Primary User Channel State Prediction Based on Channel Allocation and DBHMM

Miantezila, Milembolo; Guo, Bin; Zhang, Chenjie; Bai, Xuemei

doi:10.1109/cyberc49757.2020.00063

Cited by 4 publications

(7 citation statements)

References 9 publications

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“…Ḓ[n(t)nt-nτ(t)] =

), [ 2 ] the array is i.i.d and

(

). From here, the function of log-likelihood can be expressed,

…”

Section: Methodsmentioning

confidence: 99%

“…This has opened the flow gate to research interest and scientific dissertation. Over a decade, spectra were mainly shared within the system by applying opportunistic techniques through cognitive radio [ 1 , 2 ]. These techniques were made feasible through spectrum sensing [ 3 ] and data management localization [ 4 ] by combining the two techniques through radio mapping [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Interference Cancellation Based Spectrum Sharing for Massive MIMO Communication Systems

Miantezila

Guo

Zhang

et al. 2021

Sensors

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Cellular network operators are predicting an increase in space of more than 200 percent to carry the move and tremendous increase of total users in data traffic. The growing of investments in infrastructure such as a large number of small cells, particularly the technologies such as LTE-Advanced and 6G Technology, can assist in mitigating this challenge moderately. In this paper, we suggest a projection study in spectrum sharing of radar multi-input and multi-output, and mobile LTE multi-input multi-output communication systems near m base stations (BS). The radar multi-input multi-output and mobile LTE communication systems split different interference channels. The new approach based on radar projection signal detection has been proposed for free interference disturbance channel with radar multi-input multi-output and mobile LTE multi-input multi-output by using a new proposed interference cancellation algorithm. We chose the channel of interference with the best free channel, and the detected signal of radar was projected to null space. The goal is to remove all interferences from the radar multi-input multi-output and to cancel any disturbance sources from a chosen mobile Communication Base Station. The experimental results showed that the new approach performs very well and can optimize Spectrum Access.

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“…Ḓ[n(t)nt-nτ(t)] =

), [ 2 ] the array is i.i.d and

(

). From here, the function of log-likelihood can be expressed,

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interference Cancellation Based Spectrum Sharing for Massive MIMO Communication Systems

Miantezila

Guo

Zhang

et al. 2021

Sensors

Self Cite

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show abstract

“…Drone's dynamic states. After analysis, we find out that the progress of emission states of drone over time T, can be represented as a finite states engine and can be described as two states Markov process [28][29][30][31][32]] R = {R 0 , R 1 }. If we consider the drone as active and moving with emission states R 1 at time t, then the survival probability of an active drone can be written,…”

Section: Plos Onementioning

confidence: 99%

“…Drone's states prediction. As known, in the Bayesian approach [32,34], we analyse the unknown quantity, as a random variable. We recursively estimate the conditional posterior distribution.…”

mentioning

confidence: 99%

Sensing spectrum sharing based massive MIMO radar for drone tracking and interception

Miantezila

Guo

2022

PLoS ONE

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Radar sensors are becoming crucial for environmental perception in a world with the tremendous growth of unmanned aerial vehicles (UAVs) or drones. When public safety is a concern, the localization of drones are of great significance. However, a drone used for a wrong motive can cause a serious problem for the environment and public safety, given the fact that the dynamic movement of a drone’s emission signal and location tracking is different from existing positioning. This study proposes a safety zone characterized by the presence of N radars sensors with a goal to track and destabilized rogue drones attending to penetrate safety zones (stadium and school). Specifically, a new joint estimation based on a Gaussian filter has been introduced for spectrum sharing and detection awareness. The profit of this novel sensing method can be clearly seen when the two joint hidden states are taken into consideration. Therefore, the drone’s emission state is analyzed by estimating its movement jointly. Considering the drone’s unknown states and actual positioning, an algorithm is developed based on dynamic states space model. Where Bernoulli filter model is designed to estimate recursively the unknown stages of the drone and its changing location based on time. Meanwhile a power control acted from the radar to the targeted drones so that rogue drones are optimally tracked and destabilized over time. Furthermore, an expanding mechanism has been generated to accurately track the drone and enhance detection. A thoughtful result of the experimentation shows clearly that, even when the drone is moving, spectral detection can be performed accurately by chasing its positions. Its demonstrates at 90% of credibility that the original signal has a direct effect on the propagated signal. Therefore, the magnitude of the Doppler shift increases with frequency. And the clue of its positioning can be used for cognitive radio optimization.

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“…Therefore, instead of investigating PUs' activities on a primary channel, researches in the literature have been focusing on modeling primary channel behavior (i.e. busy when the channel is occupied by PUs or free when the channel is vacant) [39]. Besides, the primary channel behavior is often considered as a 2-state Discrete-Time Markov Chain process, in which the transition probabilities between two states (busy or free) were well-studied in the literature [40].…”

Section: Activity Model Of Multiple Primary Channelsmentioning

confidence: 99%

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

et al. 2021

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The performance of data aggregation in industrial wireless communications can be degraded by environmental interference on Industrial Scientific Medical (ISM) channels. In this paper, the opportunistic spectrum access capability of cognitive radio (CR) was applied to enable devices to share primary channels with the aim of enhancing the transmission performance of the WirelessHART network. We considered a linear convergecast network, where the packets generated at each device were routed to the gateway (GW) through the aid of neighboring devices. The solar-powered cognitive access points (CAPs) were deployed to improve the successful transmission probability of the packets among field devices by opportunistically allocating the primary channels to the devices for data transmissions. In this paper, we formulate the scheduling problem of long-term throughput maximization as a framework of a Markov decision process by considering the constraints of the minimum delay, the number of required ISM channels, and the harvested energy at the CAPs. Then, we propose a deep reinforcement learning-based scheduling scheme to optimally assign multiple ISM and primary channels to the field devices in each superframe to maximize the received packets at the GW. The simulation results confirmed the superiority of the proposed scheme compared to existing methods.INDEX TERMS wirelessHART, cognitive radio, markov decision process, industrial scientific medical

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Primary User Channel State Prediction Based on Channel Allocation and DBHMM

Cited by 4 publications

References 9 publications

Interference Cancellation Based Spectrum Sharing for Massive MIMO Communication Systems

Interference Cancellation Based Spectrum Sharing for Massive MIMO Communication Systems

Sensing spectrum sharing based massive MIMO radar for drone tracking and interception

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

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