Effect of Adaptive Modulation and Coding Schemes on Scheduling Algorithms for LTE Downlink

Long Term Evolution (LTE) network uses Radio Resource Management (RRM) mechanisms such as Scheduling and Adaptive Modulation and Coding (AMC) for realizing Quality of Service (QoS) requirements and optimizing system performance. Scheduling is the process of dynamically allocating physical resources to User Equipments (UEs) based on scheduling algorithms implemented at MAC sublayer of the LTE base station. Whereas AMC is Link Adaptation functionality of LTE Physical layer to enhance higher system performance. AMC scheme adopted in LTE Downlink depends on the channel quality Index (CQI) feedback from User Equipments. Hence in this paper, an attempt has been made to study and compare the performance of Blind Equal Throughput (BET), Maximum Throughput (MT) and Proportional Fair (PF) scheduling algorithms for Downlink connections with AMC (64 QAM, 16 QAM and QPSK regions) for Constant Bit Rate (CBR) traffic scenario. Performance metrics considered for simulation studies are throughput, delay, jitter and fairness. From the simulation results it is evident that the throughput, delay, jitter and fairness performances of the considered scheduling algorithms are better in 64QAM region. Also, MT scheduling algorithm achieves better throughput and BET scheduling algorithm achieves better fairness

Dynamic Bandwidth Allocation in Heterogeneous OFDMA-PONs Featuring Intelligent LTE-A Traffic Queuing

This work was supported by the ACCORDANCE project, through the 7th ICT Framework Programme. This is an Accepted Manuscript of an article accepted for publication in Journal of Lightwave Technology following peer review. © 2014 IEEE Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A heterogeneous, optical/wireless dynamic bandwidth allocation framework is presented, exhibiting intelligent traffic queuing for practically controlling the quality-of-service (QoS) of mobile traffic, backhauled via orthogonal frequency division multiple access–PON (OFDMA-PON) networks. A converged data link layer is presented between long term evolution-advanced (LTE-A) and next-generation passive optical network (NGPON) topologies, extending beyond NGPON2. This is achieved by incorporating in a new protocol design, consistent mapping of LTE-A QCIs and OFDMA-PON queues. Novel inter-ONU algorithms have been developed, based on the distribution of weights to allocate subcarriers to both enhanced node B/optical network units (eNB/ONUs) and residential ONUs, sharing the same infrastructure. A weighted, intra-ONU scheduling mechanism is also introduced to control further the QoS across the network load. The inter and intra-ONU algorithms are both dynamic and adaptive, providing customized solutions to bandwidth allocation for different priority queues at different network traffic loads exhibiting practical fairness in bandwidth distribution. Therefore, middle and low priority packets are not unjustifiably deprived in favor of high priority packets at low network traffic loads. Still the protocol adaptability allows the high priority queues to automatically over perform when the traffic load has increased and the available bandwidth needs to be rationally redistributed. Computer simulations have confirmed that following the application of adaptive weights the fairness index of the new scheme (representing the achieved throughput for each queue), has improved across the traffic load to above 0.9. Packet delay reduction of more than 40ms has been recorded as a result for the low priority queues, while high priories still achieve sufficiently low packet delays in the range of 20 to 30msPeer reviewe

An Optimal Application-Aware Resource Block Scheduling in LTE

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.Comment: 5 page

Joint Dynamic Radio Resource Allocation and Mobility Load Balancing in 3GPP LTE Multi-Cell Network

Load imbalance, together with inefficient utilization of system resource, constitute major factors responsible for poor overall performance in Long Term Evolution (LTE) network. In this paper, a novel scheme of joint dynamic resource allocation and load balancing is proposed to achieve a balanced performance improvement in 3rd Generation Partnership Project (3GPP) LTE Self-Organizing Networks (SON). The new method which aims at maximizing network resource efficiency subject to inter-cell interference and intra-cell resource constraints is implemented in two steps. In the first step, an efficient resource allocation, including user scheduling and power assignment, is conducted in a distributed manner to serve as many users in the whole network as possible. In the second step, based on the resource allocation scheme, the optimization objective namely network resource efficiency can be calculated and load balancing is implemented by switching the user that can maximize the objective function. Lagrange Multipliers method and heuristic algorithm are used to resolve the formulated optimization problem. Simulation results show that our algorithm achieves better performance in terms of user throughput, fairness, load balancing index and unsatisfied user number compared with the traditional approach which takes resource allocation and load balancing into account, respectively

Cross-layer scheduling and resource allocation for heterogeneous traffic in 3G LTE

3G long term evolution (LTE) introduces stringent needs in order to provide different kinds of traffic with Quality of Service (QoS) characteristics. The major problem with this nature of LTE is that it does not have any paradigm scheduling algorithm that will ideally control the assignment of resources which in turn will improve the user satisfaction. This has become an open subject and different scheduling algorithms have been proposed which are quite challenging and complex. To address this issue, in this paper, we investigate how our proposed algorithm improves the user satisfaction for heterogeneous traffic, that is, best-effort traffic such as file transfer protocol (FTP) and real-time traffic such as voice over internet protocol (VoIP). Our proposed algorithm is formulated using the cross-layer technique. The goal of our proposed algorithm is to maximize the expected total user satisfaction (total-utility) under different constraints. We compared our proposed algorithm with proportional fair (PF), exponential proportional fair (EXP-PF), and U-delay. Using simulations, our proposed algorithm improved the performance of real-time traffic based on throughput, VoIP delay, and VoIP packet loss ratio metrics while PF improved the performance of best-effort traffic based on FTP traffic received, FTP packet loss ratio, and FTP throughput metrics