liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Large-scale-fading decoding in cellular Massive MIMO systems with spatially correlated channels
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering. (Communication Systems Division)
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-2093-5466
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering. (Communication Systems Division)ORCID iD: 0000-0002-5954-434X
2019 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 67, no 4, p. 2746-2762Article in journal (Refereed) Published
Abstract [en]

Massive multiple-input–multiple-output (MIMO) systems can suffer from coherent intercell interference due to the phenomenon of pilot contamination. This paper investigates a two-layer decoding method that mitigates both coherent and non-coherent interference in multi-cell Massive MIMO. To this end, each base station (BS) first estimates the channels to intra-cell users using either minimum mean-squared error (MMSE) or element-wise MMSE estimation based on uplink pilots. The estimates are used for local decoding on each BS followed by a second decoding layer where the BSs cooperate to mitigate inter-cell interference. An uplink achievable spectral efficiency (SE) expression is computed for arbitrary two-layer decoding schemes. A closed form expression is then obtained for correlated Rayleigh fading, maximum-ratio combining, and the proposed large-scale fading decoding (LSFD) in the second layer. We also formulate a sum SE maximization problem with both the data power and LSFD vectors as optimization variables. Since this is an NP-hard problem, we develop a low-complexity algorithm based on the weighted MMSE approach to obtain a local optimum. The numerical results show that both data power control and LSFD improve the sum SE performance over single-layer decoding multi-cell Massive MIMO systems.

Place, publisher, year, edition, pages
IEEE Communications Society, 2019. Vol. 67, no 4, p. 2746-2762
Keywords [en]
Massive MIMO, Large-Scale Fading Decoding, Sum Spectral Efficiency Optimization, Channel Estimation
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-156657DOI: 10.1109/TCOMM.2018.2889090ISI: 000465242700011Scopus ID: 2-s2.0-85059010638OAI: oai:DiVA.org:liu-156657DiVA, id: diva2:1313614
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Note

Funding agencies: European Unions Horizon 2020 Research and Innovation Programme [641985]; ELLIIT; CENIIT

Available from: 2019-05-04 Created: 2019-05-04 Last updated: 2019-12-09Bibliographically approved
In thesis
1. Spatial Resource Allocation in Massive MIMO Communications: From Cellular to Cell-Free
Open this publication in new window or tab >>Spatial Resource Allocation in Massive MIMO Communications: From Cellular to Cell-Free
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Massive MIMO (multiple-input multiple-output) is considered as an heir of the multi-user MIMO technology and it has gained lots of attention from both academia and industry since the last decade. By equipping base stations (BSs) with hundreds of antennas in a compact array or a distributed manner, this new technology can provide very large multiplexing gains by serving many users on the same time-frequency resources and thereby bring significant improvements in spectral efficiency (SE) and energy efficiency (EE) over the current wireless networks. The transmit power, pilot training, and spatial transmission resources need to be allocated properly to the users to achieve the highest possible performance. This is called resource allocation and can be formulated as design utility optimization problems. If the resource allocation in Massive MIMO is optimized, the technology can handle the exponential growth in both wireless data traffic and number of wireless devices, which cannot be done by the current cellular network technology.

In this thesis, we focus on the five different resource allocation aspects in Massive MIMO communications: The first part of the thesis studies if power control and advanced coordinated multipoint (CoMP) techniques are able to bring substantial gains to multi-cell Massive MIMO systems compared to the systems without using CoMP. More specifically, we consider a network topology with no cell boundary where the BSs can collaborate to serve the users in the considered coverage area. We focus on a downlink (DL) scenario in which each BS transmits different data signals to each user. This scenario does not require phase synchronization between BSs and therefore has the same backhaul requirements as conventional Massive MIMO systems, where each user is preassigned to only one BS. The scenario where all BSs are phase synchronized to send the same data is also included for comparison. We solve a total transmit power minimization problem in order to observe how much power Massive MIMO BSs consume to provide the requested quality of service (QoS) of each user. A max-min fairness optimization is also solved to provide every user with the same maximum QoS regardless of the propagation conditions.

The second part of the thesis considers a joint pilot design and uplink (UL) power control problem in multi-cell Massive MIMO. The main motivation for this work is that the pilot assignment and pilot power allocation is momentous in Massive MIMO since the BSs are supposed to construct linear detection and precoding vectors from the channel estimates. Pilot contamination between pilot-sharing users leads to more interference during data transmission. The pilot design is more difficult if the pilot signals are reused frequently in space, as in Massive MIMO, which leads to greater pilot contamination effects. Related works have only studied either the pilot assignment or the pilot power control, but not the joint optimization. Furthermore, the pilot assignment is usually formulated as a combinatorial problem leading to prohibitive computational complexity. Therefore, in the second part of this thesis, a new pilot design is proposed to overcome such challenges by treating the pilot signals as continuous optimization variables. We use those pilot signals to solve different max-min fairness optimization problems with either ideal hardware or hardware impairments.

The third part of this thesis studies a two-layer decoding method that mitigates inter-cell interference in multi-cell Massive MIMO systems. In layer one, each BS estimates the channels to intra-cell users and uses the estimates for local decoding within the cell. This is followed by a second decoding layer where the BSs cooperate to mitigate inter-cell interference. An UL achievable SE expression is computed for arbitrary two-layer decoding schemes, while a closed form expression is obtained for correlated Rayleigh fading channels, maximum-ratio combining (MRC), and largescale fading decoding (LSFD) in the second layer. We formulate a sum SE maximization problem with both the data power and LSFD vectors as optimization variables. Since the problem is non-convex, we develop an algorithm based on the weighted minimum mean square error (MMSE) approach to obtain a stationary point with low computational complexity.

Motivated by recent successes of deep learning in predicting the solution to an optimization problem with low runtime, the fourth part of this thesis investigates the use of deep learning for power control optimization in Massive MIMO. We formulate the joint data and pilot power optimization for maximum sum SE in multi-cell Massive MIMO systems, which is a non-convex problem. We propose a new optimization algorithm, inspired by the weighted MMSE approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime. The solution is suitable for online optimization.

Finally, the fifth part of this thesis considers a large-scale distributed antenna system that serves the users by coherent joint transmission called Cell-free Massive MIMO. For a given user set, only a subset of the access points (APs) is likely needed to satisfy the users' performance demands. To find a flexible and energy-efficient implementation, we minimize the total power consumption at the APs in the DL, considering both the hardware consumed and transmit powers, where APs can be turned off to reduce the former part. Even though this is a nonconvex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program (SOCP). We also propose low-complexity algorithms that exploit group-sparsity or received power strength in the problem formulation.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2020. p. 66
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2036
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-162582 (URN)10.3384/diss.diva-162582 (DOI)9789179299415 (ISBN)
Public defence
2020-01-23, Ada Lovelace, Building B, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2019-12-09 Created: 2019-12-09 Last updated: 2020-01-24Bibliographically approved

Open Access in DiVA

fulltext(1174 kB)371 downloads
File information
File name FULLTEXT02.pdfFile size 1174 kBChecksum SHA-512
52a3decace3bc59c8bb35abd8ec9aab7e3afccdb931a7705ef9b7b0eb990feacf749d875e18a957e0bc24800b9367cf3cc134c5993e10ac4c21c10a98dca876d
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

Van Chien, TrinhMollén, ChristopherBjörnson, Emil

Search in DiVA

By author/editor
Van Chien, TrinhMollén, ChristopherBjörnson, Emil
By organisation
Communication SystemsFaculty of Science & Engineering
In the same journal
IEEE Transactions on Communications
Communication Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 371 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 236 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf