357 research outputs found
Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing
The latest satellite communication (SatCom) missions are characterized by a
fully reconfigurable on-board software-defined payload, capable of adapting
radio resources to the temporal and spatial variations of the system traffic.
As pure optimization-based solutions have shown to be computationally tedious
and to lack flexibility, machine learning (ML)-based methods have emerged as
promising alternatives. We investigate the application of energy-efficient
brain-inspired ML models for on-board radio resource management. Apart from
software simulation, we report extensive experimental results leveraging the
recently released Intel Loihi 2 chip. To benchmark the performance of the
proposed model, we implement conventional convolutional neural networks (CNN)
on a Xilinx Versal VCK5000, and provide a detailed comparison of accuracy,
precision, recall, and energy efficiency for different traffic demands. Most
notably, for relevant workloads, spiking neural networks (SNNs) implemented on
Loihi 2 yield higher accuracy, while reducing power consumption by more than
100 as compared to the CNN-based reference platform. Our findings point
to the significant potential of neuromorphic computing and SNNs in supporting
on-board SatCom operations, paving the way for enhanced efficiency and
sustainability in future SatCom systems.Comment: currently under review at IEEE Transactions on Machine Learning in
Communications and Networkin
A Unified NOMA Framework in Beam-Hopping Satellite Communication Systems
This paper investigates the application of a unified non-orthogonal multiple
access framework in beam hopping (U-NOMA-BH) based satellite communication
systems. More specifically, the proposed U-NOMA-BH framework can be applied to
code-domain NOMA based BH (CD-NOMA-BH) and power-domain NOMA based BH
(PD-NOMA-BH) systems. To satisfy dynamic-uneven traffic demands, we formulate
the optimization problem to minimize the square of discrete difference by
jointly optimizing power allocation, carrier assignment and beam scheduling.
The non-convexity of the objective function and the constraint condition is
solved through Dinkelbach's transform and variable relaxation. As a further
development, the closed-from and asymptotic expressions of outage probability
are derived for CD/PD-NOMA-BH systems. Based on approximated results, the
diversity orders of a pair of users are obtained in detail. In addition, the
system throughput of U-NOMA-BH is discussed in delay-limited transmission mode.
Numerical results verify that: i) The gap between traffic requests of
CD/PD-NOMA-BH systems appears to be more closely compared with orthogonal
multiple access based BH (OMA-BH); ii) The CD-NOMA-BH system is capable of
providing the enhanced traffic request and capacity provision; and iii) The
outage behaviors of CD/PD-NOMA-BH are better than that of OMA-BH
Joint Beamforming and Power Allocation for Satellite-Terrestrial Integrated Networks With Non-Orthogonal Multiple Access
In this paper, we propose a joint optimization design for a non-orthogonal multiple access (NOMA)-based satellite-terrestrial integrated network (STIN), where a satellite multicast communication network shares the millimeter wave spectrum with a cellular network employing NOMA technology. By assuming that the satellite uses multibeam antenna array and the base station employs uniform planar array, we first formulate a constrained optimization problem to maximize the sum rate of the STIN while satisfying the constraint of per-antenna transmit power and quality-of-service requirements of both satellite and cellular users. Since the formulated optimization problem is NP-hard and mathematically intractable, we develop a novel user pairing scheme so that more than two users can be grouped in a cluster to exploit the NOMA technique. Based on the user clustering, we further propose to transform the non-convex problem into an equivalent convex one, and present an iterative penalty function-based beamforming (BF) scheme to obtain the BF weight vectors and power coefficients with fast convergence. Simulation results confirm the effectiveness and superiority of the proposed approach in comparison with the existing works
Energy-efficient optimal power allocation in integrated wireless sensor and cognitive satellite terrestrial networks
This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach’s method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint
Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G
Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th
Generation (6G) networks, providing ubiquitous, continuous, and scalable
services. Satellites emerge as the primary enabler for NTN, leveraging their
extensive coverage, stable orbits, scalability, and adherence to international
regulations. However, satellite-based NTN presents unique challenges, including
long propagation delay, high Doppler shift, frequent handovers, spectrum
sharing complexities, and intricate beam and resource allocation, among others.
The integration of NTNs into existing terrestrial networks in 6G introduces a
range of novel challenges, including task offloading, network routing, network
slicing, and many more. To tackle all these obstacles, this paper proposes
Artificial Intelligence (AI) as a promising solution, harnessing its ability to
capture intricate correlations among diverse network parameters. We begin by
providing a comprehensive background on NTN and AI, highlighting the potential
of AI techniques in addressing various NTN challenges. Next, we present an
overview of existing works, emphasizing AI as an enabling tool for
satellite-based NTN, and explore potential research directions. Furthermore, we
discuss ongoing research efforts that aim to enable AI in satellite-based NTN
through software-defined implementations, while also discussing the associated
challenges. Finally, we conclude by providing insights and recommendations for
enabling AI-driven satellite-based NTN in future 6G networks.Comment: 40 pages, 19 Figure, 10 Tables, Surve
Energy Efficient Sparse Precoding Design for Satellite Communication System
Through precoding, the spectral efficiency of the
system can be improved; thus, more users can benefit from
5G and beyond broadband services. However, complete precoding (using all precoding coefficients) may not be possible in
practice due to the high signal processing complexity involved
in calculating a large number of precoding coefficients and
combining them with symbols for transmission. In this paper,
we propose an energy-efficient sparse precoding design, where
only a few precoding coefficients are used with lower power
consumption depending on the demand. In this context, we
formulate an optimization problem that minimizes the number of
in-use precoding coefficients and the system power consumption
while matching the per beam demand. This problem is nonconvex. Hence, we apply Lagrangian relaxation and successive convex approximation to convexify it. The proposed solution
outperforms the benchmark scheme in power consumption and demand satisfaction with the additional advantage of sparse precoding design
Rate-Splitting for Max-Min Fair Multigroup Multicast Beamforming in Overloaded Systems
In this paper, we consider the problem of achieving max-min fairness amongst
multiple co-channel multicast groups through transmit beamforming. We
explicitly focus on overloaded scenarios in which the number of transmitting
antennas is insufficient to neutralize all inter-group interference. Such
scenarios are becoming increasingly relevant in the light of growing
low-latency content delivery demands, and also commonly appear in multibeam
satellite systems. We derive performance limits of classical beamforming
strategies using DoF analysis unveiling their limitations; for example, rates
saturate in overloaded scenarios due to inter-group interference. To tackle
interference, we propose a strategy based on degraded beamforming and
successive interference cancellation. While the degraded strategy resolves the
rate-saturation issue, this comes at a price of sacrificing all spatial
multiplexing gains. This motivates the development of a unifying strategy that
combines the benefits of the two previous strategies. We propose a beamforming
strategy based on rate-splitting (RS) which divides the messages intended to
each group into a degraded part and a designated part, and transmits a
superposition of both degraded and designated beamformed streams. The
superiority of the proposed strategy is demonstrated through DoF analysis.
Finally, we solve the RS beamforming design problem and demonstrate significant
performance gains through simulations
Applicability and Challenges of Deep Reinforcement Learning for Satellite Frequency Plan Design
The study and benchmarking of Deep Reinforcement Learning (DRL) models has
become a trend in many industries, including aerospace engineering and
communications. Recent studies in these fields propose these kinds of models to
address certain complex real-time decision-making problems in which classic
approaches do not meet time requirements or fail to obtain optimal solutions.
While the good performance of DRL models has been proved for specific use cases
or scenarios, most studies do not discuss the compromises and generalizability
of such models during real operations. In this paper we explore the tradeoffs
of different elements of DRL models and how they might impact the final
performance. To that end, we choose the Frequency Plan Design (FPD) problem in
the context of multibeam satellite constellations as our use case and propose a
DRL model to address it. We identify 6 different core elements that have a
major effect in its performance: the policy, the policy optimizer, the state,
action, and reward representations, and the training environment. We analyze
different alternatives for each of these elements and characterize their
effect. We also use multiple environments to account for different scenarios in
which we vary the dimensionality or make the environment nonstationary. Our
findings show that DRL is a potential method to address the FPD problem in real
operations, especially because of its speed in decision-making. However, no
single DRL model is able to outperform the rest in all scenarios, and the best
approach for each of the 6 core elements depends on the features of the
operation environment. While we agree on the potential of DRL to solve future
complex problems in the aerospace industry, we also reflect on the importance
of designing appropriate models and training procedures, understanding the
applicability of such models, and reporting the main performance tradeoffs
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