Mohammad aljraah


Mohammad A. Al-Jarrah received his MSc. degree in Electrical Engineering/Wireless Communications from Jordan University of Science and Technology (JUST). Currently, he is a Lab instructor in the Department of Electrical and Computer Engineering, Khalifa University, United Arab Emirates. His area of research includes distributed decision fusion in wireless sensors networks, statistical signal processing, target tracking in RFID networks, and cooperative spectrum sensing in cognitive radio networks.


Research updates – Period 1

Performance Analysis of Wireless Mesh Backhauling Using Intelligent Reflecting Surface

This work considers the deployment of intelligent reflecting surfaces (IRSs) technology for integrated access and backhauling (IAB) of multiple base-stations (BSs) connected in a mesh topology. In addition, the total number of hops is assumed random. The performance of the proposed architecture is evaluated in terms of outage probability, symbol error probability and received signal power in Rician fading channels. Closed-form expressions are derived and demonstrated to be accurate for several cases of interest. The analytical results corroborated by simulation, show that the IRS based IAB architecture has several desired features that can be exploited to overcome some of the backhauling challenges, particularly the severe attenuation at high frequencies. Fig. 1 shows the considered model with 4 small BSs (sBSs), 1 main BS (mBS), and 2 IRS panels. A single hop system is considered first, and then the analysis is generalized for multiple hops and for the case of random number of hops.

Given that IRS perfectly compensates for the channel phases, results show that the effective received SNR manages to boost the effective received SNR. Consequently, for energy neutral (EN) networks with limited power budget, the deployment of IRS can significantly increase the network longevity. Therefore, future work will consider the comparison between the longevity of IRS based communications and the traditional network (without IRS).





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