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In contrary with the seamless connectivity in a telecom network, successful hand over (HO) of the user from one cell to another is an important factor that affects the network performance. The two popular HO model named as Margin Based Hand over (MBH) and Quality Based Hand over were designed for the existing LTE networks. In this paper, we discuss...
Citations
... They concluded that increase in feedback delay of UE with proportional fair MAC scheduler improved the network SNR and throughput. In [16] the authors proposed an optimum proportional fair MAC scheduler that improved the throughput of the UE on the same Signal to Noise Ration of the EUTRAN network. In [17] the authors discussed in the UE handover performance in EUTRAN and proposed new handover method that improved the network performance. ...
The requirement of the wireless network is increasing with the developments in every field of society. As it helps to use contactless controls and communications with different applications. From the fifth generation and beyond, the need of wireless networks is to fulfil the capacity requirement and offered better connectivity between peer entities. To offer high throughput within the wireless network, beamforming is one among the optimum solutions that provides better connectivity and bandwidth among peer entities. In the work, we assessed the wireless capacity of beamforming wireless network using the Frequency Division Duplexing (TDD) and Time Division Duplexing (TDD) type for multiple Transmission Modes (TM) under different radio conditions. We modify the channel properties with respect to varying the Signal to Noise Ratio (SNR) and measured the performance of both systems independently in terms of throughput, and Block Error for both the duplexing using quantitative and ratio analysis. Further, we conclude that the beamforming for higher TMs offered high throughput in best to worst channel conditions.
... It is possible to estimate path loss propagation in a mobile environment based on simulation of empirical propagation models. For optimum performance, some of these models have been optimized for the environment of interest ( Saxena and Sindal, 2018). However, simulation- based models are not able to predict the propagation environment with high accuracy as desired. ...
Analysis of predicted and measured path loss over a Lagoon environment is presented. Propagation
measurements were carried out at 1800 MHz, within a quarter of a year (May to August 2017) using Huawei
Technologies drive test equipment. Measured data comprising of the received signal strength was taken for the initial
measurements, measurements after first month, measurements after second month, and measurements after the third
month. Measured path loss was compared with predictions made by free-space, log-distance, two-ray, COST 231-
Hata, and Stanford University Interim (SUI) models. The COST-231 Hata model showed the most accurate
performance with root mean square errors (RMSEs) of 10.03 dB, 12.38 dB, 17.59 dB, and 7.67 dB for the initial
measurements, measurements after first month, measurements after second month, and measurements after third
month, respectively. In order to achieve a more accurate prediction, the COST 231 Hata model was optimized using
the least square algorithm. The optimized model showed improved signal prediction with RMSEs of 7.90 dB, 9.28
dB, 14.82 dB and 5.28 dB, respectively. The average RMSEs of the optimized COST 231 Hata model showed 9.32
dB compared with 11.92 dB predicted by the actual COST 231 Hata model. This accounts for about 21.81%
improvements over the existing COST 231 Hata model. Therefore, the optimized COST 231-Hata model could be
used to characterize radio channels in the investigated environments
... Further authors proposed a combined model on Markov chain and measured the system performance in terms of blocking probability, mean queue length, and transmission delay. In [14], the authors discussed in the UE handover performance in EUTRAN and proposed new handover method that improved the network performance. ...
The seamless connectivity of the user equipment (UE) with an LTE base station is an important challenge which impacts the network performance. The network resource for handover procedure is controlled through different handover algorithms. These algorithms work on two aspects, that is power margin and quality of the reference signals. In this paper, we study and evaluate the performance of both algorithms on a typical experimental setup (using network simulator-3) with a different UE mobility scenario. The performance evaluation was performed under a set of key performance indicator i.e. throughput and hand over counts. In simulation results, we found that margin based algorithm offers better performance while comparing with the quality-based algorithm in terms of system throughput.
The handover procedure is critical for a smooth connection in a Long Term Evolution Advanced (LTE-A) network. However, when the number of user equipment (UE) in a cell grows, the performance of the handover method suffers. This study focuses on identifying the best target cell or evolved Node B (eNB) to address this problem. The target cell in this method is selected using multi-objective functions like Reference Signal Received Quality (RSRQ), Reference Signal Received Power (RSRP), and uplink Signal to Interference Plus Noise Ratio (SINR). The UE from the loaded cell would provide optimum target eNB after eNB selection. Research has integrated a Tent chaotic map, Adaptive inertia weight, Opposition-based learning into the Whale Optimization Algorithm (TAOWOA). Integration has been made to enhance the likelihood of looking for optimal global solutions. The performance of the expected handover scheme was improved by lowering the call dropping ratio (CDR), call blocking ratio (CBR), handover failure, and handover ping-pong as well as increasing throughput and energy efficiency, according to simulation findings. Proposed scheme gives better results than previous research in terms of call blocking and dropping probabilities as well as failures, throughput, and ping-pong handovers.
Long Term Evolution (LTE) networks are designed with overlap between cells to support mobility along the network cells and to avoid out of coverage area; especially in suburban and rural area because high mobility. The overlapping results in interferences which reduce network capacity so this paper aims to bring customer demands for high-quality networks. This paper involves a good understanding of radio network planning and optimization of LTE and perform a case study in Taiz governorate with a selected suburban and rural area of 86.846 km². Self-organizing networks (SONs) are widely considered to improve the end users’ quality of experience. The simulation was performed using ATOLL software. The radio frequency (RF) optimization involves Inter-Cell Interference Coordination (ICIC). The downlink (DL) LTE coverage area is enhanced and increased to 84 km² with probability of 97%. In addition, the overlapping zone is reduced to 0.4% and the block error rate (BLER ≥ 0.2) is reduced to 2.1% in the DL and (BLER ≥ 0.2) to 0.6% in the uplink (UL).
In evolved UMTS terrestrial radio access network, long‐term evolution is known as the fourth‐generation mobile network with a leading radio performance metric as a throughput and signal‐to‐noise ratio (SNR) that affect the radio links. In this paper, we analytically describe the different long‐term evolution propagation model and measure and compare the throughput and SNR on different Doppler frequencies. To validate the performance of channel, we implement network on 3 standard models, viz, as pedestrian (EPA), vehicular (EVA), and typical urban (ETU) at different Doppler frequency (5, 70, 300, and 750 Hz), and different value of SNR (10, 13, 16, and 19 dB) on downlink channel and measured the channel throughput rate. We conclude that a performance of the channel for high SNR values severely affected by the high value of the Doppler frequency while for the channels at low SNR values is a negligible effect with high Doppler frequency.
