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Radio Resource Management in Massive MIMO Communication Systems

Project leader: Emil Björnson

Figure 1: Illustration of beamforming transmission in a massive MIMO system with 10 user terminals and an access point with 100 antenna elements.

Background and industrial relevance

The amount of wireless communications has doubled every 2.5 year, since the early 20th century (known as Cooper's law). This incredible success story for wireless connectivity is expected to continue over the foreseeable future [1]. While the everyday users are continuing to embrace modern wireless applications, such as video streaming and social networking, the exponential increase in wireless data traffic (32% or more per year) forces the telecommunication operators to continuously evolve their existing network equipment and install new network infrastructure. To keep up with the rapidly increasing demand for wireless data, the fifth generation (5G) of wireless networks is expected to be deployed at around 2020. The next five years will, thus, be exciting for wireless communication researchers in both industry and academia, because everyone has an opportunity to impact the upcoming standards and thereby the nature of the networks that will eventually be built.
     The ambitious but necessary goal for 5G networks is to handle a 1000x increase in wireless data traffic. The key to handling such an orders-of-magnitude increase is efficient radio resource management; that is, how to best utilize the available time and frequency resources for wireless data transmission. Although it is possible to acquire new frequency bands for wireless networks, it is physically impossible to find more than twice the spectrum in the bands suitable for wide-area coverage. To reach up to 1000x, one also needs to investigate techniques that reuse a given amount of spectrum in the spatial domain; that is, to improve the data throughput per area unit by having as many concurrent transmissions per area unit as possible. One of the most promising techniques to improve the area data throughput is massive MIMO (multiple-input multiple-output), where wireless access points (APs) are equipped with arrays with hundreds of active antenna elements. These antennas are phase-synchronized and can thus steer each data signal by beamforming towards its intended user terminal, instead of spreading the signal energy blindly over the coverage area. The more antennas that are deployed, the finer the beamforming and the less interference is caused in non-intended directions. This allows for serving tens or a hundred users in parallel using the same frequencies; see Figure 1. As long as the users are sufficiently spaced apart, massive MIMO beamforming provides strong signals with little inter-user interference.
     Massive MIMO seems to be a commercially attractive solution since one can at least achieve a 100x increase in area throughput by upgrading existing APs, instead of installing 100x more APs (with high costs for site renting and wired backhaul infrastructure).
     The academic research on massive MIMO has so far focused on physical-layer aspects [2-8]; that is, how to acquire channel state information (CSI) by different methods and how to compute the data rates that are achievable when the acquired CSI is utilized for beamforming. This was a natural first step because the fading in wireless channels makes it necessary to re-estimate the channels many times per second; hence, the acquisition of CSI for a large number of terminals and antennas per AP requires the design of new scalable transmission protocols [2]. CSI is typically acquired by transmitting predefined pilot sequences. As in any cellular network, it is necessary to reuse pilot sequences across cells, either in each cell or with a certain reuse pattern. This creates unavoidable interference from neighboring cells during the CSI acquisition, so-called pilot contamination, which has been shown to be one of the main performance limiting factors in massive MIMO [2, 3, 7].
     The project leader has previously contributed to the massive MIMO research by investigating how hardware imperfections affect the performance [9] and has, for example, proved that the design constraints on hardware can be greatly relaxed [10]—an important result for cost-efficient commercial solutions. Moreover, the project leader has developed techniques for reducing the computational complexity of beamforming [11] and CSI estimation [12].

Project description

As described above, many research challenges in the physical layer of massive MIMO systems have been solved, or are currently receiving large attention from the research community. In contrast, the resource management that takes place in the media access control (MAC) layer of wireless systems has received little attention in the context of massive MIMO. This leaves the door open for major research contributions, because the large gains in area throughput offered by massive MIMO physical-layer techniques need to be canalized by novel resource management schemes that can exploit the unique system characteristics.
     It was pointed out in [8] that the conventional techniques for resource management can be simplified and robustified in massive MIMO systems, since there is no need to cope with the randomness induced by small-scale fading—the short-term fluctuations in channel quality over the time and frequency domain average out when there are many antennas at the APs. On the other hand, massive MIMO systems open up new opportunities and challenges. The importance of resource management for massive MIMO was exemplified in [13], where it was shown that simple location-aware user scheduling algorithms can greatly improve the performance as compared to the rudimentary random scheduling usually assumed in the massive MIMO literature.
This project will continue the line of work initiated by [13] by considering three main research directions in the scope of radio resource management for massive MIMO systems. These are divided into three work packages (WPs) below, and the project intends to make major research contributions in all of these WPs. The project has already conducted research within all of these WPs and results have been published at international conferences and are under review for top-level international journals.
  • WP1: Interference- and Distortion-Aware Resource Allocation
    Publications: [J1], [J3], [J4], [C1], [C2], [C3], [C9].
  • WP2: Coordinated Beamforming and Load Balancing.
    Publications: [J2*], [J5], [C4], [C5], [C8].
  • WP3: Proactive and Traffic-Aware Scheduling.
    Publications: [J6*], [C6], [C7], [C10].

Long term vision of the project

The long-term vision is to make a thorough impact on the research community's view of massive MIMO techniques and how it can be implemented in 5G wireless networks. This will also establish the project leader and his collaborators as some of the world-leading experts on massive MIMO and modern radio resource management.

This is a summary of key steps taken towards realizing this vision:

  • Half-day tutorial on "The Massive MIMO Paradigm: Fundamentals and State-of-the-Art" will be given at IEEE GLOBECOM 2016, 5Gwireless Summer School 2016, and the 2016 Tyrrhenian International Workshop on Digital Communications.
  • The overview article "Massive MIMO: Ten Myths and One Critical Question" as been published in IEEE Communications Magazine, February 2016.
  • Half-day tutorial on "Massive MIMO for 5G" was given at IEEE SPAWC 2015 (Stockholm, Sweden, June 29, 2015) and at IEEE ICC 2015 (London, UK, June 8, 2015), jointly with Prof. Erik G. Larsson.
  • Keynote speech on "Massive MIMO: Bringing the Magic of Asymptotic Analysis to Wireless Networks" was given at IEEE International Workshop on Computer-Aided Modeling Analysis and Design of Communication Links and Networks (CAMAD), Athens, Greece (December 2014).
  • Reproducible research is promoted by making simulation code publicly available for some of the key publications, such as [M1].

Connections to industry and other CENIIT projects

This project is supported by Ericsson Research. Research results are presented to Ericsson at regularly meetings.
     There are no connections to other CENIIT projects. The project leader is also active in the MAMMOET project, which deals with other research questions related to massive MIMO technology.

List of publications

Magazine articles:

[M1] Emil Björnson, Erik G. Larsson, Thomas L. Marzetta, “Massive MIMO: Ten Myths and One Critical Question,” IEEE Communications Magazine, vol. 54, no. 2, pp. 114-123, February 2016.

Journal articles:

[J6*] Emil Björnson, Elisabeth de Carvalho, Jesper H. Sørensen, Erik G. Larsson, Petar Popovski, “A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems,” IEEE Transactions on Wireless Communications, Submitted for publication.

[J5] Trinh Van Chien, Emil Björnson, Erik G. Larsson, “Joint Power Allocation and User Association Optimization for Massive MIMO Systems,” IEEE Transactions on Wireless Communications, To appear.

[J4] Rami Mochaourab, Emil Björnson, Mats Bengtsson, “Adaptive Pilot Clustering in Heterogeneous Massive MIMO Networks,” IEEE Transactions on Wireless Communications, vol. 15, no. 8, pp. 5555-5568, August 2016.

[J3] Jiayi Zhang, Linglong Dai, Xinling Zhang, Emil Björnson, Zhaocheng Wang, “Achievable Rate of Rician Large-Scale MIMO Channels with Transceiver Hardware Impairments,” IEEE Transactions on Vehicular Technology, To appear.

[J2*] Xueru Li, Emil Björnson, Erik G. Larsson, Shidong Zhou, Jing Wang, “Massive MIMO with Multi-cell MMSE Processing: Exploiting All Pilots for Interference Suppression,” IEEE Transactions on Wireless Communications, Submitted for publication.

[J1] Xinlin Zhang, Michail Matthaiou, Emil Björnson, Mikael Coldrey, “Impact of residual transmit RF impairments on training-based MIMO systems,” IEEE Transactions on Communications, vol. 63, no. 8, pp. 2899-2911, August 2015.

Star-marked journal articles are currently under review.

Conference papers:

[C10] Emil Björnson, Elisabeth de Carvalho, Erik G. Larsson, Petar Popovski, “Random Access Protocol for Massive MIMO: Strongest-User Collision Resolution (SUCR),” IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, May 2016.

[C9] Daniel Verenzuela, Emil Björnson, Luca Sanguinetti, “Optimal Design of Wireless Networks for Broadband Access with Minimum Power Consumption,” IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, May 2016.

[C8] Trinh Van Chien, Emil Björnson, Erik G. Larsson, “Downlink Power Control for Massive MIMO Cellular Systems with Optimal User Association,” IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, May 2016.

[C7] Elisabeth de Carvalho, Emil Björnson, Erik G. Larsson, Petar Popovski, “Random Access for Massive MIMO Systems with Intra-Cell Pilot Contamination,” Proceedings of IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP), Shanghai, China, March 2016.

[C6] Emil Björnson, Erik G. Larsson, “Three Practical Aspects of Massive MIMO: Intermittent User Activity, Pilot Synchronism, and Asymmetric Deployment,” Proceedings of IEEE Global Communications Conference (GLOBECOM), Workshop on Massive MIMO: From theory to practice, San Diego, California, USA, December 2015.

[C5] Xueru Li, Emil Björnson, Erik G. Larsson, Shidong Zhou, Jing Wang, “A Multi-cell MMSE Detector for Massive MIMO Systems and New Large System Analysis,” Proceedings of IEEE Global Communications Conference (GLOBECOM), San Diego, California, USA, December 2015.

[C4] Xueru Li, Emil Björnson, Erik G. Larsson, Shidong Zhou, Jing Wang, “A Multi-cell MMSE Precoder for Massive MIMO Systems and New Large System Analysis,” Proceedings of IEEE Global Communications Conference (GLOBECOM), San Diego, California, USA, December 2015.

[C3] Emil Björnson, Michail Matthaiou, Antonios Pitarokoilis, Erik G. Larsson, “Distributed Massive MIMO in Cellular Networks: Impact of Imperfect Hardware and Number of Oscillators,” Proceedings of the 23rd European Signal Processing Conference (EUSIPCO-2015), Nice, France, September 2015.

[C2] Hei Victor Cheng, Emil Björnson, Erik G. Larsson, “Uplink Pilot and Data Power Control for Single Cell Massive MIMO Systems with MRC,” Proceedings of International Symposium on Wireless Communication Systems (ISWCS), Brussels, Belgium, August 2015.

[C1] Rami Mochaourab, Emil Björnson, Mats Bengtsson, “Pilot Clustering in Asymmetric Massive MIMO Networks,” Proceedings of IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Stockholm, Sweden, July 2015.


[1] Cisco, "Cisco visual networking index: Global mobile data traffic forecast update, 2013-2018," 2014.
[2] T. L. Marzetta, "Noncooperative cellular wireless with unlimited numbers of base station antennas," IEEE Trans. Wireless Commun., vol. 9, no. 11, pp. 3590-3600, Nov. 2010.
[3] J. Jose, A. Ashikhmin, T. L. Marzetta, and S. Vishwanath, "Pilot contamination and precoding in multi-cell TDD systems," IEEE Trans. Commun., vol. 10, no. 8, pp. 2640-2651, Aug. 2011.
[4] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, and F. Tufvesson, "Scaling up MIMO: Opportunities and challenges with very large arrays," IEEE Signal Process. Mag., vol. 30, no. 1, pp. 40-60, Jan. 2013.
[5] J. Hoydis, K. Hosseini, S. ten Brink, and M. Debbah, "Making smart use of excess antennas: Massive MIMO, small cells, and TDD," Bell Labs Technical Journal, vol. 18, no. 2, pp. 5-21, Sep. 2013.
[6] H. Q. Ngo, E. G. Larsson, and T. L. Marzetta, "Energy and spectral efficiency of very large multiuser MIMO systems," IEEE Trans. Commun., vol. 61, no. 4, pp. 1436-1449, Apr. 2013.
[7] H. Yin, D. Gesbert, M. Filippou, and Y. Liu, "A coordinated approach to channel estimation in large-scale multiple-antenna systems," IEEE J. Sel. Areas Commun., vol. 31, no. 2, pp. 264-273, Feb. 2013.
[8] E. G. Larsson, F. Tufvesson, O. Edfors, and T. L. Marzetta, "Massive MIMO for next generation wireless systems," IEEE Commun. Mag., vol. 52, no. 2, pp. 186-195, Feb. 2014.
[9] E. Björnson, J. Hoydis, M. Kountouris, and M. Debbah, "Massive MIMO systems with non-ideal hardware: Energy efficiency, estimation, and capacity limits," IEEE Trans. Inf. Theory, vol. 60, no. 11, pp. 7112-7139, Nov. 2014.
[10] E. Björnson, M. Matthaiou, and M. Debbah, "Massive MIMO with arbitrary non-ideal arrays: Hardware scaling laws and circuit-aware design," submitted for publication.
[11] A. Kammoun, A. Muller, E. Björnson, and M. Debbah, "Linear precoding based on polynomial expansion: Large-scale multi-cell MIMO systems," IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 861-875, Oct. 2014.
[12] N. Shariati, E. Björnson, M. Bengtsson, and M. Debbah, "Low-complexity polynomial channel estimation in large-scale MIMO with arbitrary statistics," IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 815-830, Oct. 2014.
[13] H. Huh, G. Caire, H. Papadopoulos, and S. Ramprashad, "Achieving "massive MIMO" spectral efficiency with a not-so-large number of antennas," IEEE Trans. Wireless Commun., vol. 11, no. 9, pp. 3226-3239, 2012.

Page responsible: Emil Björnson
Last updated: 2016 09 06   11:46