Mathematical Problems in Engineering

Volume 2015 (2015), Article ID 947021, 8 pages

http://dx.doi.org/10.1155/2015/947021

## An Improved Tabu Search Algorithm Based on Grid Search Used in the Antenna Parameters Optimization

Shanghai Key Laboratory of Navigation and Location-Based Services, Shanghai Jiao Tong University, Shanghai 200240, China

Received 23 September 2014; Revised 29 January 2015; Accepted 6 February 2015

Academic Editor: Hsuan-Ling Kao

Copyright © 2015 Di He and Yunlv Hong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

In the mobile system covering big areas, many small cells are often used. And the base antenna’s azimuth angle, vertical down angle, and transmit power are the most important parameters to affect the coverage of an antenna. This paper makes mathematical model and analyzes different algorithm’s performance in model. Finally we propose an improved Tabu search algorithm based on grid search, to get the best parameters of antennas, which can maximize the coverage area and minimize the interference.

#### 1. Introduction

In mobile communication systems, such as the current 2G and 3G networks, small cells divided by the base stations are used to cover the entire region. So the base station should have the best possible coverage area in order to improve the quality of service (QoS). As to base station antenna, the most important factors that affect the cell coverage are antenna’s azimuth angle, vertical down angle, and transmit power. As to the terminal, we think there are two targets which have to be considered. They are RSRP (Reference Signal Receiving Power) and SINR (Signal to Interference plus Noise Ratio) [1]. The RSRP characterizes the average signal power obtained by the mobile terminal which depends on the power antenna transmits, distance between base station and terminal, and direction of terminal to the base station, and so forth. SINR is different from the noise in the propagation path such as AWGN (Additive White Gaussian Noise). It focuses on the interference from different signals which lead to the decline in SNR (Signal-to-Noise Ratio). SINR mainly originates from the interference caused by the signals from the same base station to different terminals and from different base stations to one terminal. So we should choose the right antenna parameters to maximize the RSRP and minimize the SINR.

Recent antenna parameter adjustment algorithms are mainly based on experience and manual adjustment. Besides, we can use some optimization algorithms, such as Powell search algorithm, and intelligent algorithms such as GA (Genetic Algorithm) [2], TS (Tabu Search) [3, 4], IWO (Invasive Weed Optimization) [5, 6], PSO (Particle Swarm Optimization) [7–9], SO (Stochastic Optimization) [10], and BC (Bee Colony Algorithm) [11]. Manual adjustment consumes a large amount of time. We can make predictions by Powell search algorithm, GA, TS, and some other optimization algorithms, but there are also some limitations. In a practical scene, it is actually an NPC (NP-complete) problem to get best RSRP and SINR by adjusting azimuth angle, vertical down angle, and transmit power. If we try to get the result by exhaustive attack method, the algorithm will have exponential complexity. Powell algorithm can easily get a local optimal solution, but it is easy to fall into a local extreme trap. Otherwise, intelligent algorithms such as GA, TS, IWO, PSO, SO, and BC algorithms are global algorithms. But the problem of GA algorithm is that it does not guarantee that the revolution direction of next generation is the best one. This leads to the excessive number of iterations. TS algorithm has similar problem to GA. IWO algorithm converges prematurely sometimes, and it is easy to fall into the local optimization. PSO algorithm does not perform very effectively to solve some discrete optimization problems, and it is also easy to fall into the local minimum or local optimization. As to the SO and BC algorithms, the convergence speeds are hard to be guaranteed.

In this study, we propose an improved TS algorithm based on Grid Search to solve the problem of antenna parameter adjustment and optimization. By combining both the TS algorithm and the Grid Search strategy, the advantages of both approaches can be maintained and developed. At the same time, it can be found that the time consuming of the optimization process can be reduced efficiently, and the final optimization results guarantee that it can get the global optimum, compared with that of the exhaustive attack method (EAM) in the computer simulations.

This paper mainly discusses the application of intelligent TS algorithm in antenna parameters optimization problem. By using Tabu Search algorithm based on grid search and changed-pace one-dimensional search, we can get a global extreme in short time. Finally, we use multicell joint adjustment to get a better cell coverage. Section 2 gives the practical scene in this paper. Section 3 describes the corresponding mathematical model. Section 4 introduces the proposed improved algorithm. Section 5 gives the computer simulation results, and Section 6 is the conclusion.

#### 2. Scene Model

In modern mobile communication system, we often use cell coverage to solve the problem of signal coverage of the whole system. The entire region is divided into several cells according to antenna of base station. The terminal directly communicates with the base which it belongs to. The cellular network is a typical model of cell coverage [12]. As shown in Figure 1, in a typical cellular network unit, every base has three antennas. The direction between every two figure is 120 degrees. One cell is divided into three sectors, and a base covers a rhombus shape area. The current mobile systems, such as 3G CDMA 2000, WCDMA network, and latest 3GPP LTE, all use the similar three antennas coverage model.