The next generation of cellular networks need to be much more energy-efficient than the current generation, if we should deliver 100-1000 times more data in a cost-efficient and environmentally friendly manner. In this video, I explain the methodology that can be used to design energy efficient 5G networks, and also the key role that Massive MIMO will play.
Massive MIMO supports an order of magnitude higher spectral efficiency than legacy LTE networks. The largest gains come from spatial multiplexing of many users per cell, thus these gains can only be harvested when there are many users requesting data at every given millisecond, which requires larger traffic loads than you might think since many seemingly continuous user applications only send data sporadically.
For this reason, I used to say that outdoor musical festivals, where a crowd of 100,000 people gather to see their favorite bands, would be a first deployment scenario for Massive MIMO. This is fairly similar to what now has happened: The Russian telecom operator MTS has deployed more than 40 state-of-the-art LTE sites with Massive MIMO functionality in seven cities where the 2018 FIFA World Cup in football is currently taking place. The base stations are deployed to cover the stadiums, fan zones, airports, train stations, and major parks/squares; in other words, the places where huge crowds of football fans are expected.
In the press release, Andrei Ushatsky, Vice President of MTS, says:
“This launch is one of Europe’s largest Massive MIMO deployments, covering seven Russian cities, and is a major contribution by MTS in the preparation of the country’s infrastructure for the global sporting event of the year. Our Massive MIMO technology, using Ericsson equipment, significantly increases network capacity, allowing tens of thousands of fans together in one place to enjoy high-speed mobile internet without any loss in speed or quality.”
While this is one of the first major deployments of Massive MIMO, more will certainly follow in the coming years. More research into the development and implementation of advanced signal processing and resource management schemes will also be needed for many years to come – this is just the beginning.
LTE was designed to work equally well in time-division duplex (TDD) and frequency division duplex (FDD) mode, so that operators could choose their mode of operation depending on their spectrum licenses. In contrast, Massive MIMO clearly works at its best in TDD, since the pilot overhead is prohibitive in FDD (even if there are some potential solutions that partially overcome this issue).
Clearly, we will see a larger focus on TDD in future networks, but there are some traditional disadvantages with TDD that we need to bear in mind when designing these networks. I describe the three main ones below.
Even if we allocate the same amount of time-frequency resources to uplink and downlink in TDD and FDD operation, there is an important difference. We transmit over half the bandwidth all the time in FDD, while we transmit over the whole bandwidth half of the time in TDD. Since the power amplifier is only active half of the time, if the peak power is the same, the average radiated power is effectively cut in half. This means that the SNR is 3 dB lower in TDD than in FDD, when transmitting at maximum peak power.
Massive MIMO systems are generally interference-limited and uses power control to assign a reduced transmit power to most users, thus the impact of the 3 dB SNR loss at maximum peak power is immaterial in many cases. However, there will always be some unfortunate low-SNR users (e.g., at the cell edge) that would like to communicate at maximum peak power in both uplink and downlink, and therefore suffer from the 3 dB SNR loss. If these users are still able to connect to the base station, the beamforming gain provided by Massive MIMO will probably more than compensate for the loss in link budget as compared single-antenna systems. One can discuss if it should be the peak power or average radiated power that is constrained in practice.
Everyone in the cell should operate in uplink and downlink mode at the same time in TDD. Since the users are at different distances from the base station and have different delay spreads, they will receive the end of the downlink transmission block at different time instances. If a cell center user starts to transmit in the uplink immediately after receiving the full downlink block, then users at the cell edge will receive a combination of the delayed downlink transmission and the cell center users’ uplink transmissions. To avoid such uplink-downlink interference, there is a guard period in TDD so that all users wait with uplink transmission until the outmost users are done with the downlink.
In fact, the base station gives every user a timing bias to make sure that when the uplink commences, the users’ uplink signals are received in a time-synchronized fashion at the base station. Therefore, the outmost users will start transmitting in the uplink before the cell center users. Thanks to this feature, the largest guard period is needed when switching from downlink to uplink, while the uplink to downlink switching period can be short. This is positive for Massive MIMO operation since we want to use uplink CSI in the next downlink block, but not the other way around.
The guard period in TDD must become larger when the cell size increases, meaning that a larger fraction of the transmission resources disappears. Since no guard periods are needed in FDD, the largest benefits of TDD will be seen in urban scenarios where the macro cells have a radius of a few hundred meters and the delay spread is short.
We want to avoid interference between uplink and downlink within a cell and the same thing applies for the inter-cell interference. The base stations in different cells should be fairly time-synchronized so that the uplink and downlink take place at the same time; otherwise, it might happen that a cell-edge user receives a downlink signal from its own base station and is interfered by the uplink transmission from a neighboring user that connects to another base station.
This can also be an issue between telecom operators that use neighboring frequency bands. There are strict regulations on the permitted out-of-band radiation, but the out-of-band interference can anyway be larger than the desired inband signal if the interferer is very close to the receiving inband user. Hence, it is preferred that the telecom operators are also synchronizing their switching between uplink and downlink.
Massive MIMO will bring great gains in spectral efficiency in future cellular networks, but we should not forget about the traditional disadvantages of TDD operation: 3 dB loss in SNR at peak power transmission, larger guard periods in larger cells, and time synchronization between neighboring base stations.
Contemporary base stations are equipped with analog-to-digital converters (ADCs) that take samples described by 12-16 bits. Since the communication bandwidth is up to 100 MHz in LTE Advanced, a sampling rate of a 500 Msample/s is quite sufficient for the ADC. The power consumption of such an ADC is at the order of 1 W. Hence, in a Massive MIMO base station with 100 antennas, the ADCs would consume around 100 W!
Fortunately, the 1600 bit/sample that are effectively produced by 100 16-bit ADCs are much more than what is needed to communicate at practical SINRs. For this reason, there is plenty of research on Massive MIMO base stations equipped with lower-resolution ADCs. The use of 1-bit ADCs has received particular attention. Some good paper references are provided in a previous blog post: Are 1-bit ADCs sufficient? While many early works considered narrowband channels, recent papers (e.g., Quantized massive MU-MIMO-OFDM uplink) have demonstrated that 1-bit ADCs can also be used in practical frequency-selective wideband channels. I’m impressed by the analytical depth of these papers, but I don’t think it is practically meaningful to use 1-bit ADCs.
Do we really need 1-bit ADCs?
I think the answer is no in most situations. The reason is that ADCs with a resolution of around 6 bits strike a much better balance between communication performance and power consumption. The state-of-the-art 6-bit ADCs are already very energy-efficient. For example, the paper “A 5.5mW 6b 5GS/S 4×-lnterleaved 3b/cycle SAR ADC in 65nm CMOS” from ISSCC 2015 describes a 6-bit ADC that consumes 5.5 mW and has a huge sampling rate of 5 Gsample/s, which is sufficient even for extreme mmWave applications with 1 GHz of bandwidth. In a base station equipped with 100 of these 6-bit ADCs, less than 1 W is consumed by the ADCs. That will likely be a negligible factor in the total power consumption of any base station, so what is the point in using a lower resolution than that?
The use of 1-bit ADCs comes with a substantial loss in communication rate. In contrast, there is a consensus that Massive MIMO with 3-5 bits per ADC performs very close to the unquantized case (see Paper 1, Paper 2, Paper 3, Paper 4, Paper 5). The same applies for 6-bit ADCs, which provide an additional margin that protects against strong interference. Note that there is nothing magical with 6-bit ADCs; maybe 5-bit or 7-bit ADCs will be even better, but I don’t think it is meaningful to use 1-bit ADCs.
Will 1-bit ADCs ever become useful?
To select a 1-bit ADC, instead of an ADC with higher resolution, the energy consumption of the receiving device must be extremely constrained. I don’t think that will ever be the case in base stations, because the power amplifiers are dominating their energy consumption. However, the case might be different for internet-of-things devices that are supposed to run for ten years on the same battery. To make 1-bit ADCs meaningful, we need to greatly simplify all the other hardware components as well. One potential approach is to make a dedicated spatial-temporal waveform design, as described in this paper.
No, these are two different but somewhat related concepts, as I will explain in detail below.
Contemporary multiantenna base stations for cellular communications are equipped with 2-8 antennas, which are deployed along a horizontal line. One example is a uniform linear array (ULA), as illustrated in Figure 1 below, where the antenna spacing is uniform. All the antennas in the ULA have the same physical down-tilt, with respect to the ground, and a fixed radiation pattern and directivity.
By sending the same signal from all antennas, but with different phase-shifts, we can steer beams in different angular directions and thereby make the directivity of the radiated signal different from the directivity of the individual antennas. Since the antennas are deployed on a one-dimensional horizontal line in this example, the ULA can only steer beams in the two-dimensional (2D) azimuth plane as illustrated in Figure 1. The elevation angle is the same for all beams, which is why this is called 2D beamforming. The beamwidth in the azimuth domain shrinks the more antennas are deployed. If the array is used for multiuser MIMO, then multiple beams with different azimuth angles are created simultaneously, as illustrated by the colored beams in Figure 1.
If we would rotate the ULA so that the antennas are instead deployed at different heights above the ground, then the array can instead steer beams in different elevation angles. This is illustrated in Figure 2. Note that this is still a form of 2D beamforming since every beam will have the same directivity with respect to the azimuth plane. This antenna array can be used to steer beams towards users at different floors of a building. It is also useful to serve flying objects, such as UAVs, jointly with ground users. The beamwidth in the elevation domain shrinks the more antennas are deployed.
If we instead deploy multiple ULAs on top of each other, it is possible to control both the azimuth and elevation angle of a beam. This is called 3D beamforming and is illustrated in Figure 3 using a planar array with a “massive” number of antennas. This gives the flexibility to not only steer beams towards different buildings but also towards different floors of these buildings, to provide a beamforming gain wherever the user is in the coverage area. It is not necessary to have many antennas to perform 3D beamforming – it is basically enough to have three antennas deployed in a triangle. However, as more antennas are added, the beams become narrower and easier to jointly steer in specific azimuth-elevation directions. This increases the array gain and reduces the interference between beams directed to different users, as illustrated by the colors in Figure 3.
The detailed answer to the question “3D Beamforming, is that Massive MIMO?” is as follows. Massive MIMO and 3D beamforming are two different concepts. 3D beamforming can be performed with few antennas and Massive MIMO can be deployed to only perform 2D beamforming. However, Massive MIMO and 3D beamforming is a great combination in many applications; for example, to spatially multiplex many users in a city with high-rise buildings. One should also bear in mind that, in general, only a fraction of the users are located in line-of-sight so the formation of angular beams (as shown above) might be of limited importance. The ability to control the array’s radiation pattern in 3D is nonetheless helpful to control the multipath environment such that the many signal components add constructively at the location of the intended receiver.
This is supposedly a simple question to answer; an antenna is a device that emits radio waves. However, it is easy to get confused when comparing wireless communication systems with different number of transmit antennas, because these systems might use antennas with different physical sizes and properties. In fact, you can seldom find fair comparisons between contemporary single-antenna systems and Massive MIMO in the research literature.
Each antenna type has a predefined radiation pattern, which describes its inherent directivity; that is, how the gain of the emitted signal differs in different angular directions. An ideal isotropic antenna has no directivity, but a practical antenna always has a certain directivity, measured in dBi. For example, a half-wavelength dipole antenna has 2.15 dBi, which means that there is one angular direction in which the emitted signal is 2.15 dB stronger than it would be with a corresponding isotropic antenna. On the other hand, there are other angular directions in which the emitted signal is weaker. This is not a problem as long as there will not be any receivers in those directions.
In cellular communications, we are used to deploying large vertical antenna panels that cover a 120 degree horizontal sector and have a strong directivity of 15 dBi or more. Such a panel is made up of many small radiating elements, each having a directivity of a few dBi. By feeding them with the same input signal, a higher dBi is achieved for the panel. For example, if the panel consists of 8 patch antenna elements, each having 7 dBi, then you get a 7+10·log10(8) = 16 dBi antenna.
The picture above shows a real LTE site that I found in Nanjing, China, a couple of years ago. Looking at it from above, the site is structured as illustrated to the right. The site consists of three sectors, each containing a base station with four vertical panels. If you would look inside one of the panels, you will (probably) find 8 cross-polarized vertically stacked radiating elements, as illustrated in Figure 1. There are two RF input signals per panel, one per polarization, thus each panel acts as two antennas. This is how LTE with 8TX-sectors is deployed: 4 panels with dual polarization per base station.
At the exemplified LTE site, there is a total of 8·8·3 =192 radiating elements, but only 8·3 = 24 antennas. This disparity can lead to a lot of confusion. The Massive MIMO version of the exemplified LTE site may have the same form factor, but instead of 24 antennas with 16 dBi, you would have 192 antennas with 7 dBi. More precisely, you would connect each of the existing radiating elements to a separate RF input signal to create a larger number of antennas. Therefore, I suggest to use the following antenna definition from the book Massive MIMO Networks:
Definition: An antenna consists of one or more radiating elements (e.g., dipoles) which are fed by the same RF signal. An antenna array is composed of multiple antennas with individual RF chains.
Note that, with this definition, an array that uses analog beamforming (e.g., a phased array) only constitutes one antenna. It is usually called an adaptive antenna since the radiation pattern can be changed over time, but it is nevertheless a single antenna. Massive MIMO for sub-6 GHz frequencies is all about adding RF chains (also known as antenna ports), while not necessarily adding more radiating elements than in a contemporary system.
What is the purpose of having more RF chains?
With more RF chains, you have more degrees of freedom to modify the radiation pattern of the transmitted signal based on where the receiver is located. When transmitting a precoded signal to a single user, you adjust the phases of the RF input signals to make them all combine constructively at the intended receiver.
The maximum antenna/array gain is the same when using one 16 dBi antenna and when using 8 antennas with 7 dBi. In the first case, the radiation pattern is usually static and thus only a line-of-sight user located in the center of the cell sector will obtain this gain. However, if the antenna is adaptive (i.e., supports analog beamforming), the main lobe of the radiation pattern can be also steered towards line-of-sight users located in other angular directions. This feature might be sufficient for supporting the intended single-user use-cases of mm-wave technology (see Figure 4 in this paper).
In contrast, in the second case, we can adjust the radiation pattern by 8-antenna precoding to deliver the maximum gain to any user in the sector. This feature is particularly important for non-line-of-sight users (e.g., indoor use-cases), for which the signals from the different radiating elements will likely be received with “random” phase shifts and therefore add non-constructively, unless we compensate for the phases by digital precoding.
Note that most papers on Massive MIMO keep the antenna gain constant when comparing systems with different number of antennas. There is nothing wrong with doing that, but one cannot interpret the single-antenna case in such a study as a contemporary system.
Another, perhaps more important, feature of having multiple RF chains is that we can spatially multiplex several users when having multiple antennas. For this you need at least as many RF inputs as there are users. Each of them can get the full array gain and the digital precoding can be also used to avoid inter-user interference.
Last year, I wrote a post about channel hardening. To recap, the achievable data rate of a conventional single-antenna channel varies rapidly over time due to the random small-scale fading realizations, and also over frequency due to frequency-selective fading. However, when you have many antennas at the base station and use them for coherent precoding/combining, the fluctuations in data rate average out; we then say that the channel hardens. One follow-up question that I’ve got several times is:
Can we utilize the channel hardening to estimate the channels less frequently?
Unfortunately, the answer is no. Whenever you move approximately half a wavelength, the multi-path propagation will change each element of the channel vector. The time it takes to move such a distance is called a coherence time. This time is the same irrespectively of how many antennas the base station has and, therefore, you still need to estimate the channel once per coherence time. The same applies to the frequency domain, where the coherence bandwidth is determined by the propagation environment and not the number of antennas.
The following flow-chart shows what need to happen in every channel coherence time:
When you get a new realization (at the top of the flow-chart), you compute an estimate (e.g., based on uplink pilots), then you use the estimate to compute a new receive combining vector and transmit precoding vector. It is when you have applied these vectors to the channel that the hardening phenomena appears; that is, the randomness averages out. If you use maximum ratio (MR) processing, then the random realization h1 of the channel vector turns into an almost deterministic scalar channel ||h1||2. You can communicate over the hardened channel with gain ||h1||2 until the end of the coherence time. You then start over again by estimating the new channel realization h2, applying MR precoding/combining again, and then you get ||h2||2 ≈ ||h1||2.
In conclusion, channel hardening appears after coherent combining/precoding has been applied. To maintain a hardened channel over time (and frequency), you need to estimate and update the combining/precoding as often as you would do for a single-antenna channel. If you don’t do that, you will gradually lose the array gain until the point where the channel and the combining/precoding are practically uncorrelated, so there is no array gain left. Hence, there is more to lose from estimating channels too infrequently in Massive MIMO systems than in conventional systems. This is shown in Fig. 10 in a recent measurement paper from Lund University, where you see how the array gain vanishes with time. However, the Massive MIMO system will never be worse than the corresponding single-antenna system.