The rapid growth of non-terrestrial communication necessitates its integration with existing terrestrial networks, as highlighted in 3GPP Releases 16 and 17. This paper analyses the concept of functional splits in 3D-Networks. To manage this complex structure effectively, the adoption of a Radio Access Network (RAN) architecture with Functional Split (FS) offers advantages in flexibility, scalability, and cost-efficiency. RAN achieves this by disaggregating functionalities into three separate units. Analogous to the terrestrial network approach, 3GPP is extending this concept to non-terrestrial platforms as well. This work presents a general analysis of the requested Fronthaul (FH) data rate on feeder link between a non-terrestrial platform and the ground-station. Each split option is a trade-of between FH data rate and the respected complexity. Since flying nodes face more limitations regarding power consumption and complexity on board in comparison to terrestrial ones, we are investigating the split options between lower and higher physical layer.

We propose a novel approach for downlink transmission from a satellite swarm towards a very small aperture terminal (VSAT). These swarms have the benefit of much higher spatial separation in the transmit antennas than traditional satellites with antenna arrays, promising a massive increase in spectral efficiency. The resulting precoder and equalizer have only low demands on computational complexity, inter-satellite coordination and channel estimation. This is achieved by taking knowledge about the geometry between satellites and VSAT into account. Due to the position based transceiver design, only slowly changing long-term statistics of the channel coefficient are considered. The necessity of accurate positional information is further relaxed by considering stochastic knowledge about the relative positions between satellites and VSAT rather than exact knowledge. Specifically, each satellite needs only stochastic information about the VSATs’ relative positions to calculate its precoding vector. Similarly, the VSAT requires only stochastic knowledge of the satellites’ relative positions for equalization. Furthermore, we evaluate the impact the inter-satellite distance has on the achievable data rate. Based on that, an analytic approach to arrange the satellites in a satellite swarm to maximize the rate is provided. The combination of the low complexity transceiver with suitable inter-satellite distances is proven to be capacity achieving in specific scenarios. The simulation results provide evidence that the proposed inter-satellite distance in combination with the proposed transceiver enables close-to-optimal rates in practical applications.

Low Earth Orbit (LEO) satellite-to-handheld connections herald a new era in satellite communications. Space-Division Multiple Access (SDMA) precoding is a method that mitigates interference among satellite beams, boosting spectral efficiency. While optimal SDMA precoding solutions have been proposed for ideal channel knowledge in various scenarios, addressing robust precoding with imperfect channel information has primarily been limited to simplified models. However, these models might not capture the complexity of LEO satellite applications. We use the Soft Actor-Critic (SAC) deep Reinforcement Learning (RL) method to learn robust precoding strategies without the need for explicit insights into the system conditions and imperfections. Our results show flexibility to adapt to arbitrary system configurations while performing strongly in terms of achievable rate and robustness to disruptive influences compared to analytical benchmark precoders.

Non-terrestrial networks (NTNs), particularly satel-lite networks, are key factors for ubiquitous connectivity in today's 5G terrestrial networks (TNs) and future 6G networks. They can bring great benefits, but also great challenges. The direct-to-cell (D2C) connectivity use case, i.e., direct connectivity from satellites to common terrestrial low-gain user terminals (UTs), such as handheld or IoT devices, is currently gaining strong interest. This paper focuses on innovative distributed implementations of the space segment using swarms of multiple small platforms, each embedding one or a subset of radiating elements. They are organized with regular or irregular geometries and greater distance between radiating elements, creating large virtual antenna array apertures. The use of small satellites promises reduced production and launch costs, while the distributed nature of the system provides attractive features, such as scalability and fault tolerance. This paper analyzes the multi-beam performance of two distributed implementations, comparing the results with a single platform implementation. Distributed implementations present a better performance in terms of average summed throughput, user throughput, and throughput density. An analysis of worst-case user throughput reveals that distributed implementations based on irregular geometries capable of mitigating grating lobes provide slightly lower average performance of measured key performance indicators (KPIs), but greater fairness than the implementation based on regular geometries. Swarm-based antenna arrays appear as a promising solution for the D2C connectivity use case.

The uplink from a very small aperture terminal (VSAT) towards multiple satellites is considered, in this paper. VSATs can be equipped with multiple antennas, allowing parallel transmission to multiple satellites. A low-complexity precoder based on imperfect positional information of the satellites is presented. The probability distribution of the position uncertainty and the statistics of the channel elements are related by the characteristic function of the position uncertainty. This knowledge is included in the precoder design to maximize the mean signal-to-leakage-and-noise ratio (SLNR) at the satellites. Furthermore, the performance w.r.t. the inter-satellite distance is numerically evaluated. It is shown that the proposed approach achieves the capacity for perfect position knowledge and sufficiently large inter-satellite distances. In case of imperfect position knowledge, the performance degradation of the robust precoder is relatively small.

Low Earth orbit (LEO) satellite systems enable close to global coverage and are therefore expected to become important pillars of future communication standards. However, a particular challenge faced by LEO satellites is the high orbital velocities due to which a precise channel estimation is difficult. We model this influence as an erroneous angle of departure (AoD), which corresponds to imperfect channel state information (CSI) at the transmitter (CSIT). Poor CSIT and non-orthogonal user channels degrade the performance of space-division multiple access (SDMA) precoding by increasing inter-user interference (IUI). In contrast to SDMA, there is no IUI in orthogonal multiple access (OMA), but it requires orthogonal time or frequency resources for each user. Rate-splitting multiple access (RSMA), unifying SDMA, OMA, and non-orthogonal multiple access (NOMA), has recently been proven to be a flexible approach for robust interference management considering imperfect CSIT. In this paper, we investigate RSMA as a promising strategy to manage IUI in LEO satellite downlink systems caused by non-orthogonal user channels as well as imperfect CSIT. We evaluate the optimal configuration of RSMA depending on the geometrical constellation between the satellite and users.

Direct Low Earth Orbit satellite-to-handheld links are expected to be part of a new era in satellite communications. Space-Division Multiple Access precoding is a technique that reduces interference among satellite beams, therefore increasing spectral efficiency by allowing cooperating satellites to reuse frequency. Over the past decades, optimal precoding solutions with perfect channel state information have been proposed for several scenarios, whereas robust precoding with imperfect channel state information has mostly been studied for simplified models. Such simplified models might not be accurate particularly for Low Earth Orbit satellite applications. In this paper, we use the function approximation capabilities of the Soft Actor-Critic deep Reinforcement Learning algorithm to learn robust precoding with no knowledge of the system imperfections.

Non-geostationary orbit (NGSO) satellite constellations represent a cornerstone in the NewSpace paradigm and thus have become one of the hottest topics for the industry, academia, but also for national space agencies and regulators. For instance, numerous companies worldwide, including Starlink, OneWeb, Kepler, SPUTNIX, and Amazon have started or will soon start to deploy their own NGSO constellations, which aim to provide either broadband or IoT services. One of the major drivers for such a high interest on NGSO constellations is that, with an appropriate design, they are capable of providing global coverage and connectivity.

Non-geostationary orbit (NGSO) satellite constellations represent a cornerstone in the NewSpace paradigm and thus have become one of the hottest topics for the industry, academia, but also for national space agencies and regulators. For instance, numerous companies worldwide, including Starlink, OneWeb, Kepler, SPUTNIX, and Amazon have started or will soon start to deploy their own NGSO constellations, which aim to provide either broadband or IoT services. One of the major drivers for such a high interest on NGSO constellations is that, with an appropriate design, they are capable of providing global coverage and connectivity.

In this paper, we propose a novel approach for downlink transmission from a satellite swarm towards a ground station (GS). These swarms have the benefit of much higher spatial separation in the transmit antennas than traditional satellites with antenna arrays, promising a massive increase in spectral efficiency. The resulting precoder and equalizer have very low demands on computational complexity, inter-satellite coordination and channel estimation. This is achieved by taking knowledge about the geometry between satellites and GS into account. For precoding, each satellite only requires its angles of departure (AoDs) towards the GS and it turns out that almost optimal rates can be achieved if the satellites transmit independent data streams. For the equalizer, the GS requires only knowledge about the angles of arrival (AoAs) from all satellites. Furthermore, we show that, by choosing a proper inter-satellite distance, the proposed low-complexity approach achieves the theoretical upper bound in terms of data rate. This optimal inter-satellite distance is obtained analytically under simplifying assumption and provides a heuristic for practical scenarios. Furthermore, a novel approach to increase the robustness of the proposed precoder and equalizer against imperfect AoD and AoA knowledge is proposed by exploiting the statistics of the estimation error.

Systems of small distributed satellites in low Earth orbit (LEO) transmitting cooperatively to a multiple antenna ground station (GS) are investigated. These satellite swarms have the benefit of much higher spatial separation in the transmit antennas than traditional big satellites with antenna arrays, promising a massive increase in spectral efficiency. However, this would require instantaneous perfect channel state information (CSI) and strong cooperation between satellites. In practice, orbital velocities around 7.5 km/s lead to very short channel coherence times on the order of fractions of the inter-satellite propagation delay, invalidating these assumptions. In this paper, we propose a distributed linear precoding scheme and a GS equalizer relying on local position information. In particular, each satellite only requires information about its own position and that of the GS, while the GS has complete positional information. Due to the deterministic nature of satellite movement this information is easily obtained and no inter-satellite information exchange is required during transmission. Based on the underlying geometrical channel approximation, the optimal inter-satellite distance is obtained analytically. Numerical evaluations show that the proposed scheme is, on average, within 99.8% of the maximum achievable rate for instantaneous CSI and perfect cooperation

Low Earth orbit (LEO) satellite constellations rely on inter-satellite links (ISLs) to provide global connectivity. However, one significant challenge is to establish and maintain inter-plane ISLs, which support communication between different orbital planes. This is due to the fast movement of the infrastructure and to the limited computation and communication capabilities on the satellites. In this paper, we make use of antenna arrays with either Butler matrix beam switching networks or digital beam steering to establish the inter-plane ISLs in a LEO satellite constellation. Furthermore, we present a greedy matching algorithm to establish inter-plane ISLs with the objective of maximizing the sum of rates. This is achieved by sequentially selecting the pairs, switching or pointing the beams and, finally, setting the data rates. Our results show that, by selecting an update period of 30 seconds for the matching, reliable communication can be achieved throughout the constellation, where the impact of interference in the rates is less than 0.7% when compared to orthogonal links, even for relatively small antenna arrays. Furthermore, doubling the number of antenna elements increases the rates by around one order of magnitude.

The next frontier towards truly ubiquitous connectivity is the use of Low Earth Orbit (LEO) small-satellite constellations to support 5G and Beyond-5G (B5G) networks. Besides enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC), LEO constellations can support ultra-reliable communications (URC) with relaxed latency requirements of a few tens of milliseconds. Small-satellite impairments and the use of low orbits pose major challenges to the design and performance of these networks, but also open new innovation opportunities. This paper provides a comprehensive overview of the physical and logical links, along with the essential architectural and technological components that enable the full integration of LEO constellations into 5G and B5G systems. Furthermore, we characterize and compare each physical link category and explore novel techniques to maximize the achievable data rates.

Satellite communication systems are expected to play an important role in the fifth generation mobile network (5G) for global broadband coverage and service continuity. Constellations with many cooperating small satellites offer great advantages in terms of scalability and flexibility over traditional satellite communication systems with few large satellites. Small satellites means less power and low complexity per satellite while delivering 5G services as enhanced Mobile Broadband (eMBB) by exploiting the network of cooperating satellites. Thus, we derive a Distributed Precoding (DiP) algorithm by solving an user averaged Minimum Mean Square Error (MMSE) optimization problem subject to individual satellite power constraints. To come up with a robust precoder, the proposed design is based on a contamination model taking channel estimation accuracy into account. Simulation results show that the cooperation and processing of the satellite swarm leads to an increase of the Downlink (DL) sum rate.

In this paper, a novel distributed precoding (DiP) algorithm for ultra-dense small cell (SC) networks is developed, where the SCs cooperate to perform a joint transmission to users (UEs) with limited and individual transmit powers. Different to most state of the art (SotA) DiP algorithms, the proposed precoder design is based on the assumption that each SC has only local channel state information (CSI) available. Additionally, there are no constraints on the number of antennas for each SC, but only one constraint on the sum of all transmit antennas. A solution for the considered problem based on the Lagrangian method of multipliers (MoM) is presented, by formulating the precoder design as a constrained convex optimization problem. The obtained solution can be implemented in a fully distributed way among the SCs by using the preconditioned Richardson (PR) iteration. In numerical simulations, the convergence of the proposed DiP algorithm is verified and it is shown that the sum rate significantly increases, if the SCs cooperate with each other.