Search

In order to address the challenge of carrier blind synchronization and blind demodulation for non-cooperative burst signals, a modification scheme of carrier frequency offset estimation is proposed in this paper. Due to the existence of carrier frequency offset and phase offset, the received signal constellation is circular. In the absence of prior information, a non-data-aided method is adopted to estimate carrier frequency offset of the received signal. Then the loop processing method is used to compensate and correct the carrier frequency offset and phase offset to complete the carrier recovery. Simulation results show that the proposed method has high accuracy in carrier frequency offset and phase offset correction when the signal to noise ratio is close to or higher than that of demodulation. Moreover, it has wide extension in engineering practice.

Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2018; Aachen 1 Online-Ressource (xii, 166 Seiten) : Illustrationen (2018). = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2018, The increasing demands for higher throughput in wireless communications and other emergence of services, like the internet of things and the industry 4.0, have driven the need for new technologies beyond the current generation of wireless networks. In such a context, a massive multiple-input multiple-output (MIMO) system in a multi-user (MU) setup has been recognized as a promising candidate, based on the use of a large excess of transmit antennas over served users and the time-division duplexing (TDD) operation: it supports cheap single-antenna users/terminals, and simplifies the resource allocation as each active user can occupy all of the time-frequency resources. Motivated by these properties, this dissertation focuses on a comprehensive research of massive MIMO in regard to system design aspects and applications. To fully exploit the anticipated benefits of massive MIMO, which originate from the asymptotic orthogonality between distinct propagation channels, the acquisition of channel state information (CSI) is essential. However, the pilot contamination inherent to multi-cell TDD systems poses a severe problem, which further degrades the energy and spectral efficiency in MU-Massive-MIMO. Therefore, in this dissertation, subspace-based channel estimation schemes are employed to alleviate the pilot contamination. Even in the presence of this contamination, power control algorithms are proposed to preserve the uplink energy efficiency, either in a direct way, or by minimizing the total transmit power with a guaranteed throughput. On the other hand, the beamforming training (BFT) is applied to deliver a better CSI estimate, such that the downlink spectral efficiency is improved. In case of channel aging, the optimal transmission interval has to be specifically considered to avoid reducing the BFT gain. As the excess of transmit antennas can be used to concentrate the transmit power into small areas to bring a huge enhancement in throughput and radiated energy, several applications of massive MIMO are investigated in this dissertation. In particular, distributed antennas can be deployed to provide extra degrees of freedom to security-constrained power allocation algorithms, hence an improved physical layer security in MU-Massive-MIMO. Furthermore, this practical deployment also facilitates the simultaneous wireless information and power transfer. In addition, device-to-device (D2D) communications can be assisted by a massive MIMO relaying with an extended lifetime for D2D users, provided that they are capable of wireless energy harvesting. In this dissertation, all analytical evaluations in system design aspects and applications are verified via simulations. This demonstrates the performance advantage of massive MIMO, which is therefore envisioned as a key technology component for the next generation of wireless networks., Published by Aachen

The increasing demands for higher throughput in wireless communications and other emergence of services, like the internet of things and the industry 4.0, have driven the need for new technologies beyond the current generation of wireless networks. In such a context, a massive multiple-input multiple-output (MIMO) system in a multi-user (MU) setup has been recognized as a promising candidate, based on the use of a large excess of transmit antennas over served users and the time-division duplexing (TDD) operation: it supports cheap single-antenna users/terminals, and simplifies the resource allocation as each active user can occupy all of the time-frequency resources. Motivated by these properties, this dissertation focuses on a comprehensive research of massive MIMO in regard to system design aspects and applications. To fully exploit the anticipated benefits of massive MIMO, which originate from the asymptotic orthogonality between distinct propagation channels, the acquisition of channel state information (CSI) is essential. However, the pilot contamination inherent to multi-cell TDD systems poses a severe problem, which further degrades the energy and spectral efficiency in MU-Massive-MIMO. Therefore, in this dissertation, subspace-based channel estimation schemes are employed to alleviate the pilot contamination. Even in the presence of this contamination, power control algorithms are proposed to preserve the uplink energy efficiency, either in a direct way, or by minimizing the total transmit power with a guaranteed throughput. On the other hand, the beamforming training (BFT) is applied to deliver a better CSI estimate, such that the downlink spectral efficiency is improved. In case of channel aging, the optimal transmission interval has to be specifically considered to avoid reducing the BFT gain. As the excess of transmit antennas can be used to concentrate the transmit power into small areas to bring a huge enhancement in throughput and radiated energy, several applications of massive MIMO are investigated in this dissertation. In particular, distributed antennas can be deployed to provide extra degrees of freedom to security-constrained power allocation algorithms, hence an improved physical layer security in MU-Massive-MIMO. Furthermore, this practical deployment also facilitates the simultaneous wireless information and power transfer. In addition, device-to-device (D2D) communications can be assisted by a massive MIMO relaying with an extended lifetime for D2D users, provided that they are capable of wireless energy harvesting. In this dissertation, all analytical evaluations in system design aspects and applications are verified via simulations. This demonstrates the performance advantage of massive MIMO, which is therefore envisioned as a key technology component for the next generation of wireless networks.

Passive Detection Network Composed of Multi-Radar Reconnaissance Station, which can receive multiple sets of radar signal pulse descriptor word at the same time, joint signal sorting becomes an inevitable measure of such data. This paper proposes a multi-radar time-aligned association sorting method, and builds scenario close to actual conditions for testing. The results show that method can overcome the problem of partial pulse loss leading to sorting failure, and can quickly complete complex radar signal sorting in real electromagnetic environment.