In this paper, we present an energy-efficient joint machine learning based beam-user selection and low complexity hybrid beamforming for the multiuser massive multiple-input multiple-output (MIMO) downlink system. Massive MIMO system is a key enabler of $5^{th}$ generation (5G) technology which is being used in vehicle-to-everything (V2X) communications and other Internet-of-Things (IoT). The large number of antennas at the base-station side causes high power consumption in the radio frequency (RF) chain. Hybrid beamforming techniques are used to reduce the number of RF chains with minimal performance loss. This paper proposes a low complexity orthogonal hybrid beamforming design with the aid of a machine learning based beam-user selection scheme. The Householder (HH) reflectors are used to generate the orthogonal analog beamforming (ABF) matrix. We use a feedforward neural network (FFNN) beam-user selection scheme. The proposed HH ABF + FFNN scheme provides better energy-efficiency (EE) performance in the ill-conditioned massive MIMO channel. It has been shown that the green SE-EE point (41.35 b/s/Hz , 0.8 b/Hz/J ) can be obtained by the proposed HH ABF + FFNN scheme as compared to the (33.77 b/s/Hz , 0.63 b/Hz/J ) from the state-of-the-art Discrete Fourier transform based techniques.

Adopting Artificial Intelligent Technology in an Educational Organization is often problematic due to many internal and external environmental reasons, and often fails to attain the desired goals. This study aims to design a framework for adopting AI technology in the education sector. Most of the research focuses on the acceptance of a particular technology and ignores the study of what else is needed for a technology acceptance. The framework in this study provides a step-by-step process of the Technological Transformation of an organization never designed before. We recommend that before making any technological changes in an organization, generally and in the educational organization particularly, the processes must be followed for the successful and meaningful adoption of AI technology.

This paper presents a deep reinforcement learning-based beam-user selection and hybrid beamforming design for the multiuser massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) downlink systems. The conventional hybrid beamforming in massive MIMO provides multiple directional beams, but each beam serves only one user. The integration of NOMA with the massive MIMO enables power domain multiplexing within a beam, hence increasing the system capacity. In this paper, we first design a channel gain and correlation-based users grouping algorithm per beam, and then using the deep reinforcement learning-based beam selection, a beamspace orthogonal analog precoder is obtained. The deep Q-network consists of a main network and target network with Adam optimizer. Finally, optimal power is allocated to the users in each beam. Simulation results show that at transmit SNR of 10 dB, the proposed scheme provides a 42% increase in sum-rate and energy efficiency performance as compared to the state-of-the-art $K$ -means users’ grouping and Stable Matching-based beam selection NOMA scheme.

The aim of this paper is to investigate the impact of factors like consumer perception, advanced telecomm services, ease of use, low cost and quality towards use of telecom services in Pakistan, with the specific objectives of to develop the technology acceptance model and investigate the role of these factors for adoption of telecom services in Pakistan. The study was based on primary data which was collected from 172 respondents by means of a questionnaire. Random Sampling Technique was applied and Statistical tool SPSS was used to check the correlation analysis in order to reveal the results of the research. Correlation analysis shows the significant, positive, negative, strong and weak relationship among the variables with the significant value ranges from 0.01 to 0.05. In this research there is positive and significant relationship among all the variables. The findings and recommendation of this research will help the managers to develop a deeper insight of research factors into product development and restructure their strategies to capture maximum potential customers.

In this paper, we present an energy-efficient Self Organizing Network (SON) architecture based on a tunable eNodeB (eNB) antenna tilt design for macrocells in a mobile network environment. This is an imperative element of mobility management in high speed and low latency wireless networks. The SON architecture follows a fully distributed approach with optional network information exchange with neighboring cells and core network. Antenna tilt directly affects its radiation pattern thus changes in eNB antenna tilt can be used to optimize cell coverage and reduce interference in mobile networks. We apply and compare two reinforcement machine learning techniques for optimizing the eNB antenna tilts, i.e., Deep Q-learning using Artificial Neural Network (ANN) and a simple Stochastic Cellular Learning Automata (SCLA). ANN is well known for its ability to learn from a vast number of inputs, while the stochastic learning technique relies on a simple action based probability vector updated based on system feedback. Neighboring cells for any one cell in the network environment are selected based on their separation distance and antenna orientation. We validate the data call performance of the network for edge users as they directly impact the Quality of Service (QoS) in the mobile environment. Our simulated results show that ANN performs better for edge users as compared to SCLA. The model also satisfies the SON requirement of scalability and agility. This work is a follow-up to our earlier work, where we showed that SCLA performs better than Q-learning in a similar network environment and optimizing strategy due to its low complexity, but within the same Q-learning algorithm more input learning parameters gave better performance.