Category Archives: 5G

The Dense Urban Information Society

5G cellular networks are supposed to deal with many challenging communication scenarios where today’s cellular networks fall short.  In this post, we have a look at one such scenario, where Massive MIMO is key to overcome the challenges.

The METIS research project has identified twelve test cases for 5G connectivity. One of these is the “Dense urban information society”, which is

“…concerned with the connectivity required at any place and at any time by humans in dense urban environments. We here consider both the traffic between humans and the cloud, and also direct information exchange between humans or with their environment. The particular challenge lies in the fact that users expect the same quality of experience no matter whether they are at their workplace, enjoying leisure activities such as shopping, or being on the move on foot or in a vehicle.”

Source: METIS, deliverable D1.1 “Scenarios, requirements and KPIs for 5G mobile and wireless system

Hence, the challenge is to provide ubiquitous connectivity in urban areas, where there will be massive user loads in the future: up to  200,000 devices per km2 is predicted by METIS. In their test case, each device requests one data packet per minute, which should be transferred within one second. Hence, there is on average up to 200,000/60 = 3,333 users active per km2 at any given time.

Hexagonal cellular network, with adjacent cells having different colors for clarity.

This large number of users is a challenge that Massive MIMO is particularly well-suited for. One of the key benefits of the Massive MIMO technology is the high spectral efficiency that it achieves by spatial multiplexing of tens of user per cell. Suppose, for example, that the cells are deployed in a hexagonal pattern with a base station in each cell center, as illustrated in the figure. How many simultaneously active users will there be per cell in the dense urban information society? That depends on the area of a cell. An inter-site distance (ISD) of 0.25 km is common in contemporary urban deployments. In this case, one can show that the area covered by each cell is √3×ISD2/2 = 0.05 km2.

intersite-distance

The number of active users per cell is then obtained by multiplying the cell area with the user density. Three examples are provided in the table below:

103 users/km2 104 users/km2 105 users/km2
Total number of users per cell 54 540 5400
Average active users per cell 0.9 9 90

Recall that 1/60 of the total number of users are active simultaneously, in the urban information society test case. This gives the numbers in the second row of the table.

From this table, notice that there will be tens of simultaneously active users per cell, when the user density is above 10,000 per km2. This is a number substantially smaller than the 200,000 per km2 predicted by the METIS project. Hence, there will likely be many future urban deployment scenarios with sufficiently many users to benefit from Massive MIMO.

A fraction of these users can (and probably will) be offloaded to WiFi-like networks, maybe operating at mmWave frequencies. But since local-area networks provide only patchy coverage, it is inevitable that many users and devices will rely on the cellular networks to achieve ubiquitous connectivity, with the uniform quality-of-service everywhere.

In summary, Massive MIMO is what we need to realize the dream of ubiquitous connectivity in the dense urban information society.

Cellular Multi-User MIMO: A Technology Whose Time has Come

Both the number of devices with wireless connection and the traffic that they generate have steadily grown since the early days of cellular communications. This continuously calls for improvements in the area capacity [bit/s/km2] of the networks. The use of adaptive antenna arrays was identified as a potential capacity-improving technology in the mid-eighties. An early uplink paper was “Optimum combining for indoor radio systems with multiple users” from 1987 by J. Winters at Bell Labs. An early downlink paper was “The performance enhancement of multibeam adaptive base-station antennas for cellular land mobile radio systems” by S. C. Swales et al. from 1990.

The multi-user MIMO concept, then called space-division multiple access (SDMA), was picked up by the industry in the nineties. For example, Ericsson made field-trials with antenna arrays in GSM systems, which were reported in “Adaptive antennas for GSM and TDMA systems” from 1999. ArrayComm filed an SDMA patent in 1991 and made trials in the nineties. In cooperation with the manufacturer Kyocera, this resulted in commercial deployment of SDMA as an overlay to the TDD-based Personal Handy-phone System (PHS).

Trial with a 12-element circular array by ArrayComm, in the late nineties.

 

Given this history, why isn’t multi-user MIMO a key ingredient in current cellular networks? I think there are several answers to this question:

  1. Most cellular networks use FDD spectrum. To acquire the downlink channels, the SDMA research first focused on angle-of-arrival estimation and later on beamforming codebooks. The cellular propagation environments turned out to be far more complicated than such system concepts easily can handle.
  2. The breakthroughs in information theory for multi-user MIMO happened in the early 2000s, thus there was no theoretical framework that the industry could use in the nineties to evaluate and optimize their multiple antenna concepts.
  3. In practice, it has been far easier to increase the area capacity by deploying more base stations and using more spectrum, rather than developing more advanced base station hardware. In current networks, there is typically zero, one or two users per cell active at a time, and then there is little need for multi-user MIMO.

Why is multi-user MIMO considered a key 5G technology? Basically because the three issues described above have now changed substantially. There is a renewed interest in TDD, with successful cellular deployments in Asia and WiFi being used everywhere. Massive MIMO is the refined form of multi-user MIMO, where the TDD operation enables channel estimation in any propagation environment, the many antennas allow for low-complexity signal processing, and the scalable protocols are suitable for large-scale deployments. The technology can nowadays be implemented using power-efficient off-the-shelf radio-frequency transceivers, as demonstrated by testbeds. Massive MIMO builds upon a solid ground of information theory, which shows how to communicate efficiently under practical impairments such as interference and imperfect channel knowledge.

Maybe most importantly, spatial multiplexing is needed to manage the future data traffic growth. This is because deploying many more base stations or obtaining much more spectrum are not viable options if we want to maintain network coverage—small cells at the street-level are easily shadowed by buildings and mm-wave frequency signals do not propagate well though walls. In 5G networks, a typical cellular base station might have tens of active users at a time, which is a sufficient number to benefit from the great spectral efficiency offered by Massive MIMO.

How Much does Massive MIMO Improve the Spectral Efficiency?

It is often claimed in the academic literature that Massive MIMO can greatly improve the spectral efficiency. What does it mean, qualitatively and quantitatively? This is what I will try to explain.

With spectral efficiency, we usually mean the sum spectral efficiency of the transmissions in a cell of a cellular network. It is measured in bit/s/Hz. If you multiply it with the bandwidth, you will get the cell throughput measured in bit/s. Since the bandwidth is a scarce resource, particularly at the frequencies below 5 GHz that are suitable for network coverage, it is highly desirable to improve the cell throughput by increasing the spectral efficiency rather than increasing the bandwidth.

A great way to improve the spectral efficiency is to simultaneously serve many user terminals in the cell, over the same bandwidth, by means of space division multiple access. This is where Massive MIMO is king. There is no doubt that this technology can improve the spectral efficiency. The question is rather “how much?”

Earlier this year, the joint experimental effort by the universities in Bristol and Lund demonstrated an impressive spectral efficiency of 145.6 bit/s/Hz, over a 20 MHz bandwidth in the 3.5 GHz band. The experiment was carried out in a single-cell indoor environment. Their huge spectral efficiency can be compared with 3 bit/s/Hz, which is the IMT Advanced requirement for 4G. The remarkable Massive MIMO gain was achieved by spatial multiplexing of data signals to 22 users using 256-QAM. The raw spectral efficiency is 176 bit/s/Hz, but 17% was lost for practical reasons. You can read more about this measurement campaign here:

http://www.bristol.ac.uk/news/2016/may/5g-wireless-spectrum-efficiency.html

256-QAM is generally not an option in cellular networks, due to the inter-cell interference and unfavorable cell edge conditions. Numerical simulations can, however, predict the practically achievable spectral efficiency. The figure below shows the uplink spectral efficiency for a base station with 200 antennas that serves a varying number of users. Interference from many tiers of neighboring cells is considered. Zero-forcing detection, pilot-based channel estimation, and power control that gives every user 0 dB SNR are assumed. Different curves are shown for different values of τc, which is the number of symbols per channel coherence interval. The curves have several peaks, since the results are optimized over different pilot reuse factors.

Spectral efficiency
Uplink spectral efficiency in a cellular network with 200 base station antennas.

From this simulation figure we observe that the spectral efficiency grows linearly with the number of users, for the first 30-40 users. For larger user numbers, the spectral efficiency saturates due to interference and limited channel coherence. The top value of each curve is in the range from 60 to 110 bit/s/Hz, which are remarkable improvements over the 3 bit/s/Hz of IMT Advanced.

In conclusion, 20x-40x improvements in spectral efficiency over IMT Advanced are what to expect from Massive MIMO.