Long Term Evolution Scheduler Research Articles

LTE Cellular Networks Packet Scheduling Algorithms in Downlink and Uplink Transmission: A Survey

This survey paper provides a detailed explanation of Long Term Evolution (LTE) cellular network’s packet scheduling algorithms in both downlink and uplink directions. It starts by explaining the difference between Orthogonal Frequency Division Multiple Access (OFDMA) that is used in downlink transmission, and Single Carrier – Frequency Division Multiple Access (SC-FDMA) is used in uplink. Then, it explains the difference between the LTE scheduling process in the donwlink and uplink through explaining the interaction between users and the scheduler. Then, it explains the most commonly used downlink and uplink scheduling algorithms through analyzing their formulas, characteristics, most suitable conditions for them to work in, and the main differences among them. This explanation covers the Max Carrier-toInterference (C/I), Round Robin (RR), Proportional Fair (PF), Earliest Deadline First (EDF), Modified EDF-PF, Modified-Largest Weighted Delay First (M-LWDF), Exponential Proportional Fairness (EXPPF), Token Queues Mechanism, Packet Loss Ratio (PLR), Quality Guaranteed (QG), Opportunistic Packet Loss Fair (OPLF), Low Complexity (LC), LC-Delay, PF-Delay, Maximum Throughput (MT), First Maximum Expansion (FME), and Adaptive Resource Allocation Based Packet Scheduling (ARABPS). Lastly, it provides some concluding remarks.

A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices

The prominence of Machine-to-Machine (M2M) communications in the future wide area communication networks place various challenges to the cellular technologies such as the Long Term Evolution (LTE) standard, owing to the large number of M2M devices generating small bursts of infrequent data packets with a wide range of delay requirements. The channel structure and Quality of Service (QoS) framework of LTE networks fail to support M2M traffic with multiple burst sizes and QoS requirements while a bottleneck often arises from the limited control resources to communicate future uplink resource allocations to the M2M devices. Moreover, many of the M2M devices are battery-powered and require a low-power consuming wide area technology for wide-spread deployments. To alleviate these issues, in this article we propose an adaptive semi-persistent scheduling (SPS) scheme for the LTE uplink which caters for multi-service M2M traffic classes with variable burst sizes and delay tolerances. Instead of adhering to the rigid LTE QoS framework, the proposed algorithm supports variation of uplink allocation sizes based on queued data length yet does not require control signaling to inform those allocations to the respective devices. Both the eNodeB and the M2M devices can determine the precise uplink resource allocation related parameters based on their mutual knowledge, thus omitting the burden of regular control signaling exchanges. Based on a control parameter, the algorithm can offer different capacities and levels of QoS satisfaction to different traffic classes. We also introduce a pre-emptive feature by which the algorithm can prioritize new traffic with low delay tolerance over ongoing delay-tolerant traffic. We also build a model for incorporating the Discontinuous Reception (DRX) mechanism in synchronization with the adaptive SPS transmissions so that the UE power consumption can be significantly lowered, thereby extending their battery lives. The simulation and performance analysis of the proposed scheme shows significant improvement over the traditional LTE scheduler in terms of QoS satisfaction, channel utilization and low power requirements of multi-service M2M traffic.

Design of dynamic resource allocation scheme for real-time traffic in the LTE network

The 5G wireless technology is recently standardized for meeting intense demand. The Long Term Evolution (LTE) technology provides an easy, time-saving, and low-cost method for deploying a 4G/5G network infrastructure. To support multimedia service and higher bandwidth data delivery, an LTE MAC layer has Quality of Service (QoS) support with several QoS class indicator (QCI) levels. Based on LTE current QCI priority and QoS requirements in UEs, the original Max-Rate scheduler, Proportionally Fair (PF) and Modified Largest Weighted Delay First (M-LWDF) algorithms could not achieve their goal owing to the UE’s dynamic physical capacity with a different Channel Quality Indicator (CQI) at run time. For better QoS service than LTE networks, per UE’s CQI state for each resource block (RB) must be considered simultaneously in LTE MAC layer resource allocation with cross-layer support. As downlink real estimated capacity is dynamic owing to a UE’s periodic CQI reporting, the CQI state in LTE scheduling must be considered. This study proposes a smart and flexible scheme for Enhanced Utilization Resource Allocation (EURA) including three novel mechanisms that can dynamically fit UEs’ CQI states. The simulation results in this study demonstrate that the proposed EURA scheme outperforms the contrast schemes (Max-Rate, EXP/PF and M-LWDF), can save more rare radio capacity, and improve the utilization of radio resource assignment.

Optimized two‐level scheduler for video traffic in LTE downlink framework

SummaryThe most challenging issue in long‐term evolution (LTE) mobile networks is the design of an optimal scheduler. The main target of the LTE scheduling strategy is to guarantee a certain level of quality of service (QoS) in order to satisfy users' needs. In this work, a novel queue‐optimized two‐level scheduling strategy is introduced. The queue‐optimized scheduler has the ability to support both real‐time (RT) and non‐RT (NRT) services in the LTE downlink framework. The first level of the proposed scheduler depends on an optimized queue length threshold that is dynamically adapted to adjust the assigned resources for the video traffic to maintain the throughput around its average value. The proposed scheduling strategy is utilized to efficiently enhance the radio resource allocation process for video traffic by minimizing the allocation of any excess unwanted resources, which consequently facilitates the support of more video connections and enhances the overall network spectral efficiency. The distribution of the resources among users for RT and NRT applications is actually performed at the second level of the scheduling process. The physical resource blocks (PRBs) are assigned according to the enhanced earliest due date first (EEDDF) mechanism. The proposed scheduling strategy prioritizes users according to their service requirements, to improve the QoS provision. The proposed scheduler maximizes the video applications throughput, and generally, minimizes the packet delay and the packet loss rate (PLR) for RT applications.

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

Machine-to-Machine (M2M) communication refers to autonomous communication among devices that aims for a massive number of connected devices. M2M communication can support ubiquitous communication and full mechanical automation, and it will change everything from industry to ourselves. Recent developments in communication technology make Long Term Evolution (LTE)/Long Term Evolution-Advance (LTE-A) a promising technology for supporting M2M communication. LTE can support the diverse characteristic of M2M communication due to its IP connectivity, coverage area, and scalability. Therefore, the LTE schedulers should satisfy the need for M2M communication. Motivated by these facts, in this paper, we present a survey on the classification of LTE / LTE-A scheduling methodologies from the perspective of M2M communication. We classify the schedulers based on their objectives, such as energy efficiency, spectrum efficiency, group-based, and Quality-of-Service (QoS) support for Machine Type Communication Devices (MTCDs). We also highlight the scope of future research direction for the scheduling work.

LTE Scheduler Algorithms for VANET Traffic in Smart City

A new concept such as smart city was introduced in the last years where the Intelligent Transportation system (ITS ) plays a critical role to provide road safety and manage Vehicular Ad Hoc Networks (VANETs) traffic. Nevertheless, VANETs have significant constraints like nodes high mobility, intermittent connectivity, variable network density and heterogeneity. However, the different types of traffic, the different Quality of Service requirements, the need to exchange mobile data, multi-services and data diversity leads mainly to load and time constraints in this specific and stringent type of networks. The main characteristic of this kind of networks is the very changing topology that poses supplementary constraints and makes achieving QoS constraints a very challenging task. In VANET network the vehicle generated traffic will be transferred to the data center from road side unit to the base station by using Long Term Evolution (LTE) in an urban area. Despite LTE has a larger system capacity and it provides a higher transmission speed, the network performance is affected by the implemented scheduling algorithm. In this context, we study the efficiency of LTE scheduler algorithms such as Proportional Fairness, Round Robin, Priority Set Scheduler, Maximum Throughput Scheduler and Throughput to Average Scheduler and Blind Equal Throughput mainly at the road side unit using Network Simulator 3(NS3) to determinate the most suitable scheduler for VANET traffic. Results demonstrate that the round robin algorithm is more effective for volumetric VANET traffic in terms of throughput, delay, packet loss rate and fairness.

LTE Scheduler Algorithms for VANET Traffic in Smart City

A new concept such as smart city was introduced in the last years where the Intelligent Transportation system (ITS ) plays a critical role to provide road safety and manage Vehicular Ad Hoc Networks (VANETs) traffic. Nevertheless, VANETs have significant constraints like nodes high mobility, intermittent connectivity, variable network density and heterogeneity. However, the different types of traffic, the different Quality of Service requirements, the need to exchange mobile data, multi-services and data diversity leads mainly to load and time constraints in this specific and stringent type of networks. The main characteristic of this kind of networks is the very changing topology that poses supplementary constraints and makes achieving QoS constraints a very challenging task. In VANET network the vehicle generated traffic will be transferred to the data center from road side unit to the base station by using Long Term Evolution (LTE) in an urban area. Despite LTE has a larger system capacity and it provides a higher transmission speed, the network performance is affected by the implemented scheduling algorithm. In this context, we study the efficiency of LTE scheduler algorithms such as Proportional Fairness, Round Robin, Priority Set Scheduler, Maximum Throughput Scheduler and Throughput to Average Scheduler and Blind Equal Throughput mainly at the road side unit using Network Simulator 3(NS3) to determinate the most suitable scheduler for VANET traffic. Results demonstrate that the round robin algorithm is more effective for volumetric VANET traffic in terms of throughput, delay, packet loss rate and fairness.

Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A.

Machine-to-machine (M2M) communication consists of the communication between intelligent devices without human intervention. Long term evolution (LTE) and Long-term evolution-advanced (LTE-A) cellular networks technologies are excellent candidates to support M2M communication as they offer high data rates, low latencies, high capacities and more flexibility. However, M2M communication over LTE/LTE-A networks faces some challenges. One of these challenges is the management of resource radios especially on the uplink. LTE schedulers should be able to meet the needs of M2M devices, such as power management and the support of large number of devices, in addition to handling both human-to-human (H2H) and M2M communications. Motivated by the fundamental requirement of extending the battery lives of M2M devices and managing an LTE network system, including both M2M devices and H2H users, in this paper, two channel-aware scheduling algorithms on the uplink are proposed. Both of them consider the coexistence of H2H and M2M communications and aim to reduce energy consumption in M2M devices. The first algorithm, called FDPS-carrier-by-carrier modified (CBC-M), takes into account channel quality and power consumption while allocating radio resources. Our second algorithm, recursive maximum expansion modified (RME-M), offers a balance between delay requirement and energy consumption. Depending on the system requirements, RME-M considers both channel quality and system deadlines in an adjustable manner according to M2M devices needs. Simulation results show that the proposed schedulers outperform the round-robin scheduler in terms of energy efficiency and have better cell spectral efficiency.

Optimal Features-based Channel Selection and Neural Network Learning for LTE applications

Nowadays, the number of internet users is increasing vastly. Hence, predicting the number of channels for user’s communication is a major task. Therefore, scheduling of all traffic flows within the communication services in Long Term Evolution (LTE) scheduling is done by verifying the channel information and user availability in the network. In view of that, this paper proposes a novel feature extraction and classification method to evaluate the user availability status and the channel information for the betterment of communication within the LTE network. For preprocessing, we present a Fast Independent Component Analysis (FICA) method which incorporates dimensionality reduction in the given input feature data during the training course. In this work, feature extraction algorithm is used to extract the network feature and its degree of angle by means of distance between the features set. Subsequently, analyzes the extracted categories of network architecture by the analyzing weight value of that attribute based on the responses. As a result both the feature identification and framing of network structure increases the performance of data mining analysis. Hence, proposing a novel optimization technique like Particle Connected Cuckoo Search (PCCS) optimization algorithm for selecting the best features in the training feature set. Based on the extracted features, the classification method is performed in order to predict the network category by using Multilevel Neural Network (MLNN) classification technique. At this point, a novel kernel model for classification process is incorporated to reduce the time complexity. After that, the information is passed to the LTE scheduling system for providing enhanced communication. The comparative analysis between proposed techniques with the existing methods such as naive Bayes, stacking C, DTNB, random forest, J48, Ridor, decision table, zero R, grading in terms of accuracy, RMSE, CCI, ICI assures that the effectiveness of the proposed MLNN classification method.

QoS hybrid uplink scheduler based on service type for M2M communications in LTE networks

<span lang="EN-US">The introduction of Machine-to-Machine (M2M) communications in cellular networks creates a new set of challenges because of the unique service requirements and features of M2M devices. One of these challenges is the management of radio resources, especially on the uplink because of the unfairness and poor performance that occurs when allocating resources to users. Long Term Evolution (LTE) and LTE-Advanced (LTE-A) are excellent candidates for supporting M2M communications because of their native IP connectivity and scalability for a variety of devices. Therefore, LTE schedulers should be able to meet the needs of M2M devices such as time constraints and specific Quality of Service (QoS) requirements. In this paper, these constraints are studied and analyzed, focusing on three schedulers; they are Round Robin (RR), First Maximum Expansion (FME) and Maximum Throughput (MT). These methods do not provide QoS to users who use different types of traffic flows. The solution proposed in this work is a hybrid model between two schedulers each of them is the best scheduling solution for the real-time service and the other for the non-real-time service, in order to meet QoS criteria maximizing throughput and minimizing packet loss. Video and voip were selected as real-time traffic and best effort for non-real time. The simulations results show that the proposed scheduler reaches the lowest Packet Loss Rate (PLR), delivering highest throughput and goodput among the other strategies.</span>

A Survey on Long Term Evolution Scheduling in Data Mining

Scheduling procedures implemented in wireless networks consists of varied workflows such as resource allocation, channel gain improvement, and reduction in packet arrival delay. Among these techniques, Long term evolution (LTE) scheduling is most preferable due to its high speed communication and low bandwidth consumption. LTE allocates resources to the workflow based on time and frequency domains. Normally, the information gathered prior to scheduling increases the processing time since each attributes of the users have to be verified. In order to solve this issue, parallel processing via data mining is analyzed in recent research studies. The label that is assigned to the user attributes contributes primarily on scheduling time slots effectively. The label assignment and parallel processing via data mining reduces the delay and increases the throughput respectively. Additionally, the matched data extraction from the library and the prediction of available channels with fewer dimensions posed major challenges in the LTE scheduling. This paper surveys about various LTE scheduling algorithms, dimensionality reduction techniques, optimal feature selection techniques, multi-level classification techniques, and data mining combined with LTE techniques. A brief survey illustrates the impact of each technique on 3G/4G networks, channel availability prediction, scheduling of time slots in detail. A brief comparison of the techniques involved in the respective LTE processes via tabular form reveals that the verification of channel and user availability are the primary functions of the LTE scheduling. The survey of this paper identifies the limitations such as computational complexity and poor scheduling performance in the existing systems and encourages researchers to develop novel algorithms for LTE scheduling.

A Cross-Layer Bandwidth Allocation Scheme for HTTP-Based Video Streaming in LTE Cellular Networks

This letter investigates the benefits of flexible resource allocation when performing HTTP-based adaptive streaming (HAS) across cellular systems such as long-term evolution (LTE). To guarantee video fluidity in the presence of fluctuations of the instantaneous video source rate and channel capacity, we consider an HAS-based proxy video manager and resource controller located at the cellular base station. Based on the channel quality observed by mobile clients, the manager allocates the wireless bandwidth to mobile clients for transmitting the video streams. We propose a cross-layer bandwidth allocation scheme that takes into account the channel quality as well as the video quality requirements and encoding rate fluctuations of the HAS video stream and minimizes the transmission delays experienced by users. This cross-layer bandwidth allocation achieves the optimum in terms of HAS streams delays and it outperforms different bandwidth allocations procedures and state-of-the-art LTE schedulers.

Classification of LTE Uplink Scheduling Techniques: An M2M Perspective

The Internet of Things (IoT) aims at connecting a very large number of devices using an Internet-like architecture. Machine-to-machine (M2M) networks are considered the main component of the IoT. Long-term evolution (LTE) and LTE-advanced (LTE-A) are excellent candidates for supporting M2M communications due to their native IP connectivity and scalability for massive number of devices. Therefore, LTE schedulers should be capable of satisfying the needs of M2M devices such as timing constraints and specific quality of service (QoS) requirements. In this paper, we present a survey on uplink scheduling techniques over LTE and LTE-A from an M2M perspective. We focus on the aspects associated with M2M communications; namely, power efficiency, QoS support, multihop connectivity, and scalability for massive number of devices.

Smart Downlink Scheduling for Multimedia Streaming Over LTE Networks With Hard Handoff

This paper presents a novel smart downlink scheduling scheme to enhance the performance of multimedia transmission over long-term evolution (LTE) networks. LTE represents a promising framework for the next-generation broadband multimedia services because of its significantly increased data rate over 3G cellular networks. However, the current LTE scheduling scheme has largely been designed for general data traffic without adequate consideration of multimedia characteristics. Moreover, the hard handoff (HO) procedure adopted in LTE will further degrade the multimedia services when the mobile user moves from one cell to another. Even with increased data rate, current LTE systems still cannot deliver the expected quality of service (QoS) to the mobile users under various mobility scenarios, especially when the hard HO is evoked. Aiming at overcoming these major challenges, we develop in this paper a QoS-driven smart downlink scheduling scheme for enhanced multimedia transmission over LTE networks. The proposed design shall take the following QoS metrics into consideration: 1) the delay constraint of voice-over-Internet protocol flow; 2) the packet deadline of video flow; and 3) the service degradation induced by the hard HO procedure. We achieve the design objectives by creating three QoS-driven operational control modules: 1) a transmission delay control module to ensure the on-time arrival of various types of multimedia data; 2) an HO control module to warrant continuous multimedia services when the user moves across a cell boundary; and 3) a resource allocation module to strategically map the requested flows to best fit radio resource blocks. The simulation results confirm the performance gain of the proposed scheme.

Smart M2M Uplink Scheduling Algorithm over LTE

As a long term evolution plan of 3G, currently Long Term Evolution (LTE) System is also the internationally recognized mobile communication standard for 4G. FD-LTE is the LTE system of Frequency Division Duplex (FDD) mode. It is also the mobile network system with the fastest transmission rate. For the uplink scheduling in the LTE system, due to the difference between the power of UE and the terminal capability level, the system might not be able to schedule users (UE) for maximum bandwidth transmission. So the uplink scheduling strategy and scheduling method is one of the hot topics of current research. We have looked into the M2M devices accessing the LTE networks. Based on the characteristics of small data volume, the large number of M2M communication nodes, the different time limit and diversified services, compared with the existing H2H communication, it is found that there are major differences between M2M communications and H2H communications. In the light of this, in this paper, we have conducted an in-depth research of M2M communication resource allocation and scheduling of LTE system and introduced a queue priority strategy based on existing LTE scheduling algorithm. This results in an optimized intelligent scheduling algorithm suitable for M2M communications to improve the system throughput and reduce the average delay of system service without affecting into the traditional voice and video communications. DOI: http://dx.doi.org/10.5755/j01.eee.19.10.5457

Joint energy-distortion aware algorithms for cooperative video streaming over LTE networks

Real-time video streaming over long-term evolution (LTE) networks is investigated in the presence of mobile-to-mobile collaboration. A multi-objective problem for energy and distortion minimization is formulated and an energy-efficient, quality of service (QoS) aware suboptimal approach is presented for collaborative video transmission. In the proposed approach, mobiles are grouped into collaborative clusters using a low-complexity clustering algorithm. In each cluster, collaboration is implemented by having a cluster head send the content to other cluster members using a short-range wireless communications technology. The cluster heads receive the data on the long-range LTE links, either via unicasting or multicasting. LTE scheduling is taken into account on the long range transmissions. Videos with single description coding (SDC), multiple description coding (MDC), and scalable video coding (SVC) are considered. Results show that the proposed approach leads to significant energy savings and enhanced QoS compared to the non-collaborative scenario. Furthermore, MDC is shown to outperform SDC in terms of QoS, at the cost of a slight increase in energy consumption. The same conclusions were reached with respect to SVC compared to single layer coding.