Free-flow road networks, such as suburban highways, are increasingly experiencing traffic congestion due to growing commuter inflow and limited infrastructure. Traditional control mechanisms—traffic signals or local heuristics—are ineffective or infeasible in these high-speed, signal-free environments. We introduce self-regulating cars, a reinforcement learning-based traffic control protocol that dynamically modulates vehicle speeds to optimize throughput and prevent congestion, without requiring new physical infrastructure. Our approach integrates classical traffic flow theory, gap acceptance models, and microscopic simulation into a physics-informed RL framework. By abstracting roads into super-segments, the agent captures emergent flow dynamics and learns robust speed modulation policies from instantaneous traffic observations. Evaluated in the high-fidelity PTV Vissim simulator on a real-world highway network, our method improves total throughput by 5%, reduces average delay by 13%, and decreases total stops by 3% compared to the no-control setting. It also achieves smoother, congestion-resistant flow while generalizing across varied traffic patterns—demonstrating its potential for scalable, ML-driven traffic management.

Over a quarter of the global population remains without access to the Internet in 2026, primarily due to cost and limited infrastructure in developing regions. This gap is becoming more consequential as access to information increasingly depends on AI systems that presume reliable, high-bandwidth connectivity. In response, we present WebFM, a low-cost, scalable Web and AI access solution that builds on widely deployed infrastructure in developing regions: FM radio for downlink broadcasting and SMS for personalized uplink. Prior work on web-over-FM is limited to simulations, short-range experiments, and lossless links, leaving the effects of geographically induced signal variability largely unexplored. WebFM addresses this gap through several system-level innovations to effectively transmit Web content and enable LLM interactions over sound over FM radio, in a reliable and compressed form. For example, we introduce a loss-resilient file format for encoding webpages and modify Android to leverage built-in FM tuners, allowing deployment across a wide range of devices without requiring root access. We enable ChatGPT interactions by allowing users to submit prompts over SMS and receive model responses via FM broadcast. Finally, we preemptively transmit popular pages and a subset of their internal pages while the server is idle, improving user experience during peak hours. We deployed WebFM at an FM radio station in Cameroon for six weeks with 30 participants. Our evaluation shows a sustained downlink throughput of 10 kbps, less than 20% loss for most transmissions with signal strength above -90 dBm, and strong user engagement across both web browsing and ChatGPT interactions.

Recent breakthroughs in Large Language Models (LLMs) have led to their adoption across a wide range of tasks, ranging from code generation to machine translation and sentiment analysis, etc. Red teaming/Safety alignment efforts show that fine-tuning models on benign (non-harmful) data could compromise safety. However, it remains unclear to what extent this phenomenon is influenced by different variables, including fine-tuning task, model calibrations, etc. This paper explores the task-wise safety degradation due to fine-tuning on downstream tasks such as summarization, code generation, translation, and classification across various calibration. Our results reveal that: 1) Fine-tuning LLMs for code generation and translation leads to the highest degradation in safety guardrails. 2) LLMs generally have weaker guardrails for translation and classification, with 73-92% of harmful prompts answered, across baseline and other calibrations, falling into one of two concern categories. 3) Current solutions, including guards and safety tuning datasets, lack cross-task robustness. To address these issues, we developed a new multitask safety dataset effectively reducing attack success rates across a range of tasks without compromising the model’s overall helpfulness. Our work underscores the need for generalized alignment measures to ensure safer and more robust models.

English is the predominant language on the web, powering nearly half of the world's top ten million websites. Support for multilingual content is nevertheless growing, with many websites increasingly combining English with regional or native languages in both visible content and hidden metadata. This multilingualism introduces significant barriers for users with visual impairments, as assistive technologies like screen readers frequently lack robust support for non-Latin scripts and misrender or mispronounce non-English text, compounding accessibility challenges across diverse linguistic contexts. Yet, large-scale studies of this issue have been limited by the lack of comprehensive datasets on multilingual web content. To address this gap, we introduce LangCrUX, the first large-scale dataset of 120,000 popular websites across 12 languages that primarily use non-Latin scripts. Leveraging this dataset, we conduct a systematic analysis of multilingual web accessibility and uncover widespread neglect of accessibility hints. We find that these hints often fail to reflect the language diversity of visible content, reducing the effectiveness of screen readers and limiting web accessibility. We finally propose Kizuki, a language-aware automated accessibility testing extension to account for the limited utility of language-inconsistent accessibility hints.

Recently, various Mobile Network Aggregators (MNAs) have emerged, leveraging the coverage of a few base operators to offer global connectivity. These MNAs reap the benefits of network softwarization and virtualization, such as eSIM technology or control/data-plane separation. This paper explores a new type of MNA — a thick MNA — that uses multiple base operators from different regions to provide eSIM profiles and employs public internet gateways outside the base operators' home countries. Specifically, we analyze Airalo, a thick MNA operating in 219 countries. Unlike other MNAs that our community has scrutinized, we show that Airalo often decouples the geographical location of the public internet gateway from the native country of the base operator via IPX Hub Breakout (IHBO). To map Airalo's underlying infrastructure, we ran web-based measurements that 14 volunteers performed while traveling and using an Airalo eSIM on their personal devices. We further dive into Airalo's performance by running device-based measurements (speedtest, traceroute, etc.) in 10 countries. We find that IHBO, despite offering some latency benefits over traditional Home-Routed Roaming, still results in inflated latency and degraded bandwidth compared to non-roaming traffic, introducing new challenges for user experience, content access, and privacy.

The growing demand for reliable Internet access during air travel has made in-flight connectivity (IFC) a critical service for commercial airlines. Traditional IFC systems rely on geostationary (GEO) satellites, but their high latency and limited bandwidth hinder user experience. Emerging Low Earth Orbit (LEO) constellations, such as Starlink, promise better network performance. This paper presents the first empirical comparison of IFC performance across GEO and LEO networks, using data from 25 flights operated by 7 airlines. Measurements collected via instrumented Android devices cover key metrics including latency, throughput, and CDN responsiveness. We observe that while GEO systems rely on static and often distant Internet gateways, Starlink clients connect to different gateways that align more closely with flight trajectories, enhancing end-to-end performance through shorter satellite paths. However, DNS-based content filtering on Starlink-equipped flights often affects user geolocation, introducing unnecessary terrestrial delays. Finally, our case study of TCP performance from 2 Starlink flights shows that the BBR congestion control algorithm delivers up to 35× higher throughput than Cubic and Vegas, but with significantly higher retransmissions due to aggressive bandwidth probing.

While internet usage is slowly catching up globally, it is still unclear how the web experience differs in developing and developed countries. On the one hand, the web has a notoriously large inertia, with many webpages still relying on unencrypted HTTP, deprecated web features, or old and buggy libraries. On the other hand, developing countries are expected to leapfrog and directly adopt the newest technologies by learning from the prior mistakes of more developed countries. Anecdotal evidence suggests that webpages in developing and developed regions differ significantly. In this work, we test this hypothesis by measuring differences in web development practices across the two groups of countries, using multiple dimensions: webpages' size, complexity, security, privacy, quality, technology adoption, and accessibility. Concretely, we collect the largest dataset to date that compares web development practices across developed and developing regions -- 200,000 webpages across 20 countries -- which we aim to open source along with this publication. Our findings reveal that webpages in developing regions are generally smaller and simpler, utilizing fewer requests - an adaptation that improves the performance over slower network conditions common in these areas. However, these sites are less optimized in other critical aspects: they frequently employ inefficient image formats, include unnecessary JavaScript or CSS code, or lack responsive image designs. Notably, our security assessment shows developing regions lagging in HTTPS adoption and vulnerability mitigation, possibly due to lower awareness of best practices.

The web experience in developing regions remains subpar, primarily due to the growing complexity of modern webpages and insufficient optimization by content providers. Users in these regions typically rely on low-end devices and limited bandwidth, which results in a poor user experience as they download and parse webpages bloated with excessive third-party CSS and JavaScript (JS). To address these challenges, we introduce the Mobile Application Markup Language (MAML), a flat layout-based web specification language that reduces computational and data transmission demands, while replacing the excessive bloat from JS with a new scripting language centered on essential (and popular) web functionalities. Last but not least, MAML is backward compatible as it can be transpiled to minimal HTML/JavaScript/CSS and thus work with legacy browsers. We benchmark MAML in terms of page load times and sizes, using a translator which can automatically port any webpage to MAML. When compared to the popular Google AMP, across 100 testing webpages, MAML offers webpage speedups by tens of seconds under challenging network conditions thanks to its significant size reductions. Next, we run a competition involving 25 university students porting 50 of the above webpages to MAML using a web-based editor we developed. This experiment verifies that, with little developer effort, MAML is quite effective in maintaining the visual and functional correctness of the originating webpages.

This study examines Airalo, a "thick" Mobile Network Aggregator. We analyze its unique use of multiple base operators for eSIM provisioning and its decoupling of internet gateways from operators' home countries. Our methodology combines measurements from volunteers in 24 countries using a mix of personal and instrumented Android devices, providing a comprehensive analysis of Airalo's operational model and impact on global mobile connectivity.

The technological and online experiences of billions worldwide are dominated by a handful of companies known as “Big Tech.” Despite this being a cause for concern in governmental, economic, and ethical spheres, the literature lacks a study exploring the impact of public scandals on, and the global sentiment toward, Big Tech. Here, we quantify the power of Big Tech by analyzing their acquisitions, market capitalization, and number of monthly active users. Moreover, we utilize the synthetic control method to estimate the effect of public scandals on the stock price of two Big Tech companies, and find that they had no lasting effect. We also analyze the number of tweets mentioning these scandals, and find that they quickly fade from the spotlight. To explore public sentiment, we survey 5300 participants across 25 countries, and find that those from countries with lower digital literacy and more authoritarian regimes are more trusting of Big Tech. Furthermore, we find that one in three feels they lack control over the data collected about them, and one in four feels that Big Tech knows what they are thinking, knows more about them than their best friend, and may even be secretly listening to their conversations. Additionally, one in four feels addicted to Big Tech products, have no choice but to use them, and wishes there were more companies to choose from. These findings highlight the adverse effect of the oligopolistic nature of Big Tech on consumer choice and help inform policy-makers aiming to curb their dominance.

Location tags are designed to track personal belongings. Nevertheless, there has been anecdotal evidence that location tags are also misused to stalk people. Tracking is achieved locally, e.g., via Bluetooth with a paired phone, and remotely, by piggybacking on location-reporting devices which come into proximity of a tag. This paper studies the performance of the two most popular location tags (Apple's AirTag and Samsung's SmartTag) through controlled experiments - with a known large distribution of location-reporting devices - as well as in-the-wild experiments - with no control on the number and kind of reporting devices encountered, thus emulating real-life use-cases. We find that both tags achieve similar performance, e.g., they are located 55% of the times in about 10 minutes within a 100~m radius. It follows that real time stalking to a precise location via location tags is impractical, even when both tags are concurrently deployed which achieves comparable accuracy in half the time. Nevertheless, half of a victim's exact movements can be backtracked accurately (10m error) with just a one-hour delay, which is still perilous information in the possession of a stalker.

The increasing complexity of JavaScript (JS) in modern mobile web pages has become a performance bottleneck for low-end mobile phone users, especially in developing regions. In this paper we propose SlimWeb, a novel approach that automatically derives lightweight versions of mobile web pages on-the-fly by eliminating non-essential JavaScript that does not impact the core page content and interactive functionality. SlimWeb consists of a JavaScript classification service powered by a supervised Machine Learning (ML) model that provides insights into each JavaScript element embedded in a web page. SlimWeb aims to improve the web browsing experience by predicting the class of each element, such that essential elements are preserved and non-essential elements are blocked by the browsers using the service. We motivate SlimWeb’s core design via a preference survey where 306 users overwhelmingly preferred having faster page load times over fetching various categories of non-essential JavaScript. We evaluate SlimWeb across 500 popular web pages in a developing region on real cellular networks, along with a user experience study with 20 real-world users and a usage willingness survey of 588 users. Evaluation results show that SlimWeb achieves 50% reduction in page load time compared to the original pages, and more than 30% reduction compared to competing solutions, while achieving high similarity scores to the original pages measured via a qualitative evaluation study with 62 users. SlimWeb improves the overall user experience metric (defined by Google Lighthouse combining first contentful paint, time to interactive, speed index) by more than 60% compared to the original pages, while maintaining 90-100% of the visual and functional components of most pages.

To quickly create interactive web pages, developers heavily rely on (large) general-purpose JavaScript libraries. This practice bloats web pages with complex unused functions dead code which are unnecessarily downloaded and processed by the browser. The identification and the elimination of these functions is an open problem, which this paper tackles with Muzeel, a black-box approach requiring neither knowledge of the code nor execution traces. While the state-of-the-art solutions stop analyzing JavaScript when the page loads, the core design principle of Muzeel is to address the challenge of dynamically analyzing JavaScript after the page is loaded, by emulating all possible user interactions with the page, such that the used functions (executed when interactivity events fire) are accurately identified, whereas unused functions are filtered out and eliminated. We run Muzeel against 15,000 popular web pages and show that half of the 300,000 JavaScript files used in these pages have at least 70% of unused functions, accounting for 55% of the files' sizes. To assess the impact of dead code elimination on Mobile Web performance, we serve 200 Muzeel-ed pages to several Android phones and browsers, under variable network conditions. Our evaluation shows that Muzeel can speed up page loads by 25-30% thanks to a combination of lower CPU and bandwidth usage. Most importantly, we show that such savings are achieved while maintaining the pages' visual appearance and interactive functionality.

Due to the recent "work from home" trend, recent years have seen a growing research interest in understanding existing commercial videoconferencing systems in terms of their performance and architecture. One important question left unanswered that we tackle in this paper is: what is the performance of videoconferencing in the wild? Answering this generic question is challenging because it requires, ideally, a world-wide testbed composed of diverse devices (mobile, desktop), operating systems (Windows, MacOS, Linux) and network accesses (mobile and WiFi). In this paper, we present such a testbed that we develop to evaluate videoconferencing performance in the wild via automation for Android and Chromium-based browsers. We deploy our testbed via 85 distinct devices worldwide and collect performance metrics from 58 hours' worth of more than 2,000 videoconferencing sessions from 37 unique countries in the world. This, to the best of our knowledge, is the largest collection of videoconferencing performance data in the wild.

The increasing complexity of the web has attracted a number of solutions to offer optimized versions of web pages that are lighter to process and faster to load. These solutions have been quantitatively evaluated to show significant speed-ups in load times and/or considerable savings in bandwidth/memory consumption. However, while these solutions often produce optimized versions from existing pages, they rarely evaluate the impact of their optimizations on the original content and functionality. Additionally, due to the lack of a unified metric to evaluate the similarity of the pages generated by these solutions in comparison to the original pages, it is not yet possible to fairly compare the results obtained from different user studies campaigns, unless recruiting the exact same users, which is extremely challenging. In this paper, we demonstrate the lack of qualitative evaluation metrics, and propose QLUE (QuaLitative Uniform Evaluation), a tool that automates the qualitative evaluation of web pages generated by web complexity solutions with respect to their original versions using computer vision. QLUE evaluates the content and the functionality of these pages separately using two metrics: QLUE’s Structural Similarity, to assess the former, and QLUE’s Functional Similarity to assess the latter—a task that is proven to be challenging for humans given the complex functional dependencies in modern pages. Our results show that QLUE computes comparable content and functional scores to those provided by humans. Specifically, 90% of a set of 100 pages were given a similarity score between 90% and 100% by human evaluators, while QLUE shows similar scores for more than 75% of the same pages. In terms of time complexity, QLUE shows that it is capable of evaluating an optimized web page in a few minutes.

The amount of JavaScript used in web pages has substantially grown in the past decade, leading to large and complex pages that are computationally intensive for handheld mobile devices. Due to the increasing usage of these devices to access today’s web, and to accommodate the needs of a large number of mobile web users who solely rely on low-end devices, we propose “JSAnalyzer,” an easy-to-use tool that enables web developers to quickly optimize JavaScript usage in their pages and to generate simpler versions of these pages for mobile web users. JSAnalyzer is motivated by the widespread use of non-critical JavaScript elements, i.e., those that have negligible (if any) impact on the page’s visual content and interactive functionality. JSAnalyzer allows the developer to selectively enable or disable JavaScript elements in any given page while visually observing their impact on the page to (1) accurately identify any non-critical JavaScript elements and (2) create a simplified page with these elements removed. Our quantitative evaluation shows that, given a low-end mobile phone, JSAnalyzer achieves an increase of nearly 90% in Google’s lighthouse performance score while reducing the page load time by 30%. A qualitative study of 22 users shows that the lighter pages produced by JSAnalyzer maintain more than 90% visual similarity compared to the original pages. Moreover, JSAnalyzer was evaluated by 69 developers, showing that it scores nearly 90% in terms of usefulness and usability while retaining the page’s content and functionality. Finally, we show that JSAnalyzer outperforms state-of-the-art solutions in terms of timing speedups and resource savings.

Despite increasing mobile Internet penetration in developing regions, growing web page complexity and the lack of optimization from remote content providers make the web experience poor in these areas. The high relative bandwidth cost, poor network performance, and lack of relevant local content combine to dampen the demand for the Internet and services it enables. In this paper we propose GAIUS, a content ecosystem enabling efficient creation and dissemination of locally relevant web content. At its core, GAIUS consists of the following innovations: a locally sustainable content ecosystem, and MAML, a web specification language that simplifies web pages to reduce costs and lower barriers for content creation.

This paper studies how learning techniques can be used by the congestion control algorithms employed by transport protocols over 5G wireless channels, in particular millimeter waves. We show how metrics measured at the transport layer might be valuable to ascertain the congestion level. In situations characterized by a high correlation between such parameters and the actual congestion, it is observed that the performance of unsupervised learning methods is comparable to supervised learning approaches. Exploiting the ns-3 platform to perform an in-depth, realistic assessment, allows us to study the impact of various layers of the protocol stack. We also consider different scheduling policies to discriminate whether the allocation of radio resources impacts the performance of the proposed scheme.

A significant fraction of the World Wide Web suffers from the excessive usage of JavaScript (JS). Based on an analysis of popular webpages, we observed that a considerable number of JS elements utilized by these pages are not essential for their visual and functional features. In this paper, we propose JSCleaner, a JavaScript de-cluttering engine that aims at simplifying webpages without compromising their content or functionality. JSCleaner relies on a rule-based classification algorithm that classifies JS into three main categories: non-critical, replaceable, and critical. JSCleaner removes non-critical JS from a webpage, translates replaceable JS elements with their HTML outcomes, and preserves critical JS. Our quantitative evaluation of 500 popular webpages shows that JSCleaner achieves around 30% reduction in page load times coupled with a 50% reduction in the number of requests and the page size. In addition, our qualitative user study of 103 evaluators shows that JSCleaner preserves 95% of the page content similarity, while maintaining nearly 88% of the page functionality (the remaining 12% did not have a major impact on the user browsing experience).

Despite increasing mobile Internet penetration in emerging regions, the growth of web page complexity combined with inadequate content delivery mechanisms and lack of relevant local content make the web experience poor for users in these regions. In this paper, we propose GAIUS, a content ecosystem that enables the efficient creation and dissemination of locally relevant web content. GAIUS aims at creating a community content exchange that enables users to easily create and consume relevant web content with the help of a mobile application. Based on an early deployment of GAIUS in various regions, we show that our ecosystem enables localized content creation and diffusion efficiently.

End-users in emerging markets experience poor web performance due to a combination of three factors: high server response time, limited edge bandwidth and the complexity of web pages. The absence of cloud infrastructure in developing regions and the limited bandwidth experienced by edge nodes constrain the effectiveness of conventional caching solutions for these contexts. This paper describes the design, implementation and deployment of xCache, a cloud-managed Internet caching architecture that aims to proactively profile popular web pages and maintain the liveness of popular content at software defined edge caches to enhance the cache hit rate with minimal bandwidth overhead. xCache uses a Cloud Controller that continuously analyzes active cloud-managed web pages and derives an object-group representation of web pages based on the objects of a page. Using this object-group representation, xCache computes a bandwidth-aware utility measure to derive the most valuable configuration for each edge cache. Our preliminary real-world deployment across university campuses in three developing regions demonstrates its potential compared to conventional caching by improving cache hit rates by about 15%. Our evaluations of xCache have also shown that it can be applied in conjunction with other web optimizations solutions like Shandian, and can improve page load times by more than 50%.

Modern web pages are very complex; each web page consists of hundreds of objects that are linked from various servers all over the world. While mechanisms such as caching reduce the overall number of end-to-end requests saving bandwidth and loading time, there is still a large portion of content that is re-fetched -- despite not having changed. In this demo, we present Extreme Cache, a web caching architecture that enhances the web browsing experience through a smart pre-fetching engine. Our extreme cache tries to predict the rate of change of web page objects to bring cacheable content closer to the user.

Legacy congestion controls including TCP and its variants are known to perform poorly over cellular networks due to highly variable capacities over short time scales, self-inflicted packet delays, and packet losses unrelated to congestion. To cope with these challenges, we present Verus, an end-to-end congestion control protocol that uses delay measurements to react quickly to the capacity changes in cellular networks without explicitly attempting to predict the cellular channel dynamics. The key idea of Verus is to continuously learn a delay profile that captures the relationship between end-to-end packet delay and outstanding window size over short epochs and uses this relationship to increment or decrement the window size based on the observed short-term packet delay variations. While the delay-based control is primarily for congestion avoidance, Verus uses standard TCP features including multiplicative decrease upon packet loss and slow start. Through a combination of simulations, empirical evaluations using cellular network traces, and real-world evaluations against standard TCP flavors and state of the art protocols like Sprout, we show that Verus outperforms these protocols in cellular channels. In comparison to TCP Cubic, Verus achieves an order of magnitude (> 10x) reduction in delay over 3G and LTE networks while achieving comparable throughput (sometimes marginally higher). In comparison to Sprout, Verus achieves up to 30% higher throughput in rapidly changing cellular networks.

Web access is prohibitively slow in many developing regions despite substantial effort to increase bandwidth and network penetration. In this paper, we explore the fundamental bottlenecks that cause poor web performance from a client's perspective by carefully dissecting webpage load latency contributors in Ghana. Based on our measurements from 2012 to 2014, we find several interesting issues that arise due to the increasing complexity of web pages and number of server redirections required to completely render the assets of a page. We observe that, rather than bandwidth, the primary bottleneck of web performance in Ghana is the lack of good DNS servers and caching infrastructure. The main bottlenecks are: (a) Recursive DNS query resolutions; (b) HTTP redirections; (c) TLS/SSL handshakes. We experiment with a range of well-known end-to-end latency optimizations and find that simple DNS caching, redirection caching, and the use of SPDY can all yield substantial improvements to user-perceived latency.

There has been a lot of research focusing on optimizing the Long Term Evolution (LTE) networks performance. A large part of this research focused on how to optimize the LTE radio interface to efficiently distribute the scarce radio resources among the LTE users. Since LTE offers a diverse amount of services, the Quality of Service (QoS) differentiation is a major challenge. QoS is handled in LTE at different levels; the most important one is the radio scheduler. The QoS aware radio scheduler is designed to guarantee different service requirements. However, if the LTE radio interface is fully optimized towards QoS guarantees, what happen when the LTE transport network runs into congestion? Congestion can happen at any point in the network, and normally congested LTE backhaul tends to create large unfairness among the users, even among the ones with the same QoS class. The LTE transport congestion can occur at any router in the backhaul network. In this paper, we present two novel algorithms, which focus on the LTE transport congestion. The first algorithm is a congestion detection mechanism (CD) that identifies the transport network congestion as early as possible, and triggers the congestion control function. The second algorithm preserves and optimizes the QoS performance of each user over the transport to match the radio scheduler service differentiation, i.e., achieving fairness among the users in the same QoS demand and guaranteeing the enforced radio QoS weights for different service classes.

Modern mobile devices comprise multiple interfaces for heterogeneous network technologies. However, currently implemented mechanisms to decide which one(s) to use and distribute application flows accordingly (i.e., solving the multihomed flow management problem, MFM) are rather coarse and do not leverage the opportunities. A user-centric quality-aware (QA) MFM has been proposed which optimises network use based on user-perceivable metrics such as application quality as well as energy and monetary costs. This paper refines this approach by providing a single method for both real-time and elastic (i.e., TCP-based) traffic, and uses realistic available capacity estimation. We evaluate this method in OPNET-simulated LTE and WLAN mobile networks. We study the impact of methods used to trigger the decision algorithm, and investigate the influence of an increasing number of users employing the QA-MFM technique on both the user-perceivable metrics and the global network performance. We find that on-demand triggering performs better than a static periodic method. We also demonstrate that the proposed approach out-performs classical network selection techniques in terms of application quality. We also show that the QA-MFM is not too greedy as to not scale with a number of users, and has a positive effect on the network loads, by preemptively adapting applications parameters to match network conditions.

Long Term Evolution (LTE) uses Single Carrier Frequency Division Multiple Access (SC-FDMA) as the uplink transmission scheme. The Quality of Service (QoS) provision is one of the primary objectives of the wireless network operators. In this paper, the end-to-end QoS performance of Bandwidth and QoS Aware (BQA) scheduler for LTE uplink is evaluated in heterogeneous traffic environment. The BQA scheduler is designed to provide efficient allocation of radio resources to users according to the QoS requirements of various traffic classes and the instantaneous channel conditions. The user QoS provision is ensured by using dynamic QoS weights. Additionally, the delay sensitive traffic is facilitated by employing delay thresholds. The BQA scheduler algorithm supports multi-bearer users. The end-to-end QoS performance of the scheduler is analyzed in several simulation scenarios. The results show that the proposed scheduler guarantees provision of QoS to users.

Mobile Network Virtualization (NV) is an emerging technique which has drawn increasingly research attention. Network Virtualization enables multiple network operators to share a common infrastructure (including core network, transport network and access network) so as to reduce the investment capital while improving the overall performance at the same time. This is achieved by exploring the multiplexing gain. Similarly, Load Balancing (LB) is a well-known mechanism used in mobile networks to offload excessive traffic from high-load cells (hot spots) to low-load ones within one network operator. This paper aims at investigating the potential gain of applying NV in LTE (Long Term Evolution) networks and compares it with the LB scheme gain. In this paper, we propose an LTE virtualization framework (that enables spectrum sharing) and a dynamic load balancing scheme for multi-eNB and multi-VO (Virtual Operator) systems. We compare the performance gain of both schemes for different applications, e.g. VoIP, video, HTTP and FTP. We also investigate the parameterization of both schemes, e.g. sharing intervals, LB intervals and safety margins, in order to find the optimal parameter settings. The presented results show that the LTE networks can benefit from both NV and LB techniques.

The MAC scheduler is an important and crucial entity of the Long Term Evolution (LTE) and is responsible for efficiently allocating the radio resources among mobile users who have different QoS demands. The scheduler takes different considerations into account such as throughput and fairness when deciding the allocation of the scarce radio resources. LTE is an all IP packet system in which guaranteeing QoS is a real challenge. Therefore the LTE MAC scheduler should consider not only the throughput optimization but also the QoS differentiations in an effective manner. In this paper, we propose a novel LTE downlink MAC scheduling algorithm. The proposed scheduler differentiates between the different QoS classes and their requirements. Two different QoS classifications are considered: Guaranteed Bit Rate (GBR) and non Guaranteed Bit Rate (non-GBR). The proposed scheduler also considers the different users channel conditions and tries to create a balance between the QoS guarantees and the multi-user diversity in a proportional fair manner. The simulation analysis confirms that guaranteeing the different QoS requirements is possible.

High Speed Uplink Packet Access (HSUPA) is an extension of the Universal Mobile Telecommunications System (UMTS) technology of 3GPP Rel-99, with the objective increasing the data rate (up to 5.76 Mbps) and reducing the latency of the uplink. 3GPP UMTS standard Rel-6 introduces a new "Enhanced Dedicated Channel" (E-DCH) for the uplink. The main focus of the work presented in this paper is to analyse the effect of TNL congestion control on the HSUPA performance. The congestion control function works together with the scheduler and HARQ functionality in order to control the offered load to the TNL network. In this manner, the data flow over the transport network (TNL) is adequately controlled and limited to the available TNL capacity by avoiding congestion in the transport network. The TNL congestion control consists of two independent parts: congestion detection and congestion control which have been implemented, tested and validated within the focus of this paper. The simulation results presented in this paper confirm that the congestion in the transport network can be controlled in such a way that the available TNL capacity can be effectively utilised and hence the performance of HSUPA network can be significantly improved in all aspects.

HSUPA (High Speed Uplink Packet Access) is introduced by the 3GPP Release 6 to enhance the UMTS uplink with higher data rates, reduced latency and increased capacity. This paper discusses the dimensioning of the backhaul resources on the ATM-based Iub interface in UMTS HSUPA networks. The main focuses of this paper is to (1) analyze the important factors that have influence on the transport network dimensioning and further investigate their impacts; (2) estimate the required backhaul bandwidth as a function of user QoS requirements; (3) discuss the impact when including HSDPA traffic on the downlink. In addition, this paper also provides insights of HSUPA air interface given diverse user applications as well as different number of users within one cell. The investigations and dimensioning are based on simulations.