Alternating Projection-Based Channel Estimation and Data Detection for Massive MIMO System
- 주제(키워드) Alternating Projection-Based Channel Estimation and Data Detection for Massive MIMO System
- 발행기관 아주대학교
- 지도교수 오성근
- 발행년도 2017
- 학위수여년월 2017. 2
- 학위명 석사
- 학과 및 전공 일반대학원 전자공학과
- 실제URI http://www.dcollection.net/handler/ajou/000000024195
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
A multi-antenna base station (BS) can spatially multiplex a few terminals over the same bandwidth, a technique known as multi-user, multiple-input multipleoutput (MU-MIMO). A new idea in cellular MU-MIMO is the use of a large excess of BS antennas to serve several single-antenna terminals simultaneously. This so-called "massive MIMO" promises attractive gains in spectral efficiency with time-division duplex operation. Massive MIMO (also known as Very Large MIMO, Large-Scale Antenna System) is a new communication technology, has the most advanced level of the current. In large-scale MIMO systems, with the base station (BS), for example, a few hundred antennas simultaneously serving a relatively small number, say tens, of UEs, where the large-scale antenna array may be co-located at one site or distributed geographically at different sites. Massive MIMO systems can adopt simple signal processing methods, e.g., maximum-ratio transmission (MRT) at the transmitter and maximum-ratio combining (MRC) at the receiver, and use low-cost power amplifiers, but still provide nearly optimal performance. The benefits of massive MIMO systems compared to conventional MIMO and MU-MIMO systems include big data rates, enhance reliability, potential power saving, and inexpensive components. Massive MIMO is a new research area in communication and signal processing as well as propagation and electronics, and requires a paradigm shift in the way how we think about theory, systems, and implementation. Recently, Massive MIMO has drawn great interests from both the academic community and industry, and has triggered intensive investigations of various aspects of massive MIMO by researchers from the whole world. The considered problems include performance analysis, practical system characterization, precoder and beam former designs, energy and spectral efficiency, distributed massive MIMO systems, channel estimation, pilot decontamination and implementation issues. In this thesis, we talk about pilot decontamination problem in a massive MIMO system. The pilot contamination problem is not point out when the base station does not exist in cooperation, with the number of base station antennas being increasing, the relevant noise and fast fading effect can be neglected. However, the main factor that affects the performance of the system is the inter cell interference caused by pilot contamination. In TDD systems, each base station is unable to distinguish users of the desired cell and the interfering cells, and the pilot pollution can be scalable with respect to a large-scale MIMO system. In [5], authors show that pilot contamination is not fundamental feature of large antenna array system, but just a shortcoming of linear channel estimation, e.g., ZF, LS or MMSE. When the nonlinear channel estimation method is utilized, the power control handoff is efficient to guarantee reparability between signal and interference subspaces. Therefore, nonlinear estimation methods are back to our sights, e.g., the maximum likelihood (ML). Nevertheless, because of the high computational complexity due to the multidimensional nonlinear search process involved, ML has rarely been used. The alternating projection (AP) algorithm [5], which transforms the multivariate nonlinear maximization problem into a simple one-dimensional sequence maximization problem by using a conceptually simple alternating maximization technique, is back to our sights. A channel estimation and data detection algorithm based on AP is discussed for massive MIMO systems. The performance of AP algorithms has been illustrated for both cooperative and non-cooperative multi-cell scenarios. Since the AP algorithm is nonlinear, a literal derivation of MSE is mathematically far too complicated. As a least-square (LS) channel estimation algorithm minimizes the distance between the channel output and its noiseless hypothesis and provides the performance limit for all other channel estimators, the results of a LS based algorithm can be taken as an approximation on MSE of the AP based blind channel estimation, which can help us to get some insights about SIR behaviors for the AP based blind channel estimation. Compared to the LS algorithm, the AP algorithm can improve performance in terms of bit error rates (BER). Consequently, it can reduce the effect of pilot contamination.
more목차
Chapter 1. Introduction 1
1.1 Background and Motivation.1
1.2 Thesis Contributions 3
1.3 Thesis Organization.4
Chapter 2. Massive MIMO 5
2.1 Massive MIMO 5
2.2 Pilot Contamination 20
2.3 System Model 22
2.4 Uplink Channel Estimation 24
Chapter 3. Techniques to Mitigate Pilot Contamination 26
3.1 Alternating Projection algorithm 26
3.2 AP-Based Blind Channel & Data Detection.33
3.2.1 Cooperative Multi-cell Scenario 34
3.2.2 Signal-to-interference (SIR) Behavior 36
3.2.3 Non-cooperative Multi-cell Scenario 39
Chapter 4. Simulation Setup and Numerical Results 40
4.1 Simulation Setup 40
4.2 Results and Discussion 41
Chapter 5. Conclusion 50
Reference 51
