Proportional Fair Resource Allocation in Coordinated MIMO Networks with Interference Suppression

Lei ZHONG  Yusheng JI  

IEICE TRANSACTIONS on Communications   Vol.E93-B   No.12   pp.3489-3496
Publication Date: 2010/12/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E93.B.3489
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Section on Wireless Distributed Networks)
resource allocation,  multicell MIMO,  interference suppression,  proportional fairness,  

Full Text: PDF(512.1KB)
>>Buy this Article

The biggest challenge in multi-cell MIMO multiplexing systems is how to effectively suppress the other-cell interference (OCI) since the OCI severely decrease the system performance. Cooperation among cells is one of the most promising solutions to OCI problems. However, this solution suffers greatly from delay and overhead issues, which make it impractical. A coordinated MIMO system with a simplified cooperation between the base stations is a compromise between the theory and practice. We aim to devise an effective resource allocation algorithm based on a coordinated MIMO system that largely alleviates the OCI. In this paper, we propose a joint resource allocation algorithm incorporating intra-cell beamforming multiplexing and inter-cell interference suppression, which adaptively allocates the transmitting power and schedules users while achieving close to an optimal system throughput under proportional fairness consideration. We formulate this problem as a nonlinear combinational optimization problem, which is hard to solve. Then, we decouple the variables and transform it into a problem with convex sub-problems that can be solve but still need heavy computational complexity. In order to implement the algorithm in real-time scenarios, we reduce the computational complexity by assuming an equal power allocation utility to do user scheduling before the power allocation. Extensive simulation results show that the joint resource allocation algorithm can achieve a higher throughput and better fairness than the traditional method while maintains the proportional fairness. Moreover, the low-complexity algorithm obtains a better fairness and less computational complexity with only a slight loss in throughput.