A small base station capacity and coverage optimization method based on tabu search

Abstract

The invention relates to a capacity and coverage optimization method based on tabu search, which belongs to the technical field of wireless communication. The method comprises steps: according to the initial power of the small base station, an initial value for a capacity and coverage target optimization function is calculated; a tabu search algorithm is adopted for iterative optimization: a solution vector for enabling the target optimization function to be optimal in the neighborhood of the current solution is calculated; if the solution vector enables the target optimization function value to be the optimal so far or not the optimal so far but in a non tabu state, the solution vector is marked as the current solution and is added to a tabu list, or otherwise, the solution vector is eliminated from the neighborhood of the current solution and the sub step of calculating the optimal solution vector in the neighborhood is returned; and after iterative optimization, the transmitting power of the small base station is updated, and the capacity and coverage target optimization function value for the small base station system at the time is outputted. Self optimization of the capacity and the coverage of the small base station system can be realized, the calculation complexity for the iterative optimization is reduced, the network overall performance is optimized and enhanced, and global optimum can be achieved.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, in particular to a tabu search-based small base station capacity and coverage optimization method. Background technique [0002] At present, on the one hand, the explosive growth of mobile data traffic services has put forward higher requirements on the bandwidth, capacity and coverage of mobile communication networks, which makes it difficult for traditional macro base station coverage methods to meet today's user needs. On the other hand, in the 3rd Generation Partnership Project (3GPP) LTE system, since a large amount of data services occur indoors, the data volume of indoor users is large and unevenly distributed, resulting in unsatisfactory indoor signals and local network congestion. Therefore, the deployment of small base stations with short distance and low power consumption has become an effective solution. Compared with macro base stations, small base stations have variou...

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Problems solved by technology

[0007] Existing capacity and coverage optimization algorithms include: particle swarm optimization algorithm, an optimization algorithm in which particles follow the optimal particle search in the solution space. It is easy to solve and converge quickly, but it is not good for solving discrete problems, and it is easy to fall into local optimum and difficult to achieve global Optimal; simulated annealing algorithm, a mechanism based on probabilistic heuristic search, gradually fine-tunes the parameters to be adjusted, and can accept a solution worse than the current solution with a certain probability to avoid falling into a local optimum, and finally achieve global capacity and coverage optimization , but its convergence rate is slow

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