References of "Abdullah, Osamah"
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See detailDeep Learning-Based Device-Free Localization in Wireless Sensor Networks
Abdullah, Osamah; Al-Hraishawi, Hayder UL; Chatzinotas, Symeon UL

Scientific Conference (2022)

Location-based services (LBS) are witnessing a rise in popularity owing to their key features of delivering powerful and personalized digital experiences. The recent developments in wireless sensing ... [more ▼]

Location-based services (LBS) are witnessing a rise in popularity owing to their key features of delivering powerful and personalized digital experiences. The recent developments in wireless sensing techniques make the realization of device-free localization (DFL) feasible in wireless sensor networks. The DFL is an emerging technology that utilizes radio signal information for detecting and positioning a passive target while the target is not equipped with a wireless device. However, determining the characteristics of the massive raw signals and extracting meaningful discriminative features relevant to the localization are highly intricate tasks. Thus, deep learning (DL) techniques can be utilized to address the DFL problem due to their unprecedented performance gains in many practical problems. In this direction, we propose a DFL framework consists of multiple convolutional neural network (CNN) layers along with autoencoders based on the restricted Boltzmann machines (RBM) to construct a convolutional deep belief network (CDBN) for features recognition and extracting. Each layer has stochastic pooling to sample down the feature map and reduced the dimensions of the required data for precise localization. The proposed framework is validated using real experimental dataset. The results show that our algorithm can achieve a high accuracy of 98\% with reduced data dimensions and low signal-to-noise ratios (SNRs). [less ▲]

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See detailEnergy Harvesting from Jamming Attacks in Multi-User Massive MIMO Networks
Al-Hraishawi, Hayder UL; Abdullah, Osamah; Chatzinotas, Symeon UL et al

in IEEE Transactions on Green Communications and Networking (2022)

5G communication systems enable new functions and major performance improvements but at the cost of tougher energy requirements on mobile devices. One of the effective ways to address this issue along ... [more ▼]

5G communication systems enable new functions and major performance improvements but at the cost of tougher energy requirements on mobile devices. One of the effective ways to address this issue along with alleviating the environmental effects associated with the inevitable large increase in energy usage is the energy-neutral systems, which operate with the energy harvested from radio-frequency (RF) transmissions. In this direction, this paper investigates the notion of harvesting the ambient RF signals from an unusual source. Specifically, the performance of an RF energy harvesting scheme for multi-user massive multiple-input multiple-output (MIMO) is investigated in the presence of multiple active jammers. The key idea is to exploit the jamming transmissions as an energy source to be harvested at the legitimate users. To this end, the achievable uplink sum rate expressions are derived in closed-form for two different antenna configurations. Two optimal time-switching schemes are also proposed based on maximum sum rate and user-fairness criteria. Besides, the essential trade-off between the harvested energy and achievable sum rate are quantified in closed-form. Our analysis reveals that the massive MIMO systems can exploit the surrounding RF signals of the jamming attacks for boosting the amount of harvested energy at the served users. Finally, numerical results illustrate the effectiveness of the derived closed-form expressions through simulations. [less ▲]

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