| Beamforming technology began in the 20th century,60's. It is first widely used in radar, sonar and military communications. As modern digital signal processing technology developed, it makes the use of beamforming in personal communication possible. Beamforming technology was firstly applied in the 3G standard TD-SCDMA system. In the next-generation network, it has been used again. In current version of the standards, UE-specific reference signal based single stream beamforming technology has been identified. Moreover, in the latest evolution of technology, beamforming has been great concerned that the technology highly improved spectrum efficiency.In so many features of next-generation networks, this article focuses on three technologies. They are UE-specific reference signal based beamforming, downlink power control, and inter-cell interference coordination. Through the analysis of network signal characteristic, it is showed that in the full power transmission mode, due to the introduction of beamforming, the signal interference ratio is greatly enhanced. Especially in the close position from the serving cell center, received SINR is much higher than the amount required by highest modulation level. Considering SINR is high, the received SINR will be in nonlinear relationship with the transmission power. That means increasing transmission power will not increase the effective SINR. This observation inspired us to think about how to use the network power control and power allocation methods to tap the potential gain. In this paper, we study on a series of the downlink beamforming technology with combination of power optimization problem. First, we studied interference coordination methods without real-time communication between the base stations. We make an analysis on beamforming combined with soft frequency reuse solution. Further discussions have been made on the perspective of static power compensation and user ratio of the cell-center frequency band. Then, using game theory, we study the distributed power control game of multiple base stations. This article discusses two types of power control problem, namely, fixed target SINR minimizing total power issues and the joint target SINR and transmit power optimization problem. To solve these problems, we discussed three games respectively. They are block error rate game, linear power pricing game, and maximizing total capacity game. Then, assuming not communicate between base station, one time of communication, two times of communications or three times of communications as the basic models, six kinds of distributed power control algorithm have been achieved. Among them, the paper focused on the maximum total capacity Game, and that game had been improved. We proposed a novel effective SINR constrained total system capacity utility function. Since then, the paper further discusses the integration of soft frequency reuse and distributed power control algorithm. A novel scheme namely smart soft-frequency reuse technology has been proposed. Finally, the article discusses the ideal conditions with centralized control center. For the optimal solution of joint considering user scheduling, beamforming and power allocation is still an unsolved open problem, we discussed a close to optimal but low complexity algorithms. We proposed a two-tier resource allocation model using the principle of genetic algorithm, so as to measure the performance gap between the Distributed algorithm and the optimal algorithm in theory.In the last part, the proposed algorithms were verified using the system simulator. For the combination of Beamforming and SFR, we found that it is difficult to improve overall performance significantly, whether to adjust the power compensation factor, or adjust the UE ratio. For the fixed target SINR minimize total power algorithms, we found that such algorithms improve system performance little. Even access to more accurate information of SINR, the rate is still not significantly improved. In contrast, the proposed distributed power control algorithms can achieve significantly improvement of the overall rate. Compared with the basic program, the cell-average spectral efficiency made up to 15.2% increase, while cell-edge users' spectrum efficiency improved 76.1%. Further, the proposed smart soft frequency reuse algorithm demands a lower rate between base stations, but reached a little better system performance than common distributed power control algorithms. At last, the centralized algorithm was verified. It is observed that optimal scheduling and optimal power control both greatly improve the system spectrum efficiency. However, the gain by using both technologies is not the addition of individual gains. This indicates that after using centralized scheduling, the space of power control optimization will be decreased. Finally, the contrast of smart soft frequency reuse and centralized power control technology has been made. We can see that the proposed smart soft frequency reuse scheme achieved the highest enhancement of 15.5% in cell-average spectrum efficiency, which is very close to the ideal power control program's 20.5% gain. For cell-edge user spectral efficiency, the proposed smart soft frequency reuse scheme achieved 77.6% gain compared to basic program, which is close to 118% gain of ideal power control program.
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