Huazhou Hou

Featuring energy-efficient and cost-effective communications for low power wide area network (LPWAN), LoRa (Long Range) has manifested its crucial role in the Internet-of-Things (IoT) industry. However, the existing terrestrial LoRaWAN (Long Range Wide Area Network) networks may fail to guarantee the pervasive connectivity, especially in rural and remote areas. To tackle this challenge, integrating LoRa-based low Earth orbit (LEO) satellite IoT into the LoRa ecosystem has aroused a growing interest in both academia and industry. To comprehend such a new paradigm, we investigate LoRa-based LEO satellite IoT in this article, focusing on its development opportunities, network framework and future research directions. In particular, we provide an in-depth insight into its feasibility and scalability by numerical simulations considering practical system parameters. Technical challenges are also discussed to specify its standardization progress.

With the rapid advancement of sixth-generation (6G) communications, uncrewed aerial vehicle (UAV) swarms have emerged as a key application scenario. However, their high mobility and complex electromagnetic environment pose significant communication challenges, such as frequent handovers and severe co-channel interference. Although conventional cell-free massive MIMO (CF-mMIMO) systems offer ubiquitous coverage, their high backhaul overhead and complex baseband processing limit practical deployment. To address these challenges, this paper proposes a novel cloud-edge-end architecture-based CF-mMIMO communication system to support robust communication in UAV swarm perception networks. Our designed scalable architecture leverages distributed edge units (EDUs) and user-centric distributed units (UCDUs) for collaborative processing, enabling distributed localized processing and user-centric coordination, thus supporting infinite collaboration expansion. Furthermore, we develop a hybrid signal detection scheme that combines EDU-level local minimum mean squared error estimation (MMSE) detection with UCDU-level global signal aggregation, achieving performance close to that of a centralized receiver while significantly reducing backhaul overhead. To address the problem of dynamic interference, we also propose a spatiotemporal-frequency forward continuous mean excision (FCME)-based interference detection framework. This framework effectively suppresses both narrowband and wideband interference by dynamically identifying interfered frequency bands and beams. Simulation results demonstrate that the proposed method outperforms conventional approaches in terms of signal combining and interference suppression, providing a reliable communication link for UAV swarm communications.

The integration of active user detection (AUD) and integrated sensing and communication (ISAC) enables the realization of communication and sensing functionalities over one hardware platform within the realm of ultra-massive machine-type communications (umMTC). However, the coupling of communication and sensing signals at the receiver poses challenges in accurately acquiring the respective parameters for both functionalities. In this paper, a unified tensor-based joint communication and sensing parameter estimation algorithm is proposed. First, leveraging CANDECOMP/PARAFAC decomposition (CPD), the channel model is converted into a unified tensor-based form, facilitating effective processing of the received signals. Subsequently, a two-stage CPD-based unified communication and sensing parameter estimation algorithm is developed. In the first stage, the factor matrix is estimated by utilizing the matrix subspace-based method and the Vandermonde property of the matrix. In the second stage, equivalent path parameters are extracted based on the estimated factor matrices. Furthermore, to solve the coupling problem of equivalent path parameters, a joint alternating iterative pilot-channel estimation (JAI-PCE) algorithm is proposed, effectively decoupling and accurately estimating the parameters. Simulation results verify the effectiveness of our proposed algorithm in terms of AUD, channel estimation, and radar sensing.

In this paper, we develop a distributed higher-order k -medoids clustering algorithm for networks using hop count as the distance metric, typical examples include social networks, wireless sensor networks and etc. Different than the classical k -medoids clustering where each cluster is assigned with one medoid (representative nodes in the network), the proposed algorithm is capable of partitioning the nodes into clusters dominated by multiple medoids. In this algorithm, a higher-order shortest path algorithm is first adopted to assign each node the clustering set comprising of its h ( ${h} \geq 1$ ) closest medoids using the hop count metric, then an aggregation algorithm collects the local information from each node to the medoids in its clustering set, based on which a medoids update mechanism helps each medoid decide whether to keep its medoid status or assign the status to one of its neighbors, so as to improve the clustering quality. The clustering performance of the proposed algorithm is proved to monotonically converge to a local optimum after running the algorithm finite times. Simulations results show that the proposed algorithm can achieve superior performance compared with several centralized $k$ -medoids clustering algorithms and scale well as the number of nodes increases.

This paper investigates the application of multi-agent reinforcement learning (MARL) algorithm to solve the joint spectrum and power allocation problem (JSPAP) in wireless network. The objective of JSPAP is to optimize the subband selection and transmit power levels for links, with the aim of maximizing the sum-rate utility function. To address the JSPAP with discrete subband selection and continuous power allocation, most existing algorithms rely on a centralized optimizer and the instantaneous global channel state information, which can be challenging to implement in large wireless networks with time-varying subbands. To conquer such limitation, a two-stage MARL algorithm is proposed, which comprises a top layer network for selecting subbands across all links and a bottom layer network for determining the transmit power levels for all transmitters. By utilizing the value decomposition technique in the top layer network, the links can cooperatively select transmission subbands, effectively resolving non-stationarity issues in wireless network. Additionally, in the bottom layer network of the proposed two-stage MARL algorithm, each transmitter selects the transmit power level based solely on the local information, thereby effectively reducing computational burden. Empirical experiments demonstrate the effectiveness of the proposed two-stage MARL algorithm by comparison with the state-of-the-art RL algorithms and fractional programming algorithms.

We consider the finite time input-to-state stability of autonomous discrete time systems, where the state enters a ball around the origin, with a radius determined by the input magnitude, in finite time. This extends the notion of classical input to state stability where this condition is only achieved asymptotically. We provide several types of Lyapunov functions that guarantee finite time input-to-state stability and characterize their equivalence. We also give converse Lyapunov theorems correcting a mistake in [1].

Networked nonlinear systems (NNSs) have great potential security threats because of malicious attacks. These attacks will destabilize the networked systems and disrupt the communication to the networked systems, which will affect the stability and performance of the networked control systems. Therefore, this paper aims to deal with the resilient control problem for NNSs with dynamically triggering mechanisms (DTMs) and malicious aperiodic denial-of-service (DoS) attacks. To mitigate the impact from DoS attacks and economize communication resources, a resilient dynamically triggering controller (RDTC) is designed with DTMs evolving an adaptive adjustment auxiliary variable. Thus, the resulting closed-loop system is exponentially stable by employing the piecewise Lyapunov function technique. In addition, according to the minimum inter-event time, the Zeno behavior can be excluded. Finally, the merits of the proposed controllers and theory are corroborated using the well-known nonlinear Chua circuit.