journal article·2022Open access

Power Allocation in NOMA-CR for 5G Enabled IoT Networks

Mohammed Basheri, Mohammad Haseeb Zafar, Imran Khan

Computers, Materials & Continua· Volume 72· Issue 3

Abstract

In the power domain, non-orthogonal multiple access (NOMA) supports multiple users on the same time-frequency resources, assigns different transmission powers to different users, and differentiates users by user channel gains. Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information, and multi-user detection algorithms, such as successive interference cancellation (SIC) is performed at the receiving end to demodulate the necessary user signals. In contrast to the orthogonal transmission method, the non-orthogonal method can achieve higher spectrum utilization. However, it will increase the receiver complexity. With the development of microelectronics technology, chip processing capabilities continue to increase, laying the foundation for the practical application of non-orthogonal transmission technology. In NOMA, different users are differentiated by different power levels. Therefore, the power allocation has a considerable impact on the NOMA system performance. To address this issue, the idea of splitting power into two portions, intra-subbands and inter-subbands, is proposed in this study as a useful algorithm. Then, such optimization problems are solved using proportional fair scheduling and water-filling algorithms. Finally, the error propagation was modeled and analyzed for the residual interference. The proposed technique effectively increased the system throughput and performance under various operating settings according to simulation findings. A comparison is performed with existing algorithms for performance evaluation.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Wireless Communication Technologies · IoT Networks and Protocols · Optical Wireless Communication Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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