LTE Advanced

About: LTE Advanced is a research topic. Over the lifetime, 4055 publications have been published within this topic receiving 74262 citations. The topic is also known as: Long-Term Evolution Advanced & LTE-A.

Wireless sensor networks and LTE-A network convergence

Abstract: In recent years, machine-to-machine (M2M) networks, which do not require direct human intervention, is increasing at a rapid pace. Meanwhile, the need of a wireless platform with a vast coverage and low network deployment cost for controlling and monitoring these M2M networks has not yet been met. Mobile cellular networks (MCNs) and wireless sensor networks (WSNs) are emerging as two heterogeneous networks that can meet the challenges of M2M communication through network convergence. In this paper, we propose a model for network convergence between a Long Term Evolution-Advance (LTE-A) cellular network and a wireless sensor network. Quality of service (QoS) issues are assessed by a comparative study of the network delay in tight coupling and loose coupling LTE-A configurations. Simulation results indicate that the network delay in our proposed converged network is acceptable for various M2M applications.

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

Abstract: 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.

A novel CQI calculation scheme in LTE\LTE-A systems

Abstract: We propose a novel Channel Quality Indicator (CQI) calculation scheme in LTE\LTE-A systems. SNR-CQI mapping scheme, as well as SINR-CQI mapping scheme, is extensively studied in previous works. However, these kinds of CQI mapping schemes always provide imprecise results in fading channels, since all of them ignore the channel quality degeneration caused by other factors. In this paper, we further improve CQI calculation by considering the impact of multipath delay spread. The calculation scheme is presented in details. In order to guarantee the converge of CQI calculation, a Block Error Ratio (BLER) based converge algorithm is proposed. Simulations show that our calculation scheme can select CQIs more precisely, and achieve better throughput performance than traditional schemes. Furthermore, our scheme converges much faster than previous schemes.

Performance of Uplink Carrier Aggregation in LTE-Advanced Systems

Abstract: Carrier aggregation (CA) has been proposed to aggregate two or more component carriers (CCs) to support a much wider transmission bandwidth for LTE-Advanced systems. With carrier aggregation, it is possible to schedule a user equipment (UE) on multiple component carriers simultaneously. In this paper, we evaluate the performance of uplink CA in LTE-Advanced systems with different CC allocation schemes. We first present the radio resource management (RRM) framework of multi-component carrier LTE-Advanced systems, with special attention on CC selection, adaptive transmission bandwidth, and uplink power control. Simulation results show that with CA and advanced CC selection algorithm, the cell edge user throughput of LTE-Advanced UEs maintains the same as Rel'8 UEs, but substantial performance gains can be achieved in terms of average and cell center user throughput, especially in low load traffic conditions.

In depth performance evaluation of LTE-M for M2M communications

Abstract: The Internet of Things (IoT) represents the next wave in networking and communication which will bring by 2020 tens of billions of Machine-to-Machine (M2M) devices connected through the internet. Hence, this rapid increase in Machine Type Communications (MTC) poses a challenge on cellular operators to support M2M communications without hindering the existing Quality of Service for already established Human-to-Human (H2H) communications. LTE-M is one of the candidates to support M2M communications in Long Term Evolution (LTE) cellular networks. In this paper, we appraise and present an in depth performance evaluation of LTE-M based on cross-layer network metrics. Compared with LTE Category 0 previously released by 3GPP for MTC, simulation results show that LTE-M offers additional advantages to meet M2M communication needs in terms of wider coverage, lower throughput, and a larger number of machines connected through LTE network. However, we show that LTE-M is not yet up to the level to meet future applications requirements regarding a near-zero latency and an advanced Quality of Service (QoS) for this massive number of connected Machine Type devices (MTDs).