Video distribution in a D2D enabled 5G network supporting Public Safety Services

Siebel, R. (2017) Video distribution in a D2D enabled 5G network supporting Public Safety Services.

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Abstract:5G mobile networks, which will become available around 2020, aim to support voice, video and other high demanding communication services for billions of connected devices, such as smartphones, sensors, vehicles and other Internet or Things (IoT) devices. Therefore the capabilities of 5G must extend far beyond previous generations of mobile communication. Examples of these capabilities include very high data rates, very low latency, ultra-high reliability, energy efficiency and high capacity. One of the key technologies for supporting these 5G capabilities is device-to-device (D2D) communication. D2D enables devices to communicate with each other without using the infrastructure of the network. Due to significant investments involved, governments are reluctant to renew Public Safety Networks (PSNs). Also for manufacturers and providers Public Mobile Networks (PMNs) offer a larger market and thereby delivering more profits. As a result, the technological developments for PSNs are lagging behind compared to PMNs. However, synergies can produce a number of benefits, including increased aggregate capacity, improved resiliency and enhanced radio coverage and up to date technological implementations for Public Safety Services (PSS). The convergence of both networks begun with the introduction of Proximity Services at 4G LTE exclusively offering D2D capabilities to PSS officials. This trend is continued in 5G, where Public Safety Services is one of the use cases which will have to be supported. This thesis focusses on the use of D2D communication for Public Safety Services purposes. In particular, we focus on spectral resource allocation for a group of first responders who are supported by a relay station. User equipment (UE) can be directly linked to the base station or indirectly via the relay station, with the link from the UE to the relay station being a D2D link. It is assumed that all first responders send live video streams to a Central Command Post. Our goal is to have as many UEs as possible sent their live video streams with a high as possible video quality level. The challenge here is to determine the resource allocation for all UEs and whether they should send their video streams directly to the base station or via the relay station. This depends on the video quality to be obtained, the distance from the UEs to the base station and relay station and whether it is more efficient, with respect to spectral resources, to send directly to the base station or via the relay station. All allocations of resources and route choices for all UEs should be considered in conjunction, which makes it very difficult. To this end we have investigated what the most efficient method is to allocate spectral resources for streaming video in a 5G mobile network. We also examined what the effects are of a number of key parameters, such as transmit power, required throughput and distance from the relay station to the base station, on the route choice for a UE. 4 Based on these investigations, we have developed a heuristic resource allocation algorithm. The algorithm bases its choices on the calculations regarding the required resources and route to the base station (i.e. direct or via relay) for a single UE in isolation. When for all UEs choices have been made, corrections in the resource allocation are carried out taking the calculations of all UEs into account. The heuristic algorithm is evaluated by comparing its performance to the performance of the most optimal scheduling. The optimal scheduling is not suitable for implementation as it is not scalable and calculating the most optimal resource allocation takes a long time. The results, based on simulation, show that the heuristic algorithm is a very promising, efficient and fast method for performing recourse allocation for a clustered D2D enabled 5G network for supporting Public Safety Services. In almost the entire range of the test scenarios, the UEs for both the heuristic algorithm and the optimal scheduling meet their requirements. Only when de distances to the base station become very large, it becomes clear that the heuristic algorithm performs less than the optimal scheduling. As a result, the area where the UEs meet their requirements for the heuristic algorithm is slightly smaller than that of the optimal scheduling. The resource usage of the heuristic algorithm is somewhat higher than optimal scheduling even when both meet the throughput requirements.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:53 electrotechnology, 54 computer science
Programme:Electrical Engineering MSc (60353)
Link to this item:http://purl.utwente.nl/essays/73491
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