Dynamic Beam-Layout Design for MEO High Throughput Satellite Systems

Scientific Conference (2022, December 04)

We propose a traffic-oriented beam-layout optimization framework for a medium Earth orbit (MEO) high throughput satellite (HTS) system. The designed beam-layout plans have the objective of dynamic traffic ... [more ▼]

We propose a traffic-oriented beam-layout optimization framework for a medium Earth orbit (MEO) high throughput satellite (HTS) system. The designed beam-layout plans have the objective of dynamic traffic load balancing with minimal radio resource management. We model and evaluate the high directivity and the high reconfigurability capabilities of next-generation HTS systems that are equipped with a processing power on board. The resulting payload flexibility is compared numerically against the on-ground counterpart. Adequate key performance indicators, such as the Jain’s fairness index, the load distribution gap and optimization convergence time, are used to benchmark the system against state-of-the-art solutions. Results show that a dynamic HTS in MEO is capable of efficiently attaining high quality load balancing performance under realistic spatiotemporally varying traffic demands. [less ▲]

Frequency-Packed Faster-Than-Nyquist Signaling via Symbol-Level Precoding for Multiuser MISO Redundant Transmissions

in IEEE Transactions on Wireless Communications (2022), 21(10), 8660-8674

This work addresses the issue of interference generated by co-channel users in downlink multi-antenna multicarrier systems with frequency-packed faster-than-Nyquist (FTN) signaling. The resulting ... [more ▼]

This work addresses the issue of interference generated by co-channel users in downlink multi-antenna multicarrier systems with frequency-packed faster-than-Nyquist (FTN) signaling. The resulting interference stems from an aggressive strategy for enhancing the throughput via frequency reuse across different users and the squeezing of signals in the time-frequency plane beyond the Nyquist limit. The spectral efficiency is proved to be increasing with the frequency packing and FTN acceleration factors. The lower bound for the FTN sampling period that guarantees information losslesness is derived as a function of the transmitting-filter roll-off factor, the frequency-packing factor, and the number of subcarriers. Space-time-frequency symbol-level precoders (SLPs) that trade off constructive and destructive interblock interference (IBI) at the single-antenna user terminals are proposed. Redundant elements are added as guard interval to cope with vestigial destructive IBI effects. The proposals can handle channels with delay spread longer than the multicarrier-symbol duration. The receiver architecture is simple, for it does not require digital multicarrier demodulation. Simulations indicate that the proposed SLP outperforms zero-forcing precoding and achieves a target balance between spectral and energy efficiencies by controlling the amount of added redundancy from zero (full IBI) to half (destructive IBI-free) the group delay of the equivalent channel. [less ▲]

Matching Traffic Demand in GEO Multibeam Satellites: The Joint Use of Dynamic Beamforming and Precoding Under Practical Constraints

in IEEE Transactions on Broadcasting (2022)

To adjust for the non-uniform spatiotemporal nature of traffic patterns, next-generation high throughput satellite (HTS) systems can benefit from recent technological advancements in the space-segment in ... [more ▼]

To adjust for the non-uniform spatiotemporal nature of traffic patterns, next-generation high throughput satellite (HTS) systems can benefit from recent technological advancements in the space-segment in order to dynamically design traffic-adaptive beam layout plans (ABLPs). In this work, we propose a framework for dynamic beamforming (DBF) optimization and adaptation in dynamic environments. Given realistic traffic patterns and a limited power budget, we propose a feasible DBF operation for a geostationary multibeam HTS network. The goal is to minimize the mismatch between the traffic demand and the offered capacity under practical constraints. These constraints are dictated by the traffic-aware design requirements, the on-board antenna system limitations, and the signaling considerations in the K-band. Noting that the ABLP is agnostic about the inherent inter-beam interference (IBI), we construct an interference simulation environment using irregularly shaped beams for a large-scale multibeam HTS system. To cope with IBI, the combination of on-board DBF and on-ground precoding is considered. For precoded and non-precoded HTS configurations, the proposed design shows better traffic-matching capabilities in comparison to a regular beam layout plan. Lastly, we provide trade-off analyses between system-level key performance indicators for different realistic non-uniform traffic patterns. [less ▲]

Fading-ratio-based selection for massive MIMO systems under line-of-sight propagation

in Wireless Networks (2022)

Massive multiple-input multiple-output (MIMO) enables increased throughput by using spatial multiplexing. However, the throughput may severely degrade when the number of users served by a single base ... [more ▼]

Massive multiple-input multiple-output (MIMO) enables increased throughput by using spatial multiplexing. However, the throughput may severely degrade when the number of users served by a single base station increases, especially under line-of-sight (LoS) propagation. Selecting users is a possible solution to deal with this problem. In the literature, the user selection algorithms can be divided into two classes: small-scale fading aware (SSFA) and large scale fading aware (LSFA) algorithms. The LSFA algorithms are good solutions for massive MIMO systems under non LoS propagation since the small-scale fading does not affect the system performance under this type of propagation. For the LoS case, the small scale fading has a great impact on the system performance, requiring the use of SSFA algorithms. However, disregarding the large-scale fading is equivalent to assuming that all users are equidistant from the base station and experience the same level of shadowing, which is not a reasonable approximation in practical applications. To address this shortcoming, a new user selection algorithm called the fading-ratio-based selection (FRBS) is proposed. FRBS considers both fading information to drop those users that induce the highest interference to the remaining ones. Simulation results considering LoS channels show that using FRBS yields near optimum downlink throughput, which is similar to that of the state-of-the-art algorithm, but with much lower computational complexity. Moreover, the use of FRBS with zero forcing precoder resulted in 26.28% improvement in the maximum throughput when compared with SSFA algorithms, and 35.39% improvement when compared with LSFA algorithms. [less ▲]

Maximizing the Number of Served Users in a Smart City using Reconfigurable Intelligent Surfaces

in Proceedings of IEEE Wireless Communications and Networking Conference (WCNC) (2022, April 10)

Interference Mitigation via NMF for Radio Astronomy Applications: A Feasibility Study

in 15th International Conference on Sensing Technology (ICST), Sydney 5-7 December 2022 (2022)

This work assesses the feasibility of using nonnegative matrix factorization (NMF) for radio frequency interference (RFI) mitigation in radio astronomy applications. Two NMF-based mitigation approaches ... [more ▼]

This work assesses the feasibility of using nonnegative matrix factorization (NMF) for radio frequency interference (RFI) mitigation in radio astronomy applications. Two NMF-based mitigation approaches are proposed, one using RFI frequency information extracted from the received signals and the other using an RFI template. The suitability and efficacy of these approaches are evaluated by targeting automatic dependent surveillance-broadcast (ADS-B) RFI using data collected from the Parkes radio telescope in Australia. Results show that the proposed approaches can mitigate the RFI with minimal degradation to the underlying observation of a double pulsar, and without discarding any received data, indicating the applicability of NMF-based approaches as potential RFI mitigation tools in radio astronomy applications. [less ▲]

Online Learning and Adaptive Filters

Book published by Cambridge University Press (2022)

Learn to solve the unprecedented challenges facing Online Learning and Adaptive Signal Processing in this concise, intuitive text. The ever-increasing amount of data generated every day requires new ... [more ▼]

Learn to solve the unprecedented challenges facing Online Learning and Adaptive Signal Processing in this concise, intuitive text. The ever-increasing amount of data generated every day requires new strategies to tackle issues such as: combining data from a large number of sensors; improving spectral usage, utilizing multiple-antennas with adaptive capabilities; or learning from signals placed on graphs, generating unstructured data. Solutions to all of these and more are described in a condensed and unified way, enabling you to expose valuable information from data and signals in a fast and economical way. The up-to-date techniques explained here can be implemented in simple electronic hardware, or as part of multi-purpose systems. Also featuring alternative explanations for online learning, including newly developed methods and data selection, and several easily implemented algorithms, this one-of-a-kind book is an ideal resource for graduate students, researchers, and professionals in online learning and adaptive filtering. [less ▲]

Adaptive Nonlinear Least Squares Framework for Contactless Vital Sign Monitoring

in IEEE Transactions on Microwave Theory and Techniques (2022)

Successive Decode-and-Forward Relaying with Reconfigurable Intelligent Surfaces

in IEEE International Conference on Communications, Seoul, May 2022 (2022)

Kernel Regression over Graphs using Random Fourier Features

in IEEE Transactions on Signal Processing (2022)

This paper proposes efficient batch-based and online strategies for kernel regression over graphs (KRG). The proposed algorithms do not require the input signal to be a graph signal, whereas the target ... [more ▼]

This paper proposes efficient batch-based and online strategies for kernel regression over graphs (KRG). The proposed algorithms do not require the input signal to be a graph signal, whereas the target signal is defined over the graph. We first use random Fourier features (RFF) to tackle the complexity issues associated with kernel methods employed in the conventional KRG. For batch-based approaches, we also propose an implementation that reduces complexity by avoiding the inversion of large matrices. Then, we derive two distinct online strategies using RFF, namely, the mini-batch gradient KRG (MGKRG) and the recursive least squares KRG (RLSKRG). The stochastic gradient KRG (SGKRG) is introduced as a particular case of the MGKRG. The MGKRG and the SGKRG are low-complexity algorithms that employ stochastic gradient approximations in the regression-parameter update. The RLSKRG is a recursive implementation of the RFF-based batch KRG. A detailed stability analysis is provided for the proposed online algorithms, including convergence conditions in both mean and mean-squared senses. A discussion on complexity is also provided. Numerical simulations include a synthesized-data experiment and real-data experiments on temperature prediction, brain activity estimation, and image reconstruction. Results show that the RFF-based batch implementation offers competitive performance with a reduced computational burden when compared to the conventional KRG. The MGKRG offers a convenient trade-off between performance and complexity by varying the number of mini-batch samples. The RLSKRG has a faster convergence than the MGKRG and matches the performance of the batch implementation. [less ▲]