An Intelligence-Based Recurrent Learning Scheme for Optimal Channel Allocation and Selection in Device-to-Device Communications

Al-Makhadmeh, Zafer; Tolba, Amr

doi:10.1007/s00034-019-01056-7

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…For these reasons, operators are constantly searching for a better synchronization scheme that allows for sharing very accurate timing information reliably and cost-effectively [44]. Al-Makhadmeh and Tolba [45] study one of the most popular problems in telecommunications, the unit-response (or master-slave) configuration, in which there is a leader (master) and several devices (slaves) that need to synchronize with the leader. This problem is known as master-slave synchronization of chaotic systems.…”

Section: Optimization Problems With Synchronization Issuesmentioning

confidence: 99%

Biased-Randomized Discrete-Event Heuristics for Dynamic Optimization with Time Dependencies and Synchronization

et al. 2022

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Many real-life combinatorial optimization problems are subject to a high degree of dynamism, while, simultaneously, a certain level of synchronization among agents and events is required. Thus, for instance, in ride-sharing operations, the arrival of vehicles at pick-up points needs to be synchronized with the times at which users reach these locations so that waiting times do not represent an issue. Likewise, in warehouse logistics, the availability of automated guided vehicles at an entry point needs to be synchronized with the arrival of new items to be stored. In many cases, as operational decisions are made, a series of interdependent events are scheduled for the future, thus making the synchronization task one that traditional optimization methods cannot handle easily. On the contrary, discrete-event simulation allows for processing a complex list of scheduled events in a natural way, although the optimization component is missing here. This paper discusses a hybrid approach in which a heuristic is driven by a list of discrete events and then extended into a biased-randomized algorithm. As the paper discusses in detail, the proposed hybrid approach allows us to efficiently tackle optimization problems with complex synchronization issues.

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Section: Optimization Problems With Synchronization Issuesmentioning

confidence: 99%

Biased-Randomized Discrete-Event Heuristics for Dynamic Optimization with Time Dependencies and Synchronization

et al. 2022

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“…Equations (18) and (19) represent the general calculation formulas for the airplane's turning rate and turning radius, respectively. Where arf represents the established slope; v represents the current aircraft airspeed; g represents the gravity acceleration of the area in which it is located.…”

Section: ) Horizontal Turn Taskmentioning

confidence: 99%

A New Markov Decision Process Based Behavioral Prediction System for Airborne Crews

et al. 2020

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In order to ensure the normal and stable flights in the aircraft, a variety of sensors and corresponding instrumentation systems have been applied on the aircraft to monitor/control the current flight status, and the resulted data ensure the flight safety with a heavy burden on the pilot. In views of this, nowadays, the aircraft cockpit automation assistance system has become a hot topic. This paper is based on the pilot's future operational behavior which can be predicted through different stages of flight operations after the automated assistance system is triggered, thus providing the pilot with assistance in accordance with his operating habits. We have established a MDP (Markov Decision Process) model via analyzing and modeling of pilot operational behavior and mission requirements for flight processes, and we also use value iterative algorithm to find the optimal prediction sequence, lastly, we verify the operability of the algorithm by flight operation simulation experiment. It provides a new solution for the safety of pilot operations and the intrusiveness of the cockpit adaptive automation assistance system. INDEX TERMS Cockpit automation assistance, Markov decision process, behavioral prediction.

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“…For NOMA-OFDMA single-antenna schemes, the authors of [4] projected an increased capacity. In a proposal was made for subcarrier and power distribution that would maximize system throughput in a proportionally fair manner, outperforming the OFDMA scheme in the process.…”

Section: Introductionmentioning

confidence: 99%

“…In a proposal was made for subcarrier and power distribution that would maximize system throughput in a proportionally fair manner, outperforming the OFDMA scheme in the process. When compared to the prior OFDM cross-layer scheme [6] and the prior arrangement [4], the proposed cross-layer physical and application resource organization scheme for DL OFDMA/NOMA video communiqué schemes [5,6] was shown to have better performance in terms of PSNR [7]. PHY layer [8,9], radio resource allocation [10,11], physical layer [12,13], and upper layers [14,15] have all been the subject of recent surveys.…”

Section: Introductionmentioning

confidence: 99%

Downlink Video Communications for Enhanced Hybrid NOMA using Deep Learning Model

Kaveri

Deepa

2022

Preprint

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Eventually, the wireless system is one of the most prominent and effectively overloaded with enormous amounts of data information due to the rising number of users. To effectively handle widespread congestion, the current iteration of multiple access (MA) will inevitably fail. Recently, the process of non-orthogonal multiple access (NOMA) has been brought to light as a promising one for 5G and beyond, since it can improve spectrum and power efficiency for a large number of users simultaneously. In this research, a downlink Hybrid NOMA (H-NOMA) is recommended that combines the advantages of TDMA and NOMA to enhance the robustness of the system and offer two resource management techniques to maximize both outage capacity and ergodic size in a 5G URLLC scenario Ultra-Reliable Low Latency Communications (URLLC), and we conclude by proposing a deep-learning-based network, ResNet-50, to cut down on the complexity and latency of 5G URLLC. The suggested systems re-assign subcarrier in a way that maximizes outage capacity rather than ergodic capacity alone, and they select the candidate user for gaining subcarrier in a novel approach. The proposed model relies heavily on NOMA deep residual video identification to guarantee precise user categorization and widespread interaction. Using the obtained data, we examine how the suggested method stacks up against the conventional NOMA in terms of BER. When compared to other methods, the proposed one achieves a 93% (bit/s/Hz) spectrum efficiency, a 92% (bit/Hz/Joule) energy efficiency, and a 91% (bps/Hz) attainable data rate.

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An Intelligence-Based Recurrent Learning Scheme for Optimal Channel Allocation and Selection in Device-to-Device Communications

Cited by 5 publications

References 27 publications

Biased-Randomized Discrete-Event Heuristics for Dynamic Optimization with Time Dependencies and Synchronization

Biased-Randomized Discrete-Event Heuristics for Dynamic Optimization with Time Dependencies and Synchronization

A New Markov Decision Process Based Behavioral Prediction System for Airborne Crews

Downlink Video Communications for Enhanced Hybrid NOMA using Deep Learning Model

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