Although LoRa is predominantly employed with the single-hop LoRaWAN protocol, recent advancements have extended its application to multi-hop mesh topologies. Designing efficient routing for LoRa mesh networks remains challenging due to LoRa’s low data rate and ALOHA-based MAC. Prior work often adapts conventional protocols for low-traffic, aboveground networks with strict duty cycle constraints or uses flooding-based methods in subterranean environments. However, these approaches inefficiently utilize the limited available network bandwidth in these low-data-rate networks due to excessive control overhead, acknowledgments, and redundant retransmissions. In this paper, we introduce a novel position- and energy-aware routing strategy tailored for subterranean LoRa mesh networks aimed at enhancing maximum throughput and power efficiency while also maintaining high packet delivery ratios. Our mechanism begins with a lightweight position learning phase, during which LoRa repeaters ascertain their relative positions and gather routing information. Afterwards, the network becomes fully operational with adaptive routing, leveraging standby LoRa repeaters for recovery from packet collisions and losses, and energy-aware route switching to balance battery depletion across repeaters. The simulation results on a representative subterranean network demonstrate a 185% increase in maximum throughput and a 75% reduction in energy consumption compared to a previously optimized flooding-based approach for high traffic.

Although LoRa (Long Range) wireless communication technology is most commonly used in the form of LoRaWAN, a single-hop Low Power Wide Area Network (LPWAN) protocol, recent advancements have seen the emergence of multi-hop and mesh LoRa networks. Among these, research on linear LoRa mesh networks for subterranean environments, where traditional LoRaWAN networks face challenges due to limited telecommunication infrastructure, insufficient range, and environmental constraints, is notable. In a linear LoRa mesh network, LoRa repeaters are arranged in a line to provide extended coverage in lengthy environments such as underground mines, pipelines, and tunnels. In this paper, we present a comprehensive analysis of the scalability of these networks, based on a series of simulation experiments conducted using LoRaMeshSim, the first-ever LoRa mesh network simulator, developed by us. We propose optimal selections for LoRa configurations and network properties, including the optimal repeater density, the optimal waiting period in our presented repeater algorithm featuring carrier sensing, and a strategic method of utilizing two frequency channels to enhance network performance. Employing our proposed optimizations, we demonstrate how linear LoRa mesh networks can effectively scale up to support numerous repeaters, end devices, and high-traffic loads.

Localizing text in low-light environments is challenging due to visual degradations. Although a straightforward solution involves a two-stage pipeline with low-light image enhancement (LLE) as the initial step followed by detection, LLE is primarily designed for human vision rather than machine vision and can accumulate errors. In this work, we propose an efficient and effective single-stage approach for localizing text in the dark that circumvents the need for LLE. We introduce a constrained learning module as an auxiliary mechanism during the training stage of the text detector. This module is designed to guide the text detector in preserving textual spatial features amidst feature map resizing, thus minimizing the loss of spatial information in texts under low-light visual degradations. Specifically, we incorporate spatial reconstruction and spatial semantic constraints within this module to ensure the text detector acquires essential positional and contextual range knowledge. Our approach enhances the original text detector's ability to identify text's local topological features using a dynamic snake feature pyramid network and adopts a bottom-up contour shaping strategy with a novel rectangular accumulation technique for accurate delineation of streamlined text features. In addition, we present a comprehensive low-light dataset for arbitrary-shaped text, encompassing diverse scenes and languages. Notably, our method achieves state-of-the-art results on this low-light dataset and exhibits comparable performance on standard normal light datasets. The code and dataset will be released.

This article presents an accurate phase response control (APRC) algorithm to realize directional modulation (DM), improving the physical layer security of Internet of Things (IoT) networks. Specifically, the concept of normalized array response control is developed to realize the phase response control, and the designed weight vector is constructed as the sum of an initial weight vector and an appended weight vector. The precondition of precise phase response control is theoretically derived, and a geometric interpretation of phase response adjustment is given. The APRC algorithm can derive an analytical solution to realize the precise phase response control and normalized magnitude response control jointly, and the computational complexity of the APRC algorithm is much lower than the existing approaches. Moreover, a flexible DM scheme based on the APRC algorithm is presented to realize the specific PSK symbol synthesis. Different from the existing works that are only feasible for the single direction PSK modulation design, the APRC algorithm is applicable for the more general multipath and quasistatic block Rayleigh channels. The synthesis results with fixed and random initial weights are illustrated to show the wide applicabilities of the APRC algorithm, and the bit error rate simulations are given to validate the APRC algorithm.

Designing a practical Continuous Variable (CV) Quantum Key Distribution (QKD) system requires an estimation of the quantum channel characteristics and the extraction of secure keys based on a large number of distributed quantum signals. On standard processors, it can take hours to reconcile the required number of quantum signals. This problem is exacerbated for Low Earth Orbit (LEO) satellite CV-QKD, where the satellite flyover time is less than a few minutes. A potential solution is massive parallelisation of the classical reconciliation where a large-code block is subdivided into many shorter blocks for individual decoding. However, the penalty of this procedure on the important final secured key rate is non-trivial to determine and hitherto has not been formally analysed. In this work, we fill this important knowledge gap via detailed analyses and experimental verification of a CV-QKD sliced reconciliation protocol that uses large block-length low-density parity-check decoders. Our new solution results in a significant increase in the final key rate relative to non-optimised reconciliation. In addition, it allows for the acquisition of quantum secured messages between terrestrial stations and LEO satellites within a flyover timescale even using off-the-shelf processors. Our work allows for optimised global quantum networks secured via fundamental physics.

This paper presents a beampattern synthesis scheme based on the single-point array response control with minimum pattern deviation (SPARC-MPD) method. The central concept of SPARC-MPD stems from adaptive array theory, and a closedform expression of the designed weight vector is devised. Comparing to the state-of-the-art methods like the accurate array response control (A2RC) approach, the SPARC-MPD method addresses the pattern distortion issue. Moreover, the SPARC-MPD method can realize accurate magnitude response control at one single direction and minimize the pattern deviation at any other direction simultaneously, leading to a faster convergence speed. Simulation results show the effectiveness of the SPARC-MPD method and the efficiency of the pattern synthesis scheme.

Quantum Key Distribution (QKD) has been regarded as a novel approach to establish a secure communication between two legitimate partners. With the recent success of space based experimental QKD systems, researchers are now focussing on implementing more practical and efficient QKD systems that meet the challenges faced in space-based implementations. One pressing problem is the need for efficient and easy to implement reconciliation schemes for practical QKD systems. Rate-adaptive schemes based on LDPC codes constitute an appealing implementation of the key reconciliation process since they cover the entire range of the channel parameter space with a limited set of pre-defined Mother codes. In this paper, we investigate in detail some rate- adaptive reconciliation schemes based on LDPC codes, illustrating how their performance compares to other set-ups in which fixed-rate non- adaptive LDPC codes optimised for different channel conditions are adopted. In particular, the impact rate-adaptive codes have on decoding complexity, and subsequently the overall secure key throughput, is quantified through full blown simulations of an entanglement-based version of the QKD protocol within the context of an entanglement source onboard a satellite. Our work highlights that even though rate adaptive schemes schemes for QKD reconciliation appear ideal for future space-based implementations of single- photon QKD, identification of ideal Mother codes for such schemes remains to be resolved.