Coordinated multi-point (CoMP) transmission and reception is a promising technique for interference mitigation in cellular systems. The scheduling algorithm for CoMP has a significant impact on the network processing complexity and performance. Performing exhaustive search permits centralized scheduling and thus the optimal global solution; however, it incurs a high level of computational complexity and may be impractical or lead to high cost as well as network instability. In order to provide a more realistic scheduling method while balancing performance and complexity, we propose a low complexity centralized scheduling scheme that adaptively selects users for single-cell transmission or different CoMP scheme transmission to maximize the system weighted sum capacity. We evaluate the computational complexity and system-level simulation performance in this paper. Compared to the optimal scheduling method with exhaustive search, the proposed scheme has a much lower complexity level and achieves near optimal performance.

Codebook based multiple-input multiple-output (MIMO) precoding can significantly improve the system spectral efficiency with limited feedback and has been accepted as one of the most promising techniques for the Evolved UTRA (E-UTRA). Compared with single-user (SU) MIMO, multi-user (MU) MIMO can further improve the system spectral efficiency due to increased multi-user diversity gain. MU-MIMO is preferred for the case of a large number of users,when the total feedback overhead will become a problem. In order to reduce the feedback overhead, feedback of single channel quality indicator (CQI), e.g. rank 1 CQI, is required in E-UTRA currently. The main challenge is how to obtain CQIs of other ranks at Node B for rank adaptation with single CQI feedback. In this paper, an adaptive CQI update scheme at Node B based on statistical characteristics of CQI of various ranks is proposed. To further increase the accuracy of CQI at Node B for data transmission, an adaptive CQI feedback scheme is then proposed in which single CQI with the rank same as previously scheduled is fed back. Simulation results show that our proposed CQI update scheme can achieve 2.5-5% gain compared with the conventional method with fixed backoff. Moreover, with the proposed adaptive feedback scheme, 20-40% performance gain can be obtained and the performance can approach the upper bound.