You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Future cellular networks will be dense and require key traffic management technologies for fine-grained network control. The problem gets more complicated in the presence of different network segments with bottleneck links limiting the desired quality of service (QoS) delivery to the last mile user. In this work, we first design a framework for software-defined cellular networks and then propose new mechanisms for management of QoS and non-QoS users traffic considering both access and backhaul networks, jointly. The overall SDN-LTE system and related approaches are developed and tested using network simulator in different network environments. Especially, when the users are non-uniformly distributed, the results shows that compared to other approaches, the proposed load distribution algorithm enables at least 6 and 23 increase in the average QoS user downlink throughput and the aggregate throughput of 40 users with lowest throughput (edge users), respectively. Also, the proposed system efficiently achieves desired QoS and handles the network congestion without incurring significant overhead.

Cited Abstract(s):
[1]: The growing data traffic demand is forcing network operators to deploy more base stations, culminating in dense heterogeneous networks that require a high-connectivity backhaul. This scenario imposes significant challenges for current and future cellular networks, and Software Defined Networking (SDN) has been pointed as an enabling technology to overcome existing limitations. This paper shows how the OpenFlow protocol can be integrated into existing Long Term Evolution (LTE) networks to provide the required Quality of Service (QoS) in the network infrastructure. Three OpenFlow-based mechanisms are proposed: a traffic routing, an admission control function, and a traffic coexistence mechanism. Together, they can effectively control the bandwidth usage in the backhaul infrastructure, improving the QoS and ensuring a better user experience. Simulations were performed to validate the proposed mechanisms and highlight the benefits that can be achieved with the flexibility offered by the SDN technology.

[2]: In this work we propose a novel handover (HO) algorithm, that considers system performance from both user and network perspective, in the context of heterogeneous networks (HetNets), i.e., networks composed of BSs with asymmetrical transmission power. In such an environment, conventional HO algorithms that consider only the user perspective, e.g., received signal strength (RSS)-based, might offer suboptimal performance, since they mainly push users to cells with high transmission powers. Thus, new algorithms that take into account also the network perspective, e.g., cell load, are needed. In this work, a load-aware algorithm is proposed considering the service delay that a user experiences from the network. In addition, an implementable framework based on Software Defined Networking (SDN) architecture is sketched to support the algorithm. The proposed algorithm is compared with the traditional one we meet in long-term evolution (LTE) systems and a distance-based one. Extracted cell assignment probability and user service delay performance results show that the load-aware approach outperforms both of them.

[3]: Self-Organising Network (SON) use cases are seen as key enablers of efficient Long Term Evolution (LTE) system operation. Mobility Load Balancing Optimisation (LB) is a prominent SON use case defined by 3GPP with the scope of balancing the load in a given coverage area by reconfiguring the handover thresholds. When overload is detected at a given cell, the LB reduces the handover thresholds of those user equipments that can be redirected to the underutilised neighbouring cells. This mechanism improves the user plane performance in the overloaded cell. Despite the potential benefits, the available LB mechanisms have two major deficiencies, both of them causing user experience degradation: on the one hand, they tend to overestimate the load on the air interface, resulting in unnecessary handovers; on the other hand, they are either transport agnostic or have limited capability to consider the transport load during their decision process. This paper provides an overview of the LB related problems and proposes an improved solution including accurate evaluation of the radio load and a transport aware mechanism for end-to-end optimisation. The performance of the solution was evaluated by simulations.

[4]: 

[5]: 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.

[6]: In this paper, we investigate load balancing problem in 3GPP Long Term Evolution (LTE) networks and propose our solution which considers users with different Quality-of-Service (QoS) requirements. Load unbalance among neighboring cells often yields negative impacts on user experience and network performance, and it has mainly been considered for only data services without QoS guarantee. However, 3GPP LTE network aims to support multi-class services with different QoS requirements, on which the influence of load unbalance is quite different. For those with minimum rate requirements, it may result in high block probability, while for others without rate requirements, the throughput of boundary users may be degraded. In this paper, we incorporate all the differences into a network utility maximization framework and formulate it as a multi-objective optimization problem. The objectives in the problem are load balancing index of services with QoS requirements and the total utility of other services, and the constraints are physical resource limits and QoS demands. Then we analyze the complexity of the problem, and propose our solution, which includes a QoS guaranteed hybrid scheduling scheme, handover of users with and without QoS requirements, and a call admission control algorithm. Extensive simulation is conducted and the results show that the proposed framework leads to significantly better load balancing, and thus the decrease in call block probability of users with QoS requirements, and the increase in throughput of boundary best effort users.

[7]: In promising OFDMA systems, downlink signals originating from the same base station (BS) are orthogonal, while those from different BSs interfere with each other. As a consequence, inter-cell interference (ICI) becomes major performance degradation factor. Particularly, boundary users suffer from severe ICI in addition to the inherent near-far problem. To improve cell edge performances and support a more balanced data rate among all users, partial frequency reuse (PFR) and load-balancing schemes are investigated in this paper. We have formulated a utility maximization problem with network-wide proportional fairness (PF) as an objective in a multi-cell network with PFR. To solve this problem, we propose an offline optimal algorithm and also efficient online algorithms. Our online algorithms are based on simple inter intra-handover and cell-site selection in which a metric is changed from the signal strength to the average throughput. Through extensive simulations, we demonstrate that our online algorithms can achieve network-wide PF very closely. Compared to the conventional system with a universal frequency reuse where a user is bound to the best signal strength base station, the proposed algorithms bring two types of performance gain: interference avoidance (IA) and load balancing (LB) gain. These gains improve the system performance, especially for users at the cell boundary.

[8]: In this paper, we investigate the load balancing problem with the consideration of different qualityof- service (QoS) and channel state in 3GPP long term evolution (LTE) multi-cell networks. Load imbalance among nearby cells often causes inefficient utilization of the system resources. Then in overloaded cells, users cannot get satisfied services for short resources, while in idle cells, there is plenty of unoccupied resources. However, switching users only for load balancing with no consideration of channel state often results in aggressive handover of users, which may occupy too many resources in their target cells and deteriorate the network efficiency. Thus, how to efficiently perform load balancing with the consideration of channel state becomes an important issue. Meanwhile, LTE network aims to serve users with different QoS requirements, which should be taken into account when performing load balancing. Thus we propose a multi-objective optimization problem, whose objectives are load balancing index and network average load for users with QoS requirements and a unified utility function for users without QoS requirements. The constraints are physical resource limits and users’ QoS demands. Then we analyze its complexity and propose a practical algorithm, load balancing, with the consideration of different QoS requirements and channel state services (LBQC). Extensive simulations are conducted. The results show that the proposed algorithm can approximate the optimal algorithm efficiently, and leads to significantly better performances than load balancing with no consideration of channel state, e.g. a lower new call blocking rate with a fewer system resources occupation for users with QoS requirements, a higher cell edge throughput and total throughput for users without QoS requirements.
We have divided earlier works into different categories as shown in Table . First we briefly discuss works considering backhaul and access networks separately, then we summarize the works that consider both access and backhaul jointly. & & Limited to theoretical modeling & No & No & QoS-aware [6] & & Increases average cell load & No & Both & QoS and channel-aware [8] , [4] & & No system-level procedure & No & Both, Only QoS & Delay-aware [2] & Efficient load balancing & Does not guarantee QoS & Yes & QoS & Channel-aware [7] & & & & Yes & No & & & & No & No 4 * Backhaul Network & Congestion-aware [5] & & & No & Yes & QoS-aware [1] & QoS in LTE backhaul & No access network & Yes & Yes & & & & No & NonQoS & & & & No & Only nonQoS 3 * & Mobility-driven [3] & Overload detection & & No & Only QoS & & Handover delay reduction & Limited study & Yes & Not stated