Q-Learning vertical handover scheme in two-tier LTE-A networks

Bathich, Ammar; Mansor, Mohd Asri; Suliman, Saiful Izwan; Ali, Sinan Ghassan Abid

doi:10.11591/ijece.v10i6.pp5824-5831

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…Information used: initial seed 67 and Es/No units signal-to-noise ratio (SNR) dB. RF receiver, the RF receiver model includes the elements shown in Figure 5, where the RF demodulator consists of the following stages as shown in Figure 6 [1], [23], [24]. RF receiver, the RF receiver model includes the elements shown in Figure 5, where the RF demodulator consists of the following stages as shown in Figure 6 [1], [23], [24].…”

Section: Lte Rf Reciever Modelmentioning

confidence: 99%

“…RF receiver, the RF receiver model includes the elements shown in Figure 5, where the RF demodulator consists of the following stages as shown in Figure 6 [1], [23], [24]. RF receiver, the RF receiver model includes the elements shown in Figure 5, where the RF demodulator consists of the following stages as shown in Figure 6 [1], [23], [24]. LTE direct conversion receiver, estimate the impact of non-linearity, noise, DC offset, mismatches and imbalance introduced by an RF chain composed by an low noise pre amplifier (LNA), direct conversion demodulator and variable gain amplifier (VGA) parameters illustrated in Table 1, internat protocol (IP3) which is a well-known specification that controlling linearity in radio frequency functions and stages, The output power versus input power behavior of both amplifiers shows that p -12 dB of parallel amplifier is better than single path LNA [25].…”

Section: Lte Rf Reciever Modelmentioning

confidence: 99%

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Radio frequency receiver of long-term evolution system design by MATLAB Simulink

Al-Azzawi

Abid²,

Naji

2022

TELKOMNIKA

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For wireless broadband communication long-term evolution (LTE) is a standard for also mobile devices and data terminals, by using different radio interface together with core network improvements LTE increases the capacity and speed of mobile network. In this paper radio frequency receiver of radio-frequency long-term evolution (RF-LTE) is design and simulated using MATLAB Simulink, where the design based on illustrating parameters of each stage in LTE RF receiver from generating LTE waveform to error vector magnitude (EVM) measurements, where simulation results with 8 MHz bandwidth the transmitted signal power -3200 dBm, the received signal power (-140 to -160) dBm, while the demodulated signal reaches to -60 dBm difference between main loop and side loop witch lead to high confident recovered signal, also complementary cumulative distribution function (CCDF) measurements applied on output signals so that computes the power of complementary for cumulative distribution CCDF function from signal in time domain. Where CCDF curve shows value of time that a signal stand still above the level of average power for the measured signal or the probability of signal power will be above the level of average power.

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Section: Lte Rf Reciever Modelmentioning

confidence: 99%

Section: Lte Rf Reciever Modelmentioning

confidence: 99%

Radio frequency receiver of long-term evolution system design by MATLAB Simulink

Al-Azzawi

Abid²,

Naji

2022

TELKOMNIKA

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“…-Predicted energy sensing: the EDSS combined with NLMS, Li-NLMS, or the proposed approach, predicts the presence of FBS link-down spectrum, to keep the link as long as possible to UE; initialize the handoff at the right time; reduce the number of the unnecessary handovers and avoid ping-pong effect [9], [11]. The purpose of this metric is to evaluate: the physique aspect of prediction (the steady-state convergence) and speed of convergence.…”

Section: Analysis Metricsmentioning

confidence: 99%

“…However, small cells have been widely deployed by 4G LTE network operators alongside the conventional base station structure to provide customers with better service and coverage capacity [20]- [22]. Unfortunately, the traditional technique in the 4G LTE network initiates handoff by adjusting values of handover margin (HOM) and time to trigger (TTT) which can cause significant data loss during the session due to limited 3GPP group settings like, false handoff triggering or unnecessary handoff [9], [11] due to limited 3GPP group settings [23]- [26]. Knowing that, our previous works [23] and [24], have proven by using variable step-size normalized least mean square (VSS-NLMS) adaptive filter family, more especially, a VSS-NLMS combined with spectrum sensing (SS) probability method, can predict the detection of handoff triggering spectrum in 4G LTE heterogeneous networks (HetNets) with a rather long convergence time, mainly due to the estimation error in VSS-NLMS process.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian mobility management for heterogeneous networks

Hachemi

Irid²,

Benchehima³

et al. 2022

IJEECS

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Pending the arrival of the next generation of 5G which is not yet deployed in some countries like Algeria, 4G LTE remains one of the main mobile networks to ensure adequate quality services. Mostly, the deployment of femtocells to support the macrocell structure is crucial in the handover decision process. This paper presents a new approach called the epsilon Kalman Filter with normalized least-mean-square (ϵKFNLMS) to realize the handoff triggering in two-tier long-term evolution networks to ensure communication continuity to the pedestrian UE and improve mobility management. ϵKFNLMS uses a two-step process: a tracking process and a prediction process, to produce an optimal future state estimate at ”t+p”, where ”p” is the prediction footstep. The tracking process is performed by the Kalman filter, known for its precision in the state of the signal at time ”t”. It perfectly reduces the estimation error, injected afterward in the variable step-size NLMS algorithm (VSS-NLMS). While the prediction process is performed by the VSS-NLMS algorithm, an adaptive filter known for its prediction of the future state at ”t+p”. Thus, the goal is to achieve a faster convergence with a steady-state. ϵ value provides a precise setting of the handover trigger. Through different numerical simulations in several indoor environments, the results show that the performance and effectiveness of the proposed approach (ϵKFNLMS) provide lower mean square error (MSE), stable physical appearance in the prediction process (convergence with a steady-state), and excellent speed of convergence compared to the classical Normalized LMS (NLMS) and Li-NLMS adaptive filters.

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“…In some cases, each cell will have more than one RAN, hence it requires selection of a best network. The network selection takes place by using multi criteria decision making algorithms and others [10]- [13]. In IoV, the vehicles move at high speed and hence there is frequent change in connectivity as well as topology.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence based handover decision and network selection in heterogeneous internet of vehicles

Hussain

Yusof

Asuncion

et al. 2021

IJEECS

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Internet of vehicles (IoV) is an emerging area that gives support for vehicles via internet assisted communication. IoV with 5G provides ubiquitous connectivity due to the participation of more than one radio access network. The mobility of vehicles demands to make handover in such heterogeneous network. The vehicles at short range uses dedicated short range communication (DSRC), while it has to use better technology for long range and any type of traffic. Usually, the previous work will directly select the network for handover or it connects with available radio access. Due to this, the occurrence of handover takes place frequently. In this paper, the integration of DSRC, LTE and mmWave 5G on IoV is incorporated with novel handover decision making, network selection and routing. The handover decision is to ensure whether there is a need for vertical handover by using Dynamic Q-learning algorithm that uses entropy function for threshold prediction as per the current characteristics of the environment. Then the network selection is based on fuzzy-convolution neural network (F-CNN) that creates fuzzy rules from signal strength, distance, vehicle density, data type and line of sight. V2V chain routing is proposed to select V2V pairs using jellyfish optimization algorithm (JOA) that takes in account of channel, vehicle and transmission metrics. This system is developed in OMNeT++ simulator and the performances are evaluated in terms of success probability, handover failure, unnecessary handover, mean throughput, delay and packet loss.

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Q-Learning vertical handover scheme in two-tier LTE-A networks

Cited by 5 publications

References 22 publications

Radio frequency receiver of long-term evolution system design by MATLAB Simulink

Radio frequency receiver of long-term evolution system design by MATLAB Simulink

Pedestrian mobility management for heterogeneous networks

Artificial intelligence based handover decision and network selection in heterogeneous internet of vehicles

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