journal article·2023Open access

Optimal Resource Allocation for NOMA Wireless Networks

Fahad R. Albogamy, M. A. Aiyashi, Fazirul Hisyam Hashim, Imran Khan, Bong Jun Choi

Computers, Materials & Continua· Volume 74· Issue 2

Abstract

The non-orthogonal multiple access (NOMA) method is a novel multiple access technique that aims to increase spectral efficiency (SE) and accommodate enormous user accesses. 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), are performed at the receiving end to demodulate the necessary user signals. Although its basic signal waveform, like LTE baseline, could be based on orthogonal frequency division multiple access (OFDMA) or discrete Fourier transform (DFT)-spread OFDM, NOMA superimposes numerous users in the power domain. In contrast to the orthogonal transmission method, the non-orthogonal method can achieve higher spectrum utilization. However, it will increase the complexity of its receiver. Different power allocation techniques will have a direct impact on the system’s throughput. As a result, in order to boost the system capacity, an efficient power allocation mechanism must be investigated. This research developed an efficient technique based on conjugate gradient to solve the problem of downlink power distribution. The major goal is to maximize the users’ maximum weighted sum rate. The suggested algorithm’s most notable feature is that it converges to the global optimal solution. When compared to existing methods, simulation results reveal that the suggested technique has a better power allocation capability.
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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