In February 2024, Lionel Messi's absence from a Hong Kong exhibition match ignited extensive debate across social media platforms, capturing the attention of the public and sparking controversies that extended into realms such as business partnerships and diplomatic implications. This incident not only reflects the public's reaction and pattern of stance changes toward such a complex Incident but also significantly demonstrates the close connection between cyberspace and the real world. Research on this incident has important practical significance for predicting and intervening in the evolution of similar hot incidents. In this article, the case of Messi's absence from the Hong Kong match is delved into. A stance detection method and a quantification approach for stance divergence have been devised to analyze the evolution of the stance surrounding the incident. Furthermore, by examining topic clusters over time, the topical progression of public stances is tracked. Findings reveal that nearly 60% of posts expressed an explicit stance throughout the incident, with the majority (66.9%) taking an opposing stance. Additionally, it was noted that the topics discussed at various stages followed a long-tail distribution, indicating that most discussions revolved around a few dominant themes. Within more segmented and specific topics, stance divergences were often more prominent.

In the past few years, with the rapid growth of digital technologies, Facebook, Twitter, and other social media platforms have become the digital oligarchies, which have the enormous capabilities to potentially control what is discussed in cyberspace. In the digital oligarchy era, social perception and social influence in different complex social systems have evolved quickly. In this article, we conducted large-scale empirical studies on social perception and social influence regarding the Trump phenomenon from personal perception, media, and public attention perspectives. We found that there exist obvious correlations between the posting behavior of Trump and the attention of news media. By constructing public attention networks using complex networks based on Google search information, we further reveal that digital platforms could affect social perception and social influence significantly. Especially, we obtained that the public attention can always be influenced by the political moments.

During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information publishing strategies can be informed for the COVID-19 epidemic. This article sought to fill this gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information. We found specific features in predicting the reposted amount of each type of information. The results provide data-driven insights into the information need and public attention.

The measurement of influence on online platforms has been an important issue for various applications, including viral marketing, recommender systems, and the Internet celebrity economy. Generally, the citation frequency, mention frequency, and in-degree of users are the three major criteria for evaluating online influence in existed studies. However, some online media platforms neither provide social networking functions nor support social relationship labeling, making it infeasible to measure the user influence via the above three criteria. Such platforms can be named nonexplicit-relationship platforms. In this article, we propose three new criteria, explicit conversion rate (ER), frequency of promotion (FP), and average participation density (APD), and design a novel algorithm to effectively calculate and evaluate users' influence on these platforms. The stability and sustainability of user influence are evaluated to distinguish the real influentials from the disguised ones, while the latter usually appears for temporary commercial advertisement purposes. The experiments proved the effectiveness of the proposed criteria and the algorithm in determining influentials' influence, as well as the corresponding stability and sustainability.

It is shown that a non-square (NS) 2/sup 2n+1/-ary quadrature amplitude modulation (QAM) can be decomposed into a single parity-check (SPC) block encoder and a memoryless modulator in such a way that the inherent block encoder has a recursive nature. When concatenated with a forward-error-correcting (FEC) code, iterative demodulation, demapping, and decoding of the FEC code and the inherent SPC code of NS-2/sup 2n+1/-QAM is then possible. Simulation results show that coded NS-8QAM performs nearly 2 dB better than standard 8QAM and star-8QAM, and nearly 1 dB better than 8-ary phase-shift keying when the FEC code is a rate-1/2, 16-state convolutional code, while coded NS-32QAM performs 0.75 dB better than standard 32QAM.

We derive the outage capacity region of an M-user fading multiple-access channel (MAC) under the assumption that both the transmitters and the receiver have perfect channel side information (CSI). The outage capacity region is implicitly obtained by deriving the outage probability region for a given rate vector. Given a required rate and average power constraint for each user, we find a successive decoding strategy and a power allocation policy that achieves points on the boundary of the outage probability region. We discuss the scenario where an outage must be declared simultaneously for all users (common outage) and when outages can be declared individually (individual outage) for each user.

We show that military standard (MIL-STD) shaped-offset quadrature phase-shift keying (SOQPSK), a highly bandwidth-efficient constant-envelope modulation, can be represented in the form of a cross-correlated trellis-coded quadrature modulation. Similarly, we show that offset QPSK (OQPSK) can be decomposed into a "degraded" trellis encoder and a memoryless mapper. Based on the representations of OQPSK and MIL-STD SOQPSK as trellis-coded modulations (TCMs), we investigate the potential coding gains achievable from the application of simple outer codes to form a concatenated coding structure with iterative decoding. For MIL-STD SOQPSK, we describe the optimum receiver corresponding to its TCM form and then propose a simplified receiver. The bit-error rate (BER) performances of both receivers for uncoded and coded MIL-STD SOQPSK are simulated and compared with that of OQPSK and Feher-patented QPSK (FQPSK). The asymptotic BER performance of MIL-STD SOQPSK is also analyzed and compared with that of OQPSK and FQPSK. Simulation results show that, compared with their uncoded systems, there are significant coding gains for both OQPSK and MIL-STD SOQPSK, obtained by applying iterative decoding to either the parallel concatenated coding scheme or the serial one, even when very simple outer codes are used.

We propose a decision-feedback decoder for coded signals transmitted over finite-state Markov channels. The decoder achieves maximum-likelihood sequence detection (in the absence of feedback errors) with very low complexity by exploiting previous bit decisions and the Markov structure of the channel. We also propose a similar decoder, the output-feedback decoder, that does not use previous bit decisions and therefore does not suffer from error propagation. The decoder performance is determined using a new sliding window analysis technique as well as by simulation. Both decoders exhibit excellent bit error rate performance with a relatively low complexity that is independent of the channel decorrelation time.

In multiuser wireless systems, dynamic resource allocation between users and over time significantly improves efficiency and performance. In this two-part paper, we study three types of capacity regions for fading broadcast channels and obtain their corresponding optimal resource allocation strategies: the ergodic (Shannon) capacity region, the zero-outage capacity region, and the outage capacity region with nonzero outage. We derive the ergodic capacity region of an M-user fading broadcast channel for code division (CD), time division (TD), and frequency division (FD), assuming that both the transmitter and the receivers have perfect channel side information (CSI). It is shown that by allowing dynamic resource allocation, TD, FD, and CD without successive decoding have the same ergodic capacity region, while optimal CD has a larger region. Optimal resource allocation policies are obtained for these different spectrum-sharing techniques. A simple suboptimal policy is also proposed for TD and CD without successive decoding that results in a rate region quite close to the ergodic capacity region. Numerical results are provided for different fading broadcast channels.

For pt.I see ibid., vol.47, no.3, p.1083-1102 (2002). We study three capacity regions for fading broadcast channels and obtain their corresponding optimal resource allocation strategies: the ergodic (Shannon) capacity region, the zero-outage capacity region, and the capacity region with outage. In this paper, we derive the outage capacity regions of fading broadcast channels, assuming that both the transmitter and the receivers have perfect channel side information. These capacity regions and the associate optimal resource allocation policies are obtained for code division (CD) with and without successive decoding, for time division (TD), and for frequency division (FD). We show that in an M-user broadcast system, the outage capacity region is implicitly obtained by deriving the outage probability region for a given rate vector. Given the required rate of each user, we find a strategy which bounds the outage probability region for different spectrum-sharing techniques. The corresponding optimal power allocation scheme is a multiuser generalization of the threshold-decision rule for a single-user fading channel. Also discussed is a simpler minimum common outage probability problem under the assumption that the broadcast channel is either not used at all when fading is severe or used simultaneously for all users. Numerical results for the different outage capacity regions are obtained for the Nakagami-m (1960) fading model.

We derive the outage capacity regions of an M-user fading multiple-access channel under the assumption that both the transmitters and the receiver have perfect channel side information. We show that the outage capacity region is implicitly obtained by deriving the outage probability region for a given rate vector. Given the average power constraint and the required rate of each user, we find a successive decoding strategy and a power allocation policy that bound the outage probability region. Also discussed is a simpler minimum common outage probability problem under the assumption that the multiple-access channel is either not used at all when fading is severe or is used simultaneously by all users. Iterative algorithms are proposed for obtaining the optimal decoding order and power allocation in each fading state under the given power constraint of each user.