Short-Packet Downlink Transmission with Non-Orthogonal Multiple Access

This work introduces downlink non-orthogonal multiple access (NOMA) into short-packet communications. NOMA has great potential to improve fairness and spectral efficiency with respect to orthogonal multiple access (OMA) for low-latency downlink transmission, thus making it attractive for the emerging Internet of Things. We consider a two-user downlink NOMA system with finite blocklength constraints, in which the transmission rates and power allocation are optimized. To this end, we investigate the trade-off among the transmission rate, decoding error probability, and the transmission latency measured in blocklength. Then, a one-dimensional search algorithm is proposed to resolve the challenges mainly due to the achievable rate affected by the finite blocklength and the unguaranteed successive interference cancellation. We also analyze the performance of OMA as a benchmark to fully demonstrate the benefit of NOMA. Our simulation results show that NOMA significantly outperforms OMA in terms of achieving a higher effective throughput subject to the same finite blocklength constraint, or incurring a lower latency to achieve the same effective throughput target. Interestingly, we further find that with the finite blocklength, the advantage of NOMA relative to OMA is more prominent when the effective throughput targets at the two users become more comparable.Comment: 15 pages, 9 figures. This is a longer version of a paper to appear in IEEE Transactions on Wireless Communications. Citation Information: X. Sun, S. Yan, N. Yang, Z. Ding, C. Shen, and Z. Zhong, "Short-Packet Downlink Transmission with Non-Orthogonal Multiple Access," IEEE Trans. Wireless Commun., accepted to appear [Online] https://ieeexplore.ieee.org/document/8345745

Toward a Configurational Protection Motivation Theory

Protection motivation theory (PMT) has been widely used as a theory to explain users’ adoption of health information technologies. Prior studies based on PMT tend to treat it as a variance model and explain the parallel and independent effects of its constructs. This theorization neglects the original insights about the sequence of decision making and the interdependencies between PMT constructs. To address both of these two issues, this study proposes and tests a configurational protection motivation theory (CPMT). Specifically, different configurations are identified to reflect the potential sequential effects, substitutive effects, and complementary effects. A survey of 204 mobile health service users in China is conducted to test CPMT and the data analysis results confirm the theoretical expectations. This study can contribute to protection motivation theory and e-health research and suggest practitioners to think in a holistic way during service promotion

GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning

Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance trade-offs, and fast technological advancements. Although there has been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black-box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search, and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient.Comment: Accepted to the 57th Design Automation Conference (DAC 2020); 6 pages, 8 figure

Knowledge Quality of Collaborative Editing in Wikipedia: an Integrative Perspective of Social Capital and Team Conflict

Collaborative editing has become one of the most popular forms of knowledge contribution in virtual communities. Wikipedia— the largest online encyclopaedia— is a representative example of collaborative work. Despite the abundant researches on Wikipedia, to the best of our knowledge, no one has considered the integration of social capital and conflict. Besides, extant literatures on knowledge quality just pay attention to task conflict, while relational conflict is rarely mentioned. Meanwhile, our study proposes the nonlinear relationship between task conflict and knowledge quality instead of linear relationships in prior studies. We also postulate the moderating effect of task complexity. Furthermore, there is little empirical research on the influence of social capital on conflict, especially the distinct effects of cognitive and relational capital. This paper aims at proposing a theoretical model to examine the effect of social capital and conflict, meanwhile taking the task complexity into account. We will make our efforts to verify our research model in the following phases, and we believe that the present work can make some contributions to both research and practice

Perceived Firm Attributes, Social Identification, and Intrinsic Motivation to Voice in Brand Virtual Communities: Differentiating Brand-General and Innovation-Specific Perceptions

The question about why some brand virtual communities (BVCs) successfully motivate customers to engage in value creation (e.g., voice) while others do not is still an important but understudied research issue. To fill this research gap, we propose a research model to shed light on the antecedents of intrinsic motivation to voice by focusing on the role of perceived firm attributes. Specifically, we argue that firm attributes can be classified into brand-general versus innovation-specific attributes which affect intrinsic motivation through two types of social identification namely brand identification and community identification respectively. The links between these two types of perceptions are examined too. A field study of 291 BVC users was conducted to test the research model. The results show that customer orientation and perceived openness positively affect customers’ brand identification and community identification respectively, and customer orientation has a positive effect on perceived openness. Furthermore, the impact of brand identification on intrinsic motivation is found to be fully mediated by community identification

Dual Process, Buffering/Coping Effects, and Reciprocal Dynamics: A Social Demands-Resources Model of SNS Discontinuance

Prior studies on social networking sites (SNSs) discontinuance focus on the demand side (e.g., social overload) while neglect the resource side. To address this problem, drawing upon the job demands–resources (JD–R) model, we develop the social demands–resources (SD–R) model of SNS discontinuance. Specifically, social overload and social support, as social demands and social resources, are proposed to affect discontinuance through the energetic process and the motivational process respectively. The buffering effect and the coping effect are proposed to explain the cross-links between the dual processes. We also propose the mechanism of reciprocal dynamics to capture the relationship between social support and social overload. Through a study of 479 WeChat users, the results confirm the proposed SD–R model of SNS discontinuance. The implications for research and practice are also discussed

Robust MIMO Detection With Imperfect CSI: A Neural Network Solution

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by taking into consideration the CSI uncertainties caused by both the channel estimation error and the channel variation. To address the challenging discrete optimization problem, we propose an efficient alternating direction method of multipliers (ADMM)-based algorithm, which only requires calculating closed-form solutions in each iteration. Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies. Moreover, in order to relieve the computational burden, a low-complexity ADMM-based robust detector is developed using the Gaussian approximation, and the corresponding deep unfolding network LCRADMMNet is further established. On the other hand, we also provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking method, which can effectively refine the CSI accuracy and improve the performance of the proposed robust detectors. Simulation results validate the significant performance advantages of the proposed robust detection networks over the non-robust detectors with different CSI acquisition methods.Comment: 15 pages, 8 figures, 2 tables; Accepted by IEEE TCO