Spectrum Assignment in Hardware-Constrained Cognitive Radio IoT Networks Under Varying Channel-Quality Conditions

[EN] The integration of cognitive radio (CR) technology with the future Internet-of-Things (IoT) architecture is expected to allow effective massive IoT deployment by providing huge spectrum opportunities to the IoT devices. Several communication protocols have been proposed for the CR networks while ignoring the adjacent channel interference (ACI) problem by assuming sharp filters at the transmit and receive chains of each CR device. However, in practice, such an assumption is not feasible for low-cost hardware-constrained CR-capable IoT (CR-IoT) devices. Specifically, when a large number of CR-IoT devices are operating in the same vicinity, guard-band channels (GBs) are needed to mitigate the ACI problem, introducing GB adds constraints on the efficient use of spectrum and protocol design. In this paper, we develop a channel assignment mechanism for the hardware-constrained CR-IoT networks under time-varying channel conditions with GB-awareness. The objective of our assignment is to serve the largest possible number of CR-IoT devices by assigning the least number of idle channels to each device subject to rate demand and interference constraints. The proposed channel assignment in this paper is conducted on a per-block basis for the contending CR-IoT devices while considering the time-varying channel conditions for each CRIoT transmission over each idle channel, such that spectrum efficiency is improved. Specifically, our channel assignment problem is formulated as a binary linear programming problem, which is NP-hard. Thus, we propose a polynomial-time solution using a sequential fixing algorithm that achieves a suboptimal solution. The simulation results demonstrate that our proposed assignment provides significant increase in the number of served IoT devices over existing assignment mechanisms.This work was supported in part by the QR Global Challenges Research Fund, Staffordshire University, Staffordshire, U.K.Salameh, HAB.; Al-Masri, S.; Benkhelifa, E.; Lloret, J. (2019). Spectrum Assignment in Hardware-Constrained Cognitive Radio IoT Networks Under Varying Channel-Quality Conditions. IEEE Access. 7:42816-42825. https://doi.org/10.1109/ACCESS.2019.2901902S4281642825

Spectrum Assignment in Hardware-constrained Cognitive Radio IoT Networks under Varying Channel-quality Conditions

ABSTRACT The integration of cognitive radio (CR) technology with future Internet-of-Things (IoT) architectures is expected to allow effective massive IoT deployment by providing huge spectrum opportunities to IoT devices. Several communication protocols have been proposed for CR networks while ignoring the adjacent channel interference (ACI) problem by assuming sharp filters at the transmit and receive chains of each CR device. However, in practice, such an assumption is not feasible for low-cost hardware-constrained CR-capable IoT (CR-IoT) devices. Specifically, when large number of CR-IoT devices are operating in the same vicinity, guardband channels (GBs) are needed to mitigate the ACI problem. Introducing GB constraint spectrum efficiency and protocol design. In this paper, we develop a channel assignment mechanism for hardware-constrained CR-IoT networks under time-varying channel conditions with GB-awareness. The objective of our assignment is to serve the largest possible number of CR-IoT devices by assigning the least number of idle channels to each device subject to rate demand and interference constraints. The proposed channel assignment in this paper is conducted on a per-block basis for the contending CR-IoT devices while considering the time-varying channel conditions for each CRIoT transmission over each idle channel such that spectrum efficiency is improved. Specifically, our channel assignment problem is formulated as a binary linear programming (BLP) problem, which is NP hard. Thus, we propose a polynomial-time solution using a sequential fixing algorithm that achieves a suboptimal solution. Simulation results demonstrate that our proposed assignment provides significant increase in the number of served IoT devices over existing assignment mechanisms