Inertial navigation and WiFi are two common approaches for pedestrian localization. However, conventional pedestrian dead reckoning (PDR) and WiFi fingerprinting suffer from limited adaptability to different users and poor robustness to environmental changes, respectively. Recent deep-learning-based methods address pedestrian localization by modeling sequential dependencies in inertial data, but they typically rely on a single inertial measurement unit (IMU), which is insufficient to capture the spatial correlations of human skeletal motion. In parallel, the Fine Time Measurement (FTM) procedure in IEEE 802.11mc enables round-trip time (RTT)–based ranging and localization, yet the coordinates of WiFi routers still require labor-intensive prior surveying, limiting deployment flexibility. This paper presents a pedestrian and router colocalization framework that jointly estimates pedestrian trajectories and WiFi router positions. The proposed framework employs multiple body-worn IMUs and a long short-term memory (LSTM) network to learn both spatial and temporal dependencies in human motion, thereby enabling velocity dead reckoning (VDR). The VDR-estimated pedestrian velocity is then fused with WiFi RTT measurements through factor graph optimization (FGO), in which both pedestrian and router coordinates are treated as unknown variables. Experimental results demonstrate that the multi-IMU-based VDR effectively models pedestrian velocity, while WiFi RTT ranging constrains the long-term drift of VDR. The combined VDR/WiFi RTT framework achieves meter-level positioning accuracy in both indoor and outdoor environments, without requiring pre-surveyed router coordinates, and thus provides a promising solution for pedestrian localization in the Internet of Things (IoT) era.

Modern industrial equipment’s growing complexity makes fault propagation a critical factor impacting operational efficiency and safety. Understanding the mechanisms and influencing factors of equipment fault propagation is therefore vital for enhancing reliability, optimizing maintenance strategies, and ensuring production safety. Based on the laws governing fault propagation within industrial equipment components, this study identifies three key influencing factors: interaction frequency, connection rate, and failure probability. It provides definitions and calculation methods for each. Utilizing these three factors and the SIRC infectious disease model, we construct a model to represent the fault propagation process in advanced industrial equipment. This model aims to mitigate fault propagation risk, thereby improving equipment reliability and the overall efficiency of the production system.

Indoor navigation and positioning services for pedestrians are challenging because of the lack of satellite signals and the unpredictability of pedestrian motion. The inertial measurement unit (IMU)-based pedestrian dead reckoning (PDR) algorithm can provide continuous position estimation for individual pedestrians. However, the accumulation of errors leads to inaccurate pedestrian position results. Radio signals such as ultra-wideband (UWB) can range between pedestrians and anchors and provide high-precision positioning information; nonetheless, radio positioning requires infrastructure deployment and maintenance in indoor environments, thus limiting the popularization and implementation of these technologies. In this paper, a neural network aided factor graph optimization (NN-FGO) method was proposed for collaborative pedestrian navigation (CPN). It integrates IMU and UWB sensors to implement PDR for individual pedestrians and CPN for the Ad-Hoc network, and it is infrastructure-free since all the sensors are wearable. For a small or sparse network, ranging constraints will be insufficient to implement an acceptable CPN. A neural network model was suggested for human activity recognition and position loopback detection, which provide virtual constraints for pedestrians. For the heterogeneous problem caused by multiple collaborative signals and constraints, FGO was employed to solve the motion states of multi-pedestrians and multi-epochs. The real experimental results revealed that NN-FGO can provide 92% accuracy in activity classification. Compared with the extended Kalman filter based CPN, the average position error decreased by 19.6% and 16.0% with triangular and parallel straight geometries, respectively.

This paper presents a novel quadrotor UAV attitude control algorithm to realize complex acrobatic UAV maneuvers. A nonlinear dynamic model based on the exponential coordinates parametrization of rotation is proposed. By analysing the model using Lie Group and Lie Algebra theory, cascaded linear PID controllers are designed. To further improve the controller performance, PID controllers are augmented with smith predictor and rotational trajectory planner. The experiments conducted on a real quadrotor show that our control algorithm surpasses most known quadrotor controllers.

A low complexity precoding method is proposed for practical multiple‐input multiple‐output (MIMO) orthogonal frequency‐division multiplexing (OFDM) systems. Based on the two‐step optimal precoder design algorithm that maximises the lower bound of the mutual information with finite‐alphabet inputs, the proposed method simplifies the precoder design by fixing the right singular vectors of the precoder matrix, eliminating the iterative optimisation between the two steps, and discretising the search space of the power allocation vector. For a 4 × 4 channel, the computational complexity of the proposed precoder design is reduced to 3 and 6% of that required by the original two‐step algorithm with quadrature phase shift keying (QPSK) and 8 phase‐shift keying ( 8PSK ), respectively. The proposed method achieves nearly the same mutual information as the two‐step iterative algorithm for a large range of signal‐to‐noise ratio (SNR) region, especially for large MIMO size and/or high constellation systems. The proposed precoding design method is applied to a 2 × 2 MIMO‐OFDM system with 2048 subcarriers by designing 1024 precoders for extended channel matrices of size 4 × 4. A transceiver test bed implements these precoding matrices in comparison with other existing precoding schemes. Indoor experiments are conducted for fixed‐platform non‐line‐of‐sight channels, and the data processing results show that the proposed precoding method achieves the lowest bit error rate compared with maximum diversity, classic water‐filling and channel diagonalisation methods.

We investigate the design of linear transmit precoding for multiple-input multiple-output (MIMO) broadcast channels (BC) with finite alphabet input signals. We derive an explicit expression for the achievable rate region of the MIMO BC with discrete constellation inputs, which is generally applicable to cases involving arbitrary user number and arbitrary antenna configurations. For the case where all the users employ the same modulation scheme, we further present a weighted sum rate upper-bound of the MIMO BC with identical transmit precoding matrices. The resulting bound demonstrates a serious performance loss due to multi-user interference for MIMO BC with finite alphabet inputs in high signal-to-noise ratio (SNR) region, which motivates the use of simple precoding to combat this multiuser interference. Based on a constrained optimization problem formulation, we apply the Karush-Kuhn-Tucker analysis to derive necessary conditions for MIMO BC precoders to maximize the weighted sum-rate. We then propose an iterative gradient descent algorithm with backtracking line search to optimize the linear precoders for each user. Numerical results illustrate that our proposed algorithm provides significant gains over other conventional precoding schemes including the traditional iterative water-filling (WF) design for the Gaussian input assumption.

We investigate the design of linear transmit precoder for multiple-input multiple-output (MIMO) broadcast channels (BC) with finite alphabet input signals. We first derive an explicit expression for the achievable rate region of the MIMO BC with discrete constellation inputs, which is generally applicable to cases involving arbitrary user number and arbitrary antenna number. We further present a weighted sum rate upper bound of the MIMO BC with identical transmit precoding matrices. The resulting bound exhibits a serious performance loss because of the non-uniquely decodable transmit signals for MIMO BC with finite alphabet inputs in high signal-to-noise ratio (SNR) region. This performance loss motivates the use of a simple precoding to combat the non-unique decodability. Based on a constrained optimization problem formulation, we apply the Karush-Kuhn-Tucker analysis to derive necessary conditions for MIMO BC precoders to maximize the weighted sum-rate. We then propose an iterative gradient descent algorithm with backtracking line search to optimize the linear precoders for each user. Our { simulation} results under the practical transmit symbols of discrete constellations demonstrate significant gains by the proposed algorithm over other precoding schemes including the traditional iterative water-filling (WF) design for the Gaussian input signals. For the low-density parity-check coded systems, our precoder provides considerably coded BER improvement through iterative decoding and detection.

This paper considers the precoder design for dual-hop amplify-and-forward relay networks and formulates the design from the standpoint of finite-alphabet inputs. In particular, the mutual information is employed as the utility function, which, however, results in a nonlinear and nonconcave problem. This paper exploits the structure of the optimal precoder that maximizes the mutual information and develops a two-step algorithm based on convex optimization and optimization on the Stiefel manifold. By doing so, the proposed algorithm is insensitive to initial point selection and able to achieve a near global optimal precoder solution. Besides, it converges fast and offers high mutual information gain. These advantages are verified by numerical examples, which also show the large performance gain in mutual information also represents the large gain in the coded bit-error rate.

The WiMAX technology has drawn enormous attentions from wireless communication industry in recent years. Its sophisticated physical layer (PHY) adopts such techniques as multiple-input and multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM), which are fundamentally different techniques from the previous 3G mobile system. To help the readers gain the knowledge of some key components of the WiMAX PHY before referring the lengthy standard documentation, we provide a brief overview of the physical layer of the WiMAX standard IEEE 802.16-2009. A simulation system with LDPC coded MIMO-OFDM is also established to evaluate the performance of the WiMAX transceiver. Simulations show that the iterative receiver structure can achieve good performance.

In this paper, we study linear precoding for multiple-input multiple-output (MIMO) multiple access channels (MAC) with discrete-constellation inputs. We derive the constellation-constrained capacity region for the MIMO MAC with an arbitrary number of users. Due to the non-concavity of the objective function, we obtain the necessary conditions for the weighted sum rate (WSR) maximization problem through Karush-Kuhn-Tucker (KKT) analysis. To find the optimal precoding matrices, we propose an iterative algorithm utilizing alternating optimization strategy and gradient descent update. Numerical results show that when inputs are digital modulated signals and the signal-to-noise ratio (SNR) is in the medium range, our proposed algorithm offers significantly higher sum rate than non-precoding and the traditional method which maximizes Gaussian-input sum capacity. Furthermore, the bit error rate (BER) results of a low-density parity-check (LDPC) coded system also indicate that the system with the proposed linear precoder achieves significant gains over other methods.

This paper investigates the linear precoder design that maximizes the average mutual information of multiple-input multiple-output channels with finite-alphabet inputs and statistical channel state information known at the transmitter. This linear precoder design is an important open problem and is extremely difficult to solve: First, average mutual information lacks closed-form expression and involves complicated computations; Second, the optimization problem over precoder is nonconcave. This study explores the solution to this problem and provides the following contributions: 1) A closed-form lower bound of average mutual information is derived. It achieves asymptotic optimality at low and high signal-to-noise ratio regions and, with a constant shift, offers an accurate approximation to the average mutual information; 2) The optimal structure of the precoder is revealed, and a unified two-step iterative algorithm is proposed to solve this problem. Numerical examples show the convergence and the efficacy of the proposed algorithm. Compared to its conventional counterparts, the proposed linear precoding method provides a significant performance gain.

In this paper, we investigate the optimal precoding scheme for relay networks with finite-alphabet constraints. We show that the previous work utilizing various design criteria to maximize either the diversity or the transmission rate with the Gaussian inputs assumption may lead to significant loss for a practical system with finite constellation set constraint. A linear precoding scheme is proposed to maximize the transmission rate, i.e., the mutual information, for relay networks. We exploit the structure of the optimal precoding matrix, and develop a unified two-step iterative algorithm utilizing the theory of convex optimization and optimization on the complex Stiefel manifold. Numerical examples show that this novel iterative algorithm achieves significant gains compared to its conventional counterpart.

In this paper, we study linear precoding for multiple-input multiple-output (MIMO) multiple access channels (MAC) with finite discrete inputs. We derive the constellation-constrained capacity region for the MIMO MAC with an arbitrary number of users and find that the boundary can be achieved by solving the problem of weighted sum rate maximization with constellation and individual power constraints. Due to the non-concavity of the objective function, we obtain a set of necessary conditions for the optimization problem through Karush-Kuhn-Tucker analysis. To find the optimal precoding matrices for all users, we propose an iterative algorithm utilizing alternating optimization strategy. In particular, each iteration of the algorithm involves the gradient descent update with backtracking line search. Numerical results show that when inputs are digital modulated signals and the signal-to-noise ratio is in the medium range, our proposed algorithm offers considerably higher sum rate than non-precoding and the traditional method which maximizes Gaussian-input sum capacity. Furthermore, a low-density parity-check coded system with iterative detection and decoding for MAC is presented to evaluate the bit error rate (BER) performance of precoders. BER results also indicate that the system with the proposed linear precoder achieves significant gains over the non-precoding system and the precoder designed for Gaussian inputs.

In this paper, we investigate the optimal power allocation scheme for relay networks with finite-alphabet constraints. It has been shown that the previous work utilizing various design criteria with the Gaussian inputs assumption may lead to significant loss for a practical system with finite constellation set constraint, especially when signal-to-noise ratio (SNR) is in medium-to-high regions, or when the channel coding rate is medium to high. An optimal power allocation scheme is proposed to maximize the mutual information for the relay networks under discrete-constellation input constraint. Numerical examples show that significant gain can be obtained compared to the conventional counterpart for nonfading channels and fading channels. At the same time, we show that the large performance gain on the mutual information will also represent the large gain on the bit-error rate (BER), i.e., the benefit of the power allocation scheme predicted by the mutual information can indeed be harvested and can provide considerable performance gain in a practical system.