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Genetic Algorithm Based Optimization Method of Single Frequency Network Planning for DTMB
In the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model’s training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>
) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97.
Narrow-band interference (NBI) and impulsive noise (IN) are two kinds of non-Gaussian noise that have a severe impact on vehicular communications. In this paper, a novel compressive sensing (CS)-based method of simultaneous NBI and IN mitigation is proposed, which is a double kill of the two kinds of unfavorable disturbances. A CS-based time-frequency-measuring orthogonal frequency-division multiplexing (CS-TFM-OFDM) frame structure is introduced, in which the temporal repeated training sequences (TSs) are exploited by the proposed CS-based differential measuring (CS-DM) method to acquire the CS measurement vector of the NBI. With the aid of the a priori partial support, we further proposed the a priori-aided sparsity adaptive matching pursuit (PA-SAMP) to improve the accuracy and stability of NBI recovery. Meanwhile, the measurement vector of the IN is acquired from the null subcarriers in the CS-TFM-OFDM frame. After partial support of the IN is obtained, the IN is reconstructed using the proposed PA-SAMP algorithm. Hence, the two categories of non-Gaussian noise are both thoroughly eliminated, leading to the stability and robustness of vehicular communications. The proposed CS-based approach outperforms the conventional noise suppression methods in vehicular communications environments, which is validated by theoretical analysis and computer simulations.
In this paper, a novel narrowband interference (NBI) cancelation scheme based on priori aided compressive sensing (CS) for digital terrestrial multimedia broadcasting systems is proposed. The repeated training sequences in the transmitted symbols are exploited by differential measuring to acquire the CS measuring vector of the NBI, and for joint acquisition of the NBI support priori. Using the proposed priori aided sparsity adaptive matching pursuit algorithm, the sparse high-dimensional NBI can be accurately reconstructed from the measuring vector of much smaller size obtained through the proposed CS-based differential measuring method. It is verified by theoretical analysis and simulations that the proposed method outperforms conventional anti-NBI methods under multipath broadcasting channels.
This letter proposes a time-frequency joint sparse channel estimation for multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems under the framework of structured compressive sensing (CS). The proposed scheme firstly relies on a pseudo random preamble which is identical for all transmit antennas to acquire the partial common support by utilizing the sparse common support property of the MIMO channels. And then a very small amount of frequencydomain orthogonal pilots are used for the accurate channel recovery. Simulation results show that the proposed scheme demonstrates better performance and higher spectral efficiency than the conventional MIMO-OFDM schemes. Moreover, the obtained partial common support can be further utilized to reduce the complexity of the CS algorithm as well as improve the signal recovery probability under low signal to noise ratio conditions.
Time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) has advantages in spectral efficiency and synchronization. However, its iterative interference cancellation algorithm will suffer from performance loss especially under severely fading channels with long delays and has difficulty supporting high-order modulations like 256 QAM, which may not accommodate the emerging ultra-high definition television service. To solve this problem, a channel estimation method for OFDM under the framework of compressive sensing (CS) is proposed in this paper. Firstly, by exploiting the signal structure of recently proposed time-frequency training OFDM scheme, the auxiliary channel information is obtained. Secondly, we propose the auxiliary information based subspace pursuit (A-SP) algorithm to utilize a very small amount of frequency-domain pilots embedded in the OFDM block for the exact channel estimation. Moreover, the obtained auxiliary channel information is adopted to reduce the complexity of the classical SP algorithm. Simulation results demonstrate that the CS-based OFDM outperforms the conventional dual pseudo noise padded OFDM and CS-based TDS-OFDM schemes in both static and mobile environments, especially when the channel length is close to or even larger than the guard interval length, where the conventional schemes fail to work completely.
Single frequency network (SFN) is an attractive scheme for the digital television terrestrial broadcasting (DTTB) coverage with efficient utilization of the spectral resources. In this paper, a method featuring low complexity and low workload is proposed to estimate the reception quality under the SFN environment with the maximum delay spread of the artificial multipath no longer than the guard interval. Based on the analysis of error probability and channel capacity, this method supports an unambiguous prediction of the signal-to-noise ratio (SNR) threshold according to the parameters of received signals, which is thus meaningful for system evaluation. We applied this method to the digital television/terrestrial multimedia broadcasting (DTMB) systems, and carried out the laboratory measurements for performance verification. The predicted results show a satisfactory match with the measured results, validating the accuracy and effectiveness of the proposed method.
Genetic Algorithm (GA) has been developing rapidly in recent years. A great deal of research works in China and oversea about GA is summarized and more details about its applications to power system are given in this paper. Then some valuable ideas and future works are also proposed.
Single Frequency Network Technology for Complicated Topographies
- Z Liu
- C Pan
- Z Yang
Principles and Applications of Genetic Algorithm
- S Zhou
- S Sun
Single Frequency Network Technology for Complicated Topographies
- liu