Research On Cooperative Communication Key Technologies In Physical Layer With Patial Channel State Information

With the rapid development of wireless communications systems, people put forward higher request for transmission rate and communication quality on wireless communication. The Long Time Evolve (LTE), boosted by3rd Generation Partnership Project (3GPP), has received extensive attention and research in recent years. LTE application networks are being tested in many cities in China, and will be commercialized in large scale in the next step. LTE adopts higher bandwidth and key technologies for physical layer, such as Orthogonal Frequency Division Multiple Accesss (OFDMA) and Multiple Input Multiple Output (MIMO), to provide higher uplink/downlink transmission rate and better communication quality. However, with larger amount of users and higher user mobility, future wireless communication networks not only needs to provide a higher data rate, but also wider range of network coverage and higher communication rate of cell edge users. Thus, directly applying these technologhies such as MIMO and OFDM in traditional mobile cellular networks is unable to slove these problems. Cooperative communication is a network thechnology proposed by LTE-Advanced (LTE-A). Wireless cooperative communication mainly includes distributed antenna system (DAS), basestation cooperation, relay and femtocell. Distributed antenna is a kind of new type of network architecture with cell division, also called Coordinative Multiple Point (CoMP) transmission in LTE-A. This technology obtains cell division and higher frequencty reuse by inserting a large number of new sites to close the distance between antennas and users. Basestation cooperation is similar as DAS, which needs to arrange Remote optical fiber (ROF) between adjacent sites. The difference is that the system does not concentrate all the baseband processing units of multiple basestations into a "super basestation", each basestation still has a fully baseband processing function, basestation cooperates closely through optical fiber. This paper mainly researches on cooperative MIMO technology, the main works and innovations are as follows:1、We propose a robust precoding scheme based on Minimum Mean Square Error (MMSE) criteria in downlink multi-user MIMO systems. This scheme considers not only the effect of noise power as traditional MMSE precoding scheme, but the performance loss caused by channel quantization error as well. Thus, it is a robust precoding scheme. Simulation results show that the proposed scheme has better performance than traditional schemes.2、We propose a new power allocation scheme in distributed MIMO system. In this model, user within the cell chooses one or more distributed Remote Radio Units (RRUs) for communication. User calculates the channel large-scale fading and antenna correlation informations between RRU through channel estimation, and feeds back these informations back to a central processing unit (CPU). The CPU substitutes these large-scale fading information and antenna correlation informations into the closed-form expression of Symbol Error Rate (SER), and calculates the optimal transmit power of each RRU using optimal theory. Since the change rate of channel large-scale fadings and antenna correlations is much slower than that of small-scale fadings, user only needs to feed back the large-scale fading and correlation information to basestation periodically. This scheme reduces the user feedback overhead and operation complexity greatly and effectively approaches the performance of optimal water filling. 3、We propose a new feedback scheme in base-station cooperation communication system. In this model, a basestation within a central cell serves multiple users simultaneously. Several interfering basestations are around the main basestation, these base-stations are connected by backhaul links and interchange data with each other. Each user of the system is suffered by two interferences:multi-user interferences from local cell and inter-cell interferences from adjacent cells. User not only feeds back the quantized channel state information (CSI) to local service basestation, but feeds back the interference CSI to adjacent basestation as well. This paper proposes that in the algorithm, each user adaptively allocates reasonable feedback bit number for desired CSI and interfering CSI to minimize the capacity loss caused by channel quantization error.4、In network MIMO network with limited feedback, each user chooses several basestations from a cluster for communication. Selecting different basestation numbers will cause the change of channel dimensions, thus the codebook dimension will change correspondingly. This paper proposes a basestation selection algorithm with low complexity, user only needs a codebook to quantize different dimensions of CSI, which greatly reduces the codebook storage overhead. Finally, we propose an adaptive bit feedback scheme, each user adaptively allocate feedback bits for desired CSI and interfering CSI with fixed feedback bandwidth, to maximize the transmission rate.