Precisely estimating the propagation time error induced by complex terrains and meteorological factors during the propagation of the signal along the ground, also termed the additional secondary phase factor (ASF), is crucial to improve positioning accuracy in the eLoran system. Among all of the ASF estimation algorithms, the well-known kriging-based algorithms, a kind of methods that estimate the ASF only with limited ASF data, often suffer performance reduction, as the fixed basis functions adopted lack adaptability. In this paper, we propose a trend kriging algorithm, which dynamically selects basis functions to construct the optimal trend function, for estimating the ASF under limited ASF data. Specifically, we design an optimization framework that integrates the selection task of basis functions and the search task of the correlation parameter into a single problem for jointly handling these nested tasks. We then develop an objective function based on the weighted covariance model to ensure that the detrended data follows the spatial correlation feature. To tackle this problem with multiple local optima, we employ the niching differential evolution algorithm, a recently developed metaheuristic algorithm. The performance verification results based on the simulated and practical ASF data cautiously exhibit the superiority of our proposed algorithm. For example, based on the practical data, compared with the representative IDW, the OK, the UK 1, and the UK 2, our proposed algorithm reduces the root mean square error by 21.68%, 29.26%, 10.53%, and 23.45%, respectively.

Low Earth Orbit (LEO) satellite networks, with their low latency, high bandwidth, and global coverage, are becoming key technologies for applications like real-time video transmission. As satellite networks expand, effectively managing multicast traffic and optimizing bandwidth utilization have become major challenges for efficient video distribution. Although Software-Defined Multicast (SDM) technology has made progress in bandwidth optimization, existing SDM methods are still focused on constructing Steiner trees, making it difficult to address the dynamic changes and high-load issues in LEO satellite networks. This paper frames the multicast tree construction problem as a Joint Path Optimization Game (JPOG). We propose a Cooperative Game-Theoretic Routing (CGMR) Algorithm based on game theory, which optimizes multicast path selection and achieves load balancing by introducing a link cost-sharing mechanism. Additionally, we propose a two-stage A* path generation algorithm to improve path search efficiency. Theoretically, this paper proves that JPOG is a potential game and can converge to a pure strategy Nash equilibrium (PSNE) within a finite number of iterations. The results showed that JPOG outperformed other algorithms, achieving lower link load, path cost, and superior load balancing, demonstrating its effectiveness in optimizing multicast routing and resource management in large-scale LEO satellite networks.

Satellite networks hold great promise for future 6G communications because of their benefits such as wide coverage and large capacity. Since the end-to-end (e2e) queuing delay is regarded as one key factor affecting the quality of service (QoS) in satellite networks, accurate delay prediction is a critically important topic. However, the delay prediction is complicated due to the irregular and time-varying features of inter-satellite links (ISLs) and satellite-ground links (SGLs), such as discontinuity and alternation. In this paper, we propose to establish the multi-node satellite-to-ground communication procedure as a multi-hop tandemly queuing model and present a heterogeneous heterogeneous multi-hop martingale model for queuing delay analysis. Due to the unique time-varying characteristics, we propose to model the SGL and ISL services as the stationary Markov processes using the Markov chain Monte Carlo approach. To match the intermittency and burstiness of traffic, the data arrival and service processes are handled using the Markov process. We propose to use a scaling factor for reflecting the heterogeneity of data processing capability, and then present a novel approach to ensure the stability condition requirement of the multi-hop system. Using the multi-hop heterogeneous martingale approach, the tight upper bounds of the delay and backlog in heterogeneous links are derived, and then precise delay prediction can be obtained. Finally, numerous simulations are conducted to demonstrate the effectiveness and accuracy of the proposed martingale method in analyzing the system delay and backlog when compared to the existing stochastic network calculus method.

The rapid development of satellite communication technologies has significantly enhanced global connectivity, with Low Earth Orbit (LEO) satellite networks playing a crucial role. However, the high-speed movement of LEO satellites introduces dynamic and unstable network topologies, presenting challenges in routing and traffic engineering. Traditional routing algorithms, such as Dijkstra’s, often struggle to adapt to these dynamic conditions, leading to inefficiencies in load balancing and resource utilization. In response, this paper proposes an Iterative Region-Based Probabilistic Forwarding (IRPF) strategy within a Software-Defined Satellite Networking (SDSN) framework. Our strategy dynamically assigns forwarding probabilities to satellite ports based on link traffic weights, optimizing routing paths through a feedback iteration process. To prevent the algorithm from converging to local optima, we introduce a local adjustment mechanism that fine-tunes forwarding probabilities. The experimental results show that when the scale of the satellite network is relatively small, the IRPF strategy incurs lower total link traffic costs compared to the Dijkstra algorithm. Additionally, it improves the Gini coefficient and packet loss rate by approximately 20% and 30%, respectively. These results demonstrate the effectiveness and stability of this method in routing and traffic engineering.

To meet the growing demand for joint data and energy transmission, research on wireless information and power transfer is being promoted. The resonant beam enabled information and power transfer (RBIPT), which supports long-distance, high-power, and wide-bandwidth information and power transfer, has sparked widespread interest. The point-to-multipoint RBIPT system shows great promise for enabling simultaneous RBIPT for multiple receivers. However, the enabling system architecture has not been well studied in the literature, which is holding back the system implementation. To solve this problem, we propose a time division multiplexing RBIPT (TDM-RBIPT) system for multiple access, and constract a novel metric to evaluate the information and power transfer performance. We explore the TDM-RBIPT mechanism and design the architectures of the transmitter and the receiver. For the information transfer performance evaluation, we take system latency and throughput into consideration. We propose to estimate the system delay with the martingale theory by modeling the dynamic data processing procedures as Markovian processes with the markov chain monte carlo (MCMC) method. To evaluate the power transmission performance, we consider the transmitter’s power costs and the receivers’ power benefits. Numerical results reveal the effectiveness of the proposed TDM-RBIPT system and validate the accuracy of the proposed metric.

The emerging large-scale low earth orbit (LEO) broadband satellite networks manifest great potentials in distributing videos across the globe via efficient multicast techniques. However, existing work only studied IP multicast (IPMC) for LEO constellations, which suffers from limited scalability and tree performance. In this paper, we employ the promising software defined multicast (SDM) techniques in large-scale LEO constellations to empower satellite-based Internet video distribution. We present a multi-layer rectilinear Steiner tree (ML-RST) construction algorithm for multicast routing in large-scale LEO constellations. We extend the spanning graph and edge substitution to three-dimensional (3D) scenes. Based on multi-layer spanning graphs and multi-layer edge substitution approaches, we manage to efficiently construct ML-RSTs with${O}$(${n}$log${n}$) complexity. Experimental results show that our approach can achieve an average 10% improvement in bandwidth saving compared with existing algorithms.

The emerging low earth orbit (LEO) satellite networks are expected to provide the world’s most advanced Internet services. Besides, terrestrial networks are in constant evolution and already moving to embrace the relatively new paradigm of software defined networking (SDN). In this paper, we take the advantages of SDN features and leverage traffic engineering (TE) to enhance the ISL performance in broadband LEO satellite networks. We investigate unicast and multicast TE for SDN-enhanced LEO constellations to empower satellite-based Internet services. In LEO satellite networks, unicast supports ubiquitous network access and provides basic network services, while multicast features superior satellite-based video distribution. For unicast TE in grid ISL networks, we present a simple yet efficient${k}$-segment routing based strategy with segment routing (SR) techniques, which can achieve near optimal max link utilization when compared with the multi-commodity flow solution. In the meanwhile, our solution eliminates routing tables and only imposes little routing information stored in packet headers. For multicast TE, we employ rectilinear Steiner trees (RSTs) to maximize bandwidth saving and exploit obstacle-avoiding rectilinear Steiner trees (OARSTs) to address the contention of multiple multicast groups. Based on RSTs and OARSTs, we propose an effective per-flow management scheme to balance traffic among multiple multicast flows in the presence of limited link capacities. Simulation results demonstrate the effectiveness and efficiency of our approaches on reducing routing information and accommodating more multicast groups.

Many applications in wireless sensor networks (WSNs) require that sensor observations in a given monitoring area are aggregated in a serial fashion. This demands a routing path to be constructed traversing all sensors in that area, which is also needed to linearize the network. In this article, we present SURF, a Space filling cURve construction scheme for high genus three-dimensional (3D) surFace WSNs, yielding a traversal path provably aperiodic (that is, any node is covered at most a constant number of times). SURF first utilizes the hop-count distance function to construct the iso-contour in discrete settings, and then it uses the concept of the Reeb graph and the maximum cut set to divide the network into different regions. Finally, it conducts a novel serial traversal scheme, enabling the traversal within and between regions. To the best of our knowledge, SURF is the first high genus 3D surface WSN targeted and pure connectivity-based solution for linearizing the networks. It is fully distributed and highly scalable, requiring a nearly constant storage and communication cost per node in the network. To incorporate adaptive density of the constructed space filling curve, we also design a second algorithm, called SURF + , which makes use of parameterized spiral-like curves to cover the 3D surface and thus can yield a multiresolution SFC adapting to different requirements on travel budget or fusion delay. The application combining both algorithms for in-network data storage and retrieval in high genus 3D surface WSNs is also presented. Extensive simulations on several representative networks demonstrate that both algorithms work well on high genus 3D surface WSNs.

Rate Compatible Modulation (RCM) had been shown to be capable of achieving smooth rate adjustment in highly dynamic channel scenarios. However, there still exist two main problems in RCM: (1) The theoretical bound of the achievable rate, which is the benchmark for practical RCM design, has not been obtained. (2) The iterative decoding algorithm for RCM have a high computational complexity due to the long weight set with large value and the much more symbols required by receiver for successful decoding under poor channel conditions. To tackle these problems, Firstly, we get the theoretical achievable rate upper bound of RCM via analysis of the mutual information between transmitted and received symbols, and obtain an important result, i.e., in low SNR, the achievable rates of RCM are completely the same for the cases of large or small weight sets. Based on this result, a low-complexity RCM with variable weight sets (RCM-VWS) is proposed. In addition, we investigate the messages relationship between symbol nodes and information nodes in decoding algorithm based on Bayesian interference and analyze the computational complexity of the RCM-VWS we proposed. Theoretical analysis and simulations show that, in low SNR (<8dB), the computational complexity of the proposed scheme can be reduced by 95% compared with that of the conventional RCM, while preserving the same BER performance. So the average decoding efficiency is improved significantly.

Network Coordinate System (NCS) has drawn much attention over the past years thanks to the increasing number of large-scale distributed systems that require the distance prediction service for each pair of network hosts. The existing schemes suffer seriously from either low prediction precision or unsatisfactory convergence speed. In this paper, we present a novel distributed network coordinate system based on Robust Principal Component Analysis, RNC, that uses a few local distance measurements to calculate high-precision coordinates without convergence process. To guarantee the non-negativity of predicted distances, we propose Robust Nonnegative Principal Component Analysis (RUN-PACE) which only involves convex optimization, consequently resulting in low computation complexity. Our experimental results indicate that RNC outperforms the state-of-the-art NCS schemes.

Seamless Rate Adaptation(SRA) based on rate compatible modulation(RCM) is a new receiver rate adaptive method, in which a block of bits are mapped into a series of symbols by a sparse matrix for high order modulation. According to channel state, the receiver select of the proper number of symbols to iterative demodulation by RCM. This system can achieves smooth rate adjustment under highly dynamic channel conditions. However, for fast implementation, the convolution in the horizontal processing and the vertical processing required lots of multipliers in RCM demodulation algorithm using Belief Propagation(BP), which beyond the current hardware. Therefore, it is difficult to make full use of the parallelizable of the BP. After analysised the principle of the RCM, by the proper deformation, we have proposed the LLR-RCM algorithm, in which we used the log likelihood ratio (LLR) to iterative demodulation. So the convolution in the horizontal processing are transformed into additions and a function table, all the multiplications in the vertical processing are converted to additions, what's more, it save the normalization and make the possibility for fast implementation by hardware.

An undetermined measurement matrix can capture sparse signals losslessly if the matrix satisfies the restricted isometry property (RIP) in compressed sensing (CS) framework. However, existing measurement matrices suffer from high computational burden because of their completely unstructured nature. In this study, the authors propose to construct a novel measurement matrix with a specific structure, called sparse block circulant matrix (SBCM), to reduce the computational burden. The RIP of the proposed SBCM is also guaranteed with overwhelming probability. The simulation results validate that SBCM reduces the computational burden significantly whereas keeps similar signal recovery accuracy as Gaussian random matrices.

Exploiting the unique properties of the physical layer to enhance or complement authentication strength in wireless networks has attracted a lot of research attention recently. In this paper, we propose a novel PHYsical layer Challenge-Response Authentication Mechanism (PHY-CRAM) for wireless networks. PHY-CRAM is suitable for both one-way and mutual authentication. It fully utilizes the randomness, reciprocal, and location decorrelation features of the wireless fading channel, and is immune to various passive and active attacks. In the authentication procedure, challenge-response signals are exchanged at the physical layer, which allow two devices to verify their shared secrets while not revealing these secrets to attackers. PHY-CRAM adopts orthogonal frequency-division multiplexing (OFDM) technique which separately modulates the higher layer information and shared keys on subcarriers' phases and amplitudes respectively, in order to prevent channel probing from traffic-related information. We conduct extensive simulation study and develop a prototype using field-programmable gate array (FPGA) and discrete radio frequency (RF) components to evaluate PHY-CRAM in real-world environments. It shows that PHY-CRAM achieves both high successful authentication rate and low false acceptance rate in various channel conditions and under various attacks.

Range-free localisation methods are suitable for large scale wireless ad hoc and sensor networks because of their less-demanding hardware requirements. Many existing connectivity- or hop-count-based range-free localisation methods suffer from the hop-distance ambiguity problem where a node has a same distance estimation to all of its one-hop neighbours. In this study, the authors define a new measure, called regulated neighbourhood distance (RND), to address this problem by relating the proximity of two neighbours to their neighbour partitions. Furthermore, the authors propose a new RND-based range-free localisation method, and the simulation results show that it outperforms two existing peer algorithms in uniform and non-uniform network deployments.

Energy efficient broadcasting is of paramount importance for many broadcast applications in wireless ad hoc networks. With respects network coding, it has been proved that the energy gain is upper bounded by 3. However, the coding opportunity is often highly dependent on the established routing paths, resulting in that a lot of coding opportunities could be lost in practice. By combining network coding with the Connected Dominating Set (CDS)-based broadcasting, we take full use of network coding. The intuition behind our algorithm is to intersect information flows at nodes in CDS to increase the coding opportunities. We propose a novel scheme named NCAB, a Network Coding Aware based Broadcast routing mechanism, integrating the network coding and the dynamic implementation of connected dominating set. Our experimental results show that NCAB provides up to 169% gains compared to flooding, and 41% gains compared to CDS-based broadcasting.