Long Term Evolution (LTE) is the next step (fourth generation) mobile radio communication technology that succeeds the HSPA 3GPP standardization body. LTE is expected to be the most competitive radio technology in the future to provide high-data-rate transmission, low latency, improved service and reduced costs. As known, mobile phone traffic is divided into two parts: an uplink and a downlink. This paper presents the LTE two duplexing modes: LTE-TDD (Time Division Duplexing) and LTE-FDD(Frequency Division Duplexing).

/pdf/lte-fdd-vs-lte-tdd-from-a-qos-perspective-4h143vum7s.pdf

LTE FDD vs LTE TDD from a Qos Perspective

This paper describes comparative analysis of the performance between three scheduling algorithm, Round Robin, Proportional Fair, and Maximum C / I based on Monte Carlo simulation method on the Release 8 LTE network using case studies of Bandung city Simulations performed with radio planning software Atoll 321 From the test results, Proportional Fair scheduling algorithm outperformed the other two algorithms in downlink parameters, the cumulated throughput, fairness and the average BLER

Scheduling techniques in release 8 LTE network

Wireless Sensor Networks (WSNs) is an area that has attracted a lot of attention currently worldwide. WSNs are implemented to monitor temperature, humidity, and pressure, among others within the agricultural environment. This paper addresses the traffic congestion that occurs within WSNs in the agricultural environment during packet transmission that is normally caused by head-of-line blocking. As a result, packet loss, packet delay, and network performance impairment occurred during packet distribution in the network. This paper proposed an Intelligence Traffic Routing (ITR) algorithm to manage packet flow to avoid traffic congestion in WSNs within the agricultural environment while improving Quality of Service (QoS). The LBRM (Load Balancing Routing Management) and MLCC (Machine Learning Congestion Control) algorithms were integrated to develop the proposed ITR algorithm. Network Simulator 2 (NS-2) was used to test the effectiveness of the proposed ITR algorithm. The simulation results showed that the proposed ITR algorithm reduced packet loss by 27.3%, packet delay by 43.4%, and improved network throughput by 98.4% when compared with LBRM and MLCC algorithms.

Intelligent Traffic Routing Algorithm for Wireless Sensor Networks in Agricultural Environment

Downlink scheduling is one of the essential operations when it comes to improving the quality of service in Long Term Evolution (LTE) With an increasing user base, there will be an extensive challenge in resource provisioning too A review of existing approaches shows that there is a significant possibility of improvement in this regard, and hence, the proposed manuscript presents a benchmarking model for addressing the issues associated with Best-Channel Quality Indicator (CQI), Round Robin, and Hybrid Automatic Repeat Request (HARQ) The outcome shows HARQ scheduling to offer better performance in higher throughput, higher fairness, and lower delay over different test cases

/pdf/simplified-framework-for-benchmarking-standard-downlink-enrb3dwzx0.pdf

Simplified Framework for Benchmarking Standard Downlink Scheduler over Long Term Evolution

: In Wireless Sensor Networks (WSNs), most of the issues related to management of topology, managing energy, estimation of bandwidth, calculating packet loss etc. are addressed. Several researches have addressed queue management issues, congrolling the congestion that is incorporated with Soft Computing (SC) techniques. Very few works have focused on mitigating or avoding the congestion, delay estimation and the required bandwidth for transmitting data packets in the sensor network. This paper presents an Active Queue Management (AQM) model for estimating congestion using priority Random Early Detection (RED) to mitigate the congestion. The proposed method integrates AQM model with fuzzy subsystem which controls the admission of packets into the network and the same is compared with several existing works and is simulated using Matlab Simulator. The results outperforms the existing methods in terms of packet loss probability, energy consumption, queue length, end-to-end delay and delay between intermediate nodes.

/pdf/aqm-model-to-estimate-and-mitigate-congestion-using-priority-cjujz9n9.pdf

Hemanth Kumar

Papers

Simplified Framework for Benchmarking Standard Downlink Scheduler over Long Term Evolution

AQM Model to Estimate and Mitigate Congestion using Priority RED in WSN