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Study On Related Techniques Of Mimo Limited Feedback And Multi-base Station Cooperative

Posted on:2013-02-02Degree:DoctorType:Dissertation
Country:ChinaCandidate:D S HanFull Text:PDF
GTID:1118330371459360Subject:Communication and Information System
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In the future, mobile communication system will have to support transmission rates up to several Mbps and even tens of Mbps. In the situation where the spectrum resource is gradually limited, it is necessary to make a deep discussion on MIMO and to extend the application of it. In this way, the degree of freedom in the space domain can be exploited and utilized, and thus the spectrum efficiency can be increased, as well as the performance at the edge of cells.MIMO is the universally acknowledged key technique for the next generation mobile communication system. The limited feedback technique is essentially important for obtaining the channel state information at the transmitter side, pre-coding at the transmitter and determining the power allocation strategy. While the multi base-station cooperation technique is meaningful for increasing the spectrum efficiency, improving the performances at the cell edge and satisfying the requirement of transmission rate and spectrum efficiency for future wireless mobile communication system.This paper focuses on the issues related to limited feedback techniques in MIMO system and coordinated MIMO across multi base-stations, including: performance analysis of limited feedback for single base-station MIMO, performance analysis of limited feedback for multi base-station coordinated MIMO, user scheduling for both single base-station MIMO and multi base-station coordinated MIMO, design of limited feedback for multi base-station system, and the antenna selection and base-station selection in multi base-station coordinated system. The contributions of this paper are presented as follows:1) Based on the Nakagami-m fading channel model, the performance of a beam-forming system in a MISO scenario, where the random vector quantization is employed for limited feedback, is analyzed. The closed-form expressions for average BER and outage capacity are derived. The corresponding results are further extended to support multi-antenna-selection based MIMO system. Besides, relations between system parameters and system performance are revealed by further studying the closed-form solution, which provide theoretical basis for practical system application.2) For the multi base-station coordinated beam-forming system, the impact of limited feedback on the system capacity is analyzed. The upper bound of throughput loss is derived for the situations where there is either one active user or multi active users. Based on the theoretical analysis, the relation between the upper bound of throughput loss and the number of feedback bits is obtained. In this instance that there are multi users for each cell, an optimum strategy is designed for the allocation of feedback bits across multi cells, with which, the minimum upper bound of throughput loss can be achieved for the given number of user feedback bits.3) For the downlink transmission in a limited-feedback based multi-user MIMO system, scheduling algorithms are proposed respectively for the single base-station and the multi base-station coordination. In the single base-station, users are selected by maximizing the lower bound of capacity, which is therefore employed as the target function of the proposed method. Using this method, multi user diversity gain can be achieved and thereby the system capacity is increased. In the multi base-station coordination system, an opportunistic SDMA algorithm is proposed for the user scheduling. In this algorithm, each base-station adopts an orthogonal random vector as the beam-forming vector, and the information of beam-forming is shared across based-stations involved in the coordination via high speed link. The SINR is calculated in the receiver for each user, and the user with the highest SINR will be scheduled by the transmitter. By using this algorithm, user selection does not need to consider the user selection results of interfering cells, which well resolves the difficulty of multi base-station coordination and obtains system capacity gain in addition.4) Considering the characteristic of a multi based-station coordination system, a strategy for the limited quantization feedback is proposed, which is based on error revising technique. In this strategy, the channel quantization procedure is divided into several steps, the number of which is equal to the number of base-stations involved in the coordination. The quantization error of the last step will be revised in the current step. This method can achieve a good trade-off between complexity and system performances, and suitable for base-station coordination system with multi base-station joint processing mechanism.5) Based on the characteristics of multi base-station coordination system, the antenna selection techniques are studied, which includes distributed antenna selection and base-station selection. Three antenna selection algorithms are proposed for the selection of multi base-station antennas:the Optimal Antenna Selection (OAS), the Aggregate Channel Frobenius Norm Antenna Selection (ACFAS), and the Individual Channel Frobenius Norm Antenna Selection (ICFAS). The OAS algorithm employs a exhaustive searching based strategy, and the ACFAS and the ICFAS are two suboptimal algorithms with lower complexity. The ACFAS method adopts the Frobenius norm of the aggregate channel matrix between the user and all the base-stations involved in the coordination as the antenna selection of target function, while the ICFAS algorithm adopts the Frobenius norm of the channel matrix between the user and each single base-station involved in the coordination as the target function. Considering both the base-station selection and user scheduling, a joint algorithm for selecting base-station and user is proposed, which aims to maximize the system capacity. Following a couple of steps, the base-station and user are selected jointly at both the user-end and the base-station end. Meanwhile, the beam selection is also adopted at the user-end. Therefore, the optimal system performance can be achieved.
Keywords/Search Tags:MIMO, Limited Feedback, Multi Base-Station Cooperation, Interference Cancellation, User Scheduling, Multi User Precoding, Antenna Selection
PDF Full Text Request
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