All posts by Emil Björnson

5G, Commentary, Technical insights

Achieving Spectral Efficiency, Link Reliability, and Low-Power Operation

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On January 17, I will give a 1-hour webinar in the IEEE 5G Webinar Series. I was asked to talk about “Massive MIMO for 5G below 6 GHz” since there has been a lot of focus on mmWave frequencies in the 5G discussions, although the primary band for 5G seems to be in the range 3.4-3.8 GHz, according to Ericsson.

The full title of my webinar is Massive MIMO for 5G below 6 GHz: Achieving Spectral Efficiency, Link Reliability, and Low-Power Operation. I will cover the basics of Massive MIMO and explain how the technology is not only great for enhancing the broadband access, but also for delivering the link reliability and low-power operation required by the internet of things. I have made sure that the overlap with my previous webinar is small.

If you watch the webinar live, you will have the chance to ask questions. Otherwise, you can view the recording of the webinar afterward. All the webinars in the IEEE 5G Webinar Series are available for anyone to view.

As a final note, I wrote a guest blog post at IEEE ComSoc Technology News in late December. It follows up and my previous blog post about GLOBECOM and is called: The Birth of 5G: What to do next?

 

5G, Beyond 5G, News

Wireless Communications with UAVs: Theory and Practice

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Our recent guest post about the combination of Massive MIMO and drones has received a lot of interest on social media. The use of unmanned aerial vehicles (UAVs) for wireless communications is certainly an emerging topic that deserves further attention!

While the previous blog post focused on Massive MIMO aspects of UAV communications, other theoretical research findings are reviewed in this tutorial by Walid Saad and Mehdi Bennis:

You can also check out this tutorial by Rui Zhang.

Furthermore, the team of the ERC Advanced PERFUME project, lead by Prof. David Gesbert, has recently demonstrated what appears to be the world’s first autonomous flying base station relays. This exciting achievement is demonstrated in the following video:

5G, Beyond 5G, Commentary, Technical insights

Challenges on the Path to Deployment

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Marina Bay Sands Expo and Convention Centre

I attended GLOBECOM in Singapore earlier this week. Since more and more preprints are posted online before conferences, one of the unique features of conferences is to meet other researchers and attend the invited talks and interactive panel discussions. This year I attended the panel “Massive MIMO – Challenges on the Path to Deployment”, which was organized by Ian Wong (National Instruments). The panelists were Amitava Ghosh (Nokia), Erik G. Larsson (Linköping University), Ali Yazdan (Facebook), Raghu Rao (Xilinx), and Shugong Xu (Shanghai University).

No common definition

The first discussion item was the definition of Massive MIMO. While everyone agreed that the main characteristic is that the number of controllable antenna elements is much larger than the number of spatially multiplexed users, the panelists put forward different additional requirements. The industry prefers to call everything with at least 32 antennas for Massive MIMO, irrespective of whether the beamforming is constructed from codebook-based feedback, grid-of-beams, or by exploiting uplink pilots and TDD reciprocity. This demonstrates that Massive MIMO is becoming a marketing term, rather than a well-defined technology. In contrast, academic researchers often have more restrictive definitions; Larsson suggested to specifically include the TDD reciprocity approach in the definition. This is because it is the robust and overhead-efficient way to acquire channel state information (CSI), particularly for non-line-of-sight users; see Myth 3 in our magazine paper. This narrow definition clearly rules out FDD operation, as pointed out by a member of the audience. Personally, I think that any multi-user MIMO implementation that provides performance similar to the TDD-reciprocity-based approach deserves the Massive MIMO branding, but we should not let marketing people use the name for any implementation just because it has many antennas.

Important use cases

The primary use cases for Massive MIMO in sub-6 GHz bands are to improve coverage and spectral efficiency, according to the panel. Great improvements in spectral efficiency have been demonstrated by prototyping, but the panelist agreed that these should be seen as upper bounds. We should not expect to see more than 4x improvements over LTE in the first deployments, according to Ghosh. Larger gains are expected in later releases, but there will continue to be a substantial gap between the average spectral efficiency observed in real cellular networks and the peak spectral efficiency demonstrated by prototypes. Since Massive MIMO achieves its main spectral efficiency gains by multiplexing of users, we might not need a full-blown Massive MIMO implementation today, when there are only one or two simultaneously active users in most cells. However, the networks need to evolve over time as the number of active users per cell grows.

In mmWave bands, the panel agreed that Massive MIMO is mainly for extending coverage. The first large-scale deployments of Massive MIMO will likely aim at delivering fixed wireless broadband access and this must be done in the mmWave bands; there is too little bandwidth in sub-6 GHz bands to deliver data rates that can compete with wired DSL technology.

Initial cost considerations

The deployment cost is a key factor that will limit the first generations of Massive MIMO networks. Despite all the theoretic research that has demonstrated that each antenna branch can be built using low-resolution hardware, when there are many antennas, one should not forget the higher out-of-band radiation that it can lead to. We need to comply with the spectral emission masks – spectrum is incredibly expensive so a licensee cannot accept interference from adjacent bands. For this reason, several panelists from the industry expressed the view that we need to use similar hardware components in Massive MIMO as in contemporary base stations and, therefore, the hardware cost grows linearly with the number of antennas. On the other hand, Larsson pointed out that the futuristic devices that you could see in James Bond movies 10 years ago can now be bought for $100 in any electronic store; hence, when the technology evolves and the economy of scale kicks in, the cost per antenna should not be more than in a smartphone.

A related debate is the one between analog and digital beamforming. Several panelists said that analog and hybrid approaches will be used to cut cost in the first deployments. To rely on analog technology is somewhat weird in an age when everything is becoming digital, but Yazdan pointed out that it is only a temporary solution. The long-term vision is to do fully digital beamforming, even in mmWave bands.

Another implementation challenge that was discussed is the acquisition of CSI for mobile users. This is often brought up as a showstopper since hybrid beamforming methods have such difficulties – it is like looking at a running person in a binocular and trying to follow the movement. This is a challenging issue for any radio technology, but if you rely on uplink pilots for CSI acquisition, it will not be harder than in a system of today. This has also been demonstrated by measurements.

Open problems

The panel was asked to describe the most important open problems in the Massive MIMO area, from a deployment perspective. One obvious issue, which we called the “grand question” in a previous paper, is to provide better support for Massive MIMO in FDD.

The control plane and MAC layer deserve more attention, according to Larsson. Basic functionalities such as ACK/NACK feedback is often ignored by academia, but incredibly important in practice.

The design of “cell-free” densely distributed Massive MIMO systems also deserve further attention. Connecting all existing antennas together to perform joint transmission seems to be the ultimate approach to wireless networks. Although there is no practical implementation yet, Yazdan stressed that deploying such networks might actually be more practical than it seems, given the growing interest in C-RAN technology.

10 years from now

I asked the panel what will be the status of Massive MIMO in 10 years from now. Rao predicted that we will have Massive MIMO everywhere, just as all access point supports small-scale MIMO today. Yazdan believed that the different radio technology (e.g., WiFi, LTE, NR) will converge into one interconnected system, which also allows operators to share hardware. Larsson thinks that over the next decade many more people will have understood the fundamental benefits of utilizing TDD and channel reciprocity, which will have a profound impact on the regulations and spectrum allocation.

Education, News

New Massive MIMO Book

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For the past two years, I’ve been writing on a book about Massive MIMO networks, together with my co-authors Jakob Hoydis and Luca Sanguinetti. It has been a lot of hard work, but also a wonderful experience since we’ve learned a lot in the writing process. We try to connect all dots and provide answers to many basic questions that were previously unanswered.

The book has now been published:

Emil Björnson, Jakob Hoydis and Luca Sanguinetti (2017), “Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency”, Foundations and Trends® in Signal Processing: Vol. 11, No. 3-4, pp 154–655. DOI: 10.1561/2000000093.

What is new with this book?

Marzetta et al. published Fundamentals of Massive MIMO last year. It provides an excellent, accessible introduction to the topic. By considering spatially uncorrelated channels and two particular processing schemes (MR and ZF), the authors derive closed-form capacity bounds, which convey many practical insights and also allow for closed-form power control.

In the new book, we consider spatially correlated channels and demonstrate how such correlation (which always appears in practice) affects Massive MIMO networks. This modeling uncovers new fundamental behaviors that are important for practical system design. We go deep into the signal processing aspects by covering several types of channel estimators and deriving advanced receive combining and transmit precoding schemes.

In later chapters of the book, we cover the basics of energy efficiency, transceiver hardware impairments, and various practical aspects; for example, spatial resource allocation, channel modeling, and antenna array deployment.

The book is self-contained and written for graduate students, PhD students, and senior researchers that would like to learn Massive MIMO, either in depth or at an overview level. All the analytical proofs, and the basic results on which they build, are provided in the appendices.

On the website massivemimobook.com, you will find Matlab code that reproduces all the simulation figures in the book. You can also download exercises and other supplementary material.

Update: Get a free copy of the book

From August 2018, you can download a free PDF of the authors’ version of the manuscript. This version is similar to the official printed books, but has a different front-page and is also regularly updated to correct typos that have been identified.

5G, Commentary, Technical insights

Ten Questions and Answers About Massive MIMO

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After the IEEE ComSoc Webinar that I gave this month, there was a 15 minute online Q/A session.

Unfortunately, there was not enough time for me to answer all the questions that I received, so I had to answer many of them afterwards. I have gathered ten questions and my answers below. I can also announce that I will give another Massive MIMO webinar in January 2018 and it will also be followed by a Q/A session.

1. What are the differences between 4G and 5G that will affect how Massive MIMO can be implemented?

The channel estimation must be implemented in the right way (i.e., exploiting uplink pilots and channel reciprocity) to obtain sufficiently accurate channel state information (CSI) to perform spatial multiplexing of many users, otherwise the inter-user interference will eliminate most of the gains. Accurate CSI  is hard to achieve within the 4G standard, although there are several Massive MIMO field trials for TDD LTE that show promising results. However, if 5G is designed properly, it will support Massive MIMO from scratch, while in 4G it will always be an add-on that must to adhere to the existing air interface.

2. How easy it is to deploy MIMO antennas on the current infrastructure?

Generally speaking, we can reuse the current infrastructure when deploying Massive MIMO, which is why operators show much interest in the technology. You upgrade the radio base stations but keep the same backhaul infrastructure and core network. However, since Massive MIMO supports much higher data rates, some of the backhaul connections might also need to be upgraded to deliver these rates.

3. What are the most suitable channel models for Massive MIMO?

I recommend the channel model that was developed in the MAMMOET project. It is a refinement of the COST 2100 model that takes particular phenomena of having large antenna arrays into account. Check out Deliverable D1.2 from that project.

4. For planar arrays, what is the height to width ratio that gives the highest performance?

You typically need more antennas in the horizontal direction (width) than in the vertical direction (height), because the angular variations between users is larger in the horizontal domain. For example, the array might cover a horizontal sector of 120-180 degrees, while the users’ elevation angles might only differ by a few tens of degrees. This is the reason that 8-antenna LTE base stations use linear arrays in the horizontal direction.

There is no optimal answer to the question. It depends on the deployment scenario. If you have high-rise buildings, users at different floors can have rather different elevation angles (it can differ up to 90 degrees) and you can benefit more from having many antennas in the vertical direction. If all users have almost the same elevation angle, it is preferable to have many antennas in the horizontal direction. These things are further discussed in Sections 7.3 and 7.4 in my new book.

5. What are the difficulties we face in deploying Massive MIMO in FDD systems?

The difficulty is to acquire channel state information at the base station for the frequency band used in the downlink, since it is very resource-demanding to send downlink pilots from a large array; particularly, if you want to spatially multiplex many users. This is an important but challenging problem that researchers have been working on since the 1990s. You can read more about it in Myth 3 and the grand question in the paper Massive MIMO: ten myths and one grand question.

6. Do you believe that there is a value in coordinated resource allocation schemes for Massive MIMO?

Yes, but the resource allocation in Massive MIMO is different from conventional systems. Scheduling might not be so important, since you can multiplex many users spatially, but pilot assignment and power allocation are important aspects that must be addressed. I refer to these things as spatial resource allocation. You can read more about this in Sections 7.1 and 7.2 in my new book, but as you can see from those sections, there are many open problems to be solved.

7. What is channel hardening and what implications does it have on the frequency allocation (in OFDMA networks, for example)?

Channel hardening means that the effective channel after beamforming is almost constant so that the communication link behaves as if there is no small-scale fading. A consequence is that all frequency subcarriers provide almost the same channel quality to a user. Regarding channel assignment, since you can multiplex many tens of users spatially in Massive MIMO, you can assign the entire bandwidth (all subcarriers) to every user; there is no need to use OFDMA to allocate orthogonal frequency resources to the users.

8. Is it practical to estimate the channel for each subcarrier in an OFDM system?

To limit the pilot overhead, you typically place pilots only on a small subset of the subcarriers. The distance between the pilots in the frequency domain can be selected based on how frequency-selective the channels are; if a user has L strong channel taps, it is sufficient to send pilots on L subcarriers, even if you many more subcarriers than that. Based on the received pilot signals, one can either estimate the channels on every subcarrier or estimate the channels on some of them and interpolate to get estimates on the remaining subcarriers.

9. How sensitive are the Massive MIMO spectral efficiency gains to TDD frame synchronization?

If you consider an OFDM system, then timing synchronization mismatches that are smaller than the cyclic prefix can basically be ignored. This is the case in TDD LTE systems and will not change when considering Massive MIMO systems that are implemented using OFDM. However, the synchronization across cells will not be perfect. The implications are investigated in a recent paper.

10. How does the higher computational complexity and delay in Massive MIMO processing affect the system performance?

I used to think that the computational complexity would be a bottleneck, but it turns out that it is not a big deal since all of the operations are standard (i.e., matrix multiplications and matrix inversions). For example, the circuit that was developed at Lund University shows that MIMO detection and precoding for a 20 MHz channel can be implemented very efficiently and only consumes a few mW.

5G, Commentary, News

MAMMOET: Massive MIMO for Efficient Transmission

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MAMMOET (Massive MIMO for Efficient Transmission) was the first major research project on Massive MIMO that was funded by the European Union. The project took place 2014-2016 and you might have heard about its outcomes in terms of the first demonstrations of real-time Massive MIMO that was carried out by the LuMaMi testbed at Lund University. The other partners in the project were Ericsson, Imec, Infineon, KU Leuven, Linköping University, Technikon, and Telefonica. MAMMOET was an excellent example of a collaborative project, where the telecom industry defined the system requirements and the other partners designed and evaluated new algorithms and hardware implementations to reach the requirements.

This article is an interview with Prof. Liesbet Van der Perre who was the scientific leader of the project.

Liesbet Van der Perre while disseminating results from the MAMMOET project in September 2017.

In 2012, when you began to draft the project proposal, Massive MIMO was not a popular topic. Why did you initiate the work?

– Theoretically and conceptually it seemed so interesting that it would be a pity not to work on it. The main goal of the MAMMOET project was to make conceptual progress towards a spectrally and energy efficient system and to raise the confidence level by demonstrating a practical hardware implementation. We also wanted to make channel measurements to see if they would confirm what has been seen in theory.

It seems the project partners had a clear vision from the beginning?

– It was actually very easy to write this proposal because everyone was on the same wavelength and knew what we wanted to achieve. We were all eager to start the project and learn from each other. This is quite unique and explains why the project delivered much more than promised. The fact that the team got along very well has also laid the fundament for further research collaborations.

What were the main outcomes of the project?

– We learned a lot on how things change when going from small to large arrays. New channel models are required to capture the new behaviors. We are used to that high-precision hardware is needed, but all the sudden this is not true when drastically increasing the number of antennas. You can then use low-resolution hardware and simple processing, which is very different from conventional MIMO implementation.

Some of the big conceptual differences in massive MIMO turned out to be easier to solve than expected, while some things were more problematic than foreseen. For example, it is difficult to connect all the signals together. You need to do part of the processing distributive to avoid this problem. Synchronization also turned out to be a bottleneck. If we would have known that from the start, we could have designed the testbed differently, but we thought that the channel estimation and MIMO processing would be the challenging part.

What was the most rewarding aspect of leading this project?

– The cross-fertilization of people was unique. We brought people with different background and expertise together in a room to identify the crucial problems in massive MIMO and find new solutions. For example, we realized early that interference will be a main problem and that zero-forcing processing is needed, although matched filtering was popular at the time. By carefully analyzing the zero-forcing complexity, we could show that it was almost negligible compared to other necessary processing and we later demonstrated zero-forcing in real-time at the testbed. This was surprising for many people who thought that massive MIMO would be impossible to implement since 8×8 MIMO systems are terribly complex, but many things can be simplified in massive MIMO. Looking back, it might seem that the outcomes were obvious, but these are things you don’t know until you have gone through the process.

The real-time LuMaMi testbed at Lund University, the first one of its kind.

What are the big challenges that remains?

– An important challenge is how to integrate massive MIMO into a network. We assumed that there are many users and we can all give them the same time-frequency resources, but the channels and traffic are not always suitable for that. How should we decide which users to put together? We used an LTE-like frame structure, but it is important to design a frame structure that is well-suited for massive MIMO and real traffic.

There are many tradeoffs and degrees-of-freedom when designing massive MIMO systems. Would you use the technology to provide very good cell coverage or to boost small-cell capacity? Instead of delivering fiber to homes, we could use massive MIMO with very many antennas for spatial multiplexing of fixed wireless connections. Alternatively, in a mobile situation, we might not multiplex so many users. Optimizing massive MIMO for different scenarios is something that remains.

We made a lot of progress on the digital processing side in MAMMOET, while on the analog side we mainly came up with the specifications. We also did not work on the antenna design since, theoretically, it does not matter which antennas you use, but in practice it does.

The research team of the MAMMOET project at the final review meeting of the project in February 2017.

All the deliverables and publications in the MAMMOET project can be accessed online: https://mammoet-project.technikon.com/

The deliverables contain a lot information related to use cases, requirements, channel modeling, signal processing algorithms, algorithmic implementation, and hardware implementation. Some of the results can found in the research literature, but far from everything.

Note: The author of this article worked in the MAMMOET project, but did not take part in the drafting of the proposal.