Research On Adaptive Resource Allocation For Multiuser MIMO-OFDM Systems

With rapid development of mobile communication theory in recent decade, the research on personal wireless communication techniques become more popular. Among these works, how to cope with the shortage of frequency resource coursed by growing number of mobile users and demands of diverse multimedia services is a focal problem. Fortunately, the combination of MIMO(Multiple-Input Multiple-Output) and OFDM(orthogonal Frequency Division Multiplexing) has been comprehensively considered as a major candidate techniques to deal with these problems for future radio communication systems. Supported by the National High Technology Research and Development Program of China under Grant (No. 2003AA12331005 2006AA01Z277) and National Science Foundation of China under Grant No. 60496310, we focus our eyes on the advanced adaptive resource allocation schemes in multi-user environments and expect that our proposed optimal allocation could be helpful to improve the practicability of MIMO-OFDM systems.First, the wireless channel characteristic and the MIMO-OFDM system model are discussed. Conditioned by preplanned BER(Bit Error Rate) and required rate of user's diversity feedback information, the adaptive subcarrier group allocation strategy for improving entire system performance is structured. Assuming exact CSI(Channel State Information) is obtained by transmitter, the adaptive subcarrier group allocation and bit loading algorithm for the purpose of transmit power minimization was presented. Benefiting from relativity of coherent subcarriers, the allocation is transformed to subcarrier group assignment, which significantly simplifies the computation. The optimal subcarrier group allocation criteria based on statistical beamforming was deduced and proved that low computational complexity and close system performance can be obtained to assure the practicability.Secondly, a combinational adaptive subcarrier group allocation algorithm considering antenna-dimension is analyzed. The multi-user diversity gain employed by subcarrier group allocation improves entire system performance by assigning diverse resource to corresponding user in frequency-dimension, but it is clear that computational complexity increases with growing number of antennas in MIMO. A new subcarrier group allocation combined with antenna adaptive selection is proposed, which utilizes optimal capacity based antenna selection algorithm to reduce antenna number and achieve more multi- antenna efficiency. Subcarrier group allocation based on antenna selection policy was deduced and it is shown that optimal antenna subset employment simplifies complexity without obvious performance decline.Thirdly, adaptive subcarrier group allocation based on fairness consideration is investigated. Targeting maximal entire transmit rate, we propose an adaptive fairly subcarrier group allocation. Using nash bargaining fair idea for reference, we deduce a two-level resource assignment scheme based on nash bargaining criteria, which realizes fairly resource allocation with assuring entire transmit rate maximized by way of iterative calculation among and within random user groups. Leveled fair arrangement avoids high complexity caused by traditional none-linear combination optimization algorithm and improves system practicability with lower computation. While successive coherent subcarrier group assignment farther simplifies complexity without obvious loss in performance.Finally, we penetrate into investigation of combinational problem which opportunistic beamforming and adaptive subcarrier group allocation are jointly considered. When studying optimal allocation in traditional way, the requirement of full CSI feedback brings extra large bandwidth consumption and engenders heavy pressure to systems. To deal with this problem, a novel subcarrier allocation with limited feedback CSI is proposed. Based on OB(opportunistic beamforming) technique, we design an AOB(advanced OB) scheme and deduce the limited-CSI subcarrier group allocation criteria using AOB. The AOB guarantees to accomplish adaptive allocation with fewer feedback CSI(only SNR matched value), which saving much feedback bandwidth and assures close performance compared with full-CSI pattern, while fairness is also kept.