Research On Rateless Code In MIMO Systems

Multiple-Input Multiple-Output (MIMO) technology utilizes multiple antennas at the transmitter and/or the receiver to form multiple spatial channels. Compared with Single-Input Single-Output systems, MIMO systems can improve transmitting reliability and provide higher data rate.In MIMO systems, different data streams may have different QoS demands, so how to guarantee these demands is a very important problem. In the traditional way, STBC is usually used to achieve high probability, while BLAST is used to achieve low latency, which means that this kind of problem is only considered in PHY layer. However, this approach can lead to high cost and low efficiency.Data scheduling algorithm in MIMO systems is studied in this thesis in order to guarantee the different QoS demands of various data streams.In this thesis, at first, UEP (Unequal Error Protection) rateless code is adopted in single-user MIMO systems in order to guarantee the various QoS requirements. As a novel technique, a high reliability can be achieved in a very low overhead by the UEP rateless code. Besides, different data streams can get different protection by using the UEP ratelss code. Moreover, based on the study of various scheme of the UEP rateless code, this thesis provides a novel cross-layer scheduling scheme, which is implemented by combing the UEP rateless code with space-time code. Through the simulation, it has been verified that different QoS requirements can be guaranteed by this new scheme.Systematic rateless code is utilized to improve the scheduling scheme in multi-user MIMO systems. Systematic rateless code has been used in MBMS (Multimedia Broadcast Multicast Systems) and DVB-H standard. Based on the study of traditional scheduling schemes in multi-user MIMO systems, a new scheme is proposed in this thesis, which is implemented by the systematic rateless code. Through the simulation, it can be concluded that fairness among the same kind of users can be guaranteed. Besides, a perfect tradeoff can be achieved between the various QoS demands and the system capacity.