Intelligent Planning For TD-LTE Base Stations

TD-LTE is a smooth transition from TD-SCDMA to4G. TD-LTE system is different from the third generation mobile communications.The difference is that TD-LTE system use code division multiple accesses (CDMA) as access technology. The basic idea of OFDM technology is distributed to the high speed data stream for transmission on a plurality of mutually orthogonal sub-carriers, so that each sub-carrier symbol rate is greatly reduced and substantially lengthen corresponding symbol duration. And it uses way of cyclic prefix to resist multipath radio signal transmission delay spread. Therefore, it is particularly suitable for broadband communications scenarios. TD-LTE use Multiple Input Multiple Output (MIMO) technology to meet the need of high data rate and great system capacity. According to the deployment and application form, the antenna can be selected to use the transmit diversity, spatial multiplexing and beam forming. For example, for the large deployment of a base station antenna array spacing uncorrelated case, the spatial multiplexing scheme simultaneously transmit multiple data streams to achieve high data rates. To deploy the small and related base station antenna array, beamforming technology can be used to reducing interference between users by beaming to the target users. However, to control channel, such as the need of better ensurement the correctness of the reception scene, transmit diversity is a reasonable choice.The base station (BS) planning is to meet the short and long term targets of network, which can determine the number of base stations, configuration and locations.For the TD-LTE base stations planning, this paper presents a fast, efficient and intelligent way. A new mathematical model for the4G BS planning is proposed in this paper. The co-channel interference, OFDM, the cell edge rate, the reference signal received power (RSRP) and the base station density are considered in detail in this model. Network construction area is divided into many small blocks, with one point (test point) to represent the business needs of users in this small area. The objectives of the model are to minimum construction cost, maximum coverage and capacity at the same time. Before the construction of the network, selected suitable distance rasterized grid according to the type of building area, and sprinkle the candidate base station. Then the network capacity and coverage can be obtained through activating base station and it’s the configuration and resource allocation method to calculate the RSRP for each test point (TP). In the algorithm, this paper uses multi-objective evolution algorithms with local search to solve this problem. The integer encoding in this algorithm can cleverly avoid producing illegal solution while crossing. Local search can improve network coverage, reduce costs and speed up the computation by adjusting the solutions partially. Simulation results show that the according algorithm with local search operator can give a set of solutions with relatively lower cost, larger coverage, larger capacity and faster speed than the original one. The effectiveness of the proposed model can also be identified by the comparison of the solutions.