LTE-Advanced System Research Articles

Optimization-assisted Resource Exchange Framework for Device-to-device Communication in LTE-advanced System

Optimization-assisted Resource Exchange Framework for Device-to-device Communication in LTE-advanced System

AN ENHANCED RESOURCE ALLOCATION SCHEME FOR LONG TERM EVOLUTION ADVANCED NETWORK

The emerging Long-Term Evolution Advanced (LTE-A) is an extended version of Long-Term Evolution (LTE) fourth-generation (4G) wireless broadband technologies that supports high speed transmission of multimedia applications, like video streaming, multimedia online game, Voice over IP (VoIP), browsing over the web etc. The Third Generation Partnership Project (3GPP) does not define a standard packet scheduling strategy in LTE-A. Therefore, it is expected for researchers and network service providers to design efficient schemes. This paper proposed an Enhanced Resource Allocation Scheme (E-RAS) that improves the packet scheduling strategy of LTE-A by addressing issues related to delay and fairness of users with both high and low channel conditions, for both Real Time (RT) and Non-Real Time (NRT) flows. The aim was achieved by using a fair spectrum allocation mechanism that consider the priority of RT flows of cell-centre users, without neglecting the throughput requirements of edge-centre users. Moreover, the proposed algorithm introduces an efficient traffic load balancing mechanism to efficiently regulate traffic load in LTE-A network. To evaluate the proposed scheme, simulation was conducted using the MATLAB programming with LTE-A system level simulator. The result obtained shows that the proposed scheme achieves better delay for RT and NRT traffics by 7% and 11% respectively as against the Resource Allocation Scheme to Optimize the Throughput (RASOT) scheme.

A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network

Nowadays, the Long-Term Evolution-Advanced system is widely used to provide 5G communication due to its improved network capacity and less delay during communication. The main issues in the 5G network are insufficient user resources and burst errors, because it creates losses in data transmission. In order to overcome this, an effective Radio Resource Management (RRM) is required to be developed in the 5G network. In this paper, the Long Short-Term Memory (LSTM) network is proposed to develop the radio resource management in the 5G network. The proposed LSTM-RRM is used for assigning an adequate power and bandwidth to the desired user equipment of the network. Moreover, the Grid Search Optimization (GSO) is used for identifying the optimal hyperparameter values for LSTM. In radio resource management, a request queue is used to avoid the unwanted resource allocation in the network. Moreover, the losses during transmission are minimized by using frequency interleaving and guard level insertion. The performance of the LSTM-RRM method has been analyzed in terms of throughput, outage percentage, dual connectivity, User Sum Rate (USR), Threshold Sum Rate (TSR), Outdoor Sum Rate (OSR), threshold guaranteed rate, indoor guaranteed rate, and outdoor guaranteed rate. The indoor guaranteed rate of LSTM-RRM for 1400 m of building distance improved up to 75.38% compared to the existing QOC-RRM.

Advanced Handover Techniques in 5G LTE-A Networks

In High-speed and seamless access is the goal of next-generation mobile communication systems, especially for VoIP and multimedia services. The supported mobile speeds are expected to reach 500 km/h in the Fifth Generation Networks (5G), lead to the handover will take place continuously as the mobile user moves through the cells. Therefore, efficient radio resource management, including the handover, load balancing technologies is vital problems. In this work, we perform a comprehensive survey of advanced handover techniques in the next-generation mobile communication system (5G) as well as requirements and features for the LTE-Advanced system. In the LTE-Advanced system, Fractional Soft Handover (FSHO) with 5 CCs improves latency and better handover quantity when not using CA. Currently, the proposed handover techniques have many advantages but have not yet resolved important issues in the handover process. Therefore, new handover techniques are required to support a fast and seamless handover process in the LTE-Advanced system. As a result, an advanced transfer technique proposed by combining FSHO, SSHO techniques can reduce latency, enhance efficiency and reliability, especially in the border areas between the cells.

Seven Challenges for Communication in Modern Railway Systems

To meet the increasing demands for passenger data rates, modern railway communication networks face significant challenges. The advent of 5G communications after the long-term evolution (LTE) and LTE-Advanced (LTE-A) systems provides several technological advances to address these challenges. In this paper, after reviewing the main 5G communication aspects for modern railways, we describe seven main challenges faced by train connectivity, and discuss appropriate solutions. Specifically, we elaborate on techniques for ensuring connectivity and energy efficiency for the passengers' user equipment (UE) through the use of mobile relays (MRs) on top of the train wagons in conjunction with intelligent resource allocation.

An improved water-filling algorithm based on power allocation in network MIMO

Inter-Cell Interference (ICI) could affect the performance of the LTE-Advanced (LTE-A) system and can be evaluated according to the user’s Signal to Interference and Noise Ratio (SINR). Owing to this, the power and frequency allocation schemes can be utilized in order to attain an optimum trade-off within the obtained SINR and the resulting interference. In this study, power allocation was integrated into the Dynamic Fractional Frequency Reuse (DFFR) scheme and Network MIMO (NetMIMO) to reduce ICI and enhance the performance of LTE-A downlink. Moreover, this study focuses on the design and development of a novel power allocation algorithm, which is an extension to the previous Improved Water-Filling algorithm (IWF), namely, Low-Complexity Improved Water-Filling algorithm (LC-IWF). Results indicated a reduction in computations and enhancement in interference rejection performance in comparison to the existing algorithms by removing the Lagrange multiplier and water level. Moreover, simulation results revealed that the proposed algorithm enhances the throughput and fairness of up to 20% and 25%, respectively. Furthermore, the power consumption was reduced by 41% and the running time was 5–10 times faster than the IWF algorithm while attaining the best solution. The proposed power allocation algorithm provided a significant improvement in the performance of the NetMIMO LTE-A system, which can be extended for future 5G networks.

Improved resource allocation scheme for optimizing the performance of cell-edge users in LTE-A system

Improved resource allocation scheme for optimizing the performance of cell edge users in Long Term Evolution-Advanced (LTE-A) system is proposed in this paper. The proposed algorithm optimally assigns and allocates Carrier Components (CCs), Radio Blocks (RBs) and Modulation and Coding Scheme (MCS) index for the users based on the Quality of Service (QoS) provision to meet the on demand service requests. The proposed algorithm divides the cell regions into cell centre and cell edge depending on the traffic load on the network. The available RBs are allocated to meet the on demands requests of the users among these two regions. RB usage ratio for optimizing the performance of the scheduling algorithm is used during resource allocation. As the RB usage ratio reaches more than 70%, throughput and data rates of the cell may not be reached in timely and reliable manner. The proposed algorithm takes RB usage ratio into consideration during scheduling to maintain minimum QoS provisions of the users for resource allocation. Simulation result shows that, performance of the cell edge users optimally met with minimum throughput and data rates for reliable communication in the LTE-A system.

Cross-Layer Optimised Video Multicasting over LTE-A

This paper proposes an application layer forward error correction (AL-FEC) based video multicasting for Long Term Evolution-Advanced (LTE-A). A cross-layer LTE-A simulator is developed to analyse the performance of the proposed system. Simulation results show that the proposed system provides higher service coverage, i.e., 4 dB improvement in Signal to Interference Noise Ratio (SINR) and uses less radio and networks resources (i.e., provide up to 48,7% higher spectral efficiency) compared to the legacy LTE-A system.

FFR TEKNİĞİ KULLANILAN ÇOK HÜCRELİ LTE-A SİSTEMLERİNDE KAYNAK ATAMA ALGORİTMALARININ KARŞILAŞTIRILMASI

The purpose of resource allocation in LTE-A (Long Term Evolution Advanced) systems is that the time frequency resource can be used effectively by the users according to specific parameters. Scheduling algorithms are used to efficiently allocate system resources among users. In this study, when allocating frequency among cells in multi-cell LTE-A systems, the Fractional Frequency Reuse (FFR) technique, a static ICIC (Intercell Interference Coordination) technique, was first used for GPF (Generalized Proportional Fair) and AMM (Arithmetic Mean Method) schedulers. BCQI (Best Channel Quality Indicator, RR (Round Robin) and PF (Proportional Fair), AMM and GPF schedulers were compared in terms of mean user throughput, edge user throughput, peak user throughput and fairness. As a result of the simulations, it was observed that GPF schedulers increased edge user throughputs and system fairness compared to other schedulers in case of using FFR in multicellular LTE-A systems.

Adaptive beamformers using 2D-novel ULA for cellular communication

This paper presents the use of a novel two dimensional (2D) uniform linear array for adaptive beamforming algorithms. We used this novel antenna configuration to study the popular adaptive beamforming algorithms, namely, least mean square algorithm (LMS) and least normalized mean square algorithm (NLMS) and also proposed a variable step-size NLMS (VSSNLMS) algorithm. These beamformers are named as 2D-LMS, 2D-NLMS and 2D-VSSNLMS algorithms respectively. These algorithms not only improve the convergence rate but also mitigates the effect of interference by producing deep nulls in the interference directions. These algorithms are suitable for low-power, low-cost, anti-interference MIMO-WLAN, WiMAX, 4G LTE and other advanced communication systems.

Performance Analysis of STBC Channel Coding Techniques for LTE Advanced Systems

Performance Analysis of STBC Channel Coding Techniques for LTE Advanced Systems

Finding a Pareto Optimal Solution for a Multi-Objective Problem of Managing Radio Resources in LTE-A Systems: A QoS Aware Algorithm

Due to ever increasing users demands, fulfilling the needs of growing users is challenging with constrained resources for Long Term Evolution- Advanced (LTE-A). In this research paper we have proposed an innovative approach for Radio Resource Management (RRM) that makes use of the evolutionary multi-objective optimization techniques for Quality of Service (QoS) facilitation and embeds it with the modern techniques for RRM. We have proposed a novel algorithm that selects an optimal solution out of a set of a representative Pareto front in accordance with the users QoS requirements. It is completely novel as it uses the multi-objective optimization algorithms for achieving desired QoS in LTE-A system. The efficiency and performance of the proposed work has been further improved by autonomously assigning and managing resources among various users and applications. The results have been discussed that showcase efficient system performance and better user experience.

Iterative smoothing filtering schemes by using clipping noise‐assisted signals for PAPR reduction in OFDM‐based carrier aggregation systems

Aggregating a lot of subcarriers will generate high peak-to-average power ratio (PAPR) which makes the overall system performance degradation. Among the existing PAPR reduction methods for orthogonal frequency-division multiplexing (OFDM)-based systems, iterative clipping and filtering (ICF) may be easy to implement, but ICF may cause signal distortion, so that the system cannot approximate the processed signals to the original signals which degrades error rates at receivers. Here, the authors proposed a new method based on ICF with smoothed clipping signals, and find the optimised filter to achieve the minimum error vector magnitude and efficiently reduce PAPR. Owing to the lack of a wide range of continuous bandwidth, the Third Generation Partnership Project proposed carrier aggregation (CA) technology in LTE-advanced systems. CA can aggregate many contiguous or non-contiguous fragment spectrums for supporting larger bandwidth and higher peak data rate. Here, the authors also evaluate the developed PAPR reduction scheme for the use in CA scenarios. The efficacy of the proposed scheme is demonstrated in the simulation results.

Dual-Codebook Precoding Selection for LTE-A System

Long-term evolution advanced (LTE-A) is currently the most popular 4G wireless communication system worldwide. LTE-A system defines eight transmit antennas in downlink and adopts the precoding matrices to reduce the effect of interference and improve the received signal quality at the receiver. In this paper, three algorithms adopting the unique overlapped angle of direction of the beams for the LTE-A precoding matrices and the Euclidean Norm relation are proposed to reduce the calculations during precoding selection. Computer simulations and experiment results show that the proposed low-complexity precoding selection method can reduce more than 70% computational complexity and still maintains a good bit error rate performance compared with literary full search method.

Low Latency Random Access for Small Cell Toward Future Cellular Networks

In future cellular networks, performance target includes not only increasing data rate, but also reducing latency. The current LTE-Advanced systems require four message exchanges in the random access and uplink transmission procedure, thus inducing high latency. In this paper, we propose a 2-way random access scheme which effectively reduces the latency. The proposed 2-way random access requires only two messages to complete the procedure at the cost of increased number of preambles. We study how to generate such preambles and how to utilize them. According to extensive simulation results, the proposed random access scheme significantly outperforms conventional schemes by reducing latency by up to 43%. We also demonstrate that computational complexity slightly increases in the proposed scheme, while network load is reduced more than a half compared to the conventional schemes.

A Joint Allocation and Path Selection Scheme for Downlink Transmission in LTE-Advanced Relay System with Cooperative Relays

A Joint Allocation and Path Selection Scheme for Downlink Transmission in LTE-Advanced Relay System with Cooperative Relays

CTMC modelling for H2H/M2M coexistence in LTE‐A/LTE‐M networks

Machine-to-machine (M2M) devices with their expected exponential booming in the near future, will be one of the significant factors to influence all mobile networks. Inevitably, the expected huge number of M2M devices causes saturation problems, and leads to remarkable impacts on both M2M and human-to-human (H2H) traffics, services, and applications. The research free-space lack requires creating an appropriate model which describes the functionality of long-term evolution-advanced (LTE-A) and long-term evolution for machine (LTE-M), through mathematical frameworks to evaluate relevant performance metrics. In this study, we bridge this gap by proposing a continuous-time Markov chain (CTMC) model as a stochastic process tool to characterise the H2H/M2M coexistence based on analytical equations. Afterwards, the authors validate the proposed model through extensive Monte Carlo simulations. Eventually, it becomes approachable to characterise the impact of H2H/M2M coexistence in one LTE-A/LTE-M radio resource allocation in dense areas and under disaster scenarios. The simulation results show that using a prioritise LTE-A system for both M2M and H2H traffics is convenient in dense area scenarios, while in emergency cases, it is more appropriate to use a non-prioritise traffic strategy to keep H2H and M2M traffics working properly at the same time.

Resources Allocation in Multicell D2D Communications for Internet of Things

Device-to-device (D2D) communication can realize the direct communication between mobile users with short distance. It is an enabling technology for realizing Internet of Things in the long-term evolution-advanced system to the future fifth-generation mobile communication system. D2D communication multiplexes the licensed spectrum of cellular users (CUs) to D2D users (DUs) to improve resource utilization of cellular networks. In this paper, a cross-cell fractional frequency reuse-based frequency resource multiplexing (CFRM) scheme is proposed for the multicell D2D communication. In the proposed CFRM, each cell is first divided into two regions, and each region is allocated different spectrum resources to reduce the interference between neighboring cells. Then, the uplink resources of CUs are partially multiplexed by DUs, which can decrease the interference of the DUs to the CUs. The simulation results show that CFRM can reduce the interference, guarantee the quality of service of CUs, and increase the throughput of cellular networks.

Joint downlink resource allocation in LTE-Advanced heterogeneous networks

Long Term Evolution-Advanced (LTE-A) heterogeneous network constituted by macro cells and small cells has attracted wide attentions as a solution to the data surge problem in the developing wireless networks, e.g., 5G mobile communication systems, demanding for higher bandwidth and better quality. Taking also cognitive radio into account, we develop here a dynamic resource allocation algorithm for the downlink transmission which involves resource blocks, component carriers, modulation and coding schemes, and frequency partitions with an overall consideration. Specially, we consider not only to determine the multiple kinds of resources at each transmission time interval, but also to enforce the constraints specific to the LTE-A system with carrier aggregation. To this end, we conduct a mathematical programming model which involves nonlinear constraints on binary variables to formulate the stochastic optimization problem, and introduce a submodular-based greedy algorithm to resolve the high-dimensional NP-hard allocation problem involved. In addition, for the traffic and channel conditions to be time-varying in practice, our approach based on the Lyapunov drift-plus-penalty framework requires no prior knowledge of such information to alleviate the system overhead. Given that, the greedy allocation algorithm embedded is further used to guarantee the performance of allocation by means of submodularity. Finally, the numerical evaluation is conducted to verify our approach, revealing its performance benefits complementing the previous works that pay no special attention to the issues addressed here.

A Q-learning approach for machine-type communication random access in LTE-Advanced

Due to the wide proliferation of the 3GPP long term evolution (LTE) and LTE-Advanced systems as the air interface for 4th generation (4G) wireless systems and beyond, telecommunication operators became more interested in using the LTE infrastructure to meet the projected surge in demand for machine-to-machine (M2M) and IoT communications. As a result, the LTE-Advanced system must evolve to provide these services as an overlay over the original network that is originally designed for human-to-human (H2H) communication. In this setup, M2M communication devices share the same random access channel (RACH) with the H2H communication devices. Since M2M communication is expected to be massive, consequently the RACH is a new bottleneck in the LTE-A system. This triggered the need to enhance the operation of the RACH to meet the needs to support the low power and low data rates of M2M devices. The current standardized scheme to request access to the system is known to suffer from congestion and overloading in the presence of a huge number of devices. Accordingly, recent research pointed to the need of designing more efficient ways to manage the random access channel in such setups. Most of previous works focused on solving the congestion problem in RACH in the presence of M2M solely, but not with the existence of both M2M and H2H services. In this work we propose a new random access channel scheme based on Q-learning approach to reduce the congestion problem. The scheme adaptively divides the available preambles between both M2M and H2H devices in a way that provides an acceptable service for the H2H devices and maximizes the number of active M2M devices. The adaptation is done based on current demand levels from both the H2H and M2M devices and the observed service levels. The results indicate that the proposed approach provides high random access channel success probability for both M2M and H2H devices even with the huge number of M2M devices.