In the beginning of 2017 Belgium had just over 11 million people. 91% of these people own a wirelessly-connected computer and 59% owns a smartphone [1]. Now imagine all these people connecting their dishwasher, TV, heating system, and so on, to the Internet. Not only at home, also in factories or warehouses, like in Amazon’s robot warehouse [2] or on the road like the autonomous cars from Google [3], more and more ’things’ are becoming connected, this is what we call the Internet of Things (IoT). Gartner [4] expects the number of connected things to grow by 220% by 2020 compared to 2016, as shown in Figure1.1. Cisco [5] on the other hand estimates that by 2020 there will be 12 billion machine-to-machine connections, which is more than double the number of connections today. Moreover, Cisco estimates 600 million wearables and 3.1 billion Internet-enabled televisions worldwide. 50% of the Internet traffic generated by these devices will go through WiFi networks, with another 16% going through the cellular network, therefore, the majority of the traffic will be transmitted wirelessly. On top of this, the average speed per device will more than double by 2020 [5].
Most of this traffic, around 80% will come from streaming video services, especially with the move towards higher resolutions [5]. Smaller portions will come from browsing and sharing data. With all these applications combined, the average Internet user will generate up to 44 gigabytes per month. This equals a total of 2.3 zettabytes, i.e., 1021 bytes, of transmitted data bits globally during
the whole year. Needless to say, current wireless networks and infrastructures
2013 2014 2015 2016 2017 2018 2019 2020 5 10 15 20 Year Installed things [Billions of units]
Figure 1.1: The installed number of ’things’ will increase rapidly in the coming years. (Source: [4])
are not made to handle this huge amount of data generated by these massive amount of devices.
The quality of service (QoS) requirement of this massive amount of devices is also increasing, fueled by new applications, each with their own diverse requirements. An example of a group of such applications is called the Tactile Internet [6]. Each of these applications requires extremely low delay (<1 ms) and high reliability (>99.99%). Applications include fully autonomous cars who communicate with each other in order to drive close to each other and brake together. Another example is virtual reality which requires very minimal delay between the movement of the head or arms and the image on the screen. These applications are currently not yet possible, especially not when considering the constraints of wireless communication.
Another great set of applications, which is already being rolled out at the moment, relates to the notion of smart cities. Figure1.2gives an overview of the applications, detailing the different subcategories and their current installed base percentage. The total number of installed things within smart cities was around 1.6 billion in 2016. In smart cities a multitude of sensors is used to measure and gain insights into several processes. For example, sensors can be used to continuously monitor air quality or water pollution and allow regulators to take action when things go wrong. In big sites like industrial zones and office areas, the energy efficiency can be monitored. Gartner estimates that in these
WIRELESS TRAFFIC JAM EXPECTED 3
6.31 %
Public services 31.57 %
Smart commercial buildings
20.66 % Smart homes 21.18 % Transport 19.13 % Utilities 1.13 % Others
Figure 1.2: Overview of installed connected things within smart cities in 2016. (Source: [7])
big sites the IoT can save up to 30% in the cost of energy, spatial management and building maintenance. In smart homes, the biggest applications are smart TVs, smart thermostats and smart light bulbs. Gartner expects that this category will grow the most in the coming years. Each of these devices does not have high requirements, however, when combined these things require vast amounts of data throughput for their operation.
Finally, some applications require an extremely low energy consumption because they are battery powered. For example, the wireless sensor networks in the smart city example might not be easily accessible. In these remote locations it is cumbersome to replace the battery each month or even each year. These applications require battery lifetimes of around 10 years in order to make them cost effective and easy to maintain. Not only wireless sensor networks benefit from energy-efficient communication, in fact every battery powered device like your smartphone or your laptop can benefit from this, extending the battery lifetime from a few hours to a full day or even two. Besides in battery-powered devices, lowering the energy consumption is also essential from an environmental point of view. The International Energy Agency projects that in 2017 network- enabled devices will consume over 800 TWh globally [8]. That is more than ten times the overall domestic energy usage in Belgium [9].
Figure 1.3: WiFi spectrum usage at an office location on the UCLA campus. these applications have different requirements. Some need high throughput, others need extremely low delay and high reliability and others need none of the above but require a low energy consumption. On its own achieving these requirements will be challenging. The problem is even more challenging as all these applications will need to share the same networks. Each network will therefore need to be able to give some devices a highly reliable service while others need a high throughput service for example, all while taking the energy consumption into account.
This all comes at a point where the physical medium, i.e., the wireless spectrum, is getting more and more crowded. An important example is the 2.4 GHz industrial, scientific and medical (ISM) band which is used, among other things, for WiFi communication. Figure1.3shows a scan of the nearby WiFi access points at an office location at UCLA. The whole band is used, with each access point overlapping with at least five other access point. Not only the 2.4 GHz ISM band is heavily used, also other bands, especially below 1 GHz have a high utilization factor with more than 60% utilization being reported in [10]. Because of the limited amount of free wireless spectrum, it is difficult to meet the requirements for the diverse applications discussed above. The problem arises from too many devices with demanding applications trying to access the same wireless spectrum and essentially collapsing the network. Currently, most devices first sense the wireless medium for any other devices already transmitting in the vicinity. If no other device is detected, the device will transmit its data. However, in crowded places, there is a high probability that
SCOPE OF THIS THESIS 5
two or more devices will decide to transmit data at the same time, causing a wireless collision. During the collision, the radio waves from each of the transmitters are added together at the receiver, essentially corrupting the data of all the current transmitters. Because of this collision the same data needs to be transmitted a second or even a third time before it is received correctly. This means that devices are more often in a power hungry transmit or receive state than in a low power idle state. Therefore, batteries die faster and we have to charge our devices or replace the batteries more often. Also the delay and throughput are affected as it takes longer to actually transmit and receive the data.
New promising techniques are being developed that potentially solve the problems at hand. One of these techniques is in-band full duplex [11] which enables wireless devices to simultaneously use their transmitter and receiver chain on the same frequency. The basic premise of in-band full duplex is that devices can double their bi-directional throughput by transmitting and receiving data at the same time over the same link. However, we believe that in-band full duplex can do more than that, leading to the following hypotheses of this thesis.
Hypotheses of this thesis
It is possible to use in-band full duplex to improve wireless congestion in dense heterogeneous networks, essentially improving delay, throughput and energy consumption altogether.