Iraqi Journal of Information and communication technology

Sparse Code Multiple Access (SCMA) is an extremely effective non-orthogonal multiple access technology
that enables communication between users who have limited orthogonal resources currently. Traditional
SCMA methods use manually designed codebooks, potentially leading to subpar performance owing to inadequate
optimization for certain encoders. A Deep Neural Network (DNN) is used to produce a deep learning for SCMA
codebook using Stochastic Gradient Descent (SGD). This enables the model to determine the optimal weights and
biases for generating precise predictions. The proposed approach surpasses existing techniques in a Rayleigh fading
channel due to its reduced Bit Error Rate (BER), enhanced Minimum Euclidean Distance (MED), and diminished
complexity compared to prior SCMA frameworks

Edge-cloud computing paradigms increase the Quality of Experience (QoE) for real-time applications
by offering many benefits, such as reducing the response time. Many strategies are proposed to handle the data
in the edge cloud environment. Hence, where to execute the data generated by the end device is considered an
important issue. In this paper, an Artificial Intelligence (AI) model is proposed based on a neural network for
workload allocation decisions. The model deals with an AI application that runs on an edge server and a cloud
center. The model was trained using a pre-generated dataset based on several features. The features considered
are the data size, model complexity, application priority, edge server utilization, and delay of execution on the
edge and the cloud. The model decides where to perform the task generated by the end device, either in the edge
server or on the cloud. Four AI applications are considered. The model has been implemented using Tensorflow
platform with the required libraries. The proposed model employee multi-feature with multi-application addressing
workload allocation decisions in a hybrid edge-cloud environment by creating and utilizing a dataset based on
proposed algorithm. The proposed model achieved accuracy reached 98.3% and reduced the response time of task
execution compared to the based line approach considered in this paper.

Long-Range Wide-Area Networks (LoRaWAN) is among the foremost favored techniques for wireless
communication in Internet of Things (IoT) applications, owing to its simplicity and versatility. It utilizes an Adaptive
Data Rate (ADR) approach at both the network server and end device levels. In order to provide the necessary
downlink radio parameters, the network server could use the allocation of a resource strategy to control the transmit
power and spreading factor. Therefore, this work suggests a Uniform Distribution ADR (U-ADR), network servercontrolled
adaptive data rate, to deal with the fast-fluctuating signal-noise ratio of arriving packets at the network
server. The suggested approach seeks the most effective allocation of radio parameters to the end devices to reduce
the total network energy consumption and improve the average packet delivery ratio of the network. The findings
depict that the suggested approach enhances the average Packet Delivery Ratio (PDR) by 102% and 77.94% for
a single and two gateways deployment in an urban scenario respectively while in sub-urban improved by 65.14%
and 33.53%. The total Network Energy Consumption (NEC) was reduced by 9% and 16.55% for single and two
gateways in the urban scenario while in the sub-urban scenario reduced by 15.92% and 12.53%.