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.
The three basic scheduling algorithms are round robin, max sum rate, and proportional fair @cite_18 . 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 @cite_14 .
@cite_18 @cite_14
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Proportional Fair (PF) scheduling algorithms are the de facto standard in cellular networks. They exploit the users' channel state diversity (induced by fast-fading) and are optimal for stationary channel state distributions and an infinite time-horizon. However, mobile users experience a nonstationary channel, due to slow-fading (on the order of seconds), and are associated with base stations for short periods. Hence, we develop the Predictive Finite-horizon PF Scheduling ((PF)2S) Framework that exploits mobility. We present extensive channel measurement results from a 3G network and characterize mobility-induced channel state trends. We show that a user's channel state is highly reproducible and leverage that to develop a data rate prediction mechanism. We then present a few channel allocation estimation algorithms that exploit the prediction mechanism. Our trace-based simulations consider instances of the ((PF)2S) Framework composed of combinations of prediction and channel allocation estimation algorithms. They indicate that the framework can increase the throughput by 15 -55 compared to traditional PF schedulers, while improving fairness. This paper considers the allocation of time slots in a frame, as well as power and rate to multiple receivers on an energy harvesting downlink. Energy arrival times that will occur within the frame are known at the beginning of the frame. The goal is to optimize throughput in a proportionally fair way, taking into account the inherent differences of channel quality among users. Analysis of structural characteristics of the problem reveals that it can be formulated as a biconvex optimization problem, and that it has multiple optima. Due to the biconvex nature of the problem, a Block Coordinate Descent (BCD) based optimization algorithm that converges to an optimal solution is presented. However, finding the optimal allocation with BCD entails a computational complexity that increases sharply in terms of the number of users or slots. Therefore, certain structural characteristics of the optimal power-time allocation policy are derived. Building on those, two simple and computationally scalable heuristics, PTF and ProNTO are proposed. Simulation results suggest that PTF and ProNTO can closely track the performance of BCD which achieves a good balance between total throughput and fairness. In this paper, we introduce an approach for application-aware resource block scheduling of elastic and inelastic adaptive real-time traffic in fourth generation Long Term Evolution (LTE) systems. The users are assigned to resource blocks. A transmission may use multiple resource blocks scheduled over frequency and time. In our model, we use logarithmic and sigmoidal-like utility functions to represent the users applications running on different user equipments (UE)s. We present an optimal problem with utility proportional fairness policy, where the fairness among users is in utility percentage (i.e user satisfaction with the service) of the corresponding applications. Our objective is to allocate the resources to the users with priority given to the adaptive real-time application users. In addition, a minimum resource allocation for users with elastic and inelastic traffic should be guaranteed. Every user subscribing for the mobile service should have a minimum quality-of-service (QoS) with a priority criterion. We prove that our scheduling policy exists and achieves the maximum. Therefore the optimal solution is tractable. We present a centralized scheduling algorithm to allocate evolved NodeB (eNodeB) resources optimally with a priority criterion. Finally, we present simulation results for the performance of our scheduling algorithm and compare our results with conventional proportional fairness approaches. The results show that the user satisfaction is higher with our proposed method. The objective of this paper is to study the performance of a multicell vector perturbation (MVP) precoding technique under practical situations in a network multiple-input multiple-output (MIMO) scheme employing joint transmission. The conventional perturbation strategy that minimizes the total power is considered, and the power at each base station (BS) is properly scaled to enforce per-BS power constraints. In our scenario, we consider multiple-antenna users and use block diagonalization (BD) as the linear front-end precoder for VP. The sum rate for the MVP in the case of uniformly distributed input and an asymptotic upper bound on the sum rate at high signal-to-noise ratios (SNRs) are derived. In addition, using the asymptotic upper bound on the individual user rates, we propose a proportionally fair (PF) user scheduling algorithm of lower complexity and better performance compared with the benchmark fair semiorthogonal user selection (SUS) algorithm. As opposed to the PF-SUS, the proposed PF scheduling algorithm requires no predefined correlation threshold. Furthermore, we study the impact of backhaul delay on the performance of both VP and BD by deriving bounds on the sum rate. The numerical results show that MVP in the case of perfect channel state information (CSI) outperforms multicell BD. In the presence of a backhaul delay, the performance of MVP significantly degrades, but the upper bound on the sum rate for MVP is still higher than for multicell BD.