120 research outputs found
Active Attack on User Load Achieving Pilot Design in Massive MIMO Networks
In this paper, we propose an active attacking strategy on a massive
multiple-input multiple-output (MIMO) network, where the pilot sequences are
obtained using the user load-achieving pilot sequence design. The user
load-achieving design ensures that the signal-to-interference-plus-noise ratio
(SINR) requirements of all the users in the massive MIMO networks are
guaranteed even in the presence of pilot contamination. However, this design
has some vulnerabilities, such as one known pilot sequence and the correlation
among the pilot sequences, that may be exploited by active attackers. In this
work, we first identify the potential vulnerabilities in the user
load-achieving pilot sequence design and then, accordingly, develop an active
attacking strategy on the network. In the proposed attacking strategy, the
active attackers transmit known pilot sequences in the uplink training and
artificial noise in the downlink data transmission. Our examination
demonstrates that the per-cell user load region is significantly reduced by the
proposed attacking strategy. As a result of the reduced per-cell user load
region, the SINR requirements of all the users are no longer guaranteed in the
presence of the active attackers. Specifically, for the worst affected users
the SINR requirements may not be ensured even with infinite antennas at the
base station.Comment: Accepted in IEEE GlobeCOM 201
Mitigated Pilot Contamination to Achieve Higher Downlink Data Rate in 5G Massive MIMO Systems
Massive multiple-input, multiple-output (M-MIMO) is an important knowledge for fifth-generation (5G) wireless cellular networks. The pilot contamination (PC) is an issue in massive MIMO due to interference between adjacent cells. We proposed that the number of pilot sequence inside a cell could become smaller than or equal to the number of users (UEs), taking into account the different number of UEs that transmitted the same pilot sequence in the same cell. In addition, the pilot sequence became mutually orthogonal for different cells to prevent PC among cells. In this paper, we analyzed a channel estimation for time division duplex (TDD) and improved the achievable data rate by reducing the PC for limiting user capacity and using channel orthogonality for minimum mean square error (MMSE) precoding. From the simulation results, the proposed scheme provided a data rate for two several situations, with and without interference PC for an increased number of antennas. Consequently, increasing the number of coherence intervals made the channel estimation critical and provided a small data rate due to increased noise and interference at increased transmit pilot sequence
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Analysis and Design of Cell-Free Massive MIMO Systems under Spatially Correlated Fading Channels
Mención Internacional en el título de doctorWireless communications have become a key pillar in our modern society. It can be hard to
think of a service that somehow does not rely on them. Particularly, mobile networks are one of
the most necessary technologies in our daily life. This produces that the demand for data rates
is by no means stopping from increasing. The cellular architecture is facing a crucial challenge
under limited performance by interference and spectrum saturation. This involves cell-edge
users experiencing poor performance due to the close vicinity of base stations (BSs) using
the same carrier frequency. Based on a combination of the coordinated multi-point (CoMP)
technique and traditional massive multiple-input multiple-output (MIMO) systems, cell-free
(CF) massive MIMO networks have irrupted as a solution for avoiding inter-cell interference
issues and for providing uniform service in large coverage areas. This thesis focuses on the
analysis and design of CF massive MIMO networks assuming a spatially correlated fading
model. A general-purpose channel model is provided and the whole network functioning is
given in detail.
Despite the many characteristics a CF massive MIMO system shares with conventional colocated
massive MIMO its distributed nature brings along new issues that need to be carefully
accounted for. In particular, the so-called channel hardening effect that postulates that the variance
of the compound wireless channel experienced by a given user from a large number of
transmit antennas tends to vanish, effectively making the channel deterministic. This critical
assumption, which permeates most theoretical results of massive MIMO, has been well investigated
and validated in centralized architectures, however, it has received little attention in the
context of CF massive MIMO networks. Hardening in CF architectures is potentially compromised
by the different large-scale gains each access point (AP) impinges on the transmitted
signal to each user, a condition that is further stressed when not all APs transmit to all users as
proposed in the user-centric (UC) variations of CF massive MIMO. In this document, the presence
of channel hardening in this new architecture scheme is addressed using distributed and
cooperative precoders and combiners and different power control strategies. It is shown that
the line-of-sight (LOS) component, spatially correlated antennas, and clustering schemes have
an impact on how the channel hardens. In addition, we examine the existent gap between the
estimated achievable rate and the true network performance when channel hardening is compromised. Exact closed-form expressions for both a hardening metric and achievable downlink
(DL) and uplink (UL) rates are given as well.
We also look into the pilot contamination problem in the UL and DL with different degrees
of cooperation between the APs. The optimum minimum mean-squared error (MMSE) processing
can take advantage of large-scale fading coefficients for canceling the interference of
pilot-sharing users and thus achieves asymptotically unbounded capacity. However, it is computationally
demanding and can only be implemented in a fully centralized network. Here,
sub-optimal schemes are derived that provide unbounded capacity with much lower complexity
and using only local channel estimates but global channel statistics. This makes them suited for
both centralized and distributed networks. In this latter case, the best performance is achieved
with a generalized maximum ratio combiner that maximizes a capacity bound based on channel
statistics only.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Rui Dinis.- Secretario: María Julia Fernández-Getino García.- Vocal: Carmen Botella Mascarel
Superimposed Pilots are Superior for Mitigating Pilot Contamination in Massive MIMO
In this paper, superimposed pilots are introduced as an alternative to time-multiplexed pilot and data symbols for mitigating pilot contamination in massive multiple-input multiple-output (MIMO) systems. We propose a non-iterative scheme for uplink channel estimation based on superimposed pilots and derive an expression for the uplink signal-to-interference-plus-noise ratio (SINR) at the output of a matched filter employing this channel estimate. Based on this expression, we observe that power control is essential when superimposed pilots are employed. Moreover, the quality of the channel estimate can be improved by reducing the interference that results from transmitting data alongside the pilots, and an intuitive iterative data-aided scheme that reduces this component of interference is also proposed. Approximate expressions for the uplink SINR are provided for the iterative data-aided method as well. In addition, we show that a hybrid system with users utilizing both time-multiplexed and superimposed pilots is superior to an optimally designed system that employs only time-multiplexed pilots, even when the non-iterative channel estimate is used to build the detector and precoder. We also describe a simple approach to implement this hybrid system by minimizing the overall inter and intra-cell interference. Numerical simulations demonstrating the performance of the proposed channel estimation schemes and the superiority of the hybrid system are also provided
Error Bounds for Uplink and Downlink 3D Localization in 5G mmWave Systems
Location-aware communication systems are expected to play a pivotal part in
the next generation of mobile communication networks. Therefore, there is a
need to understand the localization limits in these networks, particularly,
using millimeter-wave technology (mmWave). Towards that, we address the uplink
and downlink localization limits in terms of 3D position and orientation error
bounds for mmWave multipath channels. We also carry out a detailed analysis of
the dependence of the bounds of different systems parameters. Our key findings
indicate that the uplink and downlink behave differently in two distinct ways.
First of all, the error bounds have different scaling factors with respect to
the number of antennas in the uplink and downlink. Secondly, uplink
localization is sensitive to the orientation angle of the user equipment (UE),
whereas downlink is not. Moreover, in the considered outdoor scenarios, the
non-line-of-sight paths generally improve localization when a line-of-sight
path exists. Finally, our numerical results show that mmWave systems are
capable of localizing a UE with sub-meter position error, and sub-degree
orientation error.Comment: This manuscripts is updated following two rounds of reviews at IEEE
Transactions on Wireless Communications. More discussion is included in
different parts of the paper. Results are unchanged, and are still vali
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
Foundations of User-Centric Cell-Free Massive MIMO
Imagine a coverage area where each mobile device is communicating with a
preferred set of wireless access points (among many) that are selected based on
its needs and cooperate to jointly serve it, instead of creating autonomous
cells. This effectively leads to a user-centric post-cellular network
architecture, which can resolve many of the interference issues and
service-quality variations that appear in cellular networks. This concept is
called User-centric Cell-free Massive MIMO (multiple-input multiple-output) and
has its roots in the intersection between three technology components: Massive
MIMO, coordinated multipoint processing, and ultra-dense networks. The main
challenge is to achieve the benefits of cell-free operation in a practically
feasible way, with computational complexity and fronthaul requirements that are
scalable to enable massively large networks with many mobile devices. This
monograph covers the foundations of User-centric Cell-free Massive MIMO,
starting from the motivation and mathematical definition. It continues by
describing the state-of-the-art signal processing algorithms for channel
estimation, uplink data reception, and downlink data transmission with either
centralized or distributed implementation. The achievable spectral efficiency
is mathematically derived and evaluated numerically using a running example
that exposes the impact of various system parameters and algorithmic choices.
The fundamental tradeoffs between communication performance, computational
complexity, and fronthaul signaling requirements are thoroughly analyzed.
Finally, the basic algorithms for pilot assignment, dynamic cooperation cluster
formation, and power optimization are provided, while open problems related to
these and other resource allocation problems are reviewed. All the numerical
examples can be reproduced using the accompanying Matlab code.Comment: This is the authors' version of the manuscript: \"Ozlem Tugfe Demir,
Emil Bj\"ornson and Luca Sanguinetti (2021), "Foundations of User-Centric
Cell-Free Massive MIMO", Foundations and Trends in Signal Processing: Vol.
14, No. 3-4, pp 162-47
Channel estimation in massive MIMO systems
Last years were characterized by a great demand for high data throughput, good quality and spectral efficiency in wireless communication systems. Consequently, a revolution in cellular networks has been set in motion towards to 5G. Massive multiple-input multiple-output (MIMO) is one of the new concepts in 5G and the idea is to scale up the known MIMO systems in unprecedented proportions, by deploying hundreds of antennas at base stations. Although, perfect channel knowledge is crucial in these systems for user and data stream separation in order to cancel interference.
The most common way to estimate the channel is based on pilots. However, problems such as interference and pilot contamination (PC) can arise due to the multiplicity of channels in the wireless link. Therefore, it is crucial to define techniques for channel estimation that together with pilot contamination mitigation allow best system performance and at same time low complexity.
This work introduces a low-complexity channel estimation technique based on Zadoff-Chu training sequences. In addition, different approaches were studied towards pilot contamination mitigation and low complexity schemes, with resort to iterative channel estimation methods, semi-blind subspace tracking techniques and matrix inversion substitutes.
System performance simulations were performed for the several proposed techniques in order to identify the best tradeoff between complexity, spectral efficiency and system performance
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