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The Multicell Multiuser MIMO Uplink with Very Large Antenna Arrays and a Finite-Dimensional Channel
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-7599-4367
Bell Laboratories, Alcatel-Lucent, NJ, USA .
2013 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 61, no 6, 2350-2361 p.Article in journal (Refereed) Published
Abstract [en]

We consider multicell multiuser MIMO systems with a very large number of antennas at the base station (BS). We assume that the channel is estimated by using uplink training. We further consider a physical channel model where the angular domain is separated into a finite number of distinct directions. We analyze the so-called pilot contamination effect discovered in previous work, and show that this effect persists under the finite-dimensional channel model that we consider. In particular, we consider a uniform array at the BS. For this scenario, we show that when the number of BS antennas goes to infinity, the system performance under a finite-dimensional channel model with P angular bins is the same as the performance under an uncorrelated channel model with P antennas. We further derive a lower bound on the achievable rate of uplink data transmission with a linear detector at theBS. We then specialize this lower bound to the cases of maximum-ratio combining (MRC) and zero-forcing (ZF) receivers, for a finite and an infinite number of BS antennas. Numerical results corroborate our analysis and show a comparison between the performances of MRC and ZF in terms of sum-rate.

Place, publisher, year, edition, pages
2013. Vol. 61, no 6, 2350-2361 p.
Keyword [en]
Finite-dimensional channel, multicell mutiuser MIMO, pilot contamination, very large MIMO systems
National Category
Communication Systems Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-85223DOI: 10.1109/TCOMM.2013.032713.120408ISI: 000321219100023OAI: oai:DiVA.org:liu-85223DiVA: diva2:644849
Available from: 2013-04-04 Created: 2012-11-12 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Performance Bounds for Very Large Multiuser MIMO Systems
Open this publication in new window or tab >>Performance Bounds for Very Large Multiuser MIMO Systems
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The last ten years have seen significant advances of multiuser MIMO (MU-MIMO) in wireless communication. MU-MIMO is now being introduced in several new generation wireless standards (e.g.,LTE-Advanced, 802.16m). The number of users is increasing with more and more applications. At the same time, high transmission data rate and communication reliability are required. Furthermore, there is a growing concern about green communication which relates to the effects of  the radiation emitted from wireless devices onthe human body. Therefore, the future MU-MIMO systems have to satisfy three main requirements: i) serving many autonomous users in the same time-frequency resource, ii) having high data rate and communication reliability, and iii) less energy consumption/radiation. These are seemingly contradictory requirements since the more users are served, the more interference the systems will suffer, or the more data rate is transmitted, the more power is required. MU-MIMO with very large antenna arrays seems to meet above demands and hence, it can be considered as a promising technology for next generation wireless systems. With very large antenna arrays (we mean arrays comprising say a hundred of antennas), the channel vectors are nearly-orthogonal and hence, multiuser interference can be significantly reduced. As a result, many users can be simultaneously served with high data rate. In particular, with coherent processing, transmit power can be reduced dramatically owing to array gain. In this thesis, we focus on the performance bounds of MU-MIMO with very large antenna arrays. We study the fundamental limits on the system performance when using large antenna arrays under practical constraints such as low complexity processing, imperfect channel state information, intercell interference, and finite-dimensional channels.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 23 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1562
National Category
Communication Systems Signal Processing Telecommunications
Identifiers
urn:nbn:se:liu:diva-85240 (URN)978-91-7519-729-6 (ISBN)
Presentation
2012-12-14, Systemet, Hus B, Campus Valla, Linköping University, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2012-11-21 Created: 2012-11-12 Last updated: 2016-08-31Bibliographically approved
2. Massive MIMO: Fundamentals and System Designs
Open this publication in new window or tab >>Massive MIMO: Fundamentals and System Designs
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The last ten years have seen a massive growth in the number of connected wireless devices. Billions of devices are connected and managed by wireless networks. At the same time, each device needs a high throughput to support applications such as voice, real-time video, movies, and games. Demands for wireless throughput and the number of wireless devices will always increase. In addition, there is a growing concern about energy consumption of wireless communication systems. Thus, future wireless systems have to satisfy three main requirements: i) having a high throughput; ii) simultaneously serving many users; and iii) having less energy consumption. Massive multiple-input multiple-output (MIMO) technology, where a base station (BS) equipped with very large number of antennas (collocated or distributed) serves many users in the same time-frequency resource,  can meet the above requirements, and hence, it is a promising candidate technology for next generations of wireless systems. With massive antenna arrays at the BS, for most propagation environments, the channels become favorable, i.e., the channel vectors between the users and the BS are (nearly) pairwisely orthogonal, and hence, linear processing is nearly optimal. A huge throughput and energy efficiency can be achieved due to the multiplexing gain and the array gain. In particular, with a simple power control scheme, Massive MIMO can offer uniformly good service for all users. In this dissertation, we focus on the performance of Massive MIMO. The dissertation consists of two main parts: fundamentals and system designs of Massive MIMO.

In the first part, we focus on fundamental limits of the system performance under practical constraints such as low complexity processing, limited length of each coherence interval, intercell interference, and finite-dimensional channels. We first study the potential for power savings of the Massive MIMO uplink with maximum-ratio combining (MRC), zero-forcing, and minimum mean-square error receivers, under perfect and imperfect channels. The energy and spectral efficiency tradeoff is investigated. Secondly, we consider a physical channel model where the angular domain is divided into a finite number of distinct directions. A lower bound on the capacity is derived, and the effect of pilot contamination in this finite-dimensional channel model is analyzed. Finally, some aspects of favorable propagation in Massive MIMO under Rayleigh fading and line-of-sight (LoS) channels are investigated. We show that both Rayleigh fading and LoS environments offer favorable propagation.

In the second part, based on the fundamental analysis in the first part, we propose some system designs for Massive MIMO. The acquisition of channel state information (CSI) is very importantin Massive MIMO. Typically, the channels are estimated at the BS through uplink training. Owing to the limited length of the coherence interval, the system performance is limited by pilot contamination. To reduce the pilot contamination effect, we propose an eigenvalue-decomposition-based scheme to estimate the channel directly from the received data. The proposed scheme results in better performance compared with the conventional training schemes due to the reduced pilot contamination. Another important issue of CSI acquisition in Massive MIMO is how to acquire CSI at the users. To address this issue, we propose two channel estimation schemes at the users: i) a downlink "beamforming training" scheme, and ii) a method for blind estimation of the effective downlink channel gains. In both schemes, the channel estimation overhead is independent of the number of BS antennas. We also derive the optimal pilot and data powers as well as the training duration allocation to maximize the sum spectral efficiency of the Massive MIMO uplink with MRC receivers, for a given total energy budget spent in a coherence interval. Finally, applications of Massive MIMO in relay channels are proposed and analyzed. Specifically, we consider multipair relaying systems where many sources simultaneously communicate with many destinations in the same time-frequency resource with the help of a massive MIMO relay. A massive MIMO relay is equipped with many collocated or distributed antennas. We consider different duplexing modes (full-duplex and half-duplex) and different relaying protocols (amplify-and-forward, decode-and-forward, two-way relaying, and one-way relaying) at the relay. The potential benefits of massive MIMO technology in these relaying systems are explored in terms of spectral efficiency and power efficiency.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. 45 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1642
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-112780 (URN)10.3384/lic.diva-112780 (DOI)978-91-7519-147-8 (ISBN)
Public defence
2015-03-06, Signalen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2015-01-16 Created: 2014-12-15 Last updated: 2016-08-31Bibliographically approved

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