Ultrasonic through-metal communication is critical for many applications in industrial IoT (IIoT) environments but suffers from frequency-selective fading, requiring orthogonal frequency-division multiplexing (OFDM). Accurate channel estimation with minimal pilot overhead is essential for optimizing OFDM performance. Existing methods for estimating ultrasonic through-metal communication channel ignore the sparsity of the ultrasonic through-metal channel impulse response (CIR), leading to high pilot overhead, while sparse estimation algorithms fail to exploit the CIR’s structural features. Therefore, a periodic sparsity enhanced channel estimation method via improved block sparse Bayesian learning (BSBL) for ultrasonic through-metal communication is proposed, which further improves the estimation accuracy and reduces the pilot overhead by integrating three key features of CIR—the periodic occurrence of echo blocks, their exponential attenuation, and their intrinsic waveform—into the Bayesian prior. Experiments show that the proposed algorithm significantly outperforms standard BSBL in estimation accuracy while substantially reducing pilot overhead compared to conventional non-sparse techniques. This method not only enhances estimation precision but also improves spectral efficiency and link reliability in resource-constrained and harsh IIoT environments.

5G-enabled unmanned aerial vehicles (UAVs) based emergency communications have attracted considerable attention for their rapid deployment and high-capacity links enabled by UAV mobility and mmWave capabilities. However, most existing studies prioritize service coverage optimization, often neglecting accurate 6D pose estimation of users and UAV-mounted base stations (BSs) calibration, which are crucial for robust beam alignment, positioning, and navigation. Traditional approaches generally assume known BS poses, an assumption invalidated by UAV mobility and deployment uncertainties, resulting in degraded positioning performance. To tackle the challenge of inaccurate BS poses, we first leverage multi-view geometry to estimate clock bias, thereby correcting synchronization errors, and subsequently derive an initial estimate of the user’s 6D pose using delay and angle measurements. Building on this, we propose a multi-view joint estimation framework that simultaneously estimates the user’s 6D pose and calibrates BS poses. This framework integrates a manifold-based BS pose uncertainty model with a hybrid maximum a posterior estimator, which combines prior pose information with the geometry of the Special Euclidean GroupSE(3). To benchmark performance, we derive a hybrid Cramér-Rao lower bound under uncertain BS pose priors. Simulation results verify that the proposed method significantly enhances user pose accuracy under BS pose uncertainty, achieving about 75% error reduction compared with the conventional method at a representative transmit power.

Millimeter-wave (mmWave) systems in the 30–300 GHz bands are among the fundamental enabling technologies of 5G and beyond 5G, providing large bandwidths, not only for high data rate communication but also for precise positioning services, in support of high accuracy demanding applications such as for robotics, extended reality, or remote surgery. With the possibility to introduce relatively large arrays on user devices with a small footprint, the ability to determine the user orientation becomes unlocked. The estimation of the full user pose (joint 3D position and 3D orientation) is referred to as 6D localization. Conventionally, the problem of 6D localization using antenna arrays has been considered difficult and was solved through a combination of heuristics and optimization. In this paper, we reveal a close connection between the angle-of-arrivals (AoAs) and angle-of-departures (AoDs) and the well-studied perspective projection model from computer vision. This connection allows us to solve the 6D localization problem, by adapting state-of-the-art methods from computer vision. More specifically, two problems, namely 6D pose estimation from AoAs from multiple single-antenna base stations and 6D simultaneous localization and mapping (SLAM) based on single- base station (BS) mmWave communication, are first modeled with the perspective projection model, and then solved. Numerical simulations show that the proposed estimators operate close to the theoretical performance bounds. Moreover, the proposed SLAM method is effective even in the absence of the line-of-sight (LoS) path, or knowledge of the LoS/non-line-of-sight (NLoS) condition.

Visible light communication (VLC) is seen as a potential access option for fifth-generation (5G) wireless communication (Wanget al., 2014) and (Ayyashet al., 2016) and beyond 5G (Strinatiet al., 2019). A reliable VLC system benefits from an accurate estimate of the receiver’s position and orientation. In many cases, the orientation of the receiver is estimated with an external orientation estimation device. However, these devices generally suffer from drift and misalignment, causing an uncertainty in the orientation presented to the receiver. Hence, the external device can only provide a probability distribution of the orientation to the position estimator, which can be used as prior information for the position estimation. Since the orientation of a receiver greatly affects the performance of a visible light system, the orientation uncertainty will degrade the performance of standard positioning algorithms, implying it should be taken into account when designing a robust positioning algorithm. In this paper, we design an received signal strength (RSS)-based hybrid position and orientation estimation algorithm using the hybrid maximum likelihood (ML)/maximuma posteriori(MAP) (HyMM) principle for a multiple LEDs - multiple photodiodes (PDs) (MLMP) system to take into account the presence of prior information on the orientation. The proposed HyMM estimator is compared with three existing estimators, i.e., the simultaneous position and orientation (SPO) estimator, the misspecified maximum likelihood (MML) estimator and the first-order-approximation-based positioning algorithm, subject to the orientation uncertainty. Further, in order to analytically assess the performance of the proposed estimator, the theoretical lower bound on the mean squared error (MSE), i.e. the hybrid Cramér-Rao bound (HCRB) for HyMM is derived. Computer simulations show an asymptotic tightness between the performance of the estimator and its associated theoretical lower bound.

Visible light communication (VLC) is seen as a supplement for fifth-generation (5G) wireless communication in short-range high data rate communication applications [1]. A reliable VLC system relies on an accurate estimate of the position and orientation of the receiver, which corresponds to the six-dimensional positioning problem mentioned in [2]. In this paper, we investigate the simultaneous position and orientation estimation (SPO) problem using received signal strength (RSS), for a visible light system containing multiple LEDs and multiple photodiodes (PDs) (MLMP). Although in general, the position and orientation of the receiver can be represented by a vector and a rotation matrix, respectively, the constraints imposed by the rotation matrix make the numerical optimization in the estimation process cumbersome, e.g, the commonly used constrained optimization method is often very complex and non-robust. Therefore, in this paper, we design two SPO algorithms using the principle of optimization on manifolds, which alleviates the constraints from the rotation matrix. In addition, we propose an initialization algorithm, based on the direct linear transformation (DLT) principle, to obtain an initial estimate in closed-form for the iterative algorithms. To evaluate the performance of the proposed RSS-based SPO algorithms, we derive the Cramer-Rao bound (CRB). In particular, the orientation error component of the CRB corresponds to the intrinsic CRB or the CRB on manifolds, which measures the error in the estimated rotation matrix in a physically meaningful way. Finally, computer simulations show an asymptotic tightness between the performance of the proposed algorithms and the theoretical lower bound, demonstrating the effectiveness of the proposed solutions.