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

Direct link
BETA
Van Chien, Trinh
Alternative names
Publications (10 of 11) Show all publications
Van Chien, T. (2020). Spatial Resource Allocation in Massive MIMO Communication: From Cellular to Cell-Free. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Spatial Resource Allocation in Massive MIMO Communication: 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: 2020-01-08Bibliographically approved
Van Chien, T., Mollén, C. & Björnson, E. (2019). Large-scale-fading decoding in cellular Massive MIMO systems with spatially correlated channels. IEEE Transactions on Communications, 67(4), 2746-2762
Open this publication in new window or tab >>Large-scale-fading decoding in cellular Massive MIMO systems with spatially correlated channels
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
Keywords
Massive MIMO, Large-Scale Fading Decoding, Sum Spectral Efficiency Optimization, Channel Estimation
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-156657 (URN)10.1109/TCOMM.2018.2889090 (DOI)000465242700011 ()2-s2.0-85059010638 (Scopus ID)
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
Van Chien, T., Björnson, E. & Larsson, E. G. (2019). Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning. In: ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC): . Paper presented at IEEE International Conference on Communications (ICC). IEEE Communications Society
Open this publication in new window or tab >>Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning
2019 (English)In: ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), IEEE Communications Society, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates the joint data and pilot power optimization for maximum sum spectral efficiency (SE) in multi-cell Massive MIMO systems, which is a non-convex problem. We first propose a new optimization algorithm, inspired by the weighted minimum mean square error (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, making it suitable for online optimization in real multi-cell Massive MIMO systems. The numerical result demonstrates that the solution obtained by the neural network is 1% less than the stationary point for four-cell systems, while the sum SE loss is 2% in a nine-cell system.

Place, publisher, year, edition, pages
IEEE Communications Society, 2019
Series
IEEE International Conference on Communications (ICC), ISSN 1550-3607, E-ISSN 1938-1883
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-156972 (URN)10.1109/ICC.2019.8761234 (DOI)000492038801037 ()978-1-5386-8088-9 (ISBN)978-1-5386-8089-6 (ISBN)
Conference
IEEE International Conference on Communications (ICC)
Projects
5GWirelessCENIIT
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Note

Funding agencies: European UnionEuropean Union (EU) [641985]; ELLIIT; CENIIT

Available from: 2019-05-18 Created: 2019-05-18 Last updated: 2019-11-11
Van Chien, T., Mollén, C. & Björnson, E. (2019). Two-Layer Decoding in Cellular Massive MIMO Systems with Spatial Channel Correlation. In: Proceedings of 2019 IEEE International Conference on Communications, ICC 2019: . Paper presented at IEEE International Conference on Communications (ICC), Shanghai, China, 20-24 May 2019. , Article ID 8761502.
Open this publication in new window or tab >>Two-Layer Decoding in Cellular Massive MIMO Systems with Spatial Channel Correlation
2019 (English)In: Proceedings of 2019 IEEE International Conference on Communications, ICC 2019, 2019, article id 8761502Conference paper, Published paper (Refereed)
Abstract [en]

This paper studies a two-layer decoding method that mitigates inter-cell interference in multi-cell Massive MIMO systems. In layer one, each base station (BS) estimates the channels to intra-cell users and uses the estimates 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, while a closed-form expression is obtained for correlated Rayleigh fading channels, maximum-ratio combining (MRC), and large-scale fading decoding (LSFD) in the second layer. We formulate a non-convex sum SE maximization problem with both the data power and LSFD vectors as optimization variables and develop an algorithm based on the weighted MMSE (minimum mean square error) approach to obtain a stationary point with low computational complexity.

Series
IEEE International Conference on Communications (ICC), ISSN 1550-3607
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-156971 (URN)10.1109/ICC.2019.8761502 (DOI)000492038802139 ()9781538680889 (ISBN)9781538680896 (ISBN)
Conference
IEEE International Conference on Communications (ICC), Shanghai, China, 20-24 May 2019
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Note

Funding agencies: European UnionEuropean Union (EU) [641985]; ELLIIT; CENIIT

Available from: 2019-05-18 Created: 2019-05-18 Last updated: 2019-11-11Bibliographically approved
Le, T. A., Van Chien, T. & Nakhai, M. R. (2018). A Power Efficient Pilot Design for Multi-cell Massive MIMO Systems. In: : . Paper presented at The 6th IEEE Global Conference on Signal and Information Processing (GlobalSIP). California: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A Power Efficient Pilot Design for Multi-cell Massive MIMO Systems
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we address the pilot contamination problem in multi-cell massive MIMO systems. Particularly, we propose a pilot design scheme that simultaneously minimizes the channel estimation errors of all base stations (BSs) and the total pilot power consumption of all users subject to the transmit power constraint for every user in the network. We decompose the proposed non-convex problem into distributed optimization problems to be solved at each BS, assuming the knowledge of pilot signals of the other BSs. Then, we introduce a successive optimization approach to cast each distributed optimization problem into a convex linear matrix inequality form. Simulation results confirm that the proposed approach significantly reduces pilot power while maintain the same level of channel estimation error as a recent work in [1].

Place, publisher, year, edition, pages
California: Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-154875 (URN)10.1109/GlobalSIP.2018.8646568 (DOI)000462968100167 ()978-1-7281-1295-4 (ISBN)978-1-7281-1296-1 (ISBN)
Conference
The 6th IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2019-03-03 Created: 2019-03-03 Last updated: 2019-05-14
Al-Salihi, H., Van Chien, T., Le, T. A. & Nakhai, M. R. (2018). A Successive Optimization Approach to Pilot Design for Multi-Cell Massive MIMO Systems. IEEE Communications Letters, 22(5), 1086-1089
Open this publication in new window or tab >>A Successive Optimization Approach to Pilot Design for Multi-Cell Massive MIMO Systems
2018 (English)In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 22, no 5, p. 1086-1089Article in journal (Refereed) Published
Abstract [en]

In this letter, we introduce a novel pilot designapproach that minimizes the total mean square errors of theminimum mean square error estimators of all base stations (BSs)subject to the transmit power constraints of individual users inthe network, while tackling the pilot contamination in multicellmassive MIMO systems. First, we decompose the originalnon-convex problem into distributed optimization sub-problemsat individual BSs, where each BS can optimize its own pilotsignals given the knowledge of pilot signals from the remainingBSs. We then introduce a successive optimization approach totransform each optimization sub-problem into a linear matrixinequality form, which is convex and can be solved by availableoptimization packages. Simulation results confirm the fast convergenceof the proposed approach and prevails a benchmarkscheme in terms of providing higher accuracy.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Massive MIMO, pilot design, successive optimization, distributed optimization
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-147749 (URN)10.1109/LCOMM.2018.2812891 (DOI)000431908300054 ()2-s2.0-85043381625 (Scopus ID)
Projects
5Gwireless,ELLIIT, and CENIIT
Available from: 2018-05-09 Created: 2018-05-09 Last updated: 2018-05-31Bibliographically approved
Van Chien, T., Björnson, E., Larsson, E. G. & Le, T. A. (2018). Distributed Power Control in Downlink Cellular Massive MIMO Systems. In: WSA 2018: 22nd International ITG Workshop on Smart Antennas. Paper presented at IEEE 22nd International ITG Workshop on Smart Antennas (WSA2018) (pp. 1-7). VDE Verlag GmbH
Open this publication in new window or tab >>Distributed Power Control in Downlink Cellular Massive MIMO Systems
2018 (English)In: WSA 2018: 22nd International ITG Workshop on Smart Antennas, VDE Verlag GmbH, 2018, p. 1-7Conference paper, Published paper (Refereed)
Abstract [en]

This paper compares centralized and distributed methods to solve the power minimization problem with quality-of-service (QoS) constraints in the downlink (DL) of multi-cell Massive multiple-input multiple-output (MIMO) systems. In particular, we study the computational complexity, number of parameters that need to be exchanged between base stations (BSs), and the convergence of iterative implementations. Although a distributed implementation based on dual decomposition (which only requires statistical channel knowledge at each BS) typically converges to the global optimum after a few iterations, many parameters need to be exchanged to reach convergence.

Place, publisher, year, edition, pages
VDE Verlag GmbH, 2018
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-148936 (URN)978-3-8007-4541-8 (ISBN)
Conference
IEEE 22nd International ITG Workshop on Smart Antennas (WSA2018)
Projects
5GwirelessELLIITCENIIT
Available from: 2018-06-22 Created: 2018-06-22 Last updated: 2019-06-28
Van Chien, T., Björnson, E. & Larsson, E. G. (2018). Joint Pilot Design and Uplink Power Allocation in Multi-Cell Massive MIMO Systems. IEEE Transactions on Wireless Communications, 17(3), 2000-2015
Open this publication in new window or tab >>Joint Pilot Design and Uplink Power Allocation in Multi-Cell Massive MIMO Systems
2018 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 17, no 3, p. 2000-2015Article in journal (Refereed) Published
Abstract [en]

This paper considers pilot design to mitigate pilot contamination and provide good service for everyone in multi-cell Massive multiple input multiple output (MIMO) systems. Instead of modeling the pilot design as a combinatorial assignment problem, as in prior works, we express the pilot signals using a pilot basis and treat the associated power coefficients as continuous optimization variables. We compute a lower bound on the uplink capacity for Rayleigh fading channels with maximum ratio detection that applies with arbitrary pilot signals. We further formulate the max-min fairness problem under power budget constraints, with the pilot signals and data powers as optimization variables. Because this optimization problem is non-deterministic polynomial-time hard due to signomial constraints, we then propose an algorithm to obtain a local optimum with polynomial complexity. Our framework serves as a benchmark for pilot design in scenarios with either ideal or non-ideal hardware. Numerical results manifest that the proposed optimization algorithms are close to the optimal solution obtained by exhaustive search for different pilot assignments and the new pilot structure and optimization bring large gains over the state-of-the-art suboptimal pilot design.

Place, publisher, year, edition, pages
IEEE Communications Society, 2018
Keywords
Massive MIMO, Pilot Design, Signomial Programming, Geometric Programming, Hardware Impairments.
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-145713 (URN)10.1109/TWC.2017.2787702 (DOI)000427226500042 ()2-s2.0-85040035548 (Scopus ID)
Projects
CENIIT
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, Horizon 2020, 641985
Note

Funding agencies:This work was supported in part by the European Union's Horizon 2020 Research and Innovation Programme under Grant 641985 (5Gwireless), in part by ELLIIT, and in part by CENIIT. This paper was presented at the IEEE ICC 2017. The associate editor coordinating the review of this paper and approving it for publication was T. Lok.

Available from: 2018-03-19 Created: 2018-03-19 Last updated: 2019-12-09Bibliographically approved
van Chien, T. (2017). Resource Allocation for Max-Min Fairness in Multi-Cell Massive MIMO. (Licentiate dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Resource Allocation for Max-Min Fairness in Multi-Cell Massive MIMO
2017 (English)Licentiate 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 recently gained lots of attention from both academia and industry. By equipping base stations (BSs) with hundreds of antennas, 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 two resource allocation aspects in Massive MIMO: 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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. p. 36
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1797
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-144221 (URN)10.3384/lic.diva-144221 (DOI)9789176853870 (ISBN)
Presentation
2018-01-11, Systemet, B-huset, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2018-01-11 Created: 2018-01-11 Last updated: 2019-10-12Bibliographically approved
Van Chien, T., Björnson, E. & Larsson, E. G. (2016). Downlink Power Control for Massive MIMO Cellular Systems with Optimal User Association. In: IEEE International Conference on Communications, Malaysia, May 23-27, 2016: proceedings. Paper presented at IEEE International Conference on Communications, Malaysia, May 23-27. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Downlink Power Control for Massive MIMO Cellular Systems with Optimal User Association
2016 (English)In: IEEE International Conference on Communications, Malaysia, May 23-27, 2016: proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper, Published paper (Refereed)
Abstract [en]

This paper aims to minimize the total transmit power consumption for Massive MIMO (multiple-input multiple-output) downlink cellular systems when each user is served by the optimized subset of the base stations (BSs). We derive a lower bound on the ergodic spectral efficiency (SE) for Rayleigh fading channels and maximum ratio transmission (MRT) when the BSs cooperate using non-coherent joint transmission. We solve the joint user association and downlink transmit power minimization problem optimally under fixed SE constraints. Furthermore, we solve a max-min fairness problem with user specific weights that maximizes the worst SE among the users. The optimal BS-user association rule is derived, which is different from maximum signal-to-noise-ratio (max-SNR) association. Simulation results manifest that the proposed methods can provide good SE for the users using less transmit power than in small-scale systems and that the optimal user association can effectively balance the load between BSs when needed.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
IEEE International Conference on Communications, ISSN 1550-3607
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-130168 (URN)10.1109/ICC.2016.7510950 (DOI)000390993201146 ()978-1-5090-0448-5 (ISBN)
Conference
IEEE International Conference on Communications, Malaysia, May 23-27
Available from: 2016-07-13 Created: 2016-07-13 Last updated: 2019-06-28Bibliographically approved
Organisations

Search in DiVA

Show all publications