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Register a new userEnabling Full Mutualism for Symbiotic Radio with Massive Backscatter Devices
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Added by
Jingran Xu
Publication date
2021
fields
Electronic Engineering
and research's language is
English
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Symbiotic radio is a promising technology to achieve spectrum- and energy-efficient wireless communications, where the secondary backscatter device (BD) leverages not only the spectrum but also the power of the primary signals for its own information transmission. In return, the primary communication link can be enhanced by the additional multipaths created by the BD. This is known as the mutualism relationship of symbiotic radio. However, as the backscattering link is much weaker than the direct link due to double attenuations, the improvement of the primary link brought by one single BD is extremely limited. To address this issue and enable full mutualism of symbiotic radio, in this paper, we study symbiotic radio with massive number of BDs. For symbiotic radio multiple access channel (MAC) with successive interference cancellation (SIC), we first derive the achievable rate of both the primary and secondary communications, based on which a receive beamforming optimization problem is formulated and solved. Furthermore, considering the asymptotic regime of massive number of BDs, closed-form expressions are derived for the primary and the secondary communication rates, both of which are shown to be increasing functions of the number of BDs. This thus demonstrates that the mutualism relationship of symbiotic radio can be fully exploited with massive BD access.
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In this paper, a novel bandwidth negotiation mechanism is proposed for massive devices wireless spectrum sharing, in which individual device locally negotiates bandwidth usage with neighbor devices and globally optimal spectrum utilization is achieved through distributed decision-making. Since only sparse feedback is needed, the proposed mechanism can greatly reduce the signaling overhead. In order to solve the distributed optimization problem when massive devices coexist, mean field multi-agent reinforcement learning (MF-MARL) based bandwidth decision algorithm is proposed, which allow device make globally optimal decision leveraging only neighborhood observation. In simulation, distributed bandwidth negotiation between 1000 devices is demonstrated and the spectrum utilization rate is above 95%. The proposed method is beneficial to reduce spectrum conflicts, increase spectrum utilization for massive devices spectrum sharing.
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