This paper presents a modified Root-MUSIC algorithm by which the signal DOA estimation performance can be improved when the snapshot number is limited. The operation principlesof this algorithm are described in detail...This paper presents a modified Root-MUSIC algorithm by which the signal DOA estimation performance can be improved when the snapshot number is limited. The operation principlesof this algorithm are described in detail. It is also pointed out theoretically that this is equivalentto have increased the snapshot number and can make the DOA estimation better. Finally, somesimulating results to verify the theoretical analyses are presented.展开更多
Based on the standard definition of the product (concatenation), the natural non-negative degree of the language is introduced. Root extraction is the reverse operation to it, and it can be defined in several differen...Based on the standard definition of the product (concatenation), the natural non-negative degree of the language is introduced. Root extraction is the reverse operation to it, and it can be defined in several different ways. Despite the simplicity of the formulation of the problem of extracting the root, the authors could not find any description of it in the literature (as well as on the Internet), including even its formulation. Most of the material in this article is devoted to the simplest version of the formulation: the root of the 2<sup>nd</sup> degree for the 1-letter alphabet, but many of the provisions of the article are generalized to more complex cases. Apparently, for a possible future description of a polynomial algorithm for solving at least one of the described statements of root extraction problems, it is first necessary to really analyze in detail such a special case, that is: either describe the necessary polynomial algorithm, or, conversely, show that the problem belongs to the class of NP-complete problems. Thus, in this article, we do not propose a polynomial algorithm for the problems under consideration;however, the models described here should help in constructing appropriate heuristic algorithms for their solution. A detailed description of the possible further application of such heuristic algorithms is beyond the scope of this article. .展开更多
Propagation models are the foundation for radio planning in mobile networks. They are widely used during feasibility studies and initial network deployment, or during network extensions, particularly in new cities. Th...Propagation models are the foundation for radio planning in mobile networks. They are widely used during feasibility studies and initial network deployment, or during network extensions, particularly in new cities. They can be used to calculate the power of the signal received by a mobile terminal, evaluate the coverage radius, and calculate the number of cells required to cover a given area. This paper takes into account the standard k factors model and then uses the differential evolution algorithm to set up a propagation model adapted to the physical environment of the Cameroonian cities of Bertoua. Drive tests were made on the LTE TDD network in the city of Bertoua. Differential evolution algorithm is used as the optimization algorithm to deduct a propagation model which fits the environment of the considered town. The calculation of the root mean square error between the actual data from the drive tests and the prediction data from the implemented model allows the validation of the obtained results. A comparative study made between the RMSE value obtained by the new model and those obtained by the Okumura Hata and free space models, allowed us to conclude that the new model obtained is better and more representative of our local environment than the Okumura Hata currently used. The implementation shows that Differential evolution can perform well and solve this kind of optimization problem;the newly obtained models can be used for radio planning in the city of Bertoua in Cameroon.展开更多
Network planning is essential for the construction and the development of wireless networks. The network planning cannot be possible without an appropriate propagation model which in fact is its foundation. Initially ...Network planning is essential for the construction and the development of wireless networks. The network planning cannot be possible without an appropriate propagation model which in fact is its foundation. Initially used mainly for mobile radio networks, the optimization of propagation model is becoming essential for efficient deployment of the network in different types of environment, namely rural, suburban and urban especially with the emergence of concepts such as digital terrestrial television, smart cities, Internet of Things (IoT) with wide deployment for different use cases such as smart grid, smart metering of electricity, gas and water. In this paper we use an optimization algorithm that is inspired by the principles of magnetic field theory namely Magnetic Optimization Algorithm (MOA) to tune COST231-Hata propagation model. The dataset used is the result of drive tests carry out on field in the town of Limbe in Cameroon. We take into account the standard K-factor model and then use the MOA algorithm in order to set up a propagation model adapted to the physical environment of a town. The town of Limbe is used as an implementation case, but the proposed method can be used everywhere. The calculation of the root mean square error (RMSE) between the real data from the radio measurements and the prediction data obtained after the implementation of MOA allows the validation of the results. A comparative study between the value of the RMSE obtained by the new model and those obtained by the optimization using linear regression, by the standard COST231-Hata models, and the free space model is also done, this allows us to conclude that the new model obtained using MOA for the city of Limbe is better and more representative of this local environment than the standard COST231-Hata model. The new model obtained can be used for radio planning in the city of Limbé in Cameroon.展开更多
文摘This paper presents a modified Root-MUSIC algorithm by which the signal DOA estimation performance can be improved when the snapshot number is limited. The operation principlesof this algorithm are described in detail. It is also pointed out theoretically that this is equivalentto have increased the snapshot number and can make the DOA estimation better. Finally, somesimulating results to verify the theoretical analyses are presented.
文摘Based on the standard definition of the product (concatenation), the natural non-negative degree of the language is introduced. Root extraction is the reverse operation to it, and it can be defined in several different ways. Despite the simplicity of the formulation of the problem of extracting the root, the authors could not find any description of it in the literature (as well as on the Internet), including even its formulation. Most of the material in this article is devoted to the simplest version of the formulation: the root of the 2<sup>nd</sup> degree for the 1-letter alphabet, but many of the provisions of the article are generalized to more complex cases. Apparently, for a possible future description of a polynomial algorithm for solving at least one of the described statements of root extraction problems, it is first necessary to really analyze in detail such a special case, that is: either describe the necessary polynomial algorithm, or, conversely, show that the problem belongs to the class of NP-complete problems. Thus, in this article, we do not propose a polynomial algorithm for the problems under consideration;however, the models described here should help in constructing appropriate heuristic algorithms for their solution. A detailed description of the possible further application of such heuristic algorithms is beyond the scope of this article. .
文摘Propagation models are the foundation for radio planning in mobile networks. They are widely used during feasibility studies and initial network deployment, or during network extensions, particularly in new cities. They can be used to calculate the power of the signal received by a mobile terminal, evaluate the coverage radius, and calculate the number of cells required to cover a given area. This paper takes into account the standard k factors model and then uses the differential evolution algorithm to set up a propagation model adapted to the physical environment of the Cameroonian cities of Bertoua. Drive tests were made on the LTE TDD network in the city of Bertoua. Differential evolution algorithm is used as the optimization algorithm to deduct a propagation model which fits the environment of the considered town. The calculation of the root mean square error between the actual data from the drive tests and the prediction data from the implemented model allows the validation of the obtained results. A comparative study made between the RMSE value obtained by the new model and those obtained by the Okumura Hata and free space models, allowed us to conclude that the new model obtained is better and more representative of our local environment than the Okumura Hata currently used. The implementation shows that Differential evolution can perform well and solve this kind of optimization problem;the newly obtained models can be used for radio planning in the city of Bertoua in Cameroon.
文摘Network planning is essential for the construction and the development of wireless networks. The network planning cannot be possible without an appropriate propagation model which in fact is its foundation. Initially used mainly for mobile radio networks, the optimization of propagation model is becoming essential for efficient deployment of the network in different types of environment, namely rural, suburban and urban especially with the emergence of concepts such as digital terrestrial television, smart cities, Internet of Things (IoT) with wide deployment for different use cases such as smart grid, smart metering of electricity, gas and water. In this paper we use an optimization algorithm that is inspired by the principles of magnetic field theory namely Magnetic Optimization Algorithm (MOA) to tune COST231-Hata propagation model. The dataset used is the result of drive tests carry out on field in the town of Limbe in Cameroon. We take into account the standard K-factor model and then use the MOA algorithm in order to set up a propagation model adapted to the physical environment of a town. The town of Limbe is used as an implementation case, but the proposed method can be used everywhere. The calculation of the root mean square error (RMSE) between the real data from the radio measurements and the prediction data obtained after the implementation of MOA allows the validation of the results. A comparative study between the value of the RMSE obtained by the new model and those obtained by the optimization using linear regression, by the standard COST231-Hata models, and the free space model is also done, this allows us to conclude that the new model obtained using MOA for the city of Limbe is better and more representative of this local environment than the standard COST231-Hata model. The new model obtained can be used for radio planning in the city of Limbé in Cameroon.
