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(A) study of precoding and limited feedback for massive MIMO communication systems 원문보기

  • 저자

    이병주

  • 학위수여기관

    Graduate School, Korea University

  • 학위구분

    국내박사

  • 학과

    컴퓨터電波通信工學科

  • 지도교수

    沈秉孝

  • 발행년도

    2014

  • 총페이지

    xiv, 124장

  • 키워드

    Massive MIMO Antenna group beamforming Antenna group pattern set Feedback resources allocation Vector perturbation Virtual users;

  • 언어

    eng

  • 원문 URL

    http://www.riss.kr/link?id=T13541890&outLink=K  

  • 초록

    For the past decade, the multiuser multiple-input multiple-output (MIMO) technology has been received much attention as a means to enhance throughput of the current fourth generation (4G) cellular network such as Long Term Evolution (LTE) and LTE-advanced. Among many features in the LTE-advanced, the multiuser MIMO scheme has been identified as one of the key enablers for achieving high spectral efficiency. In the multiuser MIMO downlink system, a basestation with multiple antennas transmits data streams to multiple mobile users, each with one or receive antennas. Due to the fact that the co-channel interference is hard to be managed by the receiver operation, pre-cancellation of the interference via the precoding at the transmitter has received much attention. It is now well-known that the capacity region of the multiuser MIMO downlink can be achieved by dirty paper coding (DPC). However, the shortcoming of the DPC is that system implementation leads to the high computational cost of successive encodings and decodings. As a way to reduce the complexity of DPC and also achieving a large fraction of DPC capacity, linear precoding techniques such as zero forcing (ZF) and block diagonalization (BD) and nonlinear precoding technique referred to as vector perturbation (VP) have been proposed. In order to provide high spatial multiplexing gains, capacity gain can be achieved based on multiuser diversity by selecting a subset of users with good channel conditions at each time slot. Among many requirements for achieving this promise, accurate channel state information (CSI) at the basestation would be perhaps the most important one. This dissertation investigates multiuser precoding techniques for downlink cellular systems. In the first part of the dissertation, we propose a method pursuing performance gain of VP in multiuser downlink systems. Instead of employing the maximum number of mobile users for communication, we use small part of them as virtual users for improving reliability of users participating communication. By controlling parameters of the virtual users including information and perturbation vector, we obtain improvement in the effective signal-to-noise ration (SNR). Numerical results on the realistic multiuser MISO and MIMO downlink systems show that the proposed MISO and MIMO downlink systems show that the proposed method brings performance gain over the standard vector perturbation with marginal overhead in computations. In the second part of the dissertation, we combine the BD technique with a minimum mean square error vector precoding (MMSE-VP) for achieving further gain in performance with minimal computational overhead. Two key ingredients to make our approach effective are the QR decomposition based block BD and joint optimization of transmitter and receiver parameters in the MMSE sense. In fact, by optimizing precoded signal vector and perturbation vector in the transmitter and receiver jointly, we pursue an optimal balance between the residual interference mitigation and the noise enhancement suppression. From the sum rate analysis as well as the bit error rate (BER) simulations in realistic multiuser MIMO downlink, we show that the proposed BD-MVP brings performance gain over existing multiuser MIMO algorithms. In the third part of the dissertation, we propose an antenna grouping based feedback reduction technique for frequency division duplexing (FDD)-based massive MIMO systems. Recent works on massive MIMO have shown that a potential breakthrough in capacity gains can be achieved by deploying a very large number of antennas at the basestation. In order to achieve optimal performance of massive MIMO systems, accurate CSI should be available at the basestation. Although CSI can be obtained by employing channel reciprocity in time division duplexing (TDD) systems, such is not possible for FDD based system and explicit feedback of CSI from the user terminal to the basestation is required. The proposed algorithm, dubbed antenna group beamforming (AGB), exploits the spatial correlation among transmit antenna array to reduce the dimension of the vector to be quantized. In fact, the key feature of the AGB method is to map multiple correlated antenna elements to a single representative value by using pre-designed patterns. In doing so, the AGB method can search the codeword in the codebook generated from the reduced dimension channel vector. Simulation results show that the proposed method achieves feedback overhead reduction over conventional approach under the same target sum rate requirement. In the last part of the dissertation, we propose the antenna group scheduling (AGS) algorithm that combines antenna selection and user scheduling for pursuing further reduction in feedback overhead. The AGS method selects the antenna group with the maximum channel gain to reduce the dimension of channel vector and schedules users in the group maximizing the sum rate. While the conventional approach finds the codeword vector in the codebook generated from full dimension vector quantization, the proposed AGS method uses codebook generated from the subset of full dimensional antenna array. From the numerical results in realistic multiuser massive MIMO downlink, we show that the proposed AGS method brings reduction in feedback overhead over prior existing works.


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