The emergence of mega-constellations of interconnected satellites has a major impact on the integration of cellular wireless and non-terrestrial networks, while simultaneously offering previously inconceivable data gathering capabilities. This paper studies the problem of running a federated learning (FL) algorithm within low Earth orbit satellite constellations connected with intra-orbit inter-satellite links (ISL), aiming to efficiently process collected data in situ. Satellites apply on-board machine learning and transmit local parameters to the parameter server (PS). The main contribution is a novel approach to enhance FL in satellite constellations using intra-orbit ISLs. The key idea is to rely on predictability of satellite visits to create a system design in which ISLs mitigate the impact of intermittent connectivity and transmit aggregated parameters to the PS. We first devise a synchronous FL, which is extended towards an asynchronous FL for the case of sparse satellite visits to the PS. An efficient use of the satellite resources is attained by sparsification-based compression the aggregated parameters within each orbit. Performance is evaluated in terms of accuracy and required data transmission size. We observe a sevenfold increase in convergence speed over the state-of-the-art using ISLs, and 10× reduction in communication load through the proposed in-network aggregation strategy.

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.

This work analyzes the use of reconfigurable meta-surfaces as a more energy-efficient transceiver technology than traditional active antenna arrays. A wireless link is considered, in which both the transmitter and receiver are equipped with a single antenna that illuminates a passive meta-surface placed in the vicinity of the transmit/receive antenna. The rate and energy efficiency of this system are optimized with respect to the phase shifts applied by the transmit and receive meta-surfaces. Numerical results show that the use of passive meta-surfaces can significantly improve the system energy efficiency without reducing the system rate when compared to a similar multiple-input multiple-output (MIMO) system without meta-surface.

Mega-constellations of small-size Low Earth Orbit (LEO) satellites are currently planned and deployed by various private and public entities. While global connectivity is the main rationale, these constellations also offer the potential to gather immense amount of data, e.g., for Earth observation. Power and bandwidth constraints together with motives like privacy, limiting delay, or resiliency make it desirable to process this data directly within the constellation. We consider the implementation of on-board federated learning (FL) orchestrated by an out-of-constellation parameter server (PS) and propose a novel communication scheme tailored to support FL. It leverages intraorbit inter-satellite links, the predictability of satellite movements and partial aggregating to massively reduce the training time and communication costs. In particular, for a constellation with 40 satellites equally distributed among five low Earth orbits and the PS in medium Earth orbit, we observe a 29× speed-up in the training process time and a 8× traffic reduction at the PS over the baseline.

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.

We consider globally optimal precoder design for rate splitting multiple access in Gaussian multiple-input single-output downlink channels with respect to weighted sum rate and energy efficiency maximization. The proposed algorithm solves an instance of the joint multicast and unicast beamforming problem and includes multicast-and unicast-only beamforming as special cases. Numerical results show that it outperforms state-of-the-art algorithms in terms of numerical stability and converges almost twice as fast.

Energy efficient resource allocation in interference networks is a challenging global optimization problem. The main issue is that the computational complexity grows exponentially in the number of variables. In general, resource allocation in interference networks requires optimizing jointly over achievable rates and transmit powers. However, close scrutiny reveals that the non-convexity stems mostly from the powers while the problem is linear in the rates. Conventional global optimization frameworks treat all variables as non-convex and require complicated, problem specific decomposition approaches to exploit the convexity in some variables. Another issue specific to energy-efficient resource allocation is that these frameworks are unable to deal directly with fractional objectives. The usual approach is to use Dinkelbach's algorithm which requires the solution of a sequence of auxiliary global optimization problems. This increases the computational complexity significantly. To overcome these challenges, we develop an algorithm that inherently treats fractional objectives and differentiates between convex and non-convex variables, preserving the polynomial complexity in the number of convex variables. The numerical results show a speed-up of almost four orders of magnitude over Dinkelbach's algorithm for global fractional programs.

This paper studies the maximization of the weighted sum energy efficiency (WSEE). We derive a first-order optimal algorithm applicable to a wide class of communication scenarios exhibiting very fast convergence. We also discuss how to leverage monotonic optimization and fractional programming to obtain a global optimal solution at the cost of higher computational complexity. The WSEE of interference networks is studied in detail with an application to relay-assisted multi-cell communication. This scenario is modeled as a non-regenerative multi-way relay channel and the achievable rate region is derived. We apply the proposed algorithm to this scenario and compare its performance to the global optimal algorithm. The results indicate that the proposed algorithm often achieves the global optimal solution and is close to it otherwise. Convergence is achieved within 10 iterations, while the global optimal solution may require more than 106iterations.

This paper studies the problem of resource allocation in overlay systems. A multiple-input single-output (MISO) primary link shares the spectrum with a multiple-input multiple-output (MIMO) secondary link, which in return acts as an amplify-and-forward (AF) relay, forwarding the primary message. The considered problem is the maximization of the secondary energy efficiency (EE) subject to a primary rate requirement. The resulting optimization problem is a fractional program which can not be tackled by traditional fractional optimization methods. Two algorithms are proposed, based on an interplay between fractional programming and sequential optimization theory, which trade-off performance and complexity. Numerical results demonstrate the merits of the proposed algorithms both in terms of energy-efficient performance and complexity.

Throughput and energy efficiency in 3-way relay channels are studied in this paper. Unlike previous contributions, we consider a circular message exchange. First, an outer bound and achievable sum rate expressions for different relaying protocols are derived for 3-way relay channels. The sum capacity is characterized for certain SNR regimes. Next, leveraging the derived achievable sum rate expressions, cooperative and competitive maximization of the energy efficiency are considered. For the cooperative case, both low-complexity and globally optimal algorithms for joint power allocation at the users and at the relay are designed so as to maximize the system global energy efficiency. For the competitive case, a game theoretic approach is taken, and it is shown that the best response dynamics is guaranteed to converge to a Nash equilibrium. A power consumption model for mmWave board-to-board communications is developed, and numerical results are provided to corroborate and provide insight on the theoretical findings.