The Eucharist in an Unarticulated World

Ethnographic data from the lived experiences of teenagers participating in the weekly observance of the Eucharist provided rich data for an application of Bakhtinian approaches to discourse in order to inform current practice. Bakhtin’s understanding of dialogism and heteroglossia asserts that all discourse communities are located in historical situations that involve complex interactions. Each utterance takes meaning from its “actual social life.” Bakhtin gives priority to utterances that occur in context and focuses on the intentional negotiation of meaning and interpretation between author and reader, or, in this case, researcher, participant, and community. The research provides opportunity for teenagers to “answer with their lives” the meaning of the Eucharist

Sensing-Throughput Tradeoff for Interweave Cognitive Radio System: A Deployment-Centric Viewpoint

Secondary access to the licensed spectrum is viable only if interference is avoided at the primary system. In this regard, different paradigms have been conceptualized in the existing literature. Of these, Interweave Systems (ISs) that employ spectrum sensing have been widely investigated. Baseline models investigated in the literature characterize the performance of IS in terms of a sensing-throughput tradeoff, however, this characterization assumes the knowledge of the involved channels at the secondary transmitter, which is unavailable in practice. Motivated by this fact, we establish a novel approach that incorporates channel estimation in the system model, and consequently investigate the impact of imperfect channel estimation on the performance of the IS. More particularly, the variation induced in the detection probability affects the detector's performance at the secondary transmitter, which may result in severe interference at the primary users. In this view, we propose to employ average and outage constraints on the detection probability, in order to capture the performance of the IS. Our analysis reveals that with an appropriate choice of the estimation time determined by the proposed model, the degradation in performance of the IS can be effectively controlled, and subsequently the achievable secondary throughput can be significantly enhanced.Comment: 13 pages, 10 figures, Accepted to be published in IEEE Transactions on Wireless Communication

A Low-Overhead Energy Detection Based Cooperative Sensing Protocol for Cognitive Radio Systems

Cognitive radio and dynamic spectrum access represent a new paradigm shift in more effective use of limited radio spectrum. One core component behind dynamic spectrum access is the sensing of primary user activity in the shared spectrum. Conventional distributed sensing and centralized decision framework involving multiple sensor nodes is proposed to enhance the sensing performance. However, it is difficult to apply the conventional schemes in reality since the overhead in sensing measurement and sensing reporting as well as in sensing report combining limit the number of sensor nodes that can participate in distributive sensing. In this paper, we shall propose a novel, low overhead and low complexity energy detection based cooperative sensing framework for the cognitive radio systems which addresses the above two issues. The energy detection based cooperative sensing scheme greatly reduces the quiet period overhead (for sensing measurement) as well as sensing reporting overhead of the secondary systems and the power scheduling algorithm dynamically allocate the transmission power of the cooperative sensor nodes based on the channel statistics of the links to the BS as well as the quality of the sensing measurement. In order to obtain design insights, we also derive the asymptotic sensing performance of the proposed cooperative sensing framework based on the mobility model. We show that the false alarm and mis-detection performance of the proposed cooperative sensing framework improve as we increase the number of cooperative sensor nodes.Comment: 11 pages, 8 figures, journal. To appear in IEEE Transactions on Wireless Communication

Throughput and Collision Analysis of Multi-Channel Multi-Stage Spectrum Sensing Algorithms

Multi-stage sensing is a novel concept that refers to a general class of spectrum sensing algorithms that divide the sensing process into a number of sequential stages. The number of sensing stages and the sensing technique per stage can be used to optimize performance with respect to secondary user throughput and the collision probability between primary and secondary users. So far, the impact of multi-stage sensing on network throughput and collision probability for a realistic network model is relatively unexplored. Therefore, we present the first analytical framework which enables performance evaluation of different multi-channel multi-stage spectrum sensing algorithms for Opportunistic Spectrum Access networks. The contribution of our work lies in studying the effect of the following parameters on performance: number of sensing stages, physical layer sensing techniques and durations per each stage, single and parallel channel sensing and access, number of available channels, primary and secondary user traffic, buffering of incoming secondary user traffic, as well as MAC layer sensing algorithms. Analyzed performance metrics include the average secondary user throughput and the average collision probability between primary and secondary users. Our results show that when the probability of primary user mis-detection is constrained, the performance of multi-stage sensing is, in most cases, superior to the single stage sensing counterpart. Besides, prolonged channel observation at the first stage of sensing decreases the collision probability considerably, while keeping the throughput at an acceptable level. Finally, in realistic primary user traffic scenarios, using two stages of sensing provides a good balance between secondary users throughput and collision probability while meeting successful detection constraints subjected by Opportunistic Spectrum Access communication

To Sense or Not To Sense

A longer sensing time improves the sensing performance; however, with a fixed frame size, the longer sensing time will reduce the allowable data transmission time of the secondary user (SU). In this paper, we try to address the tradeoff between sensing the primary channel for $\tau$ seconds of the time slot proceeded by randomly accessing it and randomly accessing primary channel without sensing to avoid wasting $\tau$ seconds in sensing. The SU senses primary channel to exploit the periods of silence, if the primary user (PU) is declared to be idle the SU randomly accesses the channel with some access probability $a_s$. In addition to randomly accesses the channel if the PU is sensed to be idle, it possibly accesses it if the channel is declared to be busy with some access probability $b_s$. This is because the probability of false alarm and misdetection cause significant secondary throughput degradation and affect the PU QoS. We propose variable sensing duration schemes where the SU optimizes over the optimal sensing time to achieve the maximum stable throughput for both primary and secondary queues. The results reveal the performance gains of the proposed schemes over the conventional sensing scheme, i.e., the SU senses the primary channel for $\tau$ seconds and accesses with probability 1 if the PU is declared to be idle. Also, the proposed schemes overcome random access without sensing scheme. The theoretical and numerical results show that pairs of misdetection and false alarm probabilities may exist such that sensing the primary channel for very small duration overcomes sensing it for large portion of the time slot. In addition, for certain average arrival rate to the primary queue pairs of misdetection and false alarm probabilities may exist such that the random access without sensing overcomes the random access with long sensing duration

Learning-Based Constraint Satisfaction With Sensing Restrictions

In this paper we consider graph-coloring problems, an important subset of general constraint satisfaction problems that arise in wireless resource allocation. We constructively establish the existence of fully decentralized learning-based algorithms that are able to find a proper coloring even in the presence of strong sensing restrictions, in particular sensing asymmetry of the type encountered when hidden terminals are present. Our main analytic contribution is to establish sufficient conditions on the sensing behaviour to ensure that the solvers find satisfying assignments with probability one. These conditions take the form of connectivity requirements on the induced sensing graph. These requirements are mild, and we demonstrate that they are commonly satisfied in wireless allocation tasks. We argue that our results are of considerable practical importance in view of the prevalence of both communication and sensing restrictions in wireless resource allocation problems. The class of algorithms analysed here requires no message-passing whatsoever between wireless devices, and we show that they continue to perform well even when devices are only able to carry out constrained sensing of the surrounding radio environment

Cognitive node selection and assignment algorithms for weighted cooperative sensing in radar systems

For the radar spectrum to be shared efficiently a good sensing capability within a secondary cognitive communication system is required. In this paper, the swept radar's rotation mechanism is explored to improve the sensing performance. Several node teaming algorithms are proposed for cooperative sensing along with the use of weighted sensing algorithms in a swept radar scenario. These teaming algorithms are considered in respect of the mobile team node selection and the sensing task assignments of the team nodes. Performance results show that selecting appropriate sensing nodes to join the sensing-active team in different sensing cycles and exploring their frequency diversity (to perform the sensing task at the most suitable frequency subchannels), yields a substantial improvement in performance. In addition, it is illustrated that proper node teaming algorithms should be chosen based on several key factors, including the characteristics of the primary signal and the sensing team node's computational capabilities.For the radar spectrum to be shared efficiently a good sensing capability within a secondary cognitive communication system is required. In this paper, the swept radar's rotation mechanism is explored to improve the sensing performance. Several node teaming algorithms are proposed for cooperative sensing along with the use of weighted sensing algorithms in a swept radar scenario. These teaming algorithms are considered in respect of the mobile team node selection and the sensing task assignments of the team nodes. Performance results show that selecting appropriate sensing nodes to join the sensing-active team in different sensing cycles and exploring their frequency diversity (to perform the sensing task at the most suitable frequency subchannels), yields a substantial improvement in performance. In addition, it is illustrated that proper node teaming algorithms should be chosen based on several key factors, including the characteristics of the primary signal and the sensing team node's computational capabilitie

Measure What Should be Measured: Progress and Challenges in Compressive Sensing

Is compressive sensing overrated? Or can it live up to our expectations? What will come after compressive sensing and sparsity? And what has Galileo Galilei got to do with it? Compressive sensing has taken the signal processing community by storm. A large corpus of research devoted to the theory and numerics of compressive sensing has been published in the last few years. Moreover, compressive sensing has inspired and initiated intriguing new research directions, such as matrix completion. Potential new applications emerge at a dazzling rate. Yet some important theoretical questions remain open, and seemingly obvious applications keep escaping the grip of compressive sensing. In this paper I discuss some of the recent progress in compressive sensing and point out key challenges and opportunities as the area of compressive sensing and sparse representations keeps evolving. I also attempt to assess the long-term impact of compressive sensing