Real-Time Massive MIMO DSP at 50 milliWatt

Colleagues at Lund University presented last month a working circuit that performs, in real time, zero-forcing decoding and precoding of 8 simultaneous terminals with 128 base station antennas, over a 20 MHz bandwidth at a power consumption of about 50 milliWatt.

Impressive, and important.

Granted, this number does not include the complexity of FFTs, sampling rate conversions, and several other (non-insignificant) tasks; however, it does include the bulk of the “Massive-MIMO”-specific digital processing. The design exploits a number of tricks and Massive-MIMO specific properties: diagonal dominance of the channel Gramian, in particular, in sufficiently favorable propagation.

When I started work on Massive MIMO in 2009, the common view held was that the technology would be infeasible because of computational complexity. Particularly, the sheer idea of performing zero-forcing processing in real time was met with, if not ridicule, extreme skepticism. We quickly realized, however, that a reasonable DSP implementation would require no more than some ten Watt. While that is a small number in itself, it turned out to be an overestimate by orders of magnitude!

I spoke with some of the lead inventors of the chip, to learn more about its design. First, the architectures for decoding and for precoding differ a bit. While there is no fundamental reason for why this has to be so, one motivation is the possible use of nonlinear detectors on uplink. (The need for such detectors, for most “typical” cellular Massive MIMO deployments, is not clear – but that is another story.)

Second, and more importantly, the scalability of the design is not clear. While the complexity of the matrix operations themselves scale fast with the dimension, the precision in the arithmetics may have to be increased as well – resulting in a much-faster-than-cubically overall complexity scaling. Since Massive MIMO operates at its best when multiplexing to many tens of terminals (or even thousands, in some applications), significant challenges remain for the future. That is good news for circuit engineers, algorithm designers, and communications theoreticians alike. The next ten years will be exciting.

How Much Performance is Lost by FDD Operation?

There has been a long-standing debate on the relative performance between reciprocity-based (TDD) Massive MIMO and that of FDD solutions based on grid-of-beams, or hybrid beamforming architectures. The matter was, for example, the subject of a heated debate in the 2015 Globecom industry panel “Massive MIMO vs FD-MIMO: Defining the next generation of MIMO in 5G” where on the one hand, the commercial arguments for grid-of-beams solutions were clear, but on the other hand, their real potential for high-performance spatial multiplexing was strongly contested.

While it is known that grid-of-beams solutions perform poorly in isotropic scattering, no prior experimental results are known. This new paper:

Massive MIMO Performance—TDD Versus FDD: What Do Measurements Say?

answers this performance question through the analysis of real Massive MIMO channel measurement data obtained at the 2.6 GHz band. Except for in certain line-of-sight (LOS) environments, the original reciprocity-based TDD Massive MIMO represents the only effective implementation of Massive MIMO at the frequency bands under consideration.

Teaching the Principles of Massive MIMO

In January this year, the IEEE Signal Processing Magazine contained an article by Erik G. Larsson, Danyo Danev, Mikael Olofsson, and Simon Sörman on “Teaching the Principles of Massive MIMO: Exploring reciprocity-based multiuser MIMO beamforming using acoustic waves“. It describes an exciting approach to teach the basics of Massive MIMO communication by implementing the system acoustically, using loudspeaker elements instead of antennas. The fifth-year engineering students at Linköping University have performed such implementations in 2014, 2015, and 2016, in the form of a conceive-design-implement-operate (CDIO) project.

The article details the teaching principles and experiences that the teachers and students had from the 2015 edition of the CDIO-project. This was also described in a previous blog post. In the following video, the students describe and demonstrate the end-result of the 2016 edition of the project. The acoustic testbed is now truly massive, since 64 loudspeakers were used.

Relative Value of Spectrum

What is more worth? 1 MHz bandwidth at 100 MHz carrier frequency, or 10 MHz bandwidth at 1 GHz carrier? Conventional wisdom has it that higher carrier frequencies are more valuable because “there is more bandwidth there”. In this post, I will explain why that is not entirely correct.

The basic presumption of TDD/reciprocity-based Massive MIMO is that all activity, comprising the transmission of uplink pilots, uplink data and downlink data, takes place inside of a coherence interval:

At fixed mobility, in meter/second, the dimensionality of the coherence interval is proportional to the wavelength, because the Doppler spread is proportional to the carrier frequency.

In a single cell, with max-min fairness power control (for uniform quality-of-service provision), the sum-throughput of Massive MIMO can be computed analytically and is given by the following formula:

In this formula,

  • $B$ = bandwidth in Hertz (split equally between uplink and downlink)
  • $M$ = number of base station antennas
  • $K$ = number of multiplexed terminals
  • $B_c$ = coherence bandwidth in Hertz (independent of carrier frequency)
  • $T_c$ = coherence time in seconds (inversely proportional to carrier frequency)
  • SNR = signal-to-noise ratio (“normalized transmit power”)
  • $\beta_k$ = path loss for the k:th terminal
  • $\gamma_k$ = constant, close to $\beta_k$ with sufficient pilot power

This formula assumes independent Rayleigh fading, but the general conclusions remain under other models.

The factor that pre-multiplies the logarithm depends on $K$.
The pre-log factor is maximized when $K=B_c T_c/2$. The maximal value is $B B_c T_c/8$, which is proportional to $T_c$, and therefore proportional to the wavelength. Due to the multiplication $B T_c$, one can get same pre-log factor using a smaller bandwidth by instead increasing the wavelength, i.e., reducing the carrier frequency. At the same time, assuming appropriate scaling of the number of antennas, $M$, with the number of terminals, $K$, the quantity inside of the logarithm is a constant.

Concluding, the sum spectral efficiency (in b/s/Hz) easily can double for every doubling of the wavelength: a megahertz of bandwidth at 100 MHz carrier is ten times more worth than a megahertz of bandwidth at a 1 GHz carrier. So while there is more bandwidth available at higher carriers, the potential multiplexing gains are correspondingly smaller.

In this example,

all three setups give the same sum-throughput, however, the throughput per terminal is vastly different.

More Demanding Massive MIMO Trials Using the Bristol Testbed

Last year, the 128-antenna Massive MIMO testbed at University of Bristol was used to set world records in per-cell spectral efficiency. Those measurements were conducted in a controlled indoor environment, but demonstrated that the theoretical gains of the technology are also practically achievable—at least in simple propagation scenarios.

The Bristol team has now worked with British Telecom and conducted trials at their site in Adastral Park, Suffolk, in more demanding user scenarios. In the indoor exhibition hall trial,  24 user streams were multiplexed over a 20 MHz bandwidth, resulting in a sum rate of 2 Gbit/s or a spectral efficiency of 100 bit/s/Hz/cell.

Several outdoor experiments were also conducted, which included user mobility. We are looking forward to see more details on these experiments, but in the meantime one can have a look at the following video:

Update: We have corrected the bandwidth number in this post.

Massive MIMO at the Mobile World Congress 2017

The Mobile World Congress (MWC) was held in Barcelona last week. Several major telecom companies took the opportunity to showcase and describe their pre-5G solutions based on Massive MIMO technology.

Huawei and Optus carried out an infield trial on February 26, where a sum rate of 655 Mbit/s was obtained over a 20 MHz channel by spatial multiplexing of 16 users. This corresponds to 33 bit/s/Hz or 2 bit/s/Hz/user, which are typical spectral efficiencies to expect from Massive MIMO. The base station was equipped with 128 antenna ports, but the press release provides no details on whether uplink or downlink transmission was considered.

ZTE demonstrated their TDD Massive MIMO solution, which we have described earlier on the blog. The company claimed to set a new record for single-site peak sum rate at their MWC demonstration. Spatial multiplexing of 16 data streams was considered with 256-QAM and the sum rate was 2.1 Gbit/s. Further details are found in their press release.

Nokia and Sprint demonstrated TDD-based Massive MIMO technology for LTE networks, using 64 antenna ports at the base station. Spatial multiplexing of eight commercial LTE terminals was considered. Communication theory predicts that the sum rate should grow proportionally to the number of terminals, which is consistent with the 8x improvement in uplink rates and 5x improvement in downlink rates that were reported. Further details are found in their press release or in the following video:

Ericsson and Sprint are also planning Massive MIMO tests in LTE TDD in the second half of 2017, according to another press release.

Did we miss any Massive MIMO related announcement from MWC? Please tell us in the comment field below!

Summer School on Signal Processing for 5G

If you want to learn about signal processing foundations for Massive MIMO and mmWave communications, you should attend the

2017 Joint IEEE SPS and EURASIP Summer School on Signal Processing for 5G

Signal processing is at the core of the emerging fifth generation (5G) cellular communication systems, which will bring revolutionary changes to the physical layer. Unlike other 5G events, the objective of this summer school is to teach the main physical-layer techniques for 5G from a signal-processing perspective. The lectures will provide a background on the 5G wireless communication concepts and their formulation from a signal processing perspective. Emphasis will be placed on showing specifically how cutting-edge signal processing techniques can and will be applied to 5G. Keynote speeches by leading researchers from Ericsson, Huawei, China Mobile, and Volvo complement the technical lectures.

The summer school covers the following specific topics:

  • Massive MIMO communication in TDD and FDD
  • mmWave communications and compressed sensing
  • mmWave positioning
  • Wireless access for massive machine-type communications

The school takes place in Gothenburg, Sweden, from May 29th to June 1st, in the week after ICC in Paris.

This event belongs to the successful series of IEEE SPS and EURASIP Seasonal Schools in Signal Processing. The 2017 edition is jointly organized by Chalmers University of Technology, Linköping University, The University of Texas at Austin, Aalborg University and the University of Vigo.

Registration is now open. A limited number of student travel grants will be available.

For more information and detailed program, see:

News – commentary – mythbusting