A Very Low Complexity Successive Symbol-by-Symbol Sequence Estimator for Faster-Than-Nyquist Signaling

In this paper, we investigate the sequence estimation problem of binary and quadrature phase shift keying faster-than-Nyquist (FTN) signaling and propose two novel low-complexity sequence estimation techniques based on concepts of successive interference cancellation. To the best of our knowledge, this is the first approach in the literature to detect FTN signaling on a symbol-by-symbol basis. In particular, based on the structure of the self-interference inherited in FTN signaling, we first find the operating region boundary---defined by the root-raised cosine (rRC) pulse shape, its roll-off factor, and the time acceleration parameter of the FTN signaling---where perfect estimation of the transmit data symbols on a symbol-by-symbol basis is guaranteed, assuming noise-free transmission. For noisy transmission, we then propose a novel low-complexity technique that works within the operating region and is capable of estimating the transmit data symbols on a symbol-by-symbol basis. To reduce the error propagation of the proposed successive symbol-by-symbol sequence estimator (SSSSE), we propose a successive symbol-by-symbol with go-back-$K$ sequence estimator (SSSgb$K$SE) that goes back to re-estimate up to $K$ symbols, and subsequently improves the estimation accuracy of the current data symbol. Simulation results show that the proposed sequence estimation techniques perform well for low intersymbol interference (ISI) scenarios and can significantly increase the data rate and spectral efficiency. Additionally, results reveal that choosing the value of $K$ as low as $2$ or $3$ data symbols is sufficient to significantly improve the bit-error-rate performance. Results also show that the performance of the proposed SSSgb$K$SE, with $K = 1$ or $2$, surpasses the performance of the lowest complexity equalizers reported in the literature, with reduced computational complexity.Comment: IEEE Access, accepte

Polar Coded Faster-than-Nyquist (FTN) Signaling with Symbol-by-Symbol Detection

Reduced complexity faster-than-Nyquist (FTN) signaling systems are gaining increased attention as they provide improved bandwidth utilization for an acceptable level of detection complexity. In order to have a better understanding of the tradeoff between performance and complexity of the reduced complexity FTN detection techniques, it is necessary to study these techniques in the presence of channel coding. In this paper, we investigate the performance a polar coded FTN system which uses a reduced complexity FTN detection, namely, the recently proposed successive symbol-by-symbol with go-backK sequence estimation (SSSgbKSE) technique. Simulations are performed for various intersymbol-interference (ISI) levels and for various go-back-K values. Bit error rate (BER) performance of Bahl-Cocke-Jelinek-Raviv (BCJR) detection and SSSgbKSE detection techniques are studied for both uncoded and polar coded systems. Simulation results reveal that polar codes can compensate some of the performance loss incurred in the reduced complexity SSSgbKSE technique and assist in closing the performance gap between BCJR and SSSgbKSE detection algorithms

Low-Complexity Detection of High-Order QAM Faster-than-Nyquist Signaling

Faster-than-Nyquist (FTN) signaling is a promising non-orthogonal transmission technique to considerably improve the spectral efficiency. This paper presents the first attempt in the literature to estimate the transmit data symbols of any high-order quadrature amplitude modulation (QAM) FTN signaling in polynomial time complexity. In particular, we propose a generalized approach to model the finite alphabet of any high-order QAM constellation as a high degree polynomial constraint. Then, we formulate the high-order QAM FTN signaling sequence estimation problem as a non-convex optimization problem. As an example of a high-order QAM, we consider 16-QAM FTN signaling and then transform the high degree polynomial constraint, with the help of auxiliary variables, to multiple quadratic constraints. Such transformation allows us to propose a generalized approach semidefinite relaxation (SDR)- based sequence estimation (GASDRSE) technique to efficiently provide a sub-optimal solution to the NP-hard non-convex FTN detection problem, with polynomial time complexity. For the particular case of 16-QAM FTN signaling,

A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial