The existing literature on device-to-device (D2D) architecture suffers from a dearth of analysis under imperfect channel conditions. There is a need for rigorous analyses on the policy improvement and evaluation of network performance. Accordingly, a two-stage transmit power control approach (named QSPCA) is proposed: First, a reinforcement Q-learning based power control technique and; second, a supervised learning based support vector machine (SVM) model. This model replaces the unified communication model of the conventional D2D setup with a distributed one, thereby requiring lower resources, such as D2D throughput, transmit power, and signal-tointerference-plus-noise ratio as compared to existing algorithms. Results confirm that the QSPCA technique is better than existing models by at least 15.31% and 19.5% in terms of throughput as compared to SVM and Q-learning techniques, respectively. The customizability of the QSPCA technique opens up multiple avenues and industrial communication technologies in 5G networks, such as factory automation.
Device-to-Device (D2D) communication provides real-time functioning support for IoT applications using fifth-generation (5G) technologies. In an In-band underlay D2D model, resource allocation is a key issue. To overcome this issue, a distance and power-driven model is proposed. A mathematical problem is formulated with the main objective is to maximizing the spectral efficiency of the network. This work utilizes the concept of distributed processing for achieving our goal. First of all, we proposed a distance and power-driven based resource allocation mechanism (DPRAM) for the improvement in spectral efficiency by mitigating D2D and interference between the base station and D2D users. Then, the applicability of the proposed work has been checked by using the Sigmoidal and Logarithmic based utility functions for the delay tolerance device and real-time IoT application respectively. Simulation results show that the proposed work is capable of providing the best solution as compared to random allocation methods and pure ALOHA by 63.41% and 95.12% and 60% and 96% improvement in system capacity. Analysis of performance parameters such as sigmoidal and logarithmic utility function approves that our proposed approach may be suitable for delay tolerant and real-time applications, such as connected healthcare systems, smart farming, smart grid, etc.
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