Enhancing user satisfaction in 5G networks using Network Coding

University essay from KTH/Radio Systems Laboratory (RS Lab)


Network data rates are growing rapidly. The data rates provided to the customers by their network providers vary from Mbps to Gbps. However, rarely do users get the promised peak throughput.

In cellular networks, network conditions change based on obstacles, weather conditions between the client and the base stations, and even the movement of objects and people. As a result of the changes in the radio link, the data transfer rate can change rapidly, hence devices needs to adjust their communications based on the currently available data rate.

The Transmission Control Protocol (TCP) is widely used for reliable data transfer over networks. However, TCP was initially designed when link data rates were much lower than the link data rates commonly available today. As a result, TCP does not perform well at high data rates, despite some of the changes that have been made to the protocol to support high data rate links. Moreover, TCP has problems adapting to large changes in link bandwidth (not caused by congestion), resulting in a lower average throughput than the link could potentially deliver.

This thesis evaluates two different versions of the TCP protocol (e.g., TCP Reno and Cubic TCP) and proposes a network coding scheme to enhance users’ experience when communicating over unstable radio links. The performance of the two TCP protocols and Random Linear Network Coding (RLNC) scheme were measured in an emulated network environment. The results of these measurements were analyzed and evaluated. The analysis shows that RLNC can provide a higher throughput than TCP over a network with high packet loss. However, RLNC is a UDP based solution and does not implement congestion control algorithms or reliability. A new solution is proposed that increases reliability and implements network adaptation in RLNC solutions.

The results obtained in this thesis can be used to develop a new protocol to increases the quality of users’ experience in high loss networks.

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