Radio Resources Allocation For Coordinated Multiple Points

The coordinated multiple points (CoMP) technology has been discussed since long-term evolution (LTE) release11. It allows geographically separated transmission points to work cooperatively, aiming at improving cellular coverage and cell-edge users’throughput. Radio resources allocation is the key technology for combating inter-cell interference and enhancing radio resources utilization efficiency. However, there are several problems for CoMP radio resources allocation, namely joint scheduling for uplink CoMP, optimization for multi-cell multi-user joint transmission and distributed interference distraction system. In this thesis, such three critical problems are researched.Optimal model of uplink CoMP joint scheduling is too complex to be solved and greedy cooperative scheduling degrades the performance a lot. To fill the gap, the pure binary-integer programming model is proposed. Its advantage is that it can be solved by branch-and-bound algorithm, rather than traditional exhaustive search method, so the computational overhead is saved. The system-level simulation shows that it yields14.83%and22.13%gains over the non-cooperation strategy in average spectrum efficiency and cell edge users’throughput,9.37%and9.15%gains over the greedy cooperation scheduling in those performance indicators.Power allocation for multi-cell multi-user joint transmission is difficult to obtain the closed-form solution, since it is a complicated optimization problem. It is still unknown the existence of better performance model with closed-form solution under some assumptions. To fill the gap, the virtual power pool is introduced, and then the closed-form water-filling solution is obtained. The simulation shows that it exhibits9.16%and16.13%gains when the power pool size is2W and number of cooperative base stations is4.Power allocation and cooperative scheduling problems for the distributed interference subtraction need to be solved. Hence, a two-cell sum-rate maximization model is established. The optimal power allocation is obtained and a signal-to-leakage ratio based cooperative scheduling algorithm is designed. The simulation exhibits138.65%and162.69%gains over the other methods in average spectrum efficiency.