’s non-convexity makes it computationally challenging to solve in polynomial time. To overcome these challenges, we introduce a generative artificial intelligence (GAI)-based deep reinforcement learning (DRL) framework, named Gen-DRL, which leverages generative adversarial networks to empower its agents. This framework dynamically predicts and adapts to changes in the space-air-ground network environment by optimizing key parameters such as channel states, HAP trajectories, user associations, and beamforming. Compared to conventional methods, the proposed Gen-DRL achieves significant improvements in SEE by effectively managing complex interdependencies among multiple agents and intelligently adapting to the network’s goals and constraints. Extensive simulation results demonstrate that Gen-DRL consistently outperforms existing state-of-the-art frameworks in terms of secrecy energy efficiency, robustness to dynamic user locations, and adaptability to varying network parameters. This work provides new insights into the design of secure and energy-efficient space-air-ground networks, highlighting the potential of GAI-based DRL for future communication systems.

Conventional non-orthogonal multiple access (NOMA)-based spatial modulation (SM) systems utilize only the real part of amplitude-phase modulation (APM) symbols for transmission, but quadrature spatial modulation (QSM) can extend this to two orthogonal components to improve the spectral efficiency. In this way, we propose a NOMA-based QSM system in multiple-input multiple-output scenarios, where APM symbols are divided into real and imaginary components for transmission and then these two components are sent by real and imaginary transmit antennas, respectively. To further improve the performance of our proposed system, we also propose a user grouping and a power allocation with relatively low complexity. Different from previous schemes, the user grouping scheme is designed for considering channel conditions, user locations, and channel correlations jointly. Moreover, the power allocation scheme is performed in each group to meet users’ quality-of-service requirements, i.e., target-rate requirements. Simulation results demonstrate the effectiveness of proposed schemes by comparing with existing ones.