Reconfigurable intelligent surface (RIS) is regarded as a promising technology to potentially enhance communications and localization capabilities. In this paper, we investigate a challenging received signal strength (RSS) based localization problem utilizing RIS. In particular, we consider a mixed line-of-sight and non-line-of-sight (LOS/NLOS) scenario with multiple sources and sensors as well as one RIS. Firstly, we obtain a closed-form Cramér-Rao lower bound (CRLB) expression and formulate a joint optimization problem to minimize the CRLB. Due to the non-convex objective function and coupled relationship between variables, it is difficult to solve directly. To overcome such issues, a novel two-stage alternating localization and passive beamforming (PBF) scheme is proposed, where the optimization problems corresponding to two stages, namely the joint localization and NLOS identification (JLNI) stage and the PBF optimization (PBFO) stage are alternately addressed. Specifically, for the JLNI stage, we adopt alternating optimization, parametric sparse Bayesian dictionary learning and block coordinate descent approaches to jointly estimate the locations of sources and LOS/NLOS path condition. Subsequently, for the PBFO stage, we optimize the PBF vector by exploiting semi-definite relaxation, difference-of-convex programming and successive convex approximation approaches. Finally, compared with the state-of-the-art methods, numerical simulations demonstrate the superiority and effectiveness of the proposed scheme in terms of CRLB, localization error and NLOS identification accuracy.

Beam alignment is essential to compensate for the high path loss in the millimeter-wave (mmWave) Unmanned Aerial Vehicle (UAV) network. The integrated sensing and communication (ISAC) technology has been envisioned as a promising solution to enable efficient beam alignment in the dynamic UAV network. However, since the digital identity (DID) is not contained in the reflected echoes, the conventional ISAC solution has to either periodically feed back the D-ID to distinguish beams for multi-UAVs or suffer the beam errors induced by the separation of D-ID and physical identity (P-ID). This paper presents a novel dual identity association (DIA)-based ISAC approach, the first solution that enables specific, fast, and accurate beamforming towards multiple UAVs. In particular, the P-IDs extracted from echo signals are distinguished dynamically by calculating the feature similarity according to their prevalence, and thus the DIA is accurately achieved. We also present the extended Kalman filtering scheme to track and predict P-IDs, and the specific beam is thereby effectively aligned toward the intended UAVs in dynamic networks. Numerical results show that the proposed DIA-based ISAC solution significantly outperforms the conventional methods in association accuracy and communication performance.

Detecting arrays are mathematical structures aimed at fault identification in combinatorial interaction testing. However, they cannot be directly applied to systems that have constraints on the test parameters. These constraints are prevalent in real-world systems. This paper proposes constrained detecting arrays (CDAs), an extension of detecting arrays, which can be used for systems with constraints. The properties and capabilities of CDAs are examined with rigorous arguments. Moreover, two algorithms are proposed for constructing CDAs: one is aimed at generating minimum CDAs, and the other is a heuristic algorithm aimed at fast generation of CDAs. The algorithms were experimentally evaluated using a benchmark dataset. Experimental results show that the first algorithm can generate minimum CDAs if a sufficiently long generation time is allowed, and the second algorithm can generate minimum or near-minimum CDAs in a reasonable time. CDAs extend the range of application of detecting arrays to systems with constraints. The two proposed algorithms have different advantages with respect to array size and generation time.

With more and more vehicles becoming autonomous, intelligent, and connected, paying attention to the future usage of car human-machine interface (HMI) with these vehicles should also get more relevant. While car HMI has been addressed in several scientific studies, little attention is being paid to designing and implementing interactive glazing into everyday (autonomous) driving contexts. Through reflecting on what was found before in theory and practice, we describe an engineering psychology practice and the design of six novel future user scenarios, which envision the application of a specific set of augmented reality (AR) support user interactions. We also present evaluations conducted with the scenarios and experiential prototypes and found that these AR scenarios support our target user groups in experiencing a new type of interactions. The overall evaluation was positive, with some valuable assessment results and suggestions. We envision that this paper will interest applied psychology educators who aspire to teach how to operationalize AR in a human-centered design (HCD) process to students with little preexisting expertise or little scientific knowledge about engineering psychology.

The Unmanned Aerial Vehicle (UAV) swarm networks will play a crucial role in the B5G/6G network thanks to its appealing features, such as wide coverage and on-demand deployment. Emergency communication (EC) is essential to promptly inform UAVs of potential danger to avoid accidents, whereas the conventional communication-only feedback-based methods, which separate the digital and physical identities (DPI), bring intolerable latency and disturb the unintended receivers. In this paper, we present a novel DPI-Mapping solution to match the identities (IDs) of UAVs from dual domains for visual networking, which is the first solution that enables UAVs to communicate promptly with what they see without the tedious exchange of beacons. The IDs are distinguished dynamically by defining feature similarity, and the asymmetric IDs from different domains are matched via the proposed bio-inspired matching algorithm. We also consider Kalman filtering to combine the IDs and predict the states for accurate mapping. Experiment results show that the DPI-Mapping reduces individual inaccuracy of features and significantly outperforms the conventional broadcast-based and feedback-based methods in EC latency. Furthermore, it also reduces the disturbing messages without sacrificing the hit rate.

Flying ad hoc networks (FANETs) play a crucial role in numerous military and civil applications since it shortens mission duration and enhances coverage significantly compared with a single unmanned aerial vehicle (UAV). Whereas, designing an energy-efficient FANETs routing protocol with a high packet delivery rate (PDR) and low delay is challenging owing to the dynamic topology changes. In this article, we propose a topology-aware resilient routing strategy based on adaptive$Q$-learning (TARRAQ) to accurately capture topology changes with low overhead and make routing decisions in a distributed and autonomous way. First, we analyze the dynamic behavior of UAVs nodes via the queuing theory, and then the closed-form solutions of neighbors’ change rate (NCR) and neighbors’ change interarrival time (NCIT) distribution are derived. Based on the real-time NCR and NCIT, a resilient sensing interval (SI) is determined by defining the expected sensing delay of network events. Besides, we also present an adaptive$Q$-learning approach that enables UAVs to make distributed, autonomous, and adaptive routing decisions, where the above SI ensures that the action space can be updated in time with low cost. The simulation results verify the accuracy of the topology dynamic analysis model, and also prove that our TARRAQ outperforms the$Q$-learning-based topology-aware routing (QTAR), mobility prediction-based virtual routing (MPVR), and greedy perimeter stateless routing based on energy-efficient hello (EE-Hello) in terms of 25.23%, 20.24%, and 13.73% lower overhead, 9.41%, 14.77%, and 16.70% higher PDR, and 5.12%, 15.65%, and 11.31% lower energy consumption, respectively.

Free-space optical (FSO) links offer gigabit per second data rates and low system complexity, but suffer from atmospheric loss due to fog and scintillation. Radio-frequency (RF) links have lower data rates, but are relatively insensitive to weather. Hybrid FSO/RF links combine the advantages of both links. Currently, selection or "hard-switching" is performed between FSO or RF links depending on feedback from the receiver. This technique is inefficient since only one medium is used at a time. In this paper, we develop a "soft-switching" scheme for hybrid FSO/RF links using short-length Raptor codes. Raptor encoded packets are sent simultaneously on both links and the code adapts to the conditions on either link with very limited feedback. A set of short-length Raptor codes (κ = 16 to 1024) are presented which are amenable to highspeed implementation. A practical Raptor encoder and decoder are implemented in an FPGA and shown to support a 714 Mbps data rate with a 97 mW power consumption and 26360 gate circuit scale. The performance of the switching algorithms is simulated in a realistic channel model based on climate data. For a 1 Gbps FSO link combined with a 96 Mbps WiMAX RF link, an average rate of over 472 Mbps is achieved using the implemented Raptor code while hard-switching techniques achieved 112 Mbps on average.