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Research On Signal Detection Algorithms In Multiple-Input Multiple-Output Systems

Posted on:2012-03-24Degree:MasterType:Thesis
Country:ChinaCandidate:S LeiFull Text:PDF
GTID:2178330335460130Subject:Communication and Information System
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In rich scattering environment, multiple-input multiple-output (MIMO) multiplexing technology can significantly increase the channel capacity and high-speed reliable data transmission by equipping multiple antennas at both transmit and receive sides of the system, without increasing the frequency bandwidth and the transmit power. Therefore, MIMO systems have been considered as a promising approach to achieve high data rates and high system capacity (for meeting the rapidly growing demand for data services) in future broadband mobile communication.However, while improving channel capacity, MIMO systems have also drastically increased the difficulty of signal detection at the receiver side, making the overall system performances closely related to the detection algorithms of the receiver.This thesis firstly briefly reviews the conventional signal detection algorithms in MIMO systems. Then the detailed analyses and comparisons of three most important tree search-based near-optimal detection algorithms are given:sphere decoder (SD), QRD-M algorithm, fixed-complexity sphere decoder (FSD), and their improved variants. Followed by an elaborately introduction of three novel signal detection algorithms, which have been proposed by combining the merits of the conventional or tree search-based algorithms, at last.SD is a depth-first detection algorithm by using radius constraint. It has an average but variable complexity and may suffer a very long delay. QRD-M is a breadth-first detection algorithm by using parallel processing, but its complexity is high and it needs lots of sorting. The original FSD algorithm is a clever combination of maximum-likelihood detection (MLD) algorithm which yields the optimal performance while with the highest complexity and the successive interference cancellation (SIC) algorithm which has a very low complexity but with poor performance. Though FSD can obtain near-optimal performance (like the SD and the QRD-M) with a fixed complexity and can be processed in parallel, it has a common drawback in equally handling all the branches obtained via full expansion till reaching the final minimum Euclidean distance (MED) decision, which will lead to very redundant computational cost.To reduce the complexity of FSD while maintaining near-optimal performance, motivated by the tree "pruning" method of the QRD-M algorithm, an adaptive control of the number of surviving branches is conducted to reduce the redundant computational cost while keeping parallel processing. SD algorithm prunes the inferior tree branches or paths by using an adaptive radius constraint which can be updated automatically during the search, thus, inspired by this, the idea of radius constraint are introduced for the FSD algorithm to reduce its complexity. In addition, by combining the parallel processing structure of FSD and a lately-proposed new idea of probabilistic (or statistical) tree pruning, an efficient statistical pruning algorithm are created for FSD by utilizing partial decision feedback detection. Computer simulation results demonstrate that all the three novel algorithms can approach the near-optimal performance with low complexity.
Keywords/Search Tags:multiple-input multiple-output (MIMO), detection algorithm, tree search, fixed-complexity sphere decoder (FSD), performance, complexity
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