The Open Radio Access Network (Open RAN) vision is based on the three principles of (i) open interfaces; (ii) cloudification; and (iii) automation through closed-loop control. It is a network architecture paradigm embodied and augmented through technical reference specifications of the 3GPP and the O-RAN Alliance. At the centre of Open RAN are open, programmable, and virtualized components, connected to each other through open interfaces that enable closed-loop, data-driven, and intelligent control. For instance, the O-RAN Alliance introduced two RAN Intelligent Controllers (or RICs) that connect through open interfaces to the disaggregated components of the RAN, and implement control loops that run at different time scales.

Innovation and standardization in 5G have brought advancements to every facet of the cellular architecture. This ranges from the introduction of new frequency bands and signaling technologies for the radio access network (RAN), to a core network underpinned by micro-services and network function virtualization (NFV). However, like any emerging technology, the pace of real-world deployments does not instantly match the pace of innovation. To address this discrepancy, one of the key aspects under continuous development is the RAN with the aim of making it more open, adaptive, functional, and easy to manage. In this paper, we highlight the transformative potential of embracingnovel cellular architecturesby transitioning from conventional systems to the progressive principles of Open RAN. This promises to make 6G networks more agile, cost-effective, energy-efficient, and resilient. It opens up a plethora of novel use cases, ranging from ubiquitous support for autonomous devices to cost-effective expansions in regions previously underserved. The principles of Open RAN encompass: (i) a disaggregated architecture with modular and standardized interfaces; (ii) cloudification, programmability and orchestration; and (iii) AI-enabled data-centric closed-loop control and automation. We first discuss the transformative role Open RAN principles have played in the 5G era. Then, we adopt a system-level approach and describe how these Open RAN principles will support 6G RAN and architecture innovation. We qualitatively discuss potential performance gains that Open RAN principles yield for specific 6G use cases. For each principle, we outline the steps that research, development and standardization communities ought to take to make Open RAN principles central to next-generation cellular network designs.

Enabling high data-rate uplink cellular connectivity for drones is a challenging problem, since a flying drone has a higher likelihood of having line-of-sight propagation to base stations that terrestrial UEs normally do not have line-of-sight to. This may result in uplink inter-cell interference and uplink performance degradation for the neighboring ground UEs when drones transmit at high data-rates (e.g., video streaming). We address this problem from a cellular operator’s standpoint to support drone-sourced video streaming of a point of interest. We propose a low-complexity, closed-loop control system for Open-RAN architectures that jointly optimizes the drone’s location in space and its transmission directionality to support video streaming and minimize its uplink interference impact on the network. We prototype and experimentally evaluate the proposed control system on a dedicated outdoor multi-cell RAN testbed, which is the first measurement campaign of its kind. Furthermore, we perform a large-scale simulation assessment of the proposed control system using the actual cell deployment topologies and cell load profiles of a major US cellular carrier. The proposed Open-RAN control scheme achieves an average$19\%$network capacity gain over traditional BS-constrained control solutions and satisfies the application data-rate requirements of the drone (e.g., to stream an HD video).

Fine-grained location prediction on smart phones can be used to improve app/system performance. Application scenarios include video quality adaptation as a function of the 5G network quality at predicted user locations, and augmented reality apps that speed up content rendering based on predicted user locations. Such use cases require prediction error in the same range as the GPS error, and no existing works on location prediction can achieve this level of accuracy. We propose Federated Meta-Location Learning (FMLL) on smart phones for fine-grained location prediction, based on GPSt races collected on the phones. FMLL has three components: a meta-location generation module, a prediction model, and a federated learning framework. The meta-location generation module represents the user location data as relative points in an abstract 2D space, which enables learning across different physical spaces. The model fuses Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN), where BiLSTM learns the speed and direction of the mobile users, and CNN learns information such as user movement preferences. The framework runs on the phones of the users and also on a server that coordinates learning from all users in the system. FMLL uses federated learning to protect user privacy and reduce bandwidth consumption. Our experimental results, using a dataset with over 600,000 users, demonstrate that FMLL outperforms baseline models in terms of prediction accuracy. We also demonstrate that FMLL works well in conjunction with transfer learning, which enables model reusability. Finally, benchmark results on Android phones demonstrate FMLL’s feasibility in real life.

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers’ view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.

Recent advances in computer vision and neural networks have made it possible for more surveillance videos to be automatically searched and analyzed by algorithms rather than humans. This happened in parallel with advances in edge computing where videos are analyzed over hierarchical clusters that contain edge devices, close to the video source. However, the current video analysis pipeline has several disadvantages when dealing with such advances. For example, video encoders have been designed for a long time to please human viewers and be agnostic of the downstream analysis task (e.g., object detection). Moreover, most of the video analytics systems leverage 2-tier architecture where the encoded video is sent to either a remote cloud or a private edge server but does not efficiently leverage both of them. In response to these advances, we present SIEVE, a 3-tier video analytics system to reduce the latency and increase the throughput of analytics over video streams. In SIEVE, we present a novel technique to detect objects in compressed video streams. We refer to this technique as semantic video encoding because it allows video encoders to be aware of the semantics of the downstream task (e.g., object detection). Our results show that by leveraging semantic video encoding, we achieve close to 100% object detection accuracy with decompressing only 3.5% of the video frames which results in more than 100x speedup compared to classical approaches that decompress every video frame.

Augmented reality (AR) apps where multiple users interact within the same physical space are gaining in popularity (e.g., shared AR mode in Pokemon Go, virtual graffiti in Google's Just a Line). However, multi-user AR apps running over the cellular network can experience very high end-to-end latencies (measured at 12.5 s median on a public LTE network). To characterize and understand the root causes of this problem, we perform a first-of-its-kind measurement study on both public LTE and industry LTE testbed for two popular multi-user AR applications, yielding several insights: (1) The radio access network (RAN) accounts for a significant fraction of the end-to-end latency (31.2%, or 3.9 s median), resulting in AR users experiencing high, variable delays when interacting with a common set of virtual objects in off-the-shelf AR apps; (2) AR network traffic is characterized by large intermittent spikes on a single uplink TCP connection, resulting in frequent TCP slow starts that can increase user-perceived latency; (3) Applying a common traffic management mechanism of cellular operators, QoS Class Identifiers (QCI), can help by reducing AR latency by 33% but impacts non-AR users. Based on these insights, we propose network-aware and network-agnostic AR design optimization solutions to intelligently adapt IP packet sizes and periodically provide information on uplink data availability, respectively. Our solutions help ramp up network performance, improving the end-to-end AR latency and goodput by ~40-70%.

Cellular traffic prediction enables operators to adapt to traffic demand in real-time for improving network resource utilization and user experience. To predict cellular traffic, previous studies either applied Recurrent Neural Networks (RNN) at individual base stations or adapted Convolutional Neural Networks (CNN) to work at grid-cells in a geographically defined grid. These solutions do not consider explicitly the effect of handover on the spatial characteristics of the traffic, which may lead to lower prediction accuracy. Furthermore, RNN solutions are slow to train, and CNN-grid solutions do not work for cells and are difficult to apply to base stations. This paper proposes a new prediction model, STGCN-HO, that uses the transition probability matrix of the handover graph to improve traffic prediction. STGCN-HO builds a stacked residual neural network structure incorporating graph convolutions and gated linear units to capture both spatial and temporal aspects of the traffic. Unlike RNN, STGCN-HO is fast to train and simultaneously predicts traffic demand for all base stations based on the information gathered from the whole graph. Unlike CNN-grid, STGCN-HO can make predictions not only for base stations, but also for cells within base stations. Experiments using data from a large cellular network operator demonstrate that our model outperforms existing solutions in terms of prediction accuracy.

Today's HTTP adaptive streaming applications are designed to provide high levels of Quality of Experience (QoE) across a wide range of network conditions. The adaptation logic in these applications typically needs an estimate of the future network bandwidth for quality decisions. This estimation, however, is challenging in cellular networks because of the inherent variability of bandwidth and latency due to factors like signal fading, variable load, and user mobility. In this paper, we exploit machine learning (ML) techniques on a range of radio channel metrics and throughput measurements from a commercial cellular network to improve the estimation accuracy and hence, streaming quality. We propose a novel summarization approach for input raw data samples. This approach reduces the 90th percentile of absolute prediction error from 54% to 13%. We evaluate our prediction engine in a trace-driven controlled lab environment using a popular Android video player (ExoPlayer) running on a stock mobile device and also validate it in the commercial cellular network. Our results show that the three tested adaptation algorithms register improvement across all QoE metrics when using prediction, with stall reduction up to 85% and bitrate switching reduction up to 40%, while maintaining or improving video quality. Finally, prediction improves the video QoE score by up to 33%.

In this paper we design and implement MEC-VR, a mobile VR system that uses a Mobile Edge Cloud (MEC) to deliver high quality VR content to today's mobile devices using 4G/LTE cellular networks. Our main contribution is in realizing a low latency control loop that streams VR scenes containing only the user's Field of View (FoV) and a latency-adaptive margin area around the FoV. This allows the clients to render locally at a high refresh rate to accommodate and compensate for the head movements before the next motion update arrives. Compared with prior approaches, our MEC-VR design requires no viewpoint prediction, supports dynamic and live VR content, and adapts to the real-world latency experienced in cellular networks between the MEC and mobile devices. We implement a prototype of MEC-VR and evaluate its performance on a MEC node connected to an LTE testbed. We demonstrate that MEC-VR can effectively stream live VR content up to 8K resolution over 4G/LTE networks and achieve more than 80% of bandwidth savings.

In this demo, we showcase how to stream 8K 360° video to commodity mobile devices without pre-processing or viewpoint prediction using our MEC-VR system. Users can freely select 360° video from YouTube and immediately become immersed in the video of 8K resolution using a Samsung GearVR compatible smartphone. Our system can support zoom in and zoom out while saving up to 80% bandwidth.

In this paper we design and implement Freedom, a mobile VR system that deliver high quality VR content on today's mobile devices using 4G/LTE cellular networks. Compared to existing state-of-the-art, Freedom does not rely on any video frame pre- rendering or viewpoint prediction. We send a latency-adaptive VAM frame that contains pixels around the FoV. This allows the clients to render locally at a high refresh rate of 60 Hz to accommodate and compensate for the user's head movements before the next server update arrives. We demonstrate that Freedom is the first system in the world that can support dynamic and live 8K resolution VR content, while adapting to the real-world latency variations experienced in cellular networks. Compared to streaming the whole 360° panoramic VR content, we show that Freedom achieves up to 80% bandwidth savings. Finally, we provide detailed end to end latency measurements of actual VR systems by running extensive experiments in a private LTE testbed using a Mobile Edge Cloud (MEC).

In this demo, we showcase how to stream 8K 360° video to commod- ity mobile devices without pre-processing or viewpoint prediction using our Freedom system. Users can freely select the viewpoint, zoom in and zoom out to enjoy the full quality of the 8K resolu- tion using any Samsung GearVR compatible smartphone. We also present how our system works internally to dynamically adjust margin size to accommodate to network latency and how our ap- proach can save up to 80% bandwidth compared to streaming full video to mobile devices.

Video streaming traffic is rapidly growing in mobile networks. Mobile Network Operators (MNOs) are expected to keep up with this growing demand, while maintaining a high video Quality of Experience (QoE). This makes it critical for MNOs to have a solid understanding of users' video QoE with a goal to help with network planning, provisioning and traffic management. However, designing a system to measure video QoE has several challenges: i) large scale of video traffic data and diversity of video streaming services, ii) cross-layer constraints due to complex cellular network architecture, and iii) extracting QoE metrics from network traffic. In this paper, we present VideoNOC, a prototype of a flexible and scalable platform to infer objective video QoE metrics (e.g., bitrate, rebuffering) for MNOs. We describe the design and architecture of VideoNOC, and outline the methodology to generate a novel data source for fine-grained video QoE monitoring. We then demonstrate some of the use cases of such a monitoring system. VideoNOC reveals video demand across the entire network, provides valuable insights on a number of design choices by content providers (e.g., OS-dependent performance, video player parameters like buffer size, range of encoding bitrates, etc.) and helps analyze the impact of network conditions on video QoE (e.g., mobility and high demand).

Streaming over the wireless channel is challenging due to rapid fluctuations in available throughput. Encouraged by recent advances in cellular throughput prediction based on radio link metrics, we examine the impact on Quality of Experience (QoE) when using prediction within existing algorithms based on the DASH standard. By design, DASH algorithms estimate available throughput at the application level from chunk rates and then apply some averaging function. We investigate alternatives for modifying these algorithms, by providing the algorithms direct predictions in place of estimates or feeding predictions in place of measurement samples. In addition, we explore different prediction horizons going from one to three chunk durations. Furthermore, we induce different levels of error to ideal prediction values to analyse deterioration in user QoE as a function of average error.

The bandwidth required for TV content distribution is rapidly increasing due to the evolution of high definition TV (HDTV) and ultra HDTV. Service providers are constantly trying to differentiate themselves by innovating new ways of distributing content more efficiently with lower cost and higher penetration. We propose a cost-efficient wireless architecture [wireless live TV (WiLiTV)], consisting of a mix of wireless access technologies [satellite, Wi-Fi, and LTE/5G millimeter wave (mmWave) overlay links], for delivering live TV services. In the proposed architecture, live TV content is injected into the network at selected locations, consisting of some homes and/or cellular base stations, using satellite antennas. The content is then further distributed to other homes using a house-to-house Wi-Fi network or an LTE/5G mmWave overlay. We construct an optimal content distribution network with the minimum number of satellite injection points, while preserving the highest quality of experience, for different neighborhood densities. We evaluate the framework using time-varying demand patterns and a diverse set of home location data provided from an operational content distribution network. Our study demonstrates that this architecture reduces the overall cost by 60% compared with the traditional architecture. We have also shown that the WiLiTV is robust in its support for several TV formats.

User location is of critical importance to cellular network operators. It is often used for network capacity planning and to aid in the analysis of service and network diagnostics. However, existing localization techniques rely on user-provided information (e.g., Angle-of-Arrival), which are not available to the operator, and often require a significant effort to collect training data. Our main contribution is the design and evaluation of the Network-Based Localization (NBL) System for localizing a user in a 4G LTE network. The NBL System consists of 2 stages. In an offline stage, we develop RF coverage maps based on a large-scale crowd-sourced channel measurement campaign. Then, in an online stage, we present a localization algorithm to quickly match RF measurements (which are already collected as part of normal network operation) to coverage map locations. The system is more practical than related works, as it does not make any assumptions about user mobility, nor does it require expensive manual training measurements. Despite the realistic assumptions, our extensive evaluations in a national 4G LTE network show that the NBL System achieves a localization accuracy which is comparable to related works (i.e., a median accuracy of 5% of the cell's coverage region).

Emerging commercial live content broadcasting platforms are facing great challenges to accommodate large scale dynamic viewer populations. Existing solutions constantly suffer from balancing the cost of deploying at the edge close to the viewers and the quality of content delivery. We propose LiveJack, a novel network service to allow CDN servers to seamlessly leverage ISP edge cloud resources. LiveJack can elastically scale the serving capacity of CDN servers by integrating Virtual Media Functions (VMF) in the edge cloud to accommodate flash crowds for very popular contents. LiveJack introduces minor application layer changes for streaming service providers and is completely transparent to end users. We have prototyped LiveJack in both LAN and WAN environments. Evaluations demonstrate that LiveJack can increase CDN server capacity by more than six times, and can effectively accommodate highly dynamic workloads with an improved service quality.

LTE-Advanced (LTE-A) macro-cell deployments are being enhanced with small cells, i.e., low-power base stations, to increase the network coverage and capacity. However, simultaneous co-channel transmissions from macro and small cells cause increased inter-cell interference and under-utilize the spectrum resources at the small cells. The following LTE-A design techniques are used to improve system performance in such deployments: (i) Carrier Aggregation (CA) to increase capacity by using additional carrier bandwidth; (ii) enhanced Inter-Cell Interference Coordination (elCIC), that includes (a) Cell Selection Biasing (CSB) to increase small cell spectrum utilization via cell range expansion; and (b) blanking data transmission on the macro cells for a certain duration of time to increase cell-edge user throughput Our objective is to maximize the CSB of the small cell, subject to user QoS constraints and blanking support from the macro cell. Towards this end, we develop an analytical model that captures the inter-dependency between elCIC techniques. We observe that, not accounting for the complex inter-dependencies between these techniques leads to a degraded network performance. We propose a framework that jointly optimizes elCIC and the assignment of multiple component carriers in an LTE-A deployment for increasing spectrum utilization at the small cells with appropriate blanking support from the macro cells. Our simulation results show that our approach increases the small cell spectrum utilization and aggregate cell-edge throughput by as much as 200%.

IoT drives the future of Connected Cars including smart cars and it will transform the way we interact with our vehicles. With the emergence of millions of connected cars in the horizon, the wireless infrastructure needed to support this capability has to scale efficiently. To better understand the impact on the resource utilization of the wireless core infrastructure, we provide a detailed statistical model of the control plane/signaling interactions in connected cars. Specifically, our model is based on a 40K sample data set spanning more than 2100 IoT vehicles collected over 20 hours from a national telecommunications provider. The control plane model quantifies the additional load that the infrastructure (e.g., MME) needs to handle compared to an average busy hour LTE traffic model. We identify the heavy hitters of the control plane events and run real experiments based on our models in a testbed to characterize the resource utilization for supporting total event loadings using a real world high performance virtualized MME. No personally identifiable information (PII) was gathered or used in conducting this study. To the extent any data was analyzed, it was anonymous and/or aggregated data.

HTTP-based adaptive protocols dominate today's video streaming over the Internet, and operate using multiple quality levels that video players request one segment at a time. Despite their popularity, studies have shown that performance of video streams still suffers from stalls, quality switches and startup delay. In wireless networks, it is well-known that high variability in network bandwidth affects video streaming. MultiPath TCP (MPTCP) is an emerging paradigm that could offer significant benefits to video streaming by combining bandwidth on multiple network interfaces, in particular for mobile devices that typically support both WiFi and cellular networks. In this paper, we explore whether MPTCP always benefits mobile video streaming. Our experimental study on video streaming using two wireless interfaces yields mixed results. While beneficial to user experience under ample and stable bandwidth, MPTCP may not offer any advantage under some network conditions. We find that when additional bandwidth on the secondary path is not sufficient to sustain an upgrade in video quality, it is generally better not to use MPTCP. We also identify that MPTCP can harm user experience when an unstable secondary path is added to the stable primary path.

Proportional Fair (PF) scheduling algorithms are the de facto standard in cellular networks. They exploit the users' channel state diversity (induced by fast-fading) and are optimal for stationary channel state distributions and an infinite time-horizon. However, mobile users experience a nonstationary channel, due to slow-fading (on the order of seconds), and are associated with base stations for short periods. Hence, we develop the Predictive Finite-horizon PF Scheduling ((PF)2S) Framework that exploits mobility. We present extensive channel measurement results from a 3G network and characterize mobility-induced channel state trends. We show that a user's channel state is highly reproducible and leverage that to develop a data rate prediction mechanism. We then present a few channel allocation estimation algorithms that exploit the prediction mechanism. Our trace-based simulations consider instances of the (PF)2S Framework composed of combinations of prediction and channel allocation estimation algorithms. They indicate that the framework can increase the throughput by 15%-55% compared to traditional PF schedulers, while improving fairness.

Network function virtualization (NFV) promises significant cost savings, flexibility and ease of deployment. However, potential challenges in implementing virtualized network elements that can support real-world performance requirements are still an open question. For example, traditional telecom networks have a lot of complex interdependencies that can affect performance. In this paper, we study the potential bottlenecks in virtualizing cellular core network functions. Using a combination of analysis and experimentation, we quantify the impact of software-based EPC elements on various metrics including physical processing, memory, IO, and bandwidth resource requirements. We use production grade, software-based cellular network elements running on general purpose Linux servers, driven by a variety of realistic workloads derived from a realworld cellular network, to examine the combined effects of control and data planes on an LTE enhanced packet core (EPC). In particular, we discover that the SGW handles about 33% of the control plane transactions and is a potential source for performance bottlenecks as a result of the interdependencies between control and data plane processing. Our results indicate that simply replacing existing EPC elements with virtualized equivalents can have severe performance bottlenecks and that virtualized EPC elements need to be carefully designed.

Despite its widespread adoption and popularity, the Hypertext Transfer Protocol (HTTP) suffers from fundamental performance limitations. SPDY, a recently proposed alternative to HTTP, tries to address many of the limitations of HTTP (e.g., multiple connections, setup latency). In this paper, we perform a detailed measurement study to understand the benefits of using SPDY over cellular networks. Through careful measurements conducted over 4 months, we provide a detailed analysis of the performance of HTTP and SPDY, how they interact with the various layers, and their implications on Web design. Our results show that unlike in wired and 802.11 networks, SPDY does not clearly outperform HTTP over cellular networks. We identify negative interactions between the protocols used for Web access (HTTP/SPDY over TCP) and cellular radio resource management as the underlying cause. Overall performance suffers when devices go through a cellular radio state promotion after an idle period, and the consequent increase in latency. This impacts SPDY more because of the use of a single TCP connection. We conclude that a viable solution has to account for these unique cross-layer dependencies to achieve improved Web performance over cellular networks.

Cellular providers are rapidly deploying multiple technologies like cell biasing, carrier aggregation, co-ordinated interference control/scheduling to improve capacity and coverage. In this paper, we explore a complementary transport layer approach based on multipath TCP that can concurrently use multiple interfaces to boost throughput of users with poor coverage and improve fairness. Multipath TCP has been recently standardized by IETF and requires no modifications to applications. It has been shown to improve fairness and throughput in wire line environments and individual user throughputs in wireless networks. However, in a wireless multi-user environment, it is not clear that it is always beneficial, as we show in this paper. Therefore, we examine if it is indeed beneficial for a service provider to judiciously decide whether to enable multiple cellular interfaces on a smart phone based on a global centralized view of its network. Alternatively, should a device decide independently based only on a local view? To quantify the network wide impact in a system where users have multiple cellular interfaces, we have developed centralized and distributed heuristic algorithms to evaluate this, particularly in the context of fairness across all the users. Our simulations and numerical models show that there are potential gains in fairness (15-30%) to be realized by judiciously enabling multipath connections at the cell edge. These gains diminish as the number of users in a cell increases or users behave greedily. We also quantify the delicate balance between throughput and fairness. Our analysis provides an intuition on which user(s) in a cellular network stand to benefit the most by enabling multiple interfaces. We also discuss LTE protocol mechanisms to enforce associations of specific interfaces to specific cells.

Proportional Fair (PF) scheduling algorithms are the de-facto standard in cellular networks. They exploit the users' channel state diversity (induced by fast-fading), and are optimal for stationary channel state distributions and an infinite time-horizon. However, mobile users experience a non-stationary channel, due to slow-fading (on the order of seconds), and are associated with basestations for short periods. Hence, we develop the Predictive Finite-horizon PF Scheduling ((PF)2S) Framework that exploits mobility. We present extensive channel measurement results from a 3G network and characterize mobility-induced channel state trends. We show that a user's channel state is highly reproducible and leverage that to develop a data rate prediction mechanism. We then present a few channel allocation estimation algorithms that rely on the prediction mechanism. Our trace-based simulations consider instances of the PF2S Framework composed of combinations of prediction and channel allocation estimation algorithms. They indicate that the framework can increase the throughput by 15%–55% compared to traditional PF schedulers, while improving fairness.

The explosive growth of cellular traffic and its highly dynamic nature often make it increasingly expensive for a cellular service provider to provision enough cellular resources to support the peak traffic demands. In this paper, we propose iDEAL, a novel auction-based incentive framework that allows a cellular service provider to leverage resources from third-party resource owners on demand by buying capacity whenever needed through reverse auctions. iDEAL has several distinctive features: 1) iDEAL explicitly accounts for the diverse spatial coverage of different resources and can effectively foster competition among third-party resource owners in different regions, resulting in significant savings to the cellular service provider. 2) iDEAL provides revenue incentives for third-party resource owners to participate in the reverse auction and be truthful in the bidding process. 3) iDEAL is provably efficient. 4) iDEAL effectively guards against collusion. 5) iDEAL effectively copes with the dynamic nature of traffic demands. In addition, iDEAL has useful extensions that address important practical issues. Extensive evaluation based on real traces from a large US cellular service provider clearly demonstrates the effectiveness of our approach. We further demonstrate the feasibility of iDEAL using a prototype implementation.

Despite its widespread adoption and popularity, the Hypertext Transfer Protocol (HTTP) suffers from fundamental performance limitations. SPDY, a recently proposed alternative to HTTP, tries to address many of the limitations of HTTP (e.g., multiple connections, setup latency). With cellular networks fast becoming the communication channel of choice, we perform a detailed measurement study to understand the benefits of using SPDY over cellular networks. Through careful measurements conducted over four months, we provide a detailed analysis of the performance of HTTP and SPDY, how they interact with the various layers, and their implications on web design. Our results show that unlike in wired and 802.11 networks, SPDY does not clearly outperform HTTP over cellular networks. We identify, as the underlying cause, a lack of harmony between how TCP and cellular networks interact. In particular, the performance of most TCP implementations is impacted by their implicit assumption that the network round-trip latency does not change after an idle period, which is typically not the case in cellular networks. This causes spurious retransmissions and degraded throughput for both HTTP and SPDY. We conclude that a viable solution has to account for these unique cross-layer dependencies to achieve improved performance over cellular networks.

We propose Joint-Family, a protocol that combines peer-to-peer (P2P) and adaptive bitrate (ABR) streaming for video-on-demand (VoD). While P2P for VoD and ABR have been proposed previously, they have not been studied together because they attempt to tackle problems with seemingly orthogonal goals. We motivate our approach through analysis that overcomes a misconception resulting from prior analytical work, and show that the popularity of a P2P swarm and seed staying time has a significant bearing on the achievable per-receiver download rate. Specifically, our analysis shows that popularity affects swarm efficiency when seeds stay “long enough”. We also show that ABR in a P2P setting helps viewers achieve higher playback rates and/or fewer interruptions. We develop the Joint-Family protocol based on the observations from our analysis. Peers in Joint-Family simultaneously participate in multiple swarms to exchange chunks of different bitrates. We adopt chunk, bitrate, and peer selection policies that minimize occurrence of interruptions while delivering high quality video and improving the efficiency of the system. Using traces from a large-scale commercial VoD service, we compare Joint-Family with existing approaches for P2P VoD and show that viewers in Joint-Family enjoy higher playback rates with minimal interruption, irrespective of video popularity.

The explosive growth of cellular traffic and its highly dynamic nature often make it increasingly expensive or even infeasible for a cellular service provider to provision enough cellular resources to support the peak traffic demands. Some service providers have started exploring various economic incentives, including smart data pricing, to manage network congestion. We present SpeedGate, a smart mobile data pricing testbed that allows a service provider to experiment with different dynamic pricing strategies. SpeedGate maintains persistent VPN connections to smartphones as users roam between different wireless networks (3G, 4G/LTE, WiFi). The maximum available bandwidth per user session can be adjusted according to various data pricing strategies. We report preliminary results on two trials with a total of 29 users for assessing their willingness to pay (WTP) for various speed tiers. Preliminary observations suggest the challenges of QoS guarantees through speed tiers in the field, the limited dynamic range of WTP values from individual users for different speed tiers, and potential opportunities for auction-based dynamic pricing.

The explosive growth of cellular traffic and its highly dynamic nature often make it increasingly expensive for a cellular service provider to provision enough cellular resources to support the peak traffic demands. In this paper, we propose iDEAL, a novel auction-based incentive framework that allows a cellular service provider to leverage resources from third-party resource owners on demand by buying capacity whenever needed through reverse auctions. iDEAL has several distinctive features: (i) iDEAL explicitly accounts for the diverse spatial coverage of different resources and can effectively foster competition among third-party resource owners in different regions, resulting in significant savings to the cellular service provider. (ii) iDEAL provides revenue incentives for third-party resource owners to participate in the reverse auction and be truthful in the bidding process. (iii) iDEAL is provably efficient. (iv) iDEAL effectively guards against collusion. (v) iDEAL effectively copes with the dynamic nature of traffic demands. In addition, iDEAL has useful extensions that address important practical issues. Extensive evaluation based on real traces from a large US cellular service provider clearly demonstrates the effectiveness of our approach. We further demonstrate the feasibility of iDEAL using a prototype implementation.

The 19th IEEE Workshop on Local and Metropolitan Area Networks features invited papers in addition to regular track papers from both industry and academia. We received 32 submissions for regular papers. After a careful review process, the technical program committee (TPC) selected 10 regular papers and 4 short papers for presentation in the workshop program, resulting in 31% and 44% acceptance rates, respectively. Each regular submission received at least three reviews. We also received 8 invited paper submissions from experts performing research in areas related to the theme of this year's workshop: Seamless Services. We are very pleased to have the invited papers by Peng He, Thomas Mager, Paris Charalampou, Jean-Michel Dricot, Vanniarajan Chellappan, Dan Li, Gianluca Reali, Patrick Thiran and their co-authors. Each invited paper submission received at least two reviews.

Peer-to-Peer (P2P) systems have evolved from being used for file sharing to delivering streaming video on demand (VoD). The policies adopted in P2P VoD, however, have not taken user viewing behavior - that users abandon videos - into account. We show that abandonment can result in increased interruptions and wasted resources. As a result, we reconsider the set of policies to use in the presence of abandonment. Our goal is to balance the conflicting needs of delivering videos without interruptions while minimizing wastage. We find that an Earliest-First chunk selection policy in conjunction with the Earliest-Deadline peer selection policy allows us to achieve high download rates. We take advantage of abandonment by converting peers to “partial seeds”; this increases capacity. We minimize wastage by using a playback lookahead window. We use analysis and simulation experiments using real-world traces to show the effectiveness of our approach.

Virtualized cloud-based services can take advantage of statistical multiplexing across applications to yield significant cost savings. However, achieving similar savings with real-time services can be a challenge. In this paper, we seek to lower a provider's costs for real-time IPTV services through a virtualized IPTV architecture and through intelligent time-shifting of selected services. Using Live TV and Video-on-Demand (VoD) as examples, we show that we can take advantage of the different deadlines associated with each service to effectively multiplex these services. We provide a generalized framework for computing the amount of resources needed to support multiple services, without missing the deadline for any service. We construct the problem as an optimization formulation that uses a generic cost function. We consider multiple forms for the cost function (e.g., maximum, convex and concave functions) reflecting the cost of providing the service. The solution to this formulation gives the number of servers needed at different time instants to support these services. We implement a simple mechanism for time-shifting scheduled jobs in a simulator and study the reduction in server load using real traces from an operational IPTV network. Our results show that we are able to reduce the load by ~24%(compared to a possible ~31.3% as predicted by the optimization framework).

We propose a system that makes opportunistic use of mobile computing devices and ad hoc networking to provide a transient storage service to clients in a localized geographical region. The main challenge is to offset the potential data loss caused by node mobility with internode communication. We first argue on the basis of simulation and theory that such a service is feasible, given a sufficiently high density of mobile devices. A distributed communication and storage protocol is then presented for situations where all mobile devices are within communication range of each other, and it is shown through testbed experiments and simulation that the protocol operates correctly and makes efficient use of storage space and communication bandwidth, while maximizing the longevity of stored data.

Combining content analysis of television programs with quantitative audience measurement can provide insights into customer reactions to advertisements and program content. This work introduces a system architecture that incorporates anonymous audience metrics from an operational IPTV environment with metadata from a content-based analysis of recorded programs. Evaluated on a collection of news programs, the system verifies that events derived from the audience metrics data stream correspond to media segmentation boundaries such as commercial breaks and topic changes. An automated system for executing multimodal media segmentation algorithms for commercial break and topic change detection is also discussed. Better understanding of audience reaction can help IPTV service providers plan infrastructure investments and help in managing multimedia content delivery networks.

To address network congestion issues, network providers are seeking to shape consumer's usage behavior through economic incentives such as a tiered pricing scheme and/or restrictions on unlimited data plans. Current pricing schemes, however, do not ad- dress the congestion problem directly: the prices differentiation is based on the data volume consumed by the user in a month whereas the congestion is a consequence of the availability of limited bandwidth resources (consumption speed). Furthermore, users subscribing to a costlier plan receive no real- time (higher bandwidth) priority. This paper pro- poses an alternate pricing plan for wireless networks based on Generalized Second Price (GSP) auctions with the aim of improving welfare. Our simulation- based analysis reveals that such a pricing scheme allows users with higher willingness to pay to secure higher throughput of service, without significantly sacrificing the overall system throughput or fairness.

We investigate how consumers view content using Video on Demand (VoD) in the context of an IP-based video distribution environment. Users today can use interactive stream control functions such as skip, replay, fast-forward, pause, and rewind to control their viewing. The use of these functions can place additional demands on the distribution infrastructure (servers, network, and set top boxes) and can be challenging to manage with a large subscriber base. A model of user interaction provides insight into the impact of stream control on server and bandwidth requirements, client responsiveness, etc.

Server side measurements from several Wi-Fi hot-spots deployed in a nationwide network over different types of venues from small coffee shops to large enterprises are used to highlight differences in traffic volumes and patterns. We develop a common modeling framework for the number of simultaneously present customers. Our approach has many novel elements: (a) We combine statistical clustering with Poisson regression from Generalized Linear Models to fit a non-stationary Poisson process to the arrival counts and demonstrate its remarkable accuracy; (b) We model the heavy tailed distribution of connection durations through fitting a Phase Type distribution to its logarithm so that not only the tail but also the overall distribution is well matched; (c) We obtain the distribution of the number of simultaneously present customers from an Mt/G/∞ queuing model using a novel regenerative argument that is transparent and avoids the customarily made assumption of the queue starting empty at an infinite past; (d) Most importantly, we validate our models by comparison of their predictions and confidence intervals against test data that is not used in fitting the models.

Existing studies have demonstrated that uneven and dynamic usage patterns by the primary users of license-based wireless communication systems can often lead to temporal and spatial spectrum underutilization. This provides an opportunity for the secondary users (SUs) to tap into underutilized frequency bands provided that they are capable of cognitively accessing systems without colliding or impacting the performance of the primary users (PUs). When there are multiple networks with spare spectrum, secondary users can opportunistically choose the best network to access, subject to certain constraints. In cognitive radio systems, this is referred to as the network selection problem for secondary users. This paper develops a Markov queuing model to obtain the maximum allowable arrival rate of secondary users subject to a target collision probability for the primary users. Based on this model, we design a novel Collision-Constrained Network Selection (CCNS) method that maximizes secondary users' throughput subject to a given PU collision probability. Further, we propose two approaches, referred as CCNS-Greedy and CCNS-Energy, which target to reduce collision probability and to decrease energy consumption of secondary users when the system is underloaded. This, however, has one practical drawback in that the proposed CCNS method depends on PU and SU traffic characteristics such as inter-arrival time and service time, which might not be available in real scenario. We next illustrate that a MEAsurement-based Networks Selection (MEANS) scheme can be used to perform network selection for secondary users based on online measurement of PU collision probability of each network. We evaluated the performance based on extensive simulation, which conclusively shows that the proposed schemes achieve the best performance in terms of resulting PU collision probability, SU throughput, and SU energy consumption, when compared to both Random and Greedy strategies.

The end to end system data performance over a 3G cellular network depends on many factors such as the number of users, interference, multipath propagation, radio resource management techniques as well as the interaction between these mechanisms and the transport protocol's flow and congestion mechanisms. Using controlled experiments in a public cell site, we investigate the interaction between TCP and the 3G UMTS/HSPA network's resource allocation, and its effect on fairness in the throughput achieved across multiple (up to 26) TCP flows in a loaded cell sector. Our field measurement results indicate that TCP fairness fluctuates significantly when the air interface (radio link) is the bottleneck. We also observe that TCP fairness is substantially better when the backhaul link (a fixed wired link) is the bottleneck, instead of the air interface. We speculate that the fairness of TCP flows is adversely impacted by the mismatch between the resource allocation mechanisms of TCP's flow and congestion control and that of the Radio Access Network (RAN).

We investigate the user viewing activity for broadcast TV, pre-recorded content using Digital Video Recording (DVR) and video on demand (VoD) in an IP-based content distribution environment. Advanced stream control functions (play, pause, skip, rewind, etc.) provide users with a high level of interactivity, but place demands on the distribution infrastructure (servers, network, home-network) that can be difficult to manage at large scale. To support system design as well as network capacity planning, it is necessary to have a good model of user interaction. Using traces from a well-provisioned operational environment with a large user population, we first characterize interactivity for broadcast TV, DVR and VoD. We then develop parametric models of individual users stream control operations for VoD. Our analysis shows that interactive behavior is adequately characterized by two semi-Markov models, one for weekdays and another for weekends. We propose a parametric model for the underlying sojourn time distributions and show that it results in a superior fit compared to well known distributions (generalized Pareto and Weibull). In order to validate that our models faithfully capture user behavior, we compare the workload that a VoD server experiences in response to actual traces and synthetic data generated from our proposed models.

Presence systems are widely used today to get information of user availability and their willingness to communicate. As presence and Unified Communications (UC) services are viewed as productivity enhancements within enterprises, the need to connect these services across enterprise WANs and MANs becomes more important. Adding to the availability to communicate is the growing use of mobile devices in the workforce. This paper investigates a protocol that can be efficiently supported across mobile networks and between domains of enterprises. Specifically, we propose a compact presence vocabulary that uses HTTP and SMS protocols to publish to a presence server in the network. The proposed architecture extends the current standardization efforts in OMA PAL working group for non-SIP enabled mobiles. We present a reference implementation of the middleware server and the mobile publisher and show by experiments that our compact protocol is bandwidth (38% to 89%) and energy efficient.

A Hidden Markov Modeling technique is used to evaluate the exact performance of the Viterbi algorithm for estimating states of a finite state system. This technique is applied to exact calculation of the bit error probability of the Viterbi decoder of a convolutional code in channels with independent errors and in channels with memory. The results are compared with the widely used upper bounds. The accuracy of the proposed methods is verified via computer simulation.

GeoTV is a project that explores seamless integration of mobile phones, HDTV sets, and computers in the living room to enrich the user experience of existing services. The three- screen service allows a user to navigate a world map on a smart phone to track geo-located media RSS content that matches her personal interests. The user can show a matching video clip on her phone or direct a nearby HDTV set to play the video. In addition, the user can bring up a world map on a nearby computer screen to navigate areas of interest related to the video clip. GeoTV allows all three screens to be used for what they are best for: HDTV for high resolution video, computer screen for browsing a world map, and a smart phone for personalized control at hand to select media of interest.

Recent advances in P2P technology have made it a viable alternative for the delivery of rich media by both content and service providers. However, to better understand and utilize P2P technologies in building a scalable media distribution platform, the nature of the underlying network must be taken into consideration. Although many P2P simulations have been conducted previously, very few were evaluated on a real P2P testbed with the properties of the physical network in mind. In this paper, we describe VP2P, a virtual machine-based P2P testbed, which supports P2P studies with the consideration of typical service provider networks. We use four fully equipped MacPro's to emulate 32 peers in the current implementation. Experiments of three P2P algorithms for video on demand (VoD) services: BitTorrent, Toast, and Zebra, were run on the testbed. We discuss the rationales behind the hardware and software architecture of VP2P, report our results, and conclude with lessons learned and future directions for VM-based testbeds.

Measurement studies have shown that uneven and dynamic usage patterns by the primary users of license based wireless communication systems often lead to temporal and spatial spectrum underutilization. This provides an opportunity for secondary users to tap into underutilized frequency bands provided that they are capable of cognitively accessing without colliding or impacting the performance of the primary users. When there are multiple networks with spare spectrum, secondary users can opportunistically choose the best network to access, subject to certain constraints. In cognitive radio systems, this is referred to as the network selection problem. In this paper, multiple network selection strategies namely, random, weighted, and greedy, are comprehensively evaluated. It is found that without adequate admission control, those methods cannot provide sufficient service protection for the primary users. Next, a Markov decision model is applied to obtain the maximum allowable arrival rate for secondary users subject to a target collision probability for the primary users. Based on this model, a Collision-Constrained Network Selection (CCNS) method is proposed that maximizes system throughput subject to a given collision probability. Simulations show that comparing to random, weighted, and greedy strategies CCNS achieves an improved performance in terms of system throughput and collision probability.

We describe and analyze a novel algorithm called Limited Chain Relay for on-demand multimedia content distribution over digital networks. Using VoD as an example, we show that this peer assisted scheme provides bandwidth and processing savings comparable to a periodic multicast without incurring subscriber delays inherent in the latter. This scheme's success does not depend upon the ability to forecast demand accurately. It uses minimal storage in participating peer CPEs. It incorporates many features to facilitate fast set up, redundancy to handle peer failures, and centralized OS functions including billing. It can be used in a wide variety of networks such as Fiber to the Home and Fiber to the Curb and with transport technologies like IP and ADSL. It can be used also for streaming content where individual clients may not store the entire content.

We present CPM, a unified approach that exploits server multicast, assisted by peer downloads, to provide efficient video-on-demand (VoD) in a service provider environment. We describe our architecture and show how CPM is designed to dynamically adapt to a wide range of situations including highly different peer-upload bandwidths, content popularity, user request arrival patterns, video library size, and subscriber population. We demonstrate the effectiveness of CPM using simulations (based on an actual implementation codebase) across the range of situations described above and show that CPM does significantly better than traditional unicast, different forms of multicast, as well as peer-to-peer schemes. Along with synthetic parameters, we augment our experiments using data from a deployed VoD service to evaluate the performance of CPM.

As more and more video content is made available and accessed on-demand, content and service providers face challenges of scale. Today's delivery mechanisms, especially unicast, require resources to scale linearly with the number of receivers and library sizes. Unlike these mechanisms, with multicast, the load on a server is relatively independent of the number of receivers. Adopting multicast for on-demand access, however, is challenging because of the need to temporally aggregate requests. In this paper, we investigate the importance of an intelligent scheduler and a good data model for achieving good aggregation of requests into multicast groups. We examine the use of an Earliest Deadline First (EDF)-like scheduler that aims to schedule the transmission of chunks of video according to their deadlines using multicast. We show through analysis that this approach is optimal in terms of the data transmitted by the server. Using trace data from an operational service, we show that our approach reduces server bandwidth by as much as 65% compared to traditional techniques such as unicast and cyclic multicast. Finally, our approach achieves good aggregation even when 50% of the users use a typical VoD stream-control function like skip, to view different parts of the video.

P2P file transfers and streaming have already seen a tremendous growth in Internet applications. With the rapid growth of IPTV, the need to efficiently disseminate large volumes of Video-on-Demand (VoD) content has prompted IPTV service providers to consider peer-assisted VoD content delivery. This paper describes Zebroid, a VoD solution that uses IPTV operational data on an on-going basis to determine how to pre-position popular contents in customer set-top boxes during idle hours to allow these peers to assist the VoD server in content delivery during peak hours. Latest VoD request distribution, set-top box availability, and capacity data on network components are all taken into consideration in determining the parameters used in the striping algorithm of Zebroid. We show both by simulation and emulation on a realistic IPTV testbed that the VoD server load can be significantly reduced by more than 50-80% during peak hours by using Zebroid.

2008 will be the "Mobile Eureka! Moment". With the Olympics just round the corner and the mobile market experiencing a phenomenal growth, we believe that WWW 2008 will truly be the climax and focal point in the midst of this mobile revolution. Mobile Web Initiative spearheaded by W3C is making a strong stand on how to realize the vision of pervasive mobile computing. Three screen services (TV, PC, Phone) are demanding new architectures to be developed. Furthermore, the need to go beyond technology, demands an embrace of the human-centric aspects of mobile computing. The objective of this workshop is to provide a single forum for researchers, sociologists, and technologists to discuss the state-of-the-art, present their contributions, and set future directions in personalized applications for mobile users with a focus in rich social media.

Radio Frequency Identification (RFID) technology has the potential to dramatically improve numerous industrial practices. However, it still faces many challenges, including security and reliability, which may limit its use in many application scenarios. While security has received considerable attention, reliability has escaped much of the research scrutiny. In this work, we investigate the reliability challenges in RFID-based tracking applications, where objects (e.g., pallets, packages, and people) tagged with low-cost passive RFID tags pass by the RFID reader's read zone. Our experiments show that the reliability of tag identification is affected by several factors, including the inter-tag distance, the distance between the tag and antenna, the orientation of the tag with respect to the antenna, and the location of the tag on the object. We demonstrate that RFID system reliability can be significantly improved with the application of simple redundancy techniques.

This paper investigates a novel concept of providing seamless control and portability of an IPTV viewing session. A solution employing a middleware system, a secure hardware token and a cell phone are used to demonstrate how an IPTV session can be securely controlled remotely and moved between multiple viewing stations. We have built a prototype of the system and demonstrated its flexible features. Depending on the user's protocol of choice, most remote control operations from a mobile device took less than 5 seconds to execute. An interesting capability of previewing content of other channels via the user's device while still continuing to watch the program on the viewing station differentiates it from today's IPTV offers. Finally for mobile content delivery, we address the problem of dynamic device profile selection and content adaptation using a classification algorithm to match the best content alternative destined for a mobile.

The Web is rapidly moving towards a platform for mass collaboration in content production and consumption from three screens: computers, mobile phones, and TVs. While there has been a surge of interests in making Web content accessible from mobile devices, there is a significant lack of progress when it comes to making the web experience suitable for viewing on a television. Towards this end, we describe a novel concept, namely GeoTV, where we explore a framework by which web content can be presented or pushed in a meaningful manner to create an entertainment experience for the TV audience. Fresh content on a variety of topics, people, and places is being created and made available on the Web at breathtaking speed. Navigating fresh content effectively on TV demands a new browsing paradigm that requires fewer mouse clicks or user interactions from the remote control. Novel geospatial and temporal browsing techniques are provided in GeoTV that allow users the capability of aggregating and navigating RSS-enabled content in a timely, personalized and automatic manner for viewing in an IPTV environment. This poster is an extension of our previous work on GeoTracker that utilizes both a geospatial representation and a temporal (chronological) presentation to help users spot the most relevant updates quickly within the context of a Web-enabled environment. We demonstrate 1) the usability of such a tool that greatly enhances a user.s ability in locating and browsing videos based on his or her geographical interests and 2) various innovative interface designs for showing RSS-enabled information in an IPTV environment.

The Web is rapidly moving towards a platform for mass collaboration in content production and consumption. Fresh content on a variety of topics, people, and places is being created and made available on the Web at breathtaking speed. Navigating the content effectively not only requires techniques such as aggregating various RSS-enabled feeds, but it also demands a new browsing paradigm. In this paper, we present novel geospatial and temporal browsing techniques that provide users with the capability of aggregating and navigating RSS-enabled content in a timely, personalized and automatic manner. In particular, we describe a system called GeoTracker that utilizes both a geospatial representation and a temporal (chronological) presentation to help users spot the most relevant updates quickly. Within the context of this work, we provide a middleware engine that supports intelligent aggregation and dissemination of RSS feeds with personalization to desktops and mobile devices. We study the navigation capabilities of this system on two kinds of data sets, namely, 2006 World Cup soccer data collected over two months and breaking news items that occur every day. We also demonstrate that the application of such technologies to the video search results returned by YouTube and Google greatly enhances a user.s ability in locating and browsing videos based on his or her geographical interests. Finally, we demonstrate that the location inference performance of GeoTracker compares well against machine learning techniques used in the natural language processing/information retrieval community. Despite its algorithm simplicity, it preserves high recall percentages.

This paper studies the conditions under which P2P sharing can increase the capacity of IPTV services over FTTN networks. For a typical FTTN network, our study shows a) P2P sharing is not beneficial when the total traffic in a local video office is low; b) P2P sharing increases the load on FTTN switches and routers in local video offices; c) P2P sharing is the most beneficial when the network bottleneck is experienced in the southbound segment of a local video office (equivalently a northbound segment of an FTTN switch); and d) sharing among all FTTN serving communities is not needed when network congestion problems are solved by using some other technologies such as program pre-caching or replication. Based on the analytical results, design for IPTV services which monitors FTTN network conditions and decides when and how to share videos among peers to maximize the service capacity. Simulations and bounds both validate the potential benefits of the MediaGrid IPTV service platform.

We present a system for automatic monitoring and timely dissemination of multimedia information to a range or mobile information appliances based on each user's interest profile. Multimedia processing algorithms detect and isolate relevant video segments from over twenty television broadcast programs based on a collection or words and phrases specified by the user. Content repurposing techniques are then used to convert the information into a form that is suitable for delivery to the user's mobile devices. Alerts are sent using a number of application messaging and network access protocols including email, short message service (SMS), multimedia messaging service (MMS), voice, session initiation protocol (SIP), fax, and pager protocols. The system is evaluated with respect to performance and user experiences. The MediaAlert system provides an effective and low-cost solution for the timely generation of alerts containing personal, business, and security information.

This paper describes the results of frequency-domain channel sounding in residential environments. It consists of detailed characterization of complex frequency responses of ultra-wideband (UWB) signals having a nominal center frequency of 5 GHz. A path loss model as well as a second-order autoregressive model is proposed for frequency response generation of the UWB indoor channel. Probability distributions of the model parameters for different locations are presented. Also, time-domain results such as root mean square delay spread and percent of captured power are presented.

The performance of an IEEE 802.11a wireless receiver, in the presence of multiple users transmitting ultra-wideband (UWB) interfering signals, is evaluated. The frequency band of interest for the 802.11a system is 5.21-5.23 GHz. The coexistence of the two systems is characterized in terms of 802.11a receiver's bit error rates and throughputs. Our simulations indicate that UWB interferers can cause severe degradation in the attainable throughput of the 802.11a system. However, as the UWB interferers move away from the 802.11a receivers, so does their negative effect on the throughput performance of the 802.11a system.

In this paper we address the research issues in providing personalized multimedia services, which enable a mobile user to remotely record video programs, control cameras, and request the delivery of pre-recorded or live video content to his or her own mobile device. We describe a mobile service platform that authenticates users who send service requests from various mobile devices, transcodes video content based on user and device profiles, and authorizes the delivery of content from a media server to the proper client device. The media server adapts automatically to the fluctuations of the wireless channel conditions for reasonable viewing on the client device. The mobile service platform essentially manages the control path, while the media server handles the actual content delivery. We discuss various aspects of the integration and report our successful experiments conducted on wireless LAN and CDPD networks.

We compare measured fading data with the popular Rayleigh model. Our results show that this model does not agree with the experimental data. As an alternative, we approximate the experimental data using autoregressive moving average (ARMA) models. To validate the models, we compare several characteristics of fading obtained analytically, by simulation, and from measured data.

We estimate bit error probability bounds for finite-length delay-optimised multi-input multi-output (MIMO) equalizers. These equalizers shorten the impulse response memory of frequency-selective MIMO channels by minimizing the average energy of the error sequence between the equalized MIMO channel impulse response and the target impulse response. We answer an important question in this paper namely, how much asymptotic loss in SNR do we expect as a result of this shortening? A partial distance spectrum for a 2/spl times/2 MIMO channel is evaluated with or without channel shortening equalisers. The union bound is then used to upper bound the bit error probability. Similarly, the lower bound is computed from the squared minimum Euclidean distance. Numerical results show that the expected loss is in the order of 2.5 dB for realistic wireless channel environments.

In this letter we estimate the bit error probability (BEP) of optimum multiuser detection for synchronous and asynchronous code division multiple access (CDMA) systems on Gaussian and fading channels. We first compute an upper bound and a lower bound on the bit error probability for a given spreading code, then average the bounds over a few thousand sets of spreading codes. These bounds are obtained from a partial distance spectrum. On Gaussian channels, the upper bound converges to the lower bound at moderate to large signal-to-noise ratios. However, on fading channels the upper bound does not converge, hence we present our results for the lower bound only. The numerical results show that: 1) the BEP of a 31-user CDMA system with binary random spreading codes of length 31 is only two to four times higher than the BEP of the single user system; 2) the number of users that can be accommodated in an asynchronous CDMA system is larger than the processing gain; and 3) optimum multiuser detection outperforms linear detection (e.g., the decorrelating detector) by about 2.8 to 5.7 dB.

In this paper, we evaluate the performance of optimum multiuser detection by union bounding techniques for a large number of users. It is a difficult task to analytically compute the average bit error probability of multiuser DS-CDMA systems. In this paper we aim to use bounds to accurately evaluate the optimal performance of a multiuser DS-CDMA system, both synchronous and asynchronous. We present a general algorithm to compute the partial distance spectrum of the multiuser system. Numerical results for both systems with binary random signature waveforms of length 31 are used to illustrate the method. It has been found that the optimal bounds for the asynchronous system have a lower bit error probability (BEP) than synchronous systems. Focus is given to two areas namely 1) computing bit error probability (BEP) given a particular correlation matrix, 2) applying optimal conditional importance sampling and computing bit error probability for randomly generated correlation matrices.