All posts by Emil Björnson

New Massive MIMO Book

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, 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.

Ten Questions and Answers About Massive MIMO

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.

MAMMOET: Massive MIMO for Efficient Transmission

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:

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.

Massive MIMO is Becoming a Marketing Term

I have been wondering for years if “MIMO” will always be a term exclusively used by engineers and a few well-informed consumers, or if it eventually becomes a word that most people are using. Will you ever hear kids saying: “I want a MIMO tablet for Christmas”?

I have been think that it can go either way – it is in the hands of marketing people. Advanced Wifi routers have been marketed with MIMO functionality for some years, but the impact is limited since most people get their routers as part of their internet subscriptions instead of buying them separately. Hence, the main question is: will handset manufactures and telecom operators start using the MIMO term when marketing products to end customers?

Maybe we have the answer because Sprint, an American telecom operator, is currently marketing their 2018 deployment of new LTE technology by talking publicly about “Massive MIMO”.  As I wrote back in March, Sprint and Ericsson were to conduct field tests in the second half of 2017.  Results from the tests conducted in Seattle, Washington and Plano, Texas, have now been described in a press release. The tests were carried at a carrier frequency in the 2.5 GHz band using TDD mode and an Ericsson base station with 64 transmit/receive antennas. It is fair to call this Massive MIMO, although 64 antennas is in the lower end of the interval that I would call “massive”.

The press release describes “peak speeds of more than 300 Mbps using a single 20 MHz channel”, which corresponds to a spectral efficiency of 15 bit/s/Hz. That is certainly higher than you can get in legacy LTE networks, but it is less than some previous field tests.

Hence, when the Sprint COO of Technology, Guenther Ottendorfer, describes their Massive MIMO deployment with the words “You ain’t seen nothing yet”, I hope that this means that we will see network deployments with substantially higher spectral efficiencies than 15 bit/s/Hz in the years to come.

Several videos about the field test in Seattle have recently appeared. The first one demonstrates that 100 people can simultaneously download a video, which is not possible in legacy networks. Since the base station has 64 antennas, the 100 users are probably served by a combination of spatial multiplexing and conventional orthogonal time-frequency multiplexing.

The second video provides some more technical details about the setup used in the field test.

Upcoming Massive MIMO Webinars

IEEE ComSoc is continuing to deliver webinars on 5G topics and Massive MIMO is a key part of several of them. The format is a 40 minute presentation followed by a 20 minuter Q/A session. Hence, if you attend the webinars “live”, you have the opportunity to ask questions to the presenters. Otherwise, you can also watch each webinar afterwards. For example, 5G Massive MIMO: Achieving Spectrum Efficiency, which was given in August by Liesbet Van der Perre (KU Leuven), can still be watched.

In November, the upcoming Massive MIMO webinars are:

Massive MIMO for 5G: How Big Can it Get? by Emil Björnson (Linköping University), Thursday, 9 November 2017, 3:00 PM EST, 12:00 PM PST, 20:00 GMT.

Real-time Prototyping of Massive MIMO: From Theory to Reality by Douglas Kim (NI) and Fredrik Tufvesson (Lund University), Wednesday, 15 November 2017, 12:00 PM EST, 9:00 AM PST, 17:00 GMT.

Six Differences Between MU-MIMO and Massive MIMO

Multi-user MIMO (MU-MIMO) is not a new technology, but the basic concept of using multi-antenna base stations (BSs) to serve a multitude of users has been around since the late 1980s.

An example of how MU-MIMO was illustrated prior to Massive MIMO.

I sometimes get the question “Isn’t Massive MIMO just MU-MIMO with more antennas?” My answer is no, because the key benefit of Massive MIMO over conventional MU-MIMO is not only about the number of antennas. Marzetta’s Massive MIMO concept is the way to deliver the theoretical gains of MU-MIMO under practical circumstances. To achieve this goal, we need to acquire accurate channel state information, which in general can only be done by exploiting uplink pilots and channel reciprocity in TDD mode. Thanks to the channel hardening and favorable propagation phenomena, one can also simplify the system operation in Massive MIMO.

This is how Massive MIMO is often illustrated for line-of-sight operation.

Six key differences between conventional MU-MIMO and Massive MIMO are provided below.

Conventional MU-MIMO Massive MIMO
Relation between number of BS antennas (M) and users (K) MK and both are small (e.g., below 10) K and both can be large (e.g., M=100 and K=20).
Duplexing mode Designed to work with both TDD and FDD operation Designed for TDD operation to exploit channel reciprocity
Channel acquisition Mainly based on codebooks with set of predefined angular beams Based on sending uplink pilots and exploiting channel reciprocity
Link quality after precoding/combining Varies over time and frequency, due to frequency-selective and small-scale fading Almost no variations over time and frequency, thanks to channel hardening
Resource allocation The allocation must change rapidly to account for channel quality variations The allocation can be planned in advance since the channel quality varies slowly
Cell-edge performance Only good if the BSs cooperate Cell-edge SNR increases proportionally to the number of antennas, without causing more inter-cell interference

Footnote: TDD stands for time-division duplex and FDD stands for frequency-division duplex.