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.
Specifically for VoLTE, @cite_36 proposes a resource allocation algorithm based on the prediction of channel quality change for VoLTE users. Also, in @cite_32 a semi-persistent packet scheduling algorithm is proposed for VoLTE (GBR) users over heterogenous networks of fourth generation LTE and third-generation universal mobile telecommunications service (3G UMTS). A new channel- and QoS-aware scheduling algorithm (WE-MQS) is proposed in @cite_19 for downlink scheduling of VoLTE users which considers user perception. To reduce the waste of resources when scheduling a low-rate VoLTE user on a strong channel, a scheduling algorithm is proposed in @cite_24 in which voice packets for different users are multiplexed within one LTE packet in the downlink. Compared to these works we consider both GBR and non-GBR users in LTE networks. Also, the authors of @cite_6 proposed a Resource Block Preserver scheduling algorithm for both real time and non-real time flows. It includes two layers, the upper layer is designed to satisfy the QoS of real time flows based on sub-frame aggregation, while the lower layer allocates the resource blocks to users depending on flow types. @cite_22 and @cite_4 new scheduling algorithms are developed which can manage mixed traffic including both real time and non-real time traffic.
@cite_4 @cite_22 @cite_36 @cite_32 @cite_6 @cite_24 @cite_19
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We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. 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. In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users’ energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes. Long Term Evolution (LTE) is the new generation wireless networks that support all IP transmission for multimedia services. One of the major challenges in LTE network is to provide the packet scheduling scheme that can supports the Quality-of-Service (QoS) requirements of multimedia services. In this paper, a Quality-of-Service (QoS) aware packet scheduler for Real Time (RT) downlink communication in LTE is proposed. A Resource Block (RB) preserver scheduling algorithm that has two layers is developed. The upper layer exploits the LTE frame features of aggregating multiple numbers of sub-frames. The lower layer adopts the concept of Proportional Fair (PF) algorithm to schedule the Non Real Time (NRT) flows. While for the real time flows, the Exponential Earliest Deadline (EXP ED) algorithm is utilized. Our simulation results show the proposed Resource Block Preserver (RBP) algorithm outperforms both the Exponential Proportional Fair (EXP PF) and Maximum-Largest Weighted Delay First (M-LWDF) scheduling algorithms in terms of lower Packet Loss Ratio (PLR) among users in the downlink 3GPP LTE system supports VoIP services.