Design and Evaluation of Buffer-Aided Cooperative NOMA with Direct Transmission in IoT

The high spectrum efficiency of non-orthogonal multiple access (NOMA) is attractive to solve the massive number of connections in the Internet of Things (IoT). This paper investigates a buffer-aided cooperative NOMA (C-NOMA) system in the IoT, where the intended users are equipped with buffers for cooperation. The direct transmission from the access point to the users and the buffer-aided cooperative transmission between the intended users are coordinated. In particular, a novel buffer-aided C-NOMA scheme is proposed to adaptively select a direct or cooperative transmission mode, based on the instantaneous channel state information and the buffer state. Then, the performance of the proposed scheme, in terms of the system outage probability and average delay, is theoretically derived with closed-form expressions. Furthermore, the full diversity order of three is demonstrated to be achieved for each user pair if the buffer size is not less than three, which is larger than conventional non-buffer-aided C-NOMA schemes whose diversity order is only two in the considered C-NOMA system in the IoT

Segmenting young-adult consumers in East Asia and Central and Eastern Europe – The role of consumer ethnocentrism and decision-making styles

© 2019 Elsevier Inc. The article addresses consumer ethnocentrism (CET) and consumer decision-making styles (CDMS) of young-adult consumers. We explore the level of between- and within-regional differences in CDMS in East Asia and Central and Eastern Europe. Drawing on Social identity theory, we explore various “constellations” of young-adult consumers with regards to their CDMS and assess to what extent can we discriminate between various consumer segments based on CET. We test hypotheses on matched samples' survey data from China, Japan, Slovenia and Croatia. Our study confirms low ethnocentric tendencies of young-adult consumers at regional, country and segment levels. We identify diverse CDMS archetypes between and within the respective countries and regions. Inter-regional differences are not bigger than country-level differences. We find weak pair-wise correlations between CET and some CDMS only in the case of Central and Eastern Europe

Performance Study of Cognitive Relay NOMA Networks with Dynamic Power Transmission

The cooperative non-orthogonal multiple access (NOMA) networks with one pair primary user and one pair cognitive user share the same spectrum resource via a common relay is considered in this paper. We propose a dynamic power transmission scheme for both uplink and downlink NOMA transmission in cognitive relay networks, which preserves the quality of service for the primary user. The closed-form expressions of overall outage probability and average sum rate for the proposed dynamic power transmission scheme of cognitive relay NOMA networks are derived. Both developed analytical results and Monte Carlo simulations show that the proposed dynamic power control scheme can dramatically enhance performance gain for the proposed networks, compared to other existing NOMA power allocation schemes

Enable Fully Customized Assistance: A Novel IMU-based Motor Intent Decoding Scheme

Assisting human locomotion is essentially related to the assistive force profile, which can be determined from four aspects: timing, magnitude, shape and duration. Most current methods of decoding human motor intent enable the customized determination of the assistive force profile by providing information of different subsets of the four aspects. Trustworthy human-exoskeleton interaction essentially relates to determining the assistive force profile. Current methods of decoding human motor intent enable the customized determination of the assistive force profile by providing limited information of human kinetics. In this paper, we propose and validate a novel motor intent decoding scheme that can enable a fully customized assistive force profile, where only inertial measurement units (IMUs) are used. First, we improve the robustness of the IMU-based kinematic estimation by sampling IMU measurements that well meet the hinge-joint assumption, and by online calibrating axes’ direction in order to avoid the post-hoc analysis of joint axes’ directions during the determination of the body-fixed coordinate frame. Second, using the calculated kinematics as input, we develop a computationally efficient dynamic model, through which kinetics of users can be calculated in real-time. Finally, we leverage a cable-driven ankle exoskeleton method to validate the assistive performance of our motor intent decoding scheme. We perform experiments on ten healthy subjects to evaluate the accuracy of our algorithm, and the change of metabolic rate and muscle efforts under the exoskeleton’s assistance. The results show the improvement from determining the assistive force profile by nominal curves and the feasibility of our algorithm

Continuous Prediction of Lower-Limb Kinematics From Multi-Modal Biomedical Signals

The fast-growing techniques of measuring and fusing multi-modal biomedical signals enable advanced motor intent decoding schemes of lower-limb exoskeletons, meeting the increasing demand for rehabilitative or assistive applications of take-home healthcare. Challenges of exoskeletons’ motor intent decoding schemes remain in making a continuous prediction to compensate for the hysteretic response caused by mechanical transmission. In this paper, we solve this problem by proposing an ahead-of-time continuous prediction of lower-limb kinematics, with the prediction of knee angles during level walking as a case study. Firstly, an end-to-end kinematics prediction network(KinPreNet)1, consisting of a feature extractor and an angle predictor, is proposed and experimentally compared with features and methods traditionally used in ahead-of-time prediction of gait phases. Secondly, inspired by the electromechanical delay(EMD), we further explore our algorithm’s capability of compensating response delay of mechanical transmission by validating the performance of the different sections of prediction time. And we experimentally reveal the time boundary of compensating the hysteretic response. Thirdly, a comparison of employing EMG signals or not is performed to reveal the EMG and kinematic signals’ collaborated contributions to the continuous prediction. During the experiments, EMG signals of nine muscles and knee angles calculated from inertial measurement unit (IMU) signals are recorded from ten healthy subjects. Our algorithm can predict knee angles with the averaged RMSE of 3.98 deg which is better than the 15.95-deg averaged RMSE of utilizing the traditional methods of ahead-of-time prediction. The best prediction time is in the interval of 27ms and 108ms. To the best of our knowledge, this is the first study of continuously predicting lower-limb kinematics in an ahead-of-time manner based on the electromechanical delay (EMD