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Joint Power Allocation and User Association Optimization for Massive MIMO Systems
Linköpings universitet, Institutionen för systemteknik, Kommunikationssystem. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för systemteknik, Kommunikationssystem. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-5954-434X
Linköpings universitet, Institutionen för systemteknik, Kommunikationssystem. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-7599-4367
2016 (engelsk)Inngår i: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 15, nr 9, s. 6384-6399Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper investigates the joint power allocationand user association problem in multi-cell Massive MIMO(multiple-input multiple-output) downlink (DL) systems. Thetarget is to minimize the total transmit power consumptionwhen each user is served by an optimized subset of the basestations (BSs), using non-coherent joint transmission. We firstderive a lower bound on the ergodic spectral efficiency (SE),which is applicable for any channel distribution and precodingscheme. Closed-form expressions are obtained for Rayleigh fadingchannels with either maximum ratio transmission (MRT) or zeroforcing (ZF) precoding. From these bounds, we further formulatethe DL power minimization problems with fixed SE constraintsfor the users. These problems are proved to be solvable aslinear programs, giving the optimal power allocation and BS-user association with low complexity. Furthermore, we formulatea max-min fairness problem which maximizes the worst SEamong the users, and we show that it can be solved as aquasi-linear program. Simulations manifest that the proposedmethods provide good SE for the users using less transmit powerthan in small-scale systems and the optimal user associationcan effectively balance the load between BSs when needed.Even though our framework allows the joint transmission frommultiple BSs, there is an overwhelming probability that only oneBS is associated with each user at the optimal solution.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2016. Vol. 15, nr 9, s. 6384-6399
Emneord [en]
Massive MIMO, user association, power allocation, load balancing, linear program
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-131129DOI: 10.1109/TWC.2016.2583436ISI: 000384241400040OAI: oai:DiVA.org:liu-131129DiVA, id: diva2:968009
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsTilgjengelig fra: 2016-09-11 Laget: 2016-09-11 Sist oppdatert: 2019-12-09bibliografisk kontrollert
Inngår i avhandling
1. Resource Allocation for Max-Min Fairness in Multi-Cell Massive MIMO
Åpne denne publikasjonen i ny fane eller vindu >>Resource Allocation for Max-Min Fairness in Multi-Cell Massive MIMO
2017 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2017. s. 36
Serie
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1797
HSV kategori
Identifikatorer
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 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-01-11 Laget: 2018-01-11 Sist oppdatert: 2019-10-12bibliografisk kontrollert
2. Spatial Resource Allocation in Massive MIMO Communications: From Cellular to Cell-Free
Åpne denne publikasjonen i ny fane eller vindu >>Spatial Resource Allocation in Massive MIMO Communications: From Cellular to Cell-Free
2020 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2020. s. 66
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2036
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-162582 (URN)10.3384/diss.diva-162582 (DOI)9789179299415 (ISBN)
Disputas
2020-01-23, Ada Lovelace, Building B, Campus Valla, Linköping, 13:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-12-09 Laget: 2019-12-09 Sist oppdatert: 2020-01-24bibliografisk kontrollert

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