The potential of the Non-Orthogonal Multiple Access (NOMA) approach for wireless communications in the fifth generation (5G) and beyond can not be underestimated. This is because users with favorable channel conditions can serve as relays to improve system performance by employing Successive Interference Cancellation (SIC). Lately, the combination of NOMA and the cooperative relay has attracted the interest of researchers. The analysis of cooperative relay NOMA (CR-NOMA) with a massive multiple-input multiple-output (mMIMO) system is mainly based on theoretical channel models such as the correlated-based stochastic channel model (CBSM) even though the geometric-based stochastic channel model (GBSM) has been found to provide better, practical and realistic channel properties. This, in our view, is due to computational challenges. Again, the performance of CR-NOMA systems using the GBSM channel model with large antenna transmitters and network coding schemes has attracted little attention in academia. Therefore, the need to study mMIMO CR-NOMA that considers channel properties such as path-loss, delay profile and tilt angle has become vital. Furthermore, the co-existing of large antenna transmitters with coding schemes needs further investigation. In this paper, we study the performance of a two-stage mMIMO CR-NOMA network where the transmitter is represented as a uniform rectangular array (URA) or cylindrical array (CA). The communication channel from the transmitter (TX) to the user equipment (UE) through a relay station (RS) is modeled with a 3GPP’s three-dimensional (3D) GBSM mMIMO channel model. To improve the analytical tractability of 3D GBSM, we defined the antenna element location vectors using the physical dimension of the antenna array and incorporated them into the 3D channel model. Bit-error rates, achievable rates and outage probabilities (OP) are examined using amplify-and-forward (AF) and decode-and-forward (DF) coding schemes. Results obtained show with fixed power allocation and SNR of 20 dB, far or weak users can attain a high achievable rate using DF and URA. Again, from the results, the combination of AF and CA presents better outage probabilities. Finally, the results indicate that the performance difference between CBSM and GBSM is marginal, even though the proposed 3D GBSM channel model has a higher degree of random parameters and computational complexities.
WiMAX is a popular broadband solution with diverse applications. With several advantages such as low cost applications and last mile solution for broadband wireless access, WiMAX will no doubt help bridge the ever increasing digital divide in many SubSaharan African countries. Many countries Sub of the Sahara have recently started deploying WiMAX to offer subscribers affordable broadband internet service. Because of the peculiar conditions in Sub-Sahara Africa, critical design and optimization techniques will be vital in making WiMAX networks deliver as expected by subscribers. In order to achieve maximum capacity while maintaining an acceptable grade of service and higher network performance of these newly deployed networks, the effect of interference should be catered for. This paper presents Network simulations results of a newly deployed 4G-WiMAX network deployed in Accra and Tema municipality, Ghana. A Monte Carlo simulation has been used to study the total interference in the network and the results presented. Finally the Network performance is evaluated through measurements of Received Total Wideband Power (RTWP) and outdoor interference and the results compared.
The Non-Orthogonal Multiple Access (NOMA) technique has enormous potential for wireless communications in the fifth generation (5G) and beyond. Researchers have recently become interested in the combination of NOMA and cooperative relay. Even though geometric-based stochastic channel models (GBSM) have been found to provide better, practical, and realistic channel properties of massive multiple-input multipleoutput (mMIMO) systems, the assessment of Cooperative Relay NOMA (CR-NOMA) with mMIMO system is largely based on correlated-based stochastic channel model (CBSM). We believe that this is a result of computational difficulties. Again, not many discussions have been done in academia about how well CR-NOMA systems perform when large antenna transmitters with the GBSM channel model are used. As a result, it is critical to investigate the mMIMO CR-NOMA system with the GBSM channel model that takes into account channel parameters such as path loss, delay profile, and tilt angle. Moreover, the coexistence of large antenna transmitters and coding methods requires additional research. In this research, we propose a twostage, three-dimension (3D) GBSM mMIMO channel model from the 3GPP, in which the transmitter is modelled as a cylindrical array (CA) to investigate the efficiency of CR-NOMA. By defining antenna elements placement vectors using the actual dimensions of the antenna array and incorporating them into the threedimension (3D) channel model, we were able to increase the analytical tractability of the 3D GBSM. Bit-error rates, achievable rates, and outage probabilities (OP) are investigated utilizing the decode-and-forward (DF) coding method: the results are compared with that of a system using the CBSM channel model. Despite the computational difficulties of the proposed GBSM system, there is no difference in performance between CBSM and GBSM.
In conventional healthcare, real-time monitoring of patient records and information mining for timely diagnosis of chronic diseases under certain health conditions is a crucial process. Chronic diseases, if not diagnosed in time, may result in patients’ death. In modern medical and healthcare systems, Internet of Things (IoT) driven ecosystems use autonomous sensors to sense and track patients’ medical conditions and suggest appropriate actions. In this paper, a novel IoT and machine learning (ML)-based hybrid approach is proposed that considers multiple perspectives for early detection and monitoring of 6 different chronic diseases such as COVID-19, pneumonia, diabetes, heart disease, brain tumor, and Alzheimer’s. The results from multiple ML models are compared for accuracy, precision, recall, F1 score, and area under the curve (AUC) as a performance measure. The proposed approach is validated in the cloud-based environment using benchmark and real-world datasets. The statistical analyses on the datasets using ANOVA tests show that the accuracy results of different classifiers are significantly different. This will help the healthcare sector and doctors in the early diagnosis of chronic diseases.
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