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:
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:
Recently, there has been a hype on the use of drones (also called unmanned aerial vehicles (UAVs)) for civilian and military applications. Especially, in the coming decades, lightweight miniature drones are expected to play a major role in the society. Nowadays, small drones are available in toy shops so that an individual could buy it for personal uses such as aerial videography. However, due to security reasons, the personal use of drones is limited to low altitudes (up to 120 m in most countries) and visible line-of-sight. On the other hand, it is most likely that, in many countries, government agencies and commercial firms will be allowed to use drones for a variety of services (See: link 1 and link 2.)
There are many foreseen applications that involve a large number of drones in a limited area such as disaster management, traffic monitoring, crowd management, and crop monitoring. The major communication requirements of most of the drone networks are: several tens of Mbps throughput for streaming high-resolution video, low latency for command and control, highly reliable connectivity in a three-dimensional coverage area, high-mobility support, and simultaneous support for a large number of drones.
The existing wireless systems are unsuitable for communicating with a large number of drones in long-range, high throughput, and high-altitude applications for the following reasons:
In many drone communication scenarios, the mobility and traffic patterns of drones are different from the ground users. For example, in aerial surveillance applications, the uplink traffic is much higher than the downlink traffic. Depending on the application, the drones will fly at high speed (10-50 m/s) in a 3D space.
The propagation environment in drone communication scenarios will be line-of-sight, even under high mobility.
The terrestrial wireless communication networks are optimized for indoor, short range, low mobility (e.g. WiFi), and low altitude (e.g. LTE).
In LTE, since the base station antennas are tilted towards the ground, coverage is possible only if the drones fly below 100 m altitude. Apart from coverage, the co-channel interference generated from the neighboring cells will be a major problem in satisfying the high throughput requirements of drones.
The MAC layer protocols of the existing systems have to be redesigned according to the drones’ requirements, especially regarding the re-transmission protocols which are related to latency and crucial for drone control.
Since the existing wireless systems are connected to the power grids, they might not be available during emergency situations such as earth-quake, massive flooding, and tsunami. Further, in mountain and sea environments, cellular networks are not widely available. This problem can be overcome by deploying flying UAV base stations over the sky.
For the above-mentioned reasons, instead of borrowing from existing wireless technologies, it would be better to develop a new technology, considering the specific drone networks’ requirements and propagation characteristics. As of now, spectrum allocation and standardization efforts for drone communication networks are in the initial stage of development. This is where Massive MIMO can play a key role. The attractive features of Massive MIMO, such as spatial multiplexing and range extension, can be exploited to design flexible and efficient drone communication systems. 5G is based on the concept of network slicing, where the network can be configured differently depending on the use case. Therefore, it is possible to deploy a variation of 5G for drone communications along with appropriately tilted antenna arrays to provide connectivity to the drones flying at high altitudes.
In our recent papers (1 and 2), we illustrated the use for Massive MIMO for drone communications. From these papers, we make the following observations:
The Massive MIMO performance in rich scattering is well understood by the use of ergodic rate bounds that are available in closed form. In line-of-sight, the ergodic rate performance depends on the relative positions of the drones as they move very quickly in 3D space. Interestingly, in case of line-of-sight, the uplink ergodic rate bounds (with MRC receiver) are available in closed form for some specific cases, for example, for the uniformly distributed drone positions within a spherical volume. However, more work is needed to understand the ergodic rate performance with arbitrary drone distributions.
The element-spacing in the ground station array affects the rate performance depending on the distribution of the drones. For a given distribution of the drone positions, ground station array has to be optimized to maximize the ergodic rate.
The probability of outage due to polarization mismatch can be made negligible by appropriately selecting the orientation and polarization of the individual array elements. For example, circularly polarized cross-dipole antenna elements perform much better when compared to linearly polarized dipoles. (For more details, see this paper.) This means that the use of simple antenna elements, such as cross-dipoles, reduce the concerns of
antenna pattern designs. Further, the drones can be equipped with a single cross-dipole.
The range extension due to the increased number of antennas can eliminate the need for multi-hop solutions in many drone communication scenarios.
TDD based Massive MIMO can be used for simultaneously supporting several tens of drones both at μ-wave and mm-wave frequencies.
TDD based Massive MIMO can support high-mobility drone communications. In some scenarios (e.g., deterministic trajectories), the channel can be extrapolated without sending pilot symbols.
Below are some examples of use cases of Massive MIMO enabled drone communication systems. The technical details of Massive MIMO based system design can be found in this paper. The Massive MIMO design parameters for some of the use cases can be found in this paper.
Drone racing: In recent years, drone racing, also called “the sport of the future”, is becoming popular around the world. In drone racing, low latency is important for drone control, because even a few tens of milliseconds delay might crash the drone when it moves at the speed of 40-50 m/s. Interestingly, in our digital world, analog transmission is used for sending videos from racing drones to the pilots. The reason is that, unlike digital transmission, an analog transmission does not incur any processing delay and the overall latency is about only 15 ms. Currently, the 5.8 GHz band (5650 MHz to 5925 MHz) is used for drone racing. The transmitter and receiver use frequency modulation and it requires 40 MHz frequency separation to avoid cross-talks between neighboring channels. As a result, the number of simultaneous drones in a contest is limited to eight. The video quality is also poor. By using Massive MIMO, several tens of drones can simultaneously participate in a contest and the pilots can enjoy latency-free high-quality video transmission.
Sports streaming: Utilizing drones for sports streaming will change the way we view the sports events. High resolution 4K 360-degree videos taken by multiple drones at different angles can be broadcasted to enable the viewers to have an entirely a new experience. If there are 20 drones covering a sports event, the required sum throughput will be in the order of 10 Gbps. Massive MIMO in the mm-wave frequency band can be used to achieve this high throughput. This can become reality as already there are signs towards the use of drones for covering sports events. For instance, during the 2018 Winter Olympics, drones will be extensively used.
Surveillance (or search and rescue operation) using a swarm of drones and a massive array
Surveillance/ Search and Rescue/Disaster management: During natural disasters, a network of drones can be quickly deployed to enable the rescue teams to assess the situation in real-time via high-resolution video streaming. Depending on the area to be covered and desired video quality, the sum throughput requirement will be in the order of Gbps. A Massive MIMO array deployed over a ground vehicle or a large aerial vehicle can be used for serving a swarm of drones.
Aerial survey: A swarm of drones can be used for high-resolution aerial imagery of several kilometers of landscape. There are many uses of aerial survey, including state governance, city planning, 3D cartography, and crop monitoring. Massive MIMO can be an enabler for such high throughput and long-range applications.
Backhaul for flying base stations: During emergency situations and heavy traffic conditions, UAVs could be used as flying base stations to provide wireless connectivity to the cellular users. A Massive MIMO base station can act as a high-capacity backhaul to a large number of flying base stations.
Massive MIMO for space exploration
Space exploration: Currently, it takes several hours to receive a photo taken by the Curiosity Mars rover. It is possible to use Massive MIMO to reduce the overall transmission delay. For example, by using a massive antenna array deployed in an orbiter (see the above figure), a swarm of drones and rovers roaming on the surface of another planet can send videos and images to earth. The array can be used to spatially multiplex the uplink transmission from the drones (and possibly the rovers) to the orbiter. Note that the distance between the Mars surface and the orbiter is about 400 km. If the drones fly at an altitude of a few hundred meters and spread out over the region with a few hundred kilometers of radius, the angular resolution of the array is sufficient for spatial multiplexing. The array can be used to transmit the collected images and videos to earth by exploiting the array gain. This might sound like a science fiction, but NASA is already developing a 256 element antenna array for future Mars rovers to enable direct communication with the earth.
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.
I am borrowing the title from a column written by my advisor two decades ago, in the array signal processing gold rush era.
Asymptotic analysis is a popular tool within statistical signal processing (infinite SNR or number of samples), information theory (infinitely long blocks) and more recently, [massive] MIMO wireless communications (infinitely many antennas).
Some caution is strongly advisable with respect to the latter. In fact, there are compelling reasons to avoid asymptotics in the number of antennas altogether:
First, elegant, rigorous and intuitively comprehensible capacity bound formulas are available in closed form.
The proofs of these expressions use basic random matrix theory, but no asymptotics at all.
Second, the notion of “asymptotic limit” or “asymptotic behavior” helps propagate the myth that Massive MIMO somehow relies on asymptotics or “infinite” numbers (or even exorbitantly large numbers) of antennas.
Third, many approximate performance results for Massive MIMO (particularly “deterministic equivalents”) based on asymptotic analysis are complicated, require numerical evaluation, and offer little intuitive insight. (And, the verification of their accuracy is a formidable task.)
Finally, and perhaps most importantly, careless use of asymptotic arguments may yield erroneous conclusions. For example in the effective SINRs in multi-cell Massive MIMO, the coherent interference scales with M (number of antennas) – which yields the commonly held misconception that coherent interference is the main impairment caused by pilot contamination. But in fact, in many relevant circumstances it is not (see case studies here): the main impairment for “reasonable” values of M is the reduction in coherent beamforming gain due to reduced estimation quality, which in turn is independent of M.
In addition, the number of antennas beyond which the far-field assumption is violated is actually smaller than what one might first think (problem 3.14).
I hear this being claimed now and then, and it is – of course – both correct and incorrect, at the same time. For the benefit of our readers I take the opportunity to provide some free consulting on the topic.
The important fact is that ergodic capacity can be lower-bounded by a formula of the form log2(1+SINR), where SINR is an “effective SINR” (that includes, among others, the effects of the terminal’s lack of channel knowledge).
This effective SINR scales proportionally to M (number of antennas), for fixed total radiated power. Compared to a single-antenna system, reciprocity always offers M times better “beamforming gain” regardless of the system’s operating point. (In fact one of the paradoxes of Massive MIMO is that performance always increases with M, despite the fact that there are “more unknowns to estimate”!) And yes, at very low SNR, the effective SINR is proportional to SNR^2 so reciprocity-based beamforming does “break down”, however, it is still M times better than a single-antenna link (with the same total radiated power). One will also, eventually, reach a point where the capacity bound for omnidirectional transmission (e.g. using a space-time code with appropriate dimension reduction in order to host the required downlink pilots) exceeds that of reciprocity-based beamforming, however, importantly, in this regime the bounds may be loose.
These matters, along with numerous case studies involving actual link budget calculations, are of course rigorously explained in our recent textbook.
The 5G Myth is the provocative title of a recent book by William Webb, CEO of Weightless SIG, a standard body for IoT/M2M technology. In this book, the author tells a compelling story of a stagnating market for cellular communications, where the customers are generally satisfied with the data rates delivered by the 4G networks. The revenue growth for the mobile network operators (MNOs) is relatively low and also in decay, since the current services are so good that the customers are unwilling to pay more for improved service quality. Although many new wireless services have materialized over the past decade (e.g., video streaming, social networks, video calls, mobile payment, and location-based services), the MNOs have failed to take the leading role in any of them. Instead, the customers make use of external services (e.g., Youtube, Facebook, Skype, Apple Pay, and Google Maps) and only pay the MNOs to deliver the data bits.
The author argues that, under these circumstances, the MNOs have little to gain from investing in 5G technology. Most customers are not asking for any of the envisaged 5G services and will not be inclined to pay extra for them. Webb even compares the situation with the prisoner’s dilemma: the MNOs would benefit the most from not investing in 5G, but they will anyway make investments to avoid a situation where customers switch to a competitor that has invested in 5G. The picture that Webb paints of 5G is rather pessimistic compared to a recent McKinsey report, where the more cost-efficient network operation is described as a key reason for MNOs to invest in 5G.
The author provides a refreshing description of the market for cellular communications, which is important in a time when the research community focuses more on broad 5G visions than on the customers’ actual needs. The book is thus a recommended read for 5G researchers, since we should all ask ourselves if we are developing a technology that tackles the right unsolved problems.
Webb does not only criticize the economic incentives for 5G deployment, but also the 5G visions and technologies in general. The claims are in many cases reasonable; for example, Webb accurately points out that most of the 5G performance goals are overly optimistic and probably only required by a tiny fraction of the user base. He also accurately points out that some “5G applications” already have a wireless solution (e.g., indoor IoT devices connected over WiFi) or should preferably be wired (e.g., ultra-reliable low-latency applications such as remote surgery).
However, it is also in this part of the book that the argumentation sometimes falls short. For example, Webb extrapolates a recent drop in traffic growth to claim that the global traffic volume will reach a plateau in 2027. It is plausible that the traffic growth rate will reduce as a larger and larger fraction of the global population gets access to wireless high-speed connections. But one should bear in mind that we have witnessed an exponential growth in wireless communication traffic for the past century (known as Cooper’s law), so this trend can just as well continue for a few more decades, potentially at a lower growth rate than in the past decade.
Webb also provides a misleading description of multiuser MIMO by claiming that 1) the antenna arrays would be unreasonable large at cellular frequencies and 2) the beamforming requires complicated angular beam-steering. These are two of the myths that we dispelled in the paper “Massive MIMO: Ten myths and one grand question” last year. In fact, testbeds have demonstrated that massive multiuser MIMO is feasible in lower frequency bands, and particularly useful to improve the spectral efficiency through coherent beamforming and spatial multiplexing of users. Reciprocity-based beamforming is a solution for mobile and cell-edge users, for which angular beam-steering indeed is inefficient.
The book is not as pessimistic about the future as it might seem from this review. Webb provides an alternative vision for future wireless communications, where consistent connectivity rather than higher peak rates is the main focus. This coincides with one of the 5G performance goals (i.e., 50 Mbit/s everywhere), but Webb advocates an extensive government-supported deployment of WiFi instead of 5G technology. The use WiFi is not a bad idea; I personally consume relatively little cellular data since WiFi is available at home, at work, and at many public locations in Sweden. However, the cellular services are necessary to realize the dream of consistent connectivity, particularly outdoors and when in motion. This is where a 5G cellular technology that delivers better coverage and higher data rates at the cell edge is highly desirable. Reciprocity-based Massive MIMO seems to be the solution that can deliver this, thus Webb would have had a stronger case if this technology was properly integrated into his vision.
In summary, the combination of 5G Massive MIMO for wide-area coverage and WiFi for local-area coverage might be the way to truly deliver consistent connectivity.
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.
The 5G Myth is the provocative title of a recent book by