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:
We consider scheduling and resource allocation in long-term evolution (LTE) networks across voice over LTE (VoLTE) and best-effort data users. The difference between these two is that VoLTE users get scheduling priority to receive their required quality of service. As we show, strict priority causes data services to suffer. We propose new scheduling and resource allocation algorithms to maximize the sum- or proportional fair (PF) throughout amongst data users while meeting VoLTE demands. Essentially, we use VoLTE as an example application with both a guaranteed bit-rate and strict application-specific requirements. We first formulate and solve the frame-level optimization problem for throughput maximization; however, this leads to an integer problem coupled across the LTE transmission time intervals (TTIs). We then propose a TTI-level problem to decouple scheduling across TTIs. Finally, we propose a heuristic, with extremely low complexity. The formulations illustrate the detail required to realize resource allocation in an implemented standard. Numerical results show that the performance of the TTI-level scheme is very close to that of the frame-level upper bound. Similarly, the heuristic scheme works well compared to TTI-level optimization and a baseline scheduling algorithm. Finally, we show that our PF optimization retains the high fairness index characterizing PF-scheduling.

Cited Abstract(s):
[1]: Abstract Long Term Evolution is standardized by the 3rd Generation Partnership Project to provide a high data rate up to 100 Mbps and 50 Mbps for downlink and uplink transmission respectively and can operate in different bandwidths ranging from 1.4 MHz up to 20 MHz. The incorporated scheduling mechanisms can significantly contribute to this goal. Scheduling mechanism is the process of allocated the resources (Time and Frequency) to users transmitting different flows in same time. In this paper, a novel scheduling algorithm is presented, modeled and compare to basic scheduler used in downlink LTE transmission, such as Round Robin, Max Rate and Proportional fair schedulers. Simulation results, presented in this paper, show that the newly proposed algorithm improves system capacity as compare with Proportional Fair scheduler with guaranty of 80 fairness between all users.

[2]: This paper analyses the stability and fairness of two classes of rate control algorithm for communication networks. The algorithms provide natural generalisations to large-scale networks of simple additive increase multiplicative decrease schemes, and are shown to be stable about a system optimum characterised by a proportional fairness criterion. Stability is established by showing that, with an appropriate formulation of the overall optimisation problem, the network's implicit objective function provides a Lyapunov function for the dynamical system defined by the rate control algorithm. The network's optimisation problem may be cast in primal or dual form: this leads naturally to two classes of algorithm, which may be interpreted in terms of either congestion indication feedback signals or explicit rates based on shadow prices. Both classes of algorithm may be generalised to include routing control, and provide natural implementations of proportionally fair pricing.
The three basic scheduling algorithms are round robin, max sum rate, and proportional fair [1] . The round robin scheduler assigns the PRBs in equal TTIs and in ordered manner. It allocates resources equally, regardless of the users' CQI. The max-sum rate scheduler maximizes throughput by assigning resources to users that have the highest SINR, but ignores fairness among users. The proportional fair scheduler provides a balance between fairness and the overall system throughput. Compared to the sum rate objective, achieving rate allocations with PF provides a better tradeoff between users' satisfaction (i.e., fairness) and total system throughput [2] .