In this paper, we investigate the outage probability of a multi-tier dual-hop terrestrial-low earth orbit (LEO) satellite-geostationary earth orbit (GEO) satellite hybrid wireless communication system. The system comprises multi-tier LEOs and one GEO act as relays in the uplink, which help the terrestrial ground station (S) transmit information to the terrestrial destination (D). In uplink transmission, we introduce a one-dimensional hardcore point process to model different altitudes of LEOs’ different tiers. We also use a generalized selection combining technique to achieve a trade-off between maximum ratio combining and selection combining. In downlink, GEO offers the maximum coverage to randomly distributed D. Moreover, the independent identically distributed Nakagami-m fading and shadowed Rician distribution are brought to model the different channels. Finally, Monte-Carlo simulations are presented to affirm the precision and accuracy of the derived analytical models and the proposed analysis. This framework offers crucial insights for system designers and network operators, enabling the optimization of resource allocation, relay strategies, and overall reliability in terrestrial-satellite hybrid networks.

This paper investigates the internal secrecy and external covertness of a mixed-trust autonomous aerial vehicle (AAV) communication system assisted by rate-splitting multiple access (RSMA). In this setting, a semi-trusted user with partial decoding capability poses an internal eavesdropping threat, while multiple distributed wardens attempt to detect the transmission from the AAV to the semi-trusted user, creating an external covertness challenge. To characterize these security aspects, a unified analytical framework is developed. First, the internal eavesdropping capability of the semi-trusted user is quantified by deriving a closed-form expression for its eavesdropping success probability. Based on the outcome of the eavesdropping attempt, tractable expressions for the secrecy outage probability of the legitimate user are obtained. Furthermore, the external covertness performance is analyzed by deriving closed-form false alarm probability, missed detection probability, and detection error probability (DEP) for an individual warden, together with the optimal detection threshold and the corresponding minimum DEP. The cooperative global detection performance with multiple wardens is further characterized under conservative fusion rules. Extensive Monte Carlo simulations validate the analytical results and, through a joint evaluation of secrecy, reliability, and covertness metrics, illustrate the feasible operating regions enabled by RSMA power allocation in comparison with a NOMA baseline. The results provide a comprehensive theoretical basis for the design of secure and covert AAV communication strategies in mixed-trust environments.

Achieving secure communication with a low probability of detection (covertness) is critical yet challenging, particularly when employing practical digital modulations that can compromise the statistical indistinguishability assumed in theoretical models. This paper introduces a novel end-to-end framework leveraging digitally modulated covert signal modeling, obfuscation, and deep learning to attain simultaneous covertness and reliability. Firstly, we propose a novel approach to covert performance evaluation for modulated covert signals against detection. To address the deteriorated covertness considering modulation schemes, we further propose generating Gaussianized camouflage signals via a multi-stage transmitter pipeline encompassing spreading, jitter, filtering, and non-linear transformations, designed to mimic noise statistics effectively. At the receiver, a specialized deep learning architecture, CovertSyncNet, performs robust joint dynamic synchronization and symbol recovery. This receiver incorporates dedicated components to precisely estimate time-varying chip offsets and invert the complex, nonlinear distortions inherent in the camouflaged signal, enabling accurate demodulation. Extensive simulations rigorously validate our approach, demonstrating that high reliability is maintained despite the heavy camouflage. Concurrently, enhanced covertness is confirmed through metrics indicating low statistical distinguishability from Gaussian noise. This work highlights the significant potential of deep learning to bridge the gap between theory and practice, realizing communication systems that are simultaneously reliable, secure, and highly covert, even under realistic operational conditions.

With the rapid advancement of unmanned aerial vehicle (UAV) technology in recent years, cooperative communication in UAV networks (UAV-Ns) has made significant strides. However, the effectiveness of UAV-Ns cooperative communication relies heavily on the accurate estimation of channel state information (CSI). Unlike terrestrial networks, the mobility of UAV introduces time-varying channel characteristics, which can substantially affect the overall system capacity. Therefore, this paper investigates the system capacity of UAV-Ns while accounting for the effect of jitter characteristics of UAV. Specifically, we propose a cooperative transmission model utilizing multiple UAV base stations (UAV-BSs) to enhance the signal quality received by ground users through coordinated multi-point (CoMP) transmission. Additionally, we present a jittering channel model, derive the channel autocorrelation function, and assess the capacity of the proposed system. To deal with the jitter, we introduce a jitter compensation scheme based on long short-term memory networks to counteract the effects of UAV jitter and improve the accuracy of channel precoding. Numerical results demonstrate that our approach significantly enhances the communication performance of UAV-Ns under the impact of jitter. Compared to the traditional method, our scheme improves the estimation accuracy of the channel state by up to 3.8%, highlighting the potential of distributed UAV-BSs with CoMP to strengthen UAV-Ns communication.

In recent years, in-band full duplex (IBFD) has garnered significant attention in wireless communications due to its potential to enhance spectral efficiency. To fully exploit the benefits of IBFD, we design a novel medium access control (MAC) protocol for wireless local area networks (WLANs), which takes into account the realities of asymmetric traffic. However, key challenges involve inter-station interference within asymmetric topologies and wastage of idle time slots under asymmetric traffic conditions. We propose a successive full-duplex communication (SFD) protocol, integrating a CSMA/CA-based uplink selection mechanism and a weight-based downlink selection algorithm to minimize interference probabilities. Furthermore, SFD incorporates a control frame structure, power control algorithm, and successive interference cancellation (SuIC) technique to address the challenge of control frame interference in continuous IBFD communication. Both theoretical analysis and experimental evaluations demonstrate that SFD achieves a 52% increase in throughput compared to the 802.11 Distributed Coordination Function (DCF) with Request to Send/Clear to Send (RTS/CTS) and a 25% improvement over A-Duplex.

Graphical User Interfaces (GUIs) serve as the primary medium for user interaction with mobile applications (apps). Within these GUIs, editable text views, buttons, and other visual elements exhibit different states following user actions. However, developers often present these states only in various colors without providing textual hints for blind users. This results in significant difficulties for blind users to discern the transitions in component states, thereby hindering their ability to proceed with subsequent actions. Traditional rule-based methods and attribute settings often struggle to adapt to diverse component styles and fail to address the component state changes influenced by context. Recently, pre-trained Large Language Models (LLMs) have demonstrated their generalization ability to various downstream tasks. In this work, we leverage LLMs and propose a tool called C omponent st a te s distinguishing GPT (CasGPT) to automatically distinguish component states in GUIs and provide corresponding textual hints, thereby aiding blind users in app usage. Our experiments demonstrate that CasGPT is a lightweight approach capable of accurately distinguishing component states (accuracy = 86.5%). The usefulness of our method is validated through a user study, where participants expressed positive attitudes toward it. Also, we compare and find that our method outperforms other open source LLMs and different versions of GPT.

Full-Duplex (FD) has been widely recognized as one of the core technologies to enhance the spectral efficiency (SE) in the fifth generation (5G) cellular networks. FD technology (compared to traditional Half-Duplex (HD) technology in theory) can achieve twice the SE of cellular networks, but it introduces severe self-interference (SI) and co-channel-interference (CCI). Multi-domain (antenna-, radio frequency- and digital-domain) SI cancellation (SIC) techniques are proposed to reduce SI to a level close to the noise floor. However, due to the severe impact of Doppler shift and the absence of prior information, eliminating CCI is more challenging than eliminating SI, which has become a prominent obstacle to improving the performance of FD cellular networks. To achieve high-performance CCI cancellation (CCIC), perfect channel state information (CSI) is required. In this paper, we propose a superimposed pilot (SP) scheme for CCIC in FD cellular networks. Moreover, to address the Doppler shifts in FD cellular networks, two new SP-based channel estimation algorithms are developed for CCI channel and uplink/downlink channel, respectively. Furthermore, the Cram´er-Rao Lower Bounds (CRLBs) are also theoretically derived. In addition, we derive maximum achievable sum rate with imperfect CCIC in FD cellular networks. Finally, numerical results show that our proposed SP scheme outperforms the conventional time-multiplexed pilot (TP) scheme in terms of CCIC performance, BER performance and maximum achievable sum rate.

Due to the unique merits of unmanned aerial vehicle (UAV) systems, they have already been harnessed for military, public, and civil applications. Time synchronization is a significant premise of formatting and applying UAV networks. However, the irregular high-speed mobile UAVs pose new challenges to time synchronization, especially when external time references are unavailable in some rigid scenarios. Therefore, in these harsh cases, self-time-synchronization (STS) without any external assistance should be concerned to overcome the relative velocity between UAVs caused by irregular high-speed motion. In this paper, a realistic timestamps model for the multi-UAV networks is established, and then a dynamic topology-based maximum likelihood estimator will be developed to carry out the STS. Furthermore, by introducing the information on frequency offset, a new estimator with the closed-form expression is proposed based on a two-way message exchange framework. After that, a tracking algorithm with the assistance of estimation results will be introduced to compensate for the time-varying change of the clock parameters for the dynamic topology UAV networks. To evaluate the performance of the estimator, both the estimation error and Cramér-Rao lower bound are analyzed. Numerical results show that the proposed algorithm exhibits its superiority in STS performance and computational complexity compared to the existing two-way message exchange algorithm using timestamps only.

The machine-type communication (MTC) enables a broad range of applications from mission-critical services to massive deployment of autonomous devices in the Internet of Things (IoT) networks. To release more spectrum resources for facilitating the explosive traffic of MTC in ultradense Cellular-IoT (CIoT) networks, full-duplex (FD) technology has been considered as a candidate mechanic due to its respective advantages in terms of the spectral efficiency (SE). Compared with the traditional half-duplex (HD) technology, the FD technology can (in theory) attain twice the SE gain. Nevertheless, the performance of FD technology is severely limited by both the self-interference (SI) and the mutual interference (MI) in the presence of multiple users. What is more serious is that when FD technology is used in a multiuser communication system, the system performance is very sensitive to the service load of the entire network, and it may even appear fragile in an ultrahigh load environment. In this article, we will delve into investigating the performance of the FD technique in the CIoT networks by considering the impacts of a variety of aspects, including the SI cancelation capability (SICC) of the FD-mode devices, the network’s workload, as well as the devices’ distribution density (DDD), the purpose of which is to maximize both the SE and the sum throughput (ST) of the network by optimizing those critical parameters. It is shown that the FD mode is capable of improving the ST of the CIoT networks in either the low-traffic-volume or low-device-density regime, provided that the devices’ SICC could be up to 100dB. At the same time, research results show that further increasing both the devices’ density and the traffic load, even without compromising the FD devices’ SICC, will still help improve the performance superiority of FD technology. Numerical results show that by implementing an appropriate workload-driven mode-selection scheme, we can sufficiently exploit the FD/HD gain according to the instantaneous radio frequency environment.

The hybrid nonlinear transceiver optimization problem of reconfigurable intelligent surface (RIS)-aided multi-user multiple-input multiple-output (MU-MIMO) downlink is investigated. Specifically, the Tomlinson-Harashima precoder (THP) and the hybrid transmit precoder (TPC) of the base station are jointly optimized with the linear digital receivers of mobile users. The triangular feedback matrix of the THP is optimized and the optimal solution is derived in closed form based on a matrix inequality. Moreover, in order to tackle the nonconvexity of the constant-modulus constraints imposed on the analog TPC, the Majorization-Minimization (MM) based reconfigurable optimization framework is proposed, which strikes a trade-off between the implementation complexity and system performance in a reconfigurable manner. Explicitly, our MM-based reconfigurable optimization framework is capable of optimizing the analog TPC in a dynamically reconfigurable manner on an element-by-element, column-by-column, row-by-row or block-by-block basis. Moreover, an MM-based reconfigurable algorithm is proposed for the optimization of the phase shifting matrix at RIS, which also suffers from constant-modulus constraints. In the proposed MM-based reconfigurable algorithm, the RIS can be partitioned into a series of subarrays for striking different performance vs. complexity tradeoffs. Finally, our numerical results demonstrate the performance advantages of the proposed nonlinear hybrid transceiver optimization techniques.

Satellite-aerial communication (SAC) is the most necessary and reliable way to bridge the aerial terminals with their remote command and control center when terrestrial relay infrastructure is unavailable. However, as an inescapable obstacle, adjacent space interference from neighboring satellites unavoidably leads to the loss of the performance of SACs. To uncover the impacts of space interference, in this paper, a SAC system including a satellite (S), an aerial platform (D), and a space interference (I) is considered. Specifically, S operates in a circular orbit and I, which is randomly distributed in the neighboring space around S, produces a jamming signal to interfere with the information delivery between S and D. Considering the randomness of S and I and employing geometric probability theory, an approximate and asymptotic outage probabilities of the considered SAC system are studied. Moreover, the multi-interference scenario is investigated accordingly. Finally, numerical results are presented to validate our proposed analysis and some insightful conclusions are achieved to reveal the effects of spatially random space interference on SAC.

In this paper, we consider the design of spiral constellations for channels affected by phase noise. The strength of the proposed constellations resides both on the performance and on the extreme simplicity of the design. The symbols can in fact be expressed in analytical form, and are uniquely defined through a single parameter that accounts for the phase and thermal noise variances. The performance of the proposed constellations are assessed in terms of information rate and error rate. Despite their simplicity, the new spiral constellations have excellent performance, especially when the constellation size grows large.