Small base station capacity and coverage optimization method based on adaptive tabu search

Abstract

The invention relates to a small base station capacity and coverage optimization method based on adaptive tabu search, and belongs to the technical field of wireless communication. The method comprises the steps of: according to initial power of a small base station, calculating an initial value of a capacity and coverage target optimization function; carrying out iteration optimization by adopting a tabu search algorithm, which comprises the steps of calculating a solution vector in a neighbourhood of a current solution, which enables the target optimization function to be optimal, recording as the current solution and adding the current solution into a tabu list if the solution vector enables the value of the target optimization function to be optimal currently or not to be optimal so far, but to be in a non-tabu state, or returning to calculate an optimal solution vector in the neighbourhood after removing the solution vector from the neighbourhood of the current solution, and adaptively updating a tabu length; and after completing iteration optimization, updating transmission power of the small base station, and outputting a current value of the capacity and coverage target optimization function of a small base station system. Capacity and coverage optimization of the small base station system can be implemented, complexity of optimization iteration computation is reduced, optimization and improvement of integral performance of the network are achieved, and global optimization can be achieved.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, in particular to a small base station capacity and coverage optimization method based on adaptive tabu search. 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...

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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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