Brand Attitude in Social Networks: The Role of eWoM

The aim of this study is to analyze the impact of electronic word-of-mouth (eWoM) marketing on branding attitude in social networks. We treated eWOM activities on brand awareness, brand destruction, branding, brand image, and brand competition. The data was gathered through the followers of Wiesland shoe page on Instagram's social network. We conduct the statistical analysis by Spss software and apply SmartPls software to test the hypotheses. The results confirm that eWoM plays a significant and positive role in branding, brand image, and brand awareness whereas it does not have an influence on brand destruction. On the other hand, branding and brand destruction play a crucial role in gaining a competitive advantage. Therefore, the brand relationship is enhanced through eWOM activities

Robust Channel Estimation in Multiuser Downlink 5G Systems Under Channel Uncertainties

In wireless communication, the performance of the network highly relies on the accuracy of channel state information (CSI). On the other hand, the channel statistics are usually unknown, and the measurement information is lost due to the fading phenomenon. Therefore, we propose a channel estimation approach for downlink communication under channel uncertainty. We apply the Tobit Kalman filter (TKF) method to estimate the hidden state vectors of wireless channels. To minimize the maximum estimation error, a robust minimax minimum estimation error (MSE) estimation approach is developed while the QoS requirements of wireless users is taken into account. We then formulate the minimax problem as a non-cooperative game to find an optimal filter and adjust the best behavior for the worst-case channel uncertainty. We also investigate a scenario in which the actual operating point is not exactly known under model uncertainty. Finally, we investigate the existence and characterization of a saddle point as the solution of the game. Theoretical analysis verifies that our work is robust against the uncertainty of the channel statistics and able to track the true values of the channel states. Additionally, simulation results demonstrate the superiority of the model in terms of MSE value over related techniques

Robust Data Transmission Rate Allocation to Improve Energy Efficiency in 6G Networks

The future sixth-generation (6G) network is expected to support both sensing and communications. Since the sensing performance will highly rely on the residual battery of smart devices, energy efficiency is one of the main concerns in the design of 6G. Motivated by these facts, we design an energy efficient data transmission rate allocation approach for 6G networks. To have a more realistic deployment, we assume that perfect channel state information is not available. Imperfect channel state information (CSI) might waste energy sources or degrade quality of service (QoS). Thus, we apply the maximum likelihood estimation (MLE) method to estimate the true channel characteristics for a given set of observations. The proposed approach is robust against unknown channel statistics and allows adapting UEs transmission rate to the channel quality which reduce energy consumption and guarantees QoS. Both numerical analysis and simulation results confirm the effectiveness of the proposed work in terms of energy efficiency and throughput maximization

An accurate RSS/AoA-based localization method for internet of underwater things

Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes’ locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors

A game-based power optimization for 5G femtocell networks

Spectrum sharing deployment of femtocells brings interferences which dramatically degrade network performance. Hence, interference control is a crucial challenge for femtocell networks. In this paper, we propose a power optimization approach for 5G femtocell networks consisting of macrocell and underlying femtocells to manage the interference. Firstly, we formulate the problem based on a non-cooperative game to analyze the competition among the users to access shared spectrum. We then design a pricing mechanism in the utility function to guarantee quality of service (QoS) requirements of macro users. The mechanism lets the macro users experience lower interference and achieve the minimum required data rate. As a result, QoS requirements of both macro and femto users are fulfilled in a non-cooperative manner. We also design a minimax decision rule to optimize the worst-case performance and find an optimal transmission power for each user. By adjusting the optimal power for each user, the maximum aggregate interference is minimized, and the network throughput is maximized. Finally, we develop an iterative learning-based algorithm to implement the proposed scheme and achieve the game equilibrium. Theoretical analysis and simulation results verifies the effectiveness of the proposed mechanism in terms of throughput maximization, QoS assurance and interference mitigation