Topic
LTE Advanced
About: LTE Advanced is a(n) research topic. Over the lifetime, 4055 publication(s) have been published within this topic receiving 74262 citation(s). The topic is also known as: Long-Term Evolution Advanced & LTE-A.
Papers published on a yearly basis
Papers
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08 May 2014
TL;DR: Channel estimation with partial channel state information (CSI) for downlink CoMP is aimed and pilot based channel estimation algorithms such as least square (LS) and linear minimum mean square error (LMMSE) have been evaluated for different channel models in LTE advanced joint processing CoMP downlink.
Abstract: Coordinated Multi Point transmission and reception (CoMP) has been considered as a promising concept to enhance the throughput and coverage in cell edge area by reducing inter-cellular interference (ICI) in both the downlink and the uplink of LTE Advanced system. In CoMP significant improvement in performance gain can be achieved through base station (eNodeB) coordination by interchanging information such as user data, channel information and scheduling decision. As perfect knowledge of channel is rarely available, designing transmitter based on partial (statistical) channel state information (CSI) are of predominant importance because they encompass the perfect as well as partial-knowledge paradigms. In this paper channel estimation with partial channel state information (CSI) for downlink CoMP is aimed. Pilot based channel estimation algorithms such as least square (LS) and linear minimum mean square error (LMMSE) have been evaluated for different channel models in LTE advanced joint processing CoMP downlink. Performance of these algorithms has been measured and compared in terms of bit error rate (BER) and mean square error (MSE).
01 Jan 2017
TL;DR: The HyLTEsteg uses covert timing channels, which is fitted the legitimate data stream of network, as a trigger for covert storage channels (CSC), and the CSC utilizes the sequence number fields of Radio Link Control (RLC) layer and Packet Data Convergence Protocol (PDCP) layer to transmit hidden information.
Abstract: In this paper, we have analyzed the sub-protocol stack of Long Term Evolution Advanced (LTE-A) System, and proposed a hybrid covert channel, called HyLTEsteg, designed for LTE-A System. The HyLTEsteg uses covert timing channels (CTC), which is fitted the legitimate data stream of network, as a trigger for covert storage channels (CSC). And the CSC utilizes the sequence number (SN) fields of Radio Link Control (RLC) layer and Packet Data Convergence Protocol (PDCP) layer to transmit hidden information. The performance of the HyLTEsteg’s anti-detection and hidden information transmission are evaluated and analyzed. Keywords-LTE-A; covert channel; hidden information; capacity; transmission time interval
26 Mar 2013
TL;DR: A preliminary Tunable Digital Low pass Channel Filter designed and implemented according to the 3GPP Release 10 Receiver Characteristics shows that the filter can satisfy Release 10 requirements in terms of magnitude response.
Abstract: This paper introduces a preliminary Tunable Digital Low pass Channel Filter designed and implemented according to the 3GPP Release 10 Receiver Characteristics. The filter is built using Altera's DSP Builder Design Environment and Matlab Simulink to allow for FPGA implementation at a later stage. The functional simulation shows that the filter can satisfy Release 10 requirements in terms of magnitude response. This filter is scheduled for implementation on one of Altera's FPGAs in order to assess its performance.
01 Jan 2017
TL;DR: This chapter introduces three new methodologies to enhance the performance of LTE cellular networks, which addresses the relay link problem, the throughput and RSS for the users inside public transportation vehicles as well as proposes a new algorithm called Balance Power Algorithm (BPA) that aims to minimize the transmission power consumption for Moving Relay (MR).
Abstract: This chapter introduces three new methodologies to enhance the performance of LTE cellular networks (Yahya, Aldhaibani, & Ahmed, 2014). The first methodology focuses on RN deployment in the cell and called Optimum RN Deployment (ORND) to enhance the coverage area and capacity at cell-edge region. RN is considered as a solution to address low SINR at the cell edge, resolve coverage holes due to shadowing, and to meet the access requirement of nonuniform distributed traffic in densely populated areas to improve coverage and throughput. However, the interference between stations is an important problem that is associated with the RN deployment in the cell. This methodology considers the mitigation of the interferences between the stations and ensures the best capacity with the optimization of transmission power. ORND is based on mathematical analysis of determination of the optimum location for RN, optimal number of relays per cell, suitable power for each RN, and design a frequency reuse scheme, which exploits available radio spectrum. Second methodology, called Enhance Relay Link Capacity (ERLC), addresses the relay link problem, where this link carries information generated by the RN and users attached to it to BS. Although the long distance between the proposed relay location and BS improves the coverage at cell boundaries, this distance also degrades the relay link efficiency and increases the probability of outage. On the other hand, the approximation of the relay location does not achieve the desired goals to enhance coverage at the cell-edge region. ERLC introduces active solution and easy implementation to solve relay link problem. The values of parameters which are used in these models are based on the LTE system specifications and presented by (3GPP, TS. ETSI, 2007) and mentioned in Table 4.1. Third methodology focuses on enhancing the throughput and RSS for the users inside public transportation vehicles as well as proposes a new algorithm called Balance Power Algorithm (BPA) that aims to minimize the transmission power consumption for Moving Relay (MR). Summary of research on improving coverage and capacity is shown in Fig. 3.1.