Improved local spectrum sensing in cluttered environment using a simple recursive estimator

Adardour, Haroun Errachid; Meliani, M.; Hachemi, Mohammed Hicham

doi:10.1016/j.compeleceng.2016.11.037

Cited by 13 publications

(8 citation statements)

References 14 publications

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“…We are interested to NCSS model. More specifically, we are addressing to EDSS process [37]. This process is widely less computation and implementation complexity [38].…”

Section: Non-cooperative Spectrum Sensingmentioning

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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“…We are interested to NCSS model. More specifically, we are addressing to EDSS process [37]. This process is widely less computation and implementation complexity [38].…”

Section: Non-cooperative Spectrum Sensingmentioning

confidence: 99%

Pedestrian mobility management for heterogeneous networks

Hachemi

Irid²,

Benchehima³

et al. 2022

IJEECS

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“…Furthermore, the objective of this second step is to predict the RSS5G at the receiver (or SUIoT-5G) and then employ it in the third step in order to estimate the SNR from the transmitter (or APWLANIoT-5G) to the receiver (or SUIoT-5G), for each distance linking them, as illustrated in Fig. 1 (Othmane et al, 2020;Adardour et al, 2017).…”

Section: Snr Sensitivity and Simple Recursive Estimatormentioning

confidence: 99%

“…Third step: After the received signal strength 5G (or RSS5G) at the SUIoT-5G is predicted in a congested environment, one will rely on a SRE for estimating the RSS5G at the SUIoT-5G with higher and better measurement stability. Next, one can estimate the signal-to-noise ratio (or SNR) link between the APWLANIoT-5G and the SUIoT-5G (Adardour et al, 2017).…”

mentioning

confidence: 99%

Wireless Lan for Iot 5g Applications in a Congested Indoor Environment: Improved Primary Signal Sensing at Millimeter-Wave v-Band [40 – 75 Ghz]

Adardour

Kameche

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Presently, the IEEE 802.11ad wireless network standard supports the communication capability within the V band of millimeter-Waves (or mm-Waves) [40 – 75 GHz] for Internet of Things (or IoT) technology. This paper presents an algorithm to improve the Primary Signal 5G (or PS5G) sensing at 60 GHz in WLANIoT-5G (or Wireless Local Area Network for IoT-5G). To achieve that, we aim at investigating and assessing the sensing performances of the PS5G from the Access Point of WLANIoT-5G (or APWLANIoT-5G) by the Secondary User IoT-5G (or SUIoT-5G) in a congested environment using a simple energy detector (or SED) algorithm and a simple recursive estimator (or SRE). In addition, the SUIoT-5G is regarded as a mobile user in WLANIoT-5G by employing the Gauss-Markov Random Mobility Model (or GMRMM). Through various scenario simulations, the performances and the robustness of the proposed algorithm are proved.

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“…Reliability in the spectrum sensing process is considered in Adardour et al, 12 and the issues are addressed through the weighted averaging method. The proposed model measures the signal strength of the secondary user using a simple recursive estimator.…”

Section: Introductionmentioning

confidence: 99%

An efficient adaptive threshold‐based dragonfly optimization model for cooperative spectrum sensing in cognitive radio networks

Jothiraj¹,

Sridevi²,

Rangaraj

2021

Int J Communication

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Effective utilization of spectrum resources is an important factor in wireless communication which reduces spectrum scarcity. Over the years, communication systems use different frequency bands, and the users are categorized into licensed and unlicensed users. Most of the wireless bands are typically licensed; as a result, accommodation of new technologies such as Internet of Things and machine to machine communication becomes difficult. So it is essential to obtain a wireless spectrum to adopt new technologies. Cognitive radio technology is introduced to improve such spectrum utilization. Reports state that most of the licensed spectrums are underutilized, and few spectrums are overutilized. Cognitive radio networks help to exploit the licensed spectrum and access the spectrum without any interference to the licensed user.Through its spectrum sensing and spectrum sharing process, cognitive radio network gains more attention in wireless communication. This research work proposed an efficient optimized spectrum sensing technique for cognitive radio networks through dragonfly optimization algorithm along with the adaptive threshold process. Proposed work performs better in terms of detection accuracy and efficiency when compared to conventional spectrum sensing schemes such as linear support vector machine and particle swarm optimization models.

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Improved local spectrum sensing in cluttered environment using a simple recursive estimator

Cited by 13 publications

References 14 publications

Pedestrian mobility management for heterogeneous networks

Pedestrian mobility management for heterogeneous networks

Wireless Lan for Iot 5g Applications in a Congested Indoor Environment: Improved Primary Signal Sensing at Millimeter-Wave v-Band [40 – 75 Ghz]

An efficient adaptive threshold‐based dragonfly optimization model for cooperative spectrum sensing in cognitive radio networks

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