| With the rapid development of the information technology, broadband information services have been extended to the mobile terminals based on the innovation of wireless communication technologies. The voice service based traditional mobile communication system has been evolved into a broadband communication system which can provide video, multimedia, and mobile broadband internet access services to the end users. Current 4G mobile communication technology with Gigabit per second transmission rate has been commercialized, providing a significant improvement for mobile users’ quality of service (QoS). However, with the popularization of the intelligent terminals and new mobile applications, wireless traffic data has been growing exponentially and will reach 1000 times by now in 2020. Meanwhile, the shortage of spectrum resources coupled with the low efficiency of authorized bandwidth leads to a contradiction of the supply and the demand of the spectrum resources. To solve these issues, Multiple-Input Multiple-Output (MIMO) technique has been proposed to get high transmission rate and system capacity without extra bandwidth and transmission power. Through utilizing the multiple input and output antennas and exploring the space diversity, MIMO system could dramatically improve the system energy efficiency and spectral efficiency. However, the performance of traditional MIMO system is limited by the typical settings and node configurations. For example, the limited number of antennas and the serious inter-channel interference at the receiver lead the significant system performance degradation with the non-ideal channel state information (CSI). Therefore, the concept of Massive MIMO with large-scale multi-antenna array has been proposed to form multiple radio frequency chains by making full use of the degree of space freedom, which can effectively improve the energy efficiency and the spectral efficiency for the future mobile communications.Massive MIMO technology is the extension and expansion of the traditional MIMO technique, where tens or even hundreds of antennas are set at the base station (BS) to form an antenna array and the users in cell will communicate with the BS with the same frequency at the same time through the full use of the degree of the space freedom provided by the large antenna array. Massive MIMO technology leads significant improvement to the spectral and energy efficiency. Although massive MIMO could provide such a performance enhancement, its related technologies are still in infancy. Particularly, the proper channel model as well as the modest pilot overhead during CSI acquisition still requires further studies. For massive MIMO system with TDD mode, the users in the cell send orthogonal pilot signals to the BS, then the BS makes channel estimation based on the received pilot signals and obtains the downlink channel parameters by the channel reciprocity. However, due to the limited number of pilots and the increasing number of the users, the pilot signals transmitted by the neighboring cell users may not entirely orthogonal. Therefore, the channel estimation will inevitably be interfered by the users utilizing the same pilot in neighboring cells, which results in the bottleneck "pilot contamination" of the massive MIMO system.In this thesis, we propose two new schemes to cope with the "pilot contamination" problem in the massive MIMO system with TDD mode and to improve the system transmission rate. The proposed schemes are based on the heuristic algorithm in the artificial intelligence field, named as the genetic algorithm (GeA) and artificial fish swarm algorithm (AFSA), which have been utilized to solve the optimization problems. Both of the proposed schemes can maximize the system transmission rate with a low computational complexity and decrease the influence of the "pilot contamination" to a certain extent. The simulation results show that the average rates of the AFSA and GeA schemes can reach a near optimal solution compared with the exhaustive search. Furthermore, the proposed pilot allocation schemes based on the AFSA and GeA can achieve a lower computation complexity base on the fast convergence rate. |
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