Green inter-cluster interference management in uplink of multi-cell processing systems

This paper examines the uplink of cellular systems employing base station cooperation for joint signal processing. We consider clustered cooperation and investigate effective techniques for managing inter-cluster interference to improve users' performance in terms of both spectral and energy efficiency. We use information theoretic analysis to establish general closed form expressions for the system achievable sum rate and the users' Bit-per-Joule capacity while adopting a realistic user device power consumption model. Two main inter-cluster interference management approaches are identified and studied, i.e., through: 1) spectrum re-use; and 2) users' power control. For the former case, we show that isolating clusters by orthogonal resource allocation is the best strategy. For the latter case, we introduce a mathematically tractable user power control scheme and observe that a green opportunistic transmission strategy can significantly reduce the adverse effects of inter-cluster interference while exploiting the benefits from cooperation. To compare the different approaches in the context of real-world systems and evaluate the effect of key design parameters on the users' energy-spectral efficiency relationship, we fit the analytical expressions into a practical macrocell scenario. Our results demonstrate that significant improvement in terms of both energy and spectral efficiency can be achieved by energy-aware interference management

Toward Ubiquitous Real-Time Radio Propagation Modeling: The Exploitation of Cyber Resources, GPU and Fast and Accurate EM Algorithms

Radio propagation modeling and prediction play an important role in the understanding of electromagnetic (EM) wave propagation in complex environments, as well as in the design of wireless communications and radar systems

Everyday imagery: Users' reflections on smartphone cameras and communication

User-based research into the lived experiences associated with smartphone camera practices – in particular, the taking, storing, curating and sharing of personal imagery in the digital media sphere – remains scarce, especially in contrast to its increasing ubiquity. Accordingly, this article’s detailed analysis of open-ended questionnaires from ‘millennial’ smartphone users elucidates the varied experiential, compositional and technological aspects associated with smartphone imagery in everyday life. It argues that the associated changes do more than just update previous technologies but rather open space up for emergent forms of visual communication. Specifically, our close interpretive reading indicates four key factors underlying the moments privileged when using smartphone cameras, namely: they deviate from the mundane, are related to ‘positive’ emotions, evince strong social bonds and encompass a future-oriented perspective. Relatedly, in terms of photographic composition, visual content tends to circulate around: the social presence of others, boundedness of event, perceived aesthetic value and intended shareability. Our findings question certain formulations about the gradual disappearance of media from personal consciousness in a digital age. If ceaselessness is a defining characteristic of the current era, our analysis reveals that the use of smartphone cameras is indicative of people affectively and self-consciously deploying the technology to try to arrest the ephemerality of daily life, however fleetingly. This article thus pinpoints the theoretical and methodological value of research approaches moving beyond a narrow focus on the usage patterns to uncover the spatio-temporal specificities shaping (and being shaped by) smartphone imagery and its communicative resonances

Network-based ranking in social systems: three challenges

Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (i) Rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure