Device-to-device (D2D) communication plays a vital role in communication technologies with resource management and power control, which are the major issues for researchers in the era of 2020. In 5G networks, machine learning algorithms play a crucial role to manage these issues. In the proposed work, the problem is formulated for the uplink underlaid model of D2D communication. The Lyapunov optimization technique is utilized with a combination of machine learning techniques for resource allocation in D2D communication. First, the maximization of uplink and overall system capacity is formulated with resource management, which guarantees the signal to interference noise ratio for the D2D users. The optimization is a mixed integer non-linear problem which uses the Lyapunov optimization method to optimize the bit error rate value and iterative algorithm to optimize the power value with different constraints. After attaining the optimized value, the support vector machine technique is utilized to ensure the spectral efficiency of an overall system in autonomous mode. Simulation results show that the proposed method provides higher reliability and power efficiency with higher system capacity in comparison to prevailing technologies.
The paper is focused on robust channel encoding for Massive machine type communication (mMTC) communication in 5G (NR). The performance evaluation of channel encoding is obtained at 5G New Radio (NR) PHY. The results show that reliable bit error rate (BER) against the poor channel condition or random fluctuated channel applied. Channel encoding algorithm as a forward error correction code (FEC) is applied on packet to packet basis to improve the BER performance against inter symbol interference. The concept of adaptation of code rate is valuable to reduce the payload effect and provide optimum solution between BER and throughput. Adaptive code rate selection is based on impact of earlier transmitted packet bit using feedback indicator.
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.
Device‐to‐Device (D2D) communication is one of the leading technologies which works under the aegis of fifth generation (5G) communication for the Internet of Things in Healthcare. In D2D communication, power control with resource allocation is the most challenging area for the research to make them suitable for IoT devices. In the present work, power control with resource allocation is done by using the Linear pricing game power allocation method. First, a mathematical min‐function based problem is proposed whilst taking the interference, data rate and minimum power requirement as a constraint to ensure the resource and power allocation. In the next phase, a linear pricing game (LPG) method is utilized to propose the LPG power control with resource allocation (L‐PaRAG). Mathematical analysis and theorem ensure the minimum cost function with global stability for an optimal solution of the proposed problem. Numerical analysis and results show that the proposed algorithm gives the best results for power control and resource management whilst managing the interference to maintain the quality of services (QoS) in D2D communication. Optimized power can be utilized for the communication between the base station and D2D users, especially, during disaster management.
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