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There has been a lot of research focusing on optimizing the Long Term Evolution (LTE) networks performance. A large part of this research focused on how to optimize the LTE radio interface to efficiently distribute the scarce radio resources among the LTE users. Since LTE offers a diverse amount of services, the Quality of Service (QoS) differentiation is a major challenge. QoS is handled in LTE at different levels; the most important one is the radio scheduler. The QoS aware radio scheduler is designed to guarantee different service requirements. However, if the LTE radio interface is fully optimized towards QoS guarantees, what happen when the LTE transport network runs into congestion?Congestion can happen at any point in the network, and normally congested LTE backhaul tends to create large unfairness among the users, even among the ones with the same QoS class. The LTE transport congestion can occur at any router in the backhaul network. In this paper, we present two novel algorithms, which focus on the LTE transport congestion. The first algorithm is a congestion detection mechanism (CD) that identifies the transport network congestion as early as possible, and triggers the congestion control function. The second algorithm preserves and optimizes the QoS performance of each user over the transport to match the radio scheduler service differentiation, i.e., achieving fairness among the users in the same QoS demand and guaranteeing the enforced radio QoS weights for different service classes.

Long‐Term Evolution employs a hard handover procedure. To reduce the interruption of data flow, downlink data is forwarded from the serving eNodeB (eNB) to the target eNB during handover. In cellular networks, unbalanced loads may lead to congestion in both the radio network and the backhaul network, resulting in bad end‐to‐end performance as well as causing unfairness among the users sharing the bottleneck link. This work focuses on congestion in the transport network. Handovers toward less loaded cells can help redistribute the load of the bottleneck link; such a mechanism is known as load balancing. The results show that the introduction of such a handover mechanism into the simulation environment positively influences the system performance. This is because terminals spend more time in the cell; hence, a better reception is offered. The utilization of load balancing can be used to further improve the performance of cellular systems that are experiencing congestion on a bottleneck link due to an uneven load.

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High Speed Downlink Packet Access (HSDPA) is an extension of the Universal Mobile Telecommunications System (UMTS) technology of 3GPP Rel-99, with the objective to increase the data rate and reduce the latency in the downlink. The main focus of the work presented is to analyse the effect of congestion at the Iub interface on the HSDPA performance. The data flows should be adequately controlled in order to avoid congestion in the transport network. The 3GPP (3rd Generation Partnership Project) Rel. 5 specifications highlight two congestion detection mechanisms which are based on the frame sequence number (FSN) and the Delay Reference Time (DRT) fields of HSDPA data frame. In addition to these, a third congestion detection mechanism based of Checksum of HSDPA data frame is considered. This paper discusses a congestion control scheme deploying all three congestion detection methods. It is shown, that a congestion control algorithm can effectively work using these congestion detection triggers and can control the offered load to the transport network. The simulation results presented in this paper confirm that the congestion in the transport network can be avoided, and hence the performance of HSDPA network can be significantly improved in all aspects.

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This paper presents analytical models to dimension the transport bandwidths for the S1 interface in the Long Term Evolution (LTE) Network. In this paper, we consider two major traffic types: elastic traffic and real time traffic. For each type of traffic, individual dimensioning models are proposed. For validating these analytical dimensioning models, a developed LTE system simulation model is used. The simulation results demonstrate that the proposed models can properly estimate the required performances and thus be able to be used for link dimensioning for various traffic and network scenarios.