Multiple Input Multiple Output (MIMO) technology refers to the use of multiple transmit and receive antennas at the transmitting end and the receiving end respectively. The signal is transmitted and received through multiple antennas at the transmitting end and the receiving end, thereby improving the quality of service of each user. Bit rate or data rate).
MIMO technology can greatly improve spectrum utilization for traditional single-antenna systems, enabling systems to transmit higher-speed data services in a limited wireless frequency band. At present, countries have begun or plan to conduct research on next-generation mobile communication technologies (post-3G or 4G), and strive to have a place in the future mobile communication field. With the development of technology, the future mobile communication broadband and wireless access convergence system has become a hot research topic, and MIMO system is one of the more research directions. This article focuses on the five research hotspots of MIMO technology.
Modeling and simulation of MIMO channelsIn order to make better use of MIMO technology, it is necessary to deeply study MIMO channel characteristics, especially spatial characteristics. Unlike traditional channels, MIMO channels have a certain spatial correlation in most cases, rather than being independent of each other. At the 3GPP conference in November 2001, Lucent, Nokia, Siemens and Ericsson jointly proposed a standardized MIMO channel. There are two modeling methods for link-level MIMO channels recommended by 3GPP and 3GPP2: correlation (CorrlraTIon-Based) based method and sub-path based (EAGC-A14H) based method. Although 3GPP and 3GPP2 define the channel parameters at the link level, there is no consensus on how to implement them. Studying the influence of channel correlation on system capacity has become one of the research directions of MIMO technology.
In addition, the current research on MIMO systems is assumed to be performed under ideal channel conditions, but in fact it is impossible to know the channel impulse response in the radio propagation environment of the receiving end, so channel estimation is performed. Since there is interference between antennas in channel estimation in MIMO systems, it is also a hot research topic to study the channel estimation method when there is interference between antennas.
Antenna selection technology for MIMO systemsSince multiple antennas require multiple RF RF circuits and RF is very expensive, it is attractive to find an optimal antenna subset selection technique with the advantages of MIMO antennas and low price and low complexity. The multi-antenna selection transmitting and receiving system uses a certain criterion to select the MS antenna from the M transmitting antennas for transmitting signals, and also selects the NS roots from the N receiving antennas at the receiving end for receiving signals, thus forming a selection. MS&TImes; NS MIMO system. In general, corresponding to the application of multiple antennas, the selection criteria can also be divided into two types: one is to maximize the diversity provided by multiple antennas to improve the transmission quality; the other is to maximize the capacity provided by multiple antennas. Transmission efficiency.
Signal processing for MIMO systemsMost of the early research on MIMO technology was concentrated in a single-user point-to-point environment without considering co-channel interference from other users. Recently, the focus of research has gradually shifted to multi-user MIMO channels. The use of space division multiple access (SCDMA) in the downlink of a multi-user MIMO system can bring considerable gain to system throughput. The technical difficulty of such a multi-user MIMO system is how to design a transmission vector to eliminate co-channel interference between users. Typical "best problems" include capacity issues (maximization and information rate) when power is limited or power control issues (minimizing transmit power) to meet each user's specific QoS. Although there is no closed-loop solution to both of these problems for a typical multi-user MIMO channel, a closed-loop solution can be obtained by imposing certain limitations. The most common ones include block diagonalization, successive optimization, beamforming, and space-time coding to eliminate interference between multiple users.
Capacity Analysis of Multiple Antenna Systems in Multiple Access ChannelsIn theory, the capacity domain of a multi-antenna multiple access system is already very clear, but how to make the capacity domain meet the transmission rate requirements of various users is still not well solved. From the structural point of view, this is a nonlinear optimization problem. Although the traditional convex optimization method can be solved, the calculation amount will be very large, and it is necessary to find a simple and fast method. In some special cases, such as the optimization problem of multi-user and capacity (the same rate weighting value of all users), there is a very meaningful multi-user water injection iterative algorithm in the literature, which makes full use of the original optimization problem. The structure is solved quickly using matrix theory and convex optimization theory. However, this special case does not make much sense for the actual network. Because different users in the actual network are located at different locations in the network, the same rate weighting method will result in the transmission rate of the network edge users being unguaranteed. Users with relatively low long-term transmission rates should be given a larger rate weighting value to increase the transmission rate of the user. After the priority is introduced, the multi-user and capacity transmission criteria are not applicable. The weighting and capacity criteria must be adopted. The weighting of different user rates reflects the user's priority. The higher the priority, the higher the user's rate weighting value. Big and vice versa. For the scheduling strategy and user rate allocation strategy in this case, the capacity domain formula of Gaussian scalar multiple access and the optimization algorithm are used to solve this problem.
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