Alessandro Checco

Proportional Fairness in 802.11 Wireless LANs

We provide the first rigorous analysis of proportional fairness in 802.11 WLANs. This analysis corrects prior approximate studies. We show that there exists a unique proportional fair rate allocation and completely characterise the allocation in terms of a new airtime quantity, the total air-time.

On the Interactions Between Multiple Overlapping WLANs Using Channel Bonding

Next-generation wireless local area networks (WLANs) will support the use of wider channels, which is known as channel bonding, to achieve higher throughput. However, because both the channel center frequency and the channel width are autonomously selected by each WLAN, the use of wider channels may also increase the competition with other WLANs operating in the same area for the available channel resources. In this paper, we analyze the interactions between a group of neighboring WLANs that use channel bonding and evaluate the impact of those interactions on the achievable throughput. A continuous-time Markov network model that is able to capture the coupled dynamics of a group of overlapping WLANs is introduced and validated. The results show that the use of channel bonding can provide significant performance gains, even in scenarios with a high density of WLANs, although it may also cause unfair situations in which some WLANs receive most of the transmission opportunities while others starve.

Analysis of Dynamic Channel Bonding in Dense Networks of WLANs

Dynamic Channel Bonding (DCB) allows for the dynamic selection and use of multiple contiguous basic channels in Wireless Local Area Networks (WLANs). A WLAN operating under DCB can enjoy a larger bandwidth, when available, and therefore achieve a higher throughput. However, the use of larger bandwidths also increases the contention with adjacent WLANs, which can result in longer delays in accessing the channel and consequently, a lower throughput. In this paper, a scenario consisting of multiple WLANs using DCB and operating within carrier-sensing range of one another is considered. An analytical framework for evaluating the performance of such networks is presented. The analysis is carried out using a Markov chain model that characterizes the interactions between adjacent WLANs with overlapping channels. An algorithm is proposed for systematically constructing the Markov chain corresponding to any given scenario. The analytical model is then used to highlight and explain the key properties that differentiate DCB networks of WLANs from those operating on a single shared channel. Furthermore, the analysis is applied to networks of IEEE 802.11ac WLANs operating under DCB–which do not fully comply with some of the simplifying assumptions in our analysis–to show that the analytical model can give accurate results in more realistic scenarios.

Self-configuration of scrambling codes for WCDMA small cell networks

This paper introduces the problem of Primary Scrambling Code (PSC) selection in small cell networks and proposes a novel optimisation technique. Small cells introduce challenges not present in conventional macrocell scrambling code allocation, including the need for dynamic allocation, scalable distributed allocation algorithms, and support for unplanned and organic deployments. To the best of our knowledge this is the first study addressing the issue of distributed scrambling code selection for small cell networks. We propose a decentralized learning algorithm which does not require any collaboration between the neighbouring base-stations and which finds a feasible allocation whenever one exists. The performance of the algorithm is compared against two variations of a greedy algorithm which is the current 3GPP recommendation and is shown to offer significant performance benefits.

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 behavior 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 analyzed 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.

Fair virtualization of 802.11 networks

We consider virtualization of network capacity in 802.11 WLANs and mesh networks. We show that allocating total airtime slices to ISPs is analogous to allocating a fraction of available time-slots in TDMA. We establish that the max-min fair flow rate allocation within an ISP airtime slice can be characterized independently of the rate allocation policy employed in other slices. Building on these observations, we present a lightweight, distributed algorithm for allocating airtime slices among ISP and max-min fair flow rates within each slice.

Throughput Analysis in CSMA/CA Networks Using Continuous Time Markov Networks: A Tutorial

This book chapter introduces the use of Continuous Time Markov Networks (CTMN) to analytically capture the operation of Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) networks. It is of tutorial nature, and it aims to be an introduction on this topic, providing a clear and easy-to-follow description. To illustrate how CTMN can be used, we introduce a set of representative and cutting-edge scenarios, such as Vehicular Ad-hoc Networks (VANETs), Power Line Communication networks and multiple overlapping Wireless Local Area Networks (WLANs). For each scenario, we describe the specific CTMN, obtain its stationary distribution and compute the throughput achieved by each node in the network. Taking the per-node throughput as reference, we discuss how the complex interactions between nodes using CSMA/CA have an impact on system performance.

Investigating User Perception of Gender Bias in Image Search: The Role of Sexism

There is growing evidence that search engines produce results that are socially biased, reinforcing a view of the world that aligns with prevalent social stereotypes. One means to promote greater transparency of search algorithms - which are typically complex and proprietary - is to raise user awareness of biased result sets. However, to date, little is known concerning how users perceive bias in search results, and the degree to which their perceptions differ and/or might be predicted based on user attributes. One particular area of search that has recently gained attention, and forms the focus of this study, is image retrieval and gender bias. We conduct a controlled experiment via crowdsourcing using participants recruited from three countries to measure the extent to which workers perceive a given image results set to be subjective or objective. Demographic information about the workers, along with measures of sexism, are gathered and analysed to investigate whether (gender) biases in the image search results can be detected. Amongst other findings, the results confirm that sexist people are less likely to detect and report gender biases in image search results.

Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance

In this paper we introduce the idea of estimating local topology in wireless networks by means of crowdsourced user reports. In this approach each user periodically reports to the serving basestation information about the set of neighbouring basestations observed by the user. We show that, by mapping the local topological structure of the network onto states of increasing knowledge, a crisp mathematical framework can be obtained, which allows in turn for the use of a variety of user mobility models. Using a simplified mobility model we show how obtain useful upper bounds on the expected time for a basestation to gain full knowledge of its local neighbourhood, answering the fundamental question about which classes of network deployments can effectively benefit from a crowdsourcing approach.

Modus Operandi of Crowd Workers: The Invisible Role of Microtask Work Environments

The ubiquity of the Internet and the widespread proliferation of electronic devices has resulted in flourishing microtask crowdsourcing marketplaces, such as Amazon MTurk. An aspect that has remained largely invisible in microtask crowdsourcing is that of work environments; defined as the hardware and software affordances at the disposal of crowd workers which are used to complete microtasks on crowdsourcing platforms. In this paper, we reveal the significant role of work environments in the shaping of crowd work. First, through a pilot study surveying the good and bad experiences workers had with UI elements in crowd work, we revealed the typical issues workers face. Based on these findings, we then deployed over 100 distinct microtasks on CrowdFlower, addressing workers in India and USA in two identical batches. These tasks emulate the good and bad UI element designs that characterize crowdsourcing microtasks. We recorded hardware specifics such as CPU speed and device type, apart from software specifics including the browsers used to complete tasks, operating systems on the device, and other properties that define the work environments of crowd workers. Our findings indicate that crowd workers are embedded in a variety of work environments which influence the quality of work produced. To confirm and validate our data-driven findings we then carried out semi-structured interviews with a sample of Indian and American crowd workers from this platform. Depending on the design of UI elements in microtasks, we found that some work environments support crowd workers more than others. Based on our overall findings resulting from all the three studies, we introduce ModOp, a tool that helps to design crowdsourcing microtasks that are suitable for diverse crowd work environments. We empirically show that the use of ModOp results in reducing the cognitive load of workers, thereby improving their user experience without affecting the accuracy or task completion time.