Multi-cell massive MIMO network optimization towards power consumption in suburban scenarios

In this paper, we propose a simulation-based method to design low power multi-cell multi-user massive MIMO network by optimizing the positions of the base stations. Two realistic outdoor suburban areas have been considered in Ghent, Belgium (Europe) and Kinshasa, the Democratic Republic of Congo (Africa), in which the power consumption, the energy efficiency, the network capacity and the multiplexing gain are investigated and compared with LTE networks. The results of the simulations demonstrated that massive MIMO networks provide better performance in the crowded scenario where user's mobility is relatively low. A massive MIMO BS consumes 5-8 times less power than the LTE networks, with a pilot reuse pattern of 3 that helps obtaining a good tradeoff between the higher bit rate requested and the low power requirements in cellular environment

Pilot Contamination and Mitigation Techniques in Massive MIMO Systems

A multi-antenna base station (BS) can spatially multiplex a few terminals over the same bandwidth, a technique known as multi-user, multiple-input multiple-output (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. Within a cell, the BS estimates the channel from mutually orthogonal reverse-link pilot sequences to formulate a receiver for the reverse link and (assuming reciprocity) a precoder for the forward link. The channel coherence is typically constrained in time as well as frequency, leading to a trade-off between the resources spent on pilots and those available for data symbols. This pilot overhead can be reduced by reusing pilot sequences in nearby cells, however this potentially introduces interference in the channel estimation phase, the so-called "pilot contamination" effect. In this thesis, we study the impact of pilot contamination in realistic environments and investigate schemes to mitigate it. We evaluate the mean squared error (MSE) of channel estimates in case of a plain-vanilla least-squares (LS) estimator and a minimum MSE (MMSE) estimator that exploits prior knowledge of second-order channel statistics. Next, we introduce a pilot open-loop power control (pilot OLPC) scheme to improve the SINR-fairness of received pilot signals at the BS. We evaluate the effect of relaxing the pilot reuse factor and also implement a soft pilot reuse (SPR) scheme to distribute pilot sequences efficiently. To study the trade-off between pilot and data symbols, we evaluate the achievable rate in forward link with maximum-ratio and zero-forcing precoding at the BS. We evaluate an inter-cell coordination scheme that exploits prior knowledge of all cross-channel covariance matrices to reuse pilots among spatially well-separated terminals. We simulate a 21-cell MU-MIMO setup with up to 100-antenna BSs and up to 24 single-antenna terminals per cell in an outdoor urban macro environment. We find that pilot reuse 1 causes severe impairment of the channel estimates, which can be improved with pilot OLPC. Pilot reuse 1/3 effectively mitigates pilot contamination, and can improve the achievable rate in the forward link. SPR also mitigates contamination but with a smaller increase in pilot overhead. Inter-cell coordinated pilot allocation, implemented using a greedy approach, provides gains over random allocation only for the initial few pilots. In general, maximum ratio precoding is more robust against pilot contamination than zero-forcing.A multi-antenna base station (BS) can be used to improve cellular communication performance. The signal at each antenna can be designed in a way that it increases received energy at the desired terminals, and attenuates it at other locations (reducing interference). This technique can be used to serve several terminals over the same time and frequency using independent data streams, known as multi-user MIMO (MU-MIMO). In this thesis, we investigate MU-MIMO approach for very large BS antenna arrays, also called massive MIMO. The performance of such systems depends critically on the quality of channel estimates the BS. We simulate realistic channel conditions in a multi-cell setup, which gives rise to interference during channel estimation. We evaluate the system performance in terms of quality of BS channel estimates and the achievable data rate within a cell. We evaluate different techniques for channel estimation, and for generating data streams from the BS to the terminals. Next, we evaluate schemes to improve the channel estimation. We conclude by noting the trade-offs involved in the various schemes and the conditions under which certain schemes might provide performance improvements

A Multi-cell MMSE Precoder for Massive MIMO Systems and New Large System Analysis

In this paper, a new multi-cell MMSE precoder is proposed for massive MIMO systems. We consider a multi-cell network where each cell has $K$ users and $B$ orthogonal pilot sequences are available, with $B = \beta K$ and $\beta \ge 1$ being the pilot reuse factor over the network. In comparison with conventional single-cell precoding which only uses the $K$ intra-cell channel estimates, the proposed multi-cell MMSE precoder utilizes all $B$ channel directions that can be estimated locally at a base station, so that the transmission is designed spatially to suppress both parts of the inter-cell and intra-cell interference. To evaluate the performance, a large-scale approximation of the downlink SINR for the proposed multi-cell MMSE precoder is derived and the approximation is tight in the large-system limit. Power control for the pilot and payload, imperfect channel estimation and arbitrary pilot allocation are accounted for in our precoder. Numerical results show that the proposed multi-cell MMSE precoder achieves a significant sum spectral efficiency gain over the classical single-cell MMSE precoder and the gain increases as $K$ or $\beta$ grows. Compared with the recent M-ZF precoder, whose performance degrades drastically for a large $K$, our M-MMSE can always guarantee a high and stable performance. Moreover, the large-scale approximation is easy to compute and shown to be accurate even for small system dimensions.Comment: 6 pages, 4 figures, accepted by Globecom 2015. arXiv admin note: text overlap with arXiv:1509.0175

Massive MIMO 시스템을 위한 채널 추정 및 피드백 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 이정우.To meet the demand of high throughput in next generation wireless systems, various directions for physical layer evolution are being explored. Massive multiple-input multiple-output (MIMO) systems, characterized by a large number of antennas at the transmitter, are expected to become a key enabler for spectral efficiency improvement. In massive MIMO systems, thanks to the orthogonality between different users' channels, high spectral and energy efficiency can be achieved through simple signal processing techniques. However, to get such advantages, accurate channel state information (CSI) needs to be available, and acquiring CSI in massive MIMO systems is challenging due to the increased channel dimension. In frequency division duplexing (FDD) systems, where CSI at the transmitter is achieved through downlink training and uplink feedback, the overhead for the training and feedback increases proportionally to the number of antennas, and the resource for data transmission becomes scarce in massive MIMO systems. In time division duplexing (TDD) systems, where the channel reciprocity holds and the downlink CSI can be obtained through uplink training, pilot contamination due to correlated pilots becomes a performance bottleneck when the number of antennas increases. In this dissertation, I propose efficient CSI acquisition techniques for various massive MIMO systems. First, I develop a downlink training technique for FDD massive MIMO systems, which estimates the downlink channel with small overhead. To this end, compressed sensing tools are utilized, and the training overhead can be highly reduced by exploiting the previous channel information. Next, a limited feedback scheme is developed for FDD massive MIMO systems. The proposed scheme reduces the feedback overhead using a dimension reduction technique that exploits spatial and temporal correlation of the channel. Lastly, I analyze the effect of pilot contamination, which has been regarded as a performance bottleneck in multi-cell massive MIMO systems, and propose two uplink training strategies. An iterative pilot design scheme is developed for small networks, and a scalable training framework is also proposed for networks with many cells.1 Introduction 1 1.1 Massive MIMO 1 1.2 CSI Acquisition in Massive MIMO Systems 3 1.3 Contributions and Organization 6 1.4 Notations 7 2 Compressed Sensing-Aided Downlink Training 9 2.1 Introduction 10 2.2 System Model 13 2.2.1 Channel Model 13 2.2.2 Downlink Channel Estimation 16 2.3 CS-Aided Channel Training 19 2.3.1 Training Sequence Design 20 2.3.2 Channel Estimation 21 2.3.3 Estimation Error 23 2.4 Discussions 26 2.4.1 Design of Measurement Matrix 26 2.4.2 Extension to MIMO Systems 27 2.4.3 Comparison to CS with Partial Support Information 28 2.5 Simulation Results 29 2.6 Conclusion 37 3 Projection-Based Differential Feedback 39 3.1 Introduction 40 3.2 System Model 44 3.2.1 Multi-User Beamforming with Limited Feedback 45 3.2.2 Massive MIMO Channel 47 3.3 Projection-Based Differential Feedback 48 3.3.1 Projection-Based Differential Feedback Framework 48 3.3.2 Projection for PBDF Framework 51 3.3.3 Efficient Algorithm 57 3.4 Discussions 58 3.4.1 Projection with Imperfect CSIR 58 3.4.2 Acquisition of Channel Statistics 61 3.5 Simulation Results 62 3.6 Conclusion 69 4 Mitigating Pilot Contamination via Pilot Design 71 4.1 Introduction 72 4.2 System Model 73 4.2.1 Multi-cell Massive MIMO Systems 74 4.2.2 Uplink Channel Training 75 4.2.3 Data Transmission 77 4.3 Iterative Pilot Design Algorithm 78 4.3.1 Algorithm 79 4.3.2 Proof of Convergence 81 4.4 Generalized Pilot Reuse 81 4.4.1 Concept of Pilot Reuse Schemes 81 4.4.2 Pilot Design based on Grassmannian Subspace Packing 82 4.5 Simulation Results 85 4.5.1 Iterative Pilot Design 85 4.5.2 Generalized Pilot Reuse 87 4.6 Conclusion 89 5 Conclusion 91 5.1 Summary 91 5.2 Future Directions 93 Bibliography 96 Abstract (In Korean) 109Docto

Pilot Power Allocation Through User Grouping in Multi-Cell Massive MIMO Systems

In this paper, we propose a relative channel estimation error (RCEE) metric, and derive closed-form expressions for its expectation $\rm {Exp}_{rcee}$ and the achievable uplink rate holding for any number of base station antennas $M$, with the least squares (LS) and minimum mean squared error (MMSE) estimation methods. It is found that RCEE and $\rm {Exp}_{rcee}$ converge to the same constant value when $M\rightarrow\infty$, resulting in the pilot power allocation (PPA) is substantially simplified and a PPA algorithm is proposed to minimize the average $\rm {Exp}_{rcee}$ per user with a total pilot power budget $P$ in multi-cell massive multiple-input multiple-output systems. Numerical results show that the PPA algorithm brings considerable gains for the LS estimation compared with equal PPA (EPPA), while the gains are only significant with large frequency reuse factor (FRF) for the MMSE estimation. Moreover, for large FRF and large $P$, the performance of the LS approaches to the performance of the MMSE, which means that simple LS estimation method is a very viable when co-channel interference is small. For the achievable uplink rate, the PPA scheme delivers almost the same average achievable uplink rate and improves the minimum achievable uplink rate compared with the EPPA scheme.Comment: 30 pages, 5 figures, submitted to IEEE Transactions on Communication