Beyond Diagonal RIS-Aided Networks: Performance Analysis and Sectorization Tradeoff

Reconfigurable intelligent surfaces (RISs) have emerged as a spectrum- and energy-efficient technology to enhance the coverage of wireless communications within the upcoming 6G networks. Recently, novel extensions of this technology, referred to as multi-sector beyond diagonal RIS (BD-RIS), have been proposed, where the configurable elements are divided into $L$ sectors $(L \geq 2)$ and arranged as a polygon prism, with each sector covering $1/L$ space. This paper presents a performance analysis of a multi-user communication system assisted by a multi-sector BD-RIS operating in time-switching (TS) mode. Specifically, we derive closed-form expressions for the moment-generating function (MGF), probability density function (PDF), and cumulative density function (CDF) of the signal-to-noise ratio (SNR) per user. Furthermore, closed-form expressions for the outage probability, achievable spectral and energy efficiency, symbol error probability, and diversity order for the proposed system model are derived. Moreover, a comparison is performed with the simultaneously transmitting and reflecting (STAR)-RISs, a special case of multi-sector BD-RIS with two sectors. Our analysis shows that for a fixed number of elements, increasing the sectors improves outage performance at the expense of reduced diversity order compared to STAR-RIS. This trade-off is influenced by the Rician factors of the cascaded channel and the number of configurable elements per sector. However, this superiority in slope is observed at outage probability values below $10^{-5}$, which remains below practical operating ranges of communication systems. Additionally, simulations are provided to validate the accuracy of our theoretical analyses showing a notable $182\%$ increase in spectral efficiency and a $238\%$ increase in energy efficiency when transitioning from a 2-sector to a 6-sector configuration.

Digital Twin for Non-Terrestrial Networks: Vision, Challenges, and Enabling Technologies

The ongoing digital transformation has sparked the emergence of various new network applications that demand cutting-edge technologies to enhance their efficiency and functionality. One of the promising technologies in this direction is the digital twin, which is a new approach to design and manage complicated cyber-physical systems with a high degree of automation, intelligence, and resilience. This article discusses the use of digital twin technology as a new approach for modeling non-terrestrial networks (NTNs). Digital twin technology can create accurate data-driven NTN models that operate in real-time, allowing for rapid testing and deployment of new NTN technologies and services, besides facilitating innovation and cost reduction. Specifically, we provide a vision on integrating the digital twin into NTNs and explore the primary deployment challenges, as well as the key potential enabling technologies within NTN realm. In closing, we present a case study that employs a data-driven digital twin model for dynamic and service-oriented network slicing within an open radio access network (O-RAN) NTN architecture.

An Overview of Channel Models for NGSO Satellites

Satellite communications industry is currently going through a rapid and profound transformation to adapt to the recent innovations and developments in the realm of non-geostationary orbit (NGSO) satellites. The growing popularity of NGSO systems, with cheap manufacturing and launching costs, has set to revolutionize the internet market. In this context, accurate channel characterization is crucial for the performance optimization and designing efficient NGSO communications, especially considering the dynamic propagation environment. While the Third Generation Partnership Project (3GPP) has provided some guidelines in Release 15, we observed certain divergence on the channel models considered in the literature, each with different assumptions and peculiarities. This paper provides an extensive review of the existing methods proposed for NGSO channel modeling that consider different orbits, frequency bands, user equipment, use-case and scenario peculiarities. The provided review discusses the channel modeling efforts from a contemporary perspective through trade-off analyses, classifications, and highlighting their advantages and pitfalls. The main goal is to provide a comprehensive overview of NGSO channel models to facilitate the selection of the most appropriate channel based on the scenario requirements to be evaluated and/or analysed.

Joint Beam Hopping and Carrier Aggregation in High Throughput Multi-Beam Satellite Systems

Beam hopping (BH) and carrier aggregation (CA) are two promising technologies for the next generation satellite communication systems to achieve several orders of magnitude increase in system capacity and to significantly improve the spectral efficiency. While BH allows a great flexibility in adapting the offered capacity to the heterogeneous demand, CA further enhances the user quality-of-service (QoS) by allowing it to pool resources from multiple adjacent beams. In this paper, we consider a multi-beam high throughput satellite (HTS) system that employs BH in conjunction with CA to capitalize on the mutual interplay between both techniques. Particularly, an innovative joint BH-CA scheme is proposed and analyzed in this work to utilize their individual competencies. This includes designing an efficient joint time-space beam illumination pattern for BH and multi-user aggregation strategy for CA. Through this, user-carrier assignment, transponder filling-rates, beams hopping pattern, and illumination duration are all simultaneously optimized by formulating a joint optimization problem as a multi-objective mixed integer linear programming problem (MINLP). Simulation results are provided to corroborate our analysis, demonstrate the design tradeoffs, and point out the potentials of the proposed joint BH-CA concept. Advantages of our BH-CA scheme versus the conventional BH method without employing CA are investigated and presented under the same system circumstances.

Improved Gaussian-Bernoulli Restricted Boltzmann Machines for UAV-Ground Communication Systems

Unmanned aerial vehicle (UAV) is steadily growing as a promising technology for next-generation communication systems due to their appealing features such as wide coverage with high altitude, on-demand low-cost deployment, and fast responses. UAV communications are fundamentally different from the conventional terrestrial and satellite communications owing to the high mobility and the unique channel characteristics of air-ground links. However, obtaining effective channel state information (CSI) is challenging because of the dynamic propagation environment and variable transmission delay. In this paper, a deep learning (DL)-based CSI prediction framework is proposed to address channel aging problem by extracting the most discriminative features from the UAV wireless signals. Specifically, we develop a procedure of multiple Gaussian Bernoulli restricted Boltzmann machines (GBRBM) for dimension reduction and pre-training utilization incorporated with an autoencoder-based deep neural networks (DNNs). To evaluate the proposed approach, real data measurements from an UAV communicating with base-stations within a commercial cellular network are obtained and used for training and validation. Numerical results demonstrate that the proposed method is accurate in channel acquisition for various UAV flying scenarios and outperforms the conventional DNNs.

Deep Learning-Based Device-Free Localization in Wireless Sensor Networks

Location-based services (LBS) are witnessing a rise in popularity owing to their key features of delivering powerful and personalized digital experiences. The recent developments in wireless sensing techniques make the realization of device-free localization (DFL) feasible in wireless sensor networks. The DFL is an emerging technology that utilizes radio signal information for detecting and positioning a passive target while the target is not equipped with a wireless device. However, determining the characteristics of the massive raw signals and extracting meaningful discriminative features relevant to the localization are highly intricate tasks. Thus, deep learning (DL) techniques can be utilized to address the DFL problem due to their unprecedented performance gains in many practical problems. In this direction, we propose a DFL framework consists of multiple convolutional neural network (CNN) layers along with autoencoders based on the restricted Boltzmann machines (RBM) to construct a convolutional deep belief network (CDBN) for features recognition and extracting. Each layer has stochastic pooling to sample down the feature map and reduced the dimensions of the required data for precise localization. The proposed framework is validated using real experimental dataset. The results show that our algorithm can achieve a high accuracy of 98% with reduced data dimensions and low signal-to-noise ratios (SNRs).

Multi-layer Space Information Networks: Access Design and Softwarization

In this paper, we propose an approach for constructing a multi-layer multi-orbit space information network (SIN) to provide high-speed continuous broadband connectivity for space missions (nanosatellite terminals) from the emerging space-based Internet providers. This notion has been motivated by the rapid developments in satellite technologies in terms of satellite miniaturization and reusable rocket launch, as well as the increased number of nanosatellite constellations in lower orbits for space downstream applications, such as earth observation, remote sensing, and Internet of Things (IoT) data collection. Specifically, space-based Internet providers, such as Starlink, OneWeb, and SES O3b, can be utilized for broadband connectivity directly to/from the nanosatellites, which allows a larger degree of connectivity in space network topologies. Besides, this kind of establishment is more economically efficient and eliminates the need for an excessive number of ground stations while achieving real-time and reliable space communications. This objective necessitates developing suitable radio access schemes and efficient scalable space backhauling using inter-satellite links (ISLs) and inter-orbit links (IOLs). Particularly, service-oriented radio access methods in addition to software-defined networking (SDN)-based architecture employing optimal routing mechanisms over multiple ISLs and IOLs are the most essential enablers for this novel concept. Thus, developing this symbiotic interaction between versatile satellite nodes across different orbits will lead to a breakthrough in the way that future downstream space missions and satellite networks are designed and operated.

A Survey on Non-Geostationary Satellite Systems: The Communication Perspective

Non-geostationary (NGSO) satellites are envisioned to support various new communication applications from countless industries. NGSO systems are known for a number of key features such as lower propagation delay, smaller size, and lower signal losses in comparison to the conventional geostationary (GSO) satellites, which will enable latency-critical applications to be provided through satellites. NGSO promises a dramatic boost in communication speed and energy efficiency, and thus, tackling the main inhibiting factors of commercializing GSO satellites for broader utilizations. However, there are still many NGSO deployment challenges to be addressed to ensure seamless integration not only with GSO systems but also with terrestrial networks. These unprecedented challenges are discussed in this paper, including coexistence with GSO systems in terms of spectrum access and regulatory issues, satellite constellation and architecture designs, resource management problems, and user equipment requirements. Beyond this, the promised improvements of NGSO systems have motivated this survey to provide the state-of-the-art NGSO research focusing on the communication prospects, including physical layer and radio access technologies along with the networking aspects and the overall system features and architectures. We also outline a set of innovative research directions and new opportunities for future NGSO research.

Broadband Non-Geostationary Satellite Communication Systems: Research Challenges and Key Opportunities

Besides conventional geostationary (GSO) satellite broadband communication services, non-geostationary (NGSO) satellites are envisioned to support various new communication use cases from countless industries. These new scenarios bring many unprecedented challenges that will be discussed in this paper alongside with several potential future research opportunities. NGSO systems are known for various advantages, including their important features of low cost, lower propagation delay, smaller size, and lower losses in comparison to GSO satellites. However, there are still many deployment challenges to be tackled to ensure seamless integration not only with GSO systems but also with terrestrial networks. In this paper, we discuss several key challenges including satellite constellation and architecture designs, coexistence with GSO systems in terms of spectrum access and regulatory issues, resource management algorithms, and NGSO networking requirements. Additionally, the latest progress in provisioning secure communication via NGSO systems is discussed. Finally, this paper identifies multiple important open issues and research directions to inspire further studies towards the next generation of satellite networks.