User Terminals Research Articles

Photonic Gateway Architecture Extension and Guard Time-Free Initial Connection Method with Point-to-Multipoint Control of Remote User Terminals for Metro/Access Converged All-Photonics Network

Photonic Gateway Architecture Extension and Guard Time-Free Initial Connection Method with Point-to-Multipoint Control of Remote User Terminals for Metro/Access Converged All-Photonics Network

Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links.

Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation. While there is growing interest in using satellite-to-earth microwave links (SMLs) for machine learning-based precipitation estimation, direct rainfall estimation from raw signal-to-noise ratio (SNR) data via deep learning remains underexplored. This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. We develop real-time models for rainfall detection and estimation using downlink SNR signals from satellites to user terminals. By leveraging a year-long dataset from multiple locations-including SNR measurements paired with disdrometer and rain-gauge data-we explore and evaluate various ML models. Our final models include ensemble approaches for both rainfall detection and cumulative rainfall estimation. The proposed models provide a reliable solution for estimating precipitation using Earth-satellite microwave links, potentially improving precipitation monitoring. Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a 46% reduction in the root mean squared error (RMSE) for event-based cumulative precipitation predictions.

Design of an Integrated System for Spaceborne SAR Imaging and Data Transmission.

In response to the conflicting demands between real-time satellite communication and high-resolution synthetic aperture radar (SAR) imaging, we propose a method that aligns the data transmission rate with the imaging data volume. This approach balances SAR performance with the requirements for real-time data transmission. To meet the need for mobile user terminals to access real-time SAR imagery data of their surroundings without depending on large traditional ground data transmission stations, we developed an application system based on filter bank multicarrier offset quadrature amplitude modulation (FBMC-OQAM). To address the interference problem with SAR signals' transmission and reception, we developed a signal sequence based on spaceborne SAR echo and data transmission and reception. This system enables SAR and data transmission signals to share the same frequency band, radio frequency transmission system, and antenna, creating an integrated sensing and communication system. Simulation experiments showed that, compared to the equal power allocation scheme for subcarriers, the echo image signal-to-noise ratio (SNR) improved by 2.79 dB and the data transmission rate increased by 24.075 Mbps.

ORR-CP-ABE: A secure and efficient outsourced attribute-based encryption scheme with decryption results reuse

Attribute-based encryption that supports computation outsourcing has been a promising approach to implement fine-grained access control in the Internet of Things (IoT). However, existing outsourcing schemes only focus on minimizing computation on user terminals while overlooking the significant computational burden faced by outsourcing devices. In IoT scenarios with a large volume of concurrent outsourcing requests, such burden can overwhelm outsourcing devices, leading to inefficient decryption execution. This paper, for the first time, proposes ORR-CP-ABE, an outsourced attribute-based encryption scheme with decryption results reuse. In ORR-CP-ABE, users with equivalent access privileges are grouped and issued group-oriented translation keys. Through translation, outsourcing devices can directly reuse previously calculated decryption results of other group members, eliminating redundant outsourced task execution. To further alleviate the burden on outsourcing devices, we use Named Data Networking as the underlying network layer to deploy translation functions and distributed caching for results reuse in the edge network. Furthermore, the reuse behavior in our scheme is configured to be under the control of both the data owner and the initial invoker of results. The security analysis proves the security of ORR-CP-ABE. Performance analysis indicates that it significantly reduces the burden on outsourcing devices and improves decryption efficiency by up to 90% compared to the baseline.

A compact electron beam ion source for highly charged ion experiments at large-scale user facilities

Probing and manipulating of 2D materials and their heterostructures using slow highly charged ions (HCIs) is currently a hot topic due to the ultimate surface sensitivity of electronic sputtering with profound implications for fundamental research and technological applications. To study surface modifications without the complications of sample transport from ion irradiation to complex microscopic or spectroscopic analysis tools, the development of compact and thus portable ion sources is essential. In this paper we present the first results of the electron beam ion source-Compact version 1 (EBIS-C1), a novel and highly compact source for highly charged ions manufactured by D.I.S Germany GmbH. The main focus of this paper is to demonstrate the suitability of the EBIS-C1 as an ideal source for ion scattering experiments at surfaces and at gas/liquid jet targets by presenting the first charge state spectra of extracted neon, argon and xenon ions. The results highlight the potential of this portable EBIS to become a versatile platform for the study of HCI-surface interactions, allowing investigations to be carried out at user terminals in different laboratory environments.

ASAP: IEEE 802.11ax-based seamless access point handover for moving vehicles

The increasing number of connected and automated vehicles has led to a sharp increase in the demand for network access of moving vehicles. Although 5G networks support terminals with high mobility, the traffic load is too heavy to bear if all the vehicles have a large amount of data for transmission. Therefore, IEEE 802.11-based wireless network is a complementary offload solution to provide high-speed network access for vehicles with low cost, easy deployment and high scalability. However, frequent network handover of moving vehicles between multiple roadside access points (APs) results in network performance degradation, which is one of the challenges in vehicular communications. In this paper, we propose a framework (referred to as ASAP) based on the up-to-date IEEE 802.11ax standard to provide moving vehicles with seamless handover between multiple APs. By leveraging the high efficiency (HE) sounding protocol of IEEE 802.11ax, each AP is capable to monitor the current location of moving vehicles in real time. In addition, a mechanism is also proposed for AP uplink/downlink transmissions through collaboration between the APs and the backbone network to achieve seamless handover for moving vehicles. Since ASAP is based on IEEE 802.11ax, the compatible security scheme such as IEEE 802.11i can be applied to ASAP for security enhancement. The proposed solution does not require any modification on the user terminals, making it possible to be implemented in practice. Extensive simulations show that ASAP significantly reduces the network handover delay to microsecond level, and improves network throughput up to 59% compared with the state-of-the-art methods.

Multiple Nodes Co-Carrier Cooperative Transmission in LEO Communication Networks: Developing the Diversity Gain of Satellites.

Low Earth orbit (LEO) satellite communication (SATCOM) networks have gradually been recognized as an efficient solution to enhance ground-based wireless networks. As one of the main characteristics of LEO SATCOM, the beam-edge area could be covered by multiple satellite nodes. In this case, user terminals (UTs) located at the beam-edge have the chance to connect one or more LEO satellites. To develop the diversity gain of multiple nodes in the overlapping area, we propose two high spectral efficiency cooperative transmission strategies, i.e., directly combining (DC) and selection combining (SC). In the DC scheme, signals arrived at the UT simultaneously could be combined into one enhanced signal. For downlink time division multiplexing, the SC scheme enables the UT to select the strongest signal path. Further, as there exists a significant channel gain difference of the beam-center and beam-edge areas, UTs in these two areas can be allocated in one resource block. In this case, we derive co-carriers based on DC and SC, respectively. To deeply analyze the novel methods, we study the ergodic sum-rate and outage probability while the outage diversity gain is further provided. Simulation results show that the co-carrier-based DC method has the ability to provide a higher ergodic sum-rate while the SC method performs better in terms of the outage probability.

ChirpPair: packet acquisition in uncoordinated access channels of Low Earth Orbit (LEO) satellite networks

Low Earth Orbit (LEO) satellite networks provide global data service coverage and has become increasingly popular. Uncoordinated access channels reduce data latency in LEO networks by allowing user terminals to transmit data packets at random times to the satellite without any coordination overhead. In this paper, packet acquisition in uncoordinated access channels of LEO networks is studied and a novel solution, called ChirpPair, is proposed, with which the satellite can detect the packets as well as estimating key parameters of the packets for data demodulation. With ChirpPair, the packet preamble consists of a chirp and its conjugate, where a chirp is a complex vector with constant magnitude and linearly increasing frequency. ChirpPair adopts a multi-stage process that gradually increases the estimation accuracy of the parameters without incurring high computation complexity. ChirpPair has been demonstrated in real-world experiments with over-the-air transmissions. ChirpPair has also been evaluated by simulations with the 3GPP New Radio (NR) Non-Terrestrial Network (NTN) channel model and the results show that ChirpPair achieves high accuracy despite its low computation complexity.

A Pseudo-Satellite Fingerprint Localization Method Based on Discriminative Deep Belief Networks

Pseudo-satellite technology has excellent compatibility with the BDS satellite navigation system in terms of signal systems. It can serve as a stable and reliable positioning signal source in signal-blocking environments. User terminals can achieve continuous high-precision positioning both indoors and outdoors without any modification to the navigation module. As a result, pseudo-satellite indoor positioning has gradually emerged as a research hotspot in the field. However, due to the complex and variable indoor radio propagation environment, signal propagation is interfered with by noise, multipath, non-line-of-sight (NLOS) propagation, etc. The geometric relation-based localization algorithm cannot be applied in indoor non-line-of-sight environments. Therefore, this paper proposes a pseudo-satellite fingerprint localization method based on the discriminative deep belief networks (DDBNs). The method acquires the model parameters of pseudo-satellite multi-carrier noise density signal strength in non-line-of-sight indoor spaces through a greedy unsupervised learning method and gradient descent-supervised learning method. It establishes a mapping relationship between the implied features of the pseudo-satellite multi-carrier noise density signal strength and indoor location, enabling pseudo-satellite fingerprint matching localization in indoor non-line-of-sight environments. In this paper, the performance of the positioning algorithm is verified in dynamic and static scenarios through numerous experiments in a laboratory environment. Compared to the commonly used localization algorithms based on fingerprint library matching, the results demonstrate that, in indoor non-line-of-sight test conditions, the system’s 2D static positioning has a maximum error of less than 0.24 m, an RMSE better than 0.12 m, and a 2σ (95.4%) positioning error better than 0.19 m. For 2D dynamic positioning, the maximum error is less than 0.36 m, the average error is 0.23 m, and the 2σ positioning error is better than 0.26 m. These results effectively tackle the challenge of pseudo-satellite indoor positioning in non-line-of-sight environments.

Beamforming for the Successive Relaying-Based Cooperative Non-Orthogonal Multiple Access Transmission

In this paper, a set of cooperative non-orthogonal multiple access (NOMA) transmission schemes is proposed, in which two half-duplex relaying user terminals (UTs) successively deliver the AP signals toward two NOMA UTs. The successive relaying (SR) of two HD relays emulates the full-duplex (FD) relaying transmission such that the whole transmission of one data block is finished in one time slot. Different from FD relaying, where the removal of self-interference (SI) at the relay is challenging, the SR is suffering from cross-relay interference (CRI) between the two HD relays and the handling of this CRI is the key technical challenge. This CRI suppression is implemented by beamforming with multiple antennas at the relaying UTs, while the beamforming also does the role of maximizing the NOMA transmission efficiency. Two beamforming schemes are proposed, where the first method employs semi-definite relaxation (SDR) with four dimensional (4-D) search. Here, the four dimensions come from the four parameters reflecting the CRI levels at the relaying UTs and signal strengths at the NOMA UTs. This scheme alternates until the 4-D search range is reduced and the sum throughput rate saturates. The second method is computationally simpler than the first one since it avoids the SDR and alternation, and it finds the beamformer set in one shot calculation. Both schemes rely on zero forcing of the CRI. These two schemes along with the HDR relay-based cooperative NOMA scheme are compared in the numerical experiments, and the results exhibit their pros and cons in several aspects.

Геометрический фактор односпутниковой системы определения местоположения абонентских терминалов по оценкам частоты доплеровским методом

The article presents a mathematical description of the doppler method of satellite communication systems user terminals geolocation by a retransmitted signal, as well as an analytical expression of the geometric factor of determining the location of user terminals. The analysis of the geometric factor distribution on the Earth's surface in the radio visibility zone of the repeater satellite is carried out.

Interference Situational Aware Beam Pointing Optimization for Dense LEO Satellite Communication System

Recently, the low earth orbit (LEO) mega-constellation faces serious time-varying interferences due to spectrum sharing, dense deployment, and high mobility. Therefore, it is important to study the interference avoidance techniques for the dense LEO satellite system. In this paper, the interference situational aware beam pointing optimization technique is proposed. Firstly, the angle of departure (AoD) and angle of arrival (AoA) of the interfering links are obtained to represent the time-varying interference. Then, the interference avoidance problem for dense LEO satellite systems is modeled as a non-convex optimization problem, and a particle swarm optimization (PSO) based method is proposed to obtain the optimal beam pointing of the user terminal (UT). Simulations show that the relative error of the mean signal-to-interference plus noise ratio (SINR) obtained by the proposed method is 0.51%, so the co-channel interference can be effectively mitigated for the dense LEO satellite communication system.

Coverage extended MMF-based indoor OWC using overfilled launch and diversity reception

Speckle patterns generated as coherent optical beams are reflected by scattering elements. Multimode fibers (MMFs) can modify the transverse intensity distribution of speckle patterns with macro perturbations, i.e., pressures, providing a simple and low-cost way to achieve equivalent beam-steering for indoor optical wireless communications (OWCs) with divergent optical beams. However, the received optical power (ROP) variance severely limits the mobility of user terminals. In this paper, the issue is alleviated by using the overfilled launch of MMFs and the diversity gain of multi-receivers. By adjusting the axial spatial coupling distance between the MMF and the single mode fiber (SMF) emitting coherent laser, the number of excited modes of MMF can be significantly increased at 1550 nm with negligible coupling and bending losses. In addition, the signal-to-noise ratio (SNR) enhancement obtained by applying two receivers is theoretically analyzed for the case when either thermal noise or shot noise is dominant. The experimental results demonstrate that the proposed scheme can efficiently compensate for the ROP inhomogeneity, and at the same time it can extend the achievable full steering angle up to 12° at a 1.5-m free-space distance for bit error rate (BER) values of less than 3.8 × 10−3.

Photonic Gateway accommodating all types of wavelength paths for digital-coherent and IM-DD user terminals in all-photonic metro-access converged networks

In access and metro networks, there is increasing demand for guaranteed wide-bandwidth and stringent/deterministic end-to-end latency characteristics. Converging these networks by expanding dense wavelength-division-multiplexed metro networks to access areas is a promising approach to satisfy this demand. An All-Photonics Metro-Access Converged Network (APN) dynamically and flexibly provides a wavelength connection between any two points including user premises. This evolution in network architecture presents some technical challenges. First, from the access-node architecture viewpoint, conventional reconfigurable optical add/drop multiplexers (ROADMs) do not have functions for remote and in-channel user-terminal (UT) control or turn-back connection within the access area. Therefore, the APN cannot be achieved by simply adopting ROADMs as access nodes, which are defined as Photonic Gateways. Remote and in-channel UT controls are required to control the endpoints of a wavelength path beyond the carrier control-plane network. These types of control should be independent of the protocol and modulation/detection method for the main signal. In addition, the Photonic Gateway must flexibly and efficiently support a new type of wavelength path, specifically a turn-back connection within the access area, due to metro/access convergence. Second, from the viewpoint of the process for dynamic wavelength-path provisioning, the APN controller should autonomously discover physical connectivity between a UT and the Photonic Gateway connected via field access fibers. This paper proposes a novel Photonic Gateway architecture that flexibly and scalably provides various UTs with direct optical connections including turn-back configurations. This paper also proposes a method that autonomously recognizes physical connections between newly connected UTs and the Photonic Gateway. The proposed architecture and method are experimentally verified based on all types of wavelength-path provisioning that take at most 72 s using Photonic Gateway prototypes, 100-Gb/s digital-coherent UTs, and 10-Gb/s intensity-modulation direct-detection UTs. All types of wavelength paths that are provided over the APN architecture are considered including a turn-back connection between two UTs within the same access area and connections between two UTs that traverse the metro area with or without optical/electrical conversion.

Data formats and standards for opportunistic rainfall sensors.

Opportunistic sensors are increasingly used for rainfall measurement. However, their raw data are collected by a variety of systems that are often not primarily intended for rainfall monitoring, resulting in a plethora of different data formats and a lack of common standards. This hinders the sharing of opportunistic sensing (OS) data, their automated processing, and, at the end, their practical usage and integration into standard observation systems. This paper summarises the experiences of the more than 100 members of the OpenSense Cost Action involved in the OS of rainfall. We review the current practice of collecting and storing precipitation OS data and corresponding metadata, and propose new common guidelines describing the requirements on data and metadata collection, harmonising naming conventions, and defining human-readable and machine readable file formats for data and metadata storage. We focus on three sensors identified by the OpenSense community as prominent representatives of the OS of precipitation: Commercial microwave links (CML): fixed point-to-point radio links mainly used as backhauling connections in telecommunication networks Satellite microwave links (SML): radio links between geostationary Earth orbit (GEO) satellites and ground user terminals. Personal weather stations (PWS): non-professional meteorological sensors owned by citizens. The conventions presented in this paper are primarily designed for storing, handling, and sharing historical time series and do not consider specific requirements for using OS data in real time for operational purposes. The conventions are already now accepted by the ever growing OpenSense community and represent an important step towards automated processing of OS raw data and community development of joint OS software packages.

Task offloading and resource allocation for blockchain‐enabled mobile edge computing

AbstractMobile edge computing (MEC) has risen as an effective approach to support ubiquitous and prosperous mobile applications. Due to the strict delay requirements and the increasingly complex application environments, the computation efficiency and security have become the bottleneck that restricts the MEC system. Here, a blockchain‐enabled task offloading scheme is proposed, where the sensitive computation tasks of user terminals (UTs) can be offloaded to a blockchain‐assisted base station (BS). The MEC‐assisted BS helps UTs compute tasks while the blockchain consensus protocol ensures the security of the task offloading and computing process. To manage the allocation of computing resources between task offloading and blockchain consensus, the task offloading and resource allocation are formulated as a joint optimization problem. The aim of the problem is to minimize the energy consumption of UTs while guaranteeing the delay requirement. By transforming the original problem into a Markov decision process, a collective reinforcement learning algorithm is proposed to solve the problem in an online fashion. In the simulations, the convergence and the performance of the proposed scheme are evaluated. The simulation results show the effectiveness of the scheme.

Multiuser downlink communication for MIMO-VLC system using spatial modulation and optical orthogonal codes

In this paper, a novel multiuser spatial modulation (SM) scheme is proposed and applied in downlink multiple input-multiple output (MIMO) visible light communication (VLC) system. In the transmitter side, each user's temporal symbol is first modulated onto user specific optical orthogonal code (OOC), then being emitted by the specific LED that is activated by its spatial symbol. At the front end of each user terminal (UT), array of photodetectors (PDs) convert optical into electrical signal, then chip-level detection scheme is employed to eliminate multiuser interference (MUI). Two computationally attractive detection algorithms are then proposed: Maximum likelihood (ML) algorithm premises on jointly estimating both temporal and spatial symbols. While the two-stages algorithm first identifies the index of the activated LED followed by a M-ary PAM demodulator to extract the desired user's spatial and temporal symbols sequentially. Extensive Monte Carlo simulations are undertaken to evaluate system performance in terms of the overall symbol error rate (SER). It is demonstrated that the chip-level detection scheme can effectively alleviate MUI and both ML and the two-stages detection algorithms work reliably in various scenarios. Hence, it is promising to apply the proposed scheme in multiuser MIMO-VLC system.

PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks

In next-generation mobile communication scenarios, more and more user terminals (UEs) and edge computing servers (ECSs) are connected to the network. To ensure the experience of edge computing services, we designed an unmanned aerial vehicle (UAV)-assisted edge computing network application scenario. In the considered scenario, the UAV acts as a relay node to forward edge computing tasks when the performance of the wireless channel between UEs and ECSs degrades. In order to minimize the average delay of edge computing tasks, we design the optimization problem of joint UE–ECS matching and UAV three-dimensional hovering position deployment. Further, we transform this mixed integer nonlinear programming into a continuous-variable decision process and design the corresponding Proximal Policy Optimization (PPO)-based joint optimization algorithm. Sufficient data pertaining to latency demonstrate that the suggested algorithm can obtain a seamless reward value when the number of training steps hits three million. This verifies the algorithm’s desirable convergence property. Furthermore, the algorithm’s efficacy has been confirmed through simulation in various environments. The experimental findings ascertain that the PPO-based co-optimization algorithm consistently attains a lower average latency rate and a minimum of 8% reduction in comparison to the baseline scenarios.

Channel Estimation for LEO Satellite Massive MIMO OFDM Communications

In this paper, we investigate the massive multiple-input multiple-output orthogonal frequency division multiplexing channel estimation for low-earth-orbit satellite communication systems. First, we use the angle-delay domain channel to characterize the space-frequency domain channel. Then, we show that the asymptotic minimum mean square error (MMSE) of the channel estimation can be minimized if the array response vectors of the user terminals (UTs) that use the same pilot are orthogonal. Inspired by this, we design an efficient graph-based pilot allocation strategy to enhance the channel estimation performance. In addition, we devise a novel two-stage channel estimation (TSCE) approach, in which the received signals at the satellite are manipulated with per-subcarrier space domain processing followed by per-user frequency domain processing. Moreover, the space domain processing of each UT is shown to be identical for all the subcarriers, and an asymptotically optimal vector for the per-subcarrier space domain linear processing is derived. The frequency domain processing can be efficiently implemented by means of the fast Toeplitz system solver. Simulation results show that the proposed TSCE approach can achieve a near performance to the MMSE estimation with much lower complexity.

Resource Allocation Control of UAV-Assisted IoT Communication Device

This work aims to explore the effect of Resource Allocation and Control (RAC) of the Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) communication equipment. The communication congestion problem is caused by insufficient computing resources of macro base stations when many user terminals access the IoT system. To this end, this work innovatively constructs the IoT system combined with UAVs to satisfy the user’s communication experience and allocate channel resources simultaneously. Finally, a UAV-assisted IoT resource allocation and control model is constructed and tested through experiments on the simulation software to analyze its performance. The results indicate that the transmission delay of the model reported here is significantly lower than that of other algorithms with the increased number of end users and the proportion of tasks. When the number of users is 500, the transmission delay of data resources is 47.5s. Besides, it requires lower energy consumption and shorter task completion time while maintaining lower throughput. At the same time, the uplink transmission rate of the model is generally stable at 3.10bps; the uplink transmission rate of the entire wireless communication system shows an increasing trend with the increase of the transmit power. Therefore, it is found that the model constructed here has strong robustness and achieves an excellent data resource allocation effect while ensuring lower data delay. This model provides an experimental reference and contribution to the improvement of the performance of IoT systems and the accurate allocation of network resources.