Joint resource allocation and power allocation scheme forMIMOassistedNOMAsystem

Abstract

Multiple input multiple output (MIMO) plays an integral role in improving energy efficiency for enhancing all users' quality of service (QoS). For 5G wireless communication (WC), the NOMA strategy is used with the MIMO system to improve the system's sum rate and computational complexity. In this, terahertz (THz) band communication is introduced for future demand to carry more users and obtains high multiplexing gain. Multiple antenna arrays are used in the MIMO‐NOMA system to achieve high signal transmission. However, the use of antenna arrays increases as the number of users increases. A beam‐forming‐based signal transmission is introduced in this work to overcome this issue. For performing digital and Analog beam‐forming, a hybrid minimum mean square error (MMSE) with zero‐forcing (ZF) pre‐coder is proposed to eliminate the inter‐cluster interference. Initially, user clustering is undertaken using a fuzzy k‐means clustering (FKM) algorithm, and beam‐forming is performed among the user clustering. The joint power allocation (PA) and resource allocation (RA) is done using a hybrid artificial gorilla troop with leader optimization (HGTLO) technique to achieve a maximum sum rate of the RF channel. The performance of a proposed method is analyzed using the MATLAB tool. In the experimental scenario, the performance of energy efficiency (EE), spectral efficiency (SE), achievable sum rate, throughput, bit error rate (BER), and mean square error (MSE) attains the value of 35.82 bits/Hz/Joules, 198.11 bps/Hz, 116.78 Mbps, 93.06%, .003 and .011 respectively. The performance measures prove the efficacy of a proposed method.

Beam-space MIMO-NOMA Technique for mm-Wave Communication Systems

“…This is because the energy is shared between multiple users of the same beam. Therefore, a lot of research has been revealed to study the energy allocation of NOMA-MIMO see for example the references, [17], [19][20][21][22]. The formula for optimizing the QoS with power-allocation constraint can be written as follows, The optimization expression in ( 9) aims to attain a fair quality of service (i.e., best QoS) among all the users, "𝑅 𝑡ℎ = 𝐦𝐢𝐧 𝑘 ln( 1 + Γ 𝑘𝑖 )" denotes the minimum amount of data that each user should have, or in other words, it represents the QoS to confirm the user's fairness performance, Γ 𝑘𝑖 refers to SINR, and 𝑃 𝑡 stands for the total power transmitted by the whole system.…”