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\title{Deep Reinforcement Learning Based Algorithm for Symbiotic Radio IoT
Throughput Optimization in 6G Network}



\author[1]{Samar Shaker Metwaly}%
\author[2]{Gerges M. Salama}%
\author[2]{E. G. Shehata}%
\author[1]{Ahmed. M. Abd El-Haleem}%
\affil[1]{Helwan University Faculty of Engineering (Helwan)}%
\affil[2]{Minia University Faculty of Engineering}%


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  \date{April 27, 2026}


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\begin{abstract}
\textbf{Abstract} Internet of Things (IoT) based 6G is expected to
revolutionize our world. various candidate technologies have been
proposed to meet the requirements of IoT systems based on 6G, symbiotic
radio (SR) is one of these technologies. This paper aims to use
symbiotic radio technology to support the passive internet of things and
enhance the uplink transmission performance. In SR the IoT tag is
parasitic on the primary transmission that the tags transmission shares
not only the radio spectrum of the primary transmission, but also the
power, and infrastructure of the neighbor smartphone primary system
which enhances the spectrum and energy efficiency of the system. Then
the IoT tags information is sent to cloud for analysis through the Macro
base station MBS or the wireless access point WAP where the smart phones
are used as a relay to transmit this information to the MBS or WAP. In
this paper two optimization problems are formulated to maximize the
total throughput of the system. First, a problem of achieving the
optimum mode selection of LTE or Wi-Fi Network (MBS or WAP) by
transmitting an expected tags information load from the smartphone to
MBS or WAP aiming to maximize the system throughput. The matching game
algorithm is used to solve this problem. Second, a problem of achieving
optimum clustering of tags where the tags are divided into virtual
clusters and finding which smartphones' LTE/Wi-Fi downlink signal all
cluster members can ride to maximize the system throughput. A deep
Q-network (DDQN) model is proposed for solving this optimization problem
with low complexity. Simulation results show that our proposed
algorithms increase the total system data rate by average 90\% above the
system by using LTE network first and 20\% above the system without
using DDQL algorithm. Furthermore, our proposed algorithms enhance the
capacity of the system on the average by 100\% above system using LTE
network first without DDQL algorithm.algorithm.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Deep Reinforcement Learning Based Algorithm. .docx`  available at \url{https://authorea.com/users/563099/articles/610296-deep-reinforcement-learning-based-algorithm-for-symbiotic-radio-iot-throughput-optimization-in-6g-network}

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