Today’s traffic projections speak of almost 58% video traffic across the Internet. Nearly all video traffic is encrypted, accounting for more than 50% encrypted traffic worldwide. To analyze video traffic today, or even estimate its quality in the network, a deep look into the traffic characteristics has to be done. But then, important quality metrics from the traffic behavior can be derived. Based on extensive measurements we show in this work how to measure and estimate video stalls for mobile adaptive streaming. The underlying dataset includes more than 900 hours of video footage from the native YouTube app, measured over 18 different videos in 56 network scenarios in two cities in Europe. We outline a possible approach to estimate the video playback buffer size based on uplink video chunk requests in real-time to break down the video stalls. This work is intended as a tool for network operators to receive further knowledge of the characteristics of video streaming traffic to quantify the most important QoE degradation factors of one of the most important applications today.

Responding fluently to user requests is important to keep them immersed. In this paper, we are presenting an extensive dataset to study the response time of YouTube's mobile video streaming service on Android. We illustrate the application of our dataset by studying YouTube's initial delay for a subset of 9 videos in 75 network scenarios. We find that in 41% of the cases, YouTube exceeds the attention span of a typical user, while deep immersion is only reached in 15% of the cases. Our factor analysis implies that the allocation of the initial CDN node is the critical link in this delay chain. Since our dataset includes a large variety of factors, we are describing setup, methodology, and data structure in detail. Our dataset and measurement tools are publicly available at [8].

In this paper, we develop an analytical framework to compute the Quality-of-Experience (QoE) metrics of video streaming in wireless networks. Our framework takes into account the system dynamics that arises due to the arrival and departure of flows. We also consider the possibility of users abandoning the system on account of poor QoE. Considering the coexistence of multiple services such as video streaming and elastic flows, we use a Markov chain based analysis to compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and bitrate switching. Our simulation results validate the accuracy of our model and describe the impact of scheduler at eNB on the QoE metrics.

Dynamic Adaptive Streaming over HTTP (DASH) has become the standard choice for live events and on-demand video services. In fact, by performing bitrate adaptation at the client side, DASH operates to deliver the highest possible Quality of Experience (QoE) under given network conditions. In cellular networks, in particular, video streaming services are affected by mobility and cell load variation. In this context, DASH video clients continually adapt the streaming quality to cope with channel variability. However, since they operate in a greedy manner, adaptive video clients can overload cellular network resources, degrading the QoE of other users and suffer persistent bitrate oscillations. In this paper, we tackle this problem using a new eNB scheduler, named Shadow-Enforcer, which ensures minimal number of quality switches as well as efficient and fair utilization of network resources. Our scheduler works well under dynamic scenarios and mobility, and requires minimal information, i.e., just the set of video bitrates supported by DASH video clients. It consists of the cascade of a virtual scheduler, Shadow, and the actual scheduler, Enforcer, piloted by the virtual one. Extensive simulations demonstrate the efficiency, fairness and the smooth response to channel variations of the proposed solution.

Video streaming over cellular network has become extremely popular in 4G and will be an integral part of future cellular networks. While most modern-day video clients continually adapt quality of video streams, they neither coordinate with network elements nor among each other. Consequently, a streaming client may quickly overload the cellular network, leading to poor Quality of Experience (QoE) for users in the network. Motivated by this problem, we present D-VIEWS - a scheduling paradigm that assures video bitrate stability of adaptive video streams while ensuring better system utilization. D-VIEWS only needs to be aware of the set of video bitrates and requires no changes to streaming clients and other network functions. Through simulations, we also study the performance of proportional fairness scheduler and D-VIEWS in the presence of user arrival and departure events.

Accurate cross-layer information is very useful to optimize mobile networks for specific applications. However, providing application-layer information to lower protocol layers has become very difficult due to the wide adoption of end-to-end encryption and due to the absence of cross-layer signaling standards. As an alternative, this paper presents a traffic profiling solution to passively estimate parameters of HTTP Adaptive Streaming (HAS) applications at the lower layers. By observing IP packet arrivals, our machine learning system identifies video flows and detects the state of an HAS client's play-back buffer in real time. Our experiments with YouTube's mobile client show that Random Forests achieve very high accuracy even with a strong variation of link quality. Since this high performance is achieved at IP level with a small, generic feature set, our approach requires no Deep Packet Inspection (DPI), comes at low complexity, and does not interfere with end-to-end encryption. Traffic profiling is, thus, a powerful new tool for monitoring and managing even encrypted HAS traffic in mobile networks.

This demo introduces a wrapper used for automated measurements of mobile video streaming in the Android YouTube app. The difference to traditional measurement techniques is that the measurement is done with the native YouTube app as it is provided in the Google Play Store. In addition to bandwidth or packet loss detection, the QoE of the video stream can be measured and quantified. For this, the amount of quality changes, the current playtime, the buffer level, and statistics like video and audio format are captured. Thus, detailed relationships between network parameters and streaming behavior based on many factors can be detected within the native app available in the Play Store.

Adaptive video streaming improves users' quality of experience (QoE), while using the network efficiently. In the last few years, adaptive video streaming has seen widespread adoption and has attracted significant research effort. We study a dynamic system of random arrivals and departures for different classes of users using the adaptive streaming industry standard DASH (Dynamic Adaptive Streaming over HTTP). Using a Markov chain based analysis, we compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and switching rate. We validate our model by simulations, which show a very close match. Our study of the playout buffer is based on client adaptation scheme, which makes efficient use of the network while improving users' QoE. We prove that for buffer-based variants, the average video bit-rate matches the average channel rate. Hence, we would see quality switches whenever the average channel rate does not match the available video bit rates. We give a sufficient condition for setting the playout buffer threshold to ensure that quality switches only between adjacent quality levels.

This paper describes a novel system that provides key parameters of HTTP Adaptive Streaming (HAS) sessions to the lower layers of the protocol stack. A non-intrusive traffic profiling solution is proposed that observes packet flows at the transmit queue of base stations, edge-routers, or gateways. By analyzing IP flows in real time, the presented scheme identifies different phases of an HAS session and estimates important application-layer parameters, such as play-back buffer state and video encoding rate. The introduced estimators only use IP-layer information, do not require standardization and work even with traffic that is encrypted via Transport Layer Security (TLS). Experimental results for a popular video streaming service clearly verify the high accuracy of the proposed solution. Traffic profiling, thus, provides a valuable alternative to cross-layer signaling and Deep Packet Inspection (DPI) in order to perform efficient network optimization for video streaming.

HTTP Adaptive Streaming is able to dynamically match video quality to variable network conditions. This is a key feature for multimedia delivery when quality of service cannot be granted network-wide. For instance, the end-to-end throughput towards mobile terminals may suffer short term fluctuations due to fading. Hence, robust bitrate adaptation schemes become crucial in order to avoid degraded video reproduction. The objective, in this context, is to control the filling level of the playback buffer, maximize the video quality, and avoid unnecessary quality variations which may also impair the perceived quality of experience. In this work we study bitrate adaptation algorithms leveraging on Backward-Shifted Coding (BSC), a scalable video coding scheme able to cope with the effects of end-to-end throughput fluctuations. We have proposed a new adaptation scheme able to balance video rate smoothness and high network capacity utilization. Both the throughput-based and buffer-based variants of such scheme have been designed. Extensive simulations using synthetic and real-world video traffic traces show that the proposed solution performs remarkably well even under challenging network conditions.

HTTP Adaptive Streaming (HAS) techniques are now the dominant solution for video delivery in mobile networks. Over the past few years, several HAS algorithms have been introduced in order to improve user quality-of-experience (QoE) by bit-rate adaptation. Their difference is mainly the required input information, ranging from network characteristics to application-layer parameters such as the playback buffer. Interestingly, despite the recent outburst in scientific papers on the topic, a comprehensive comparative study of the main algorithm classes is still missing. In this paper we provide such comparison by evaluating the performance of the state-of-the-art HAS algorithms per class, based on data from field measurements. We provide a systematic study of the main QoE factors and the impact of the target buffer level We conclude that this target buffer level is a critical classifier for the studied HAS algorithms. While buffer-based algorithms show superior QoE in most of the cases, their performance may differ at the low target buffer levels of live streaming services. Overall, we believe that our findings provide valuable insight for the design and choice of HAS algorithms according to networks conditions and service requirements.

Channel Quality Prediction (CQP) is an essential function for anticipatory and proactive Radio Resource Management (RRM). CQP estimates the wireless channel for multiple time slots in advance, thus, providing a new degree of freedom at the cost of a prediction error. In this paper, we carefully study the error when Kalman-based predictors are used to predict the large-scale statistics of the LTE downlink channel. After presenting our predictor assumptions, we describe optimal and practical filters to isolate the slow channel process. Then, we study the prediction error in a realistic LTE scenario. Our study verifies that practical channel predictors already reach horizons in the order of seconds at a reasonable error bound. This promising finding shows that anticipatory and proactive adaptation is already practicable in mobile networks today.

We have completed the collection of one of the richest accurately annotated mobile dataset of modes of transportation and locomotion. To do this, we developed a highly reliable Android application called DataLogger capable of recording multisensor data from multiple synchronized smartphones simultaneously. The application allows real-time data annotation. We explain how we designed the app to achieve high reliability and ease of use. We also present an evaluation of the application in a big-data collection (750 hours, 950 GB of data, 17 different sensor modalities), analysing the data loss (less than 0.4%) and battery consumption (≈ 6% on average per hour). The application is available as open source.

We explain how to obtain a highly versatile and precisely annotated dataset for the multimodal locomotion of mobile users. After presenting the experimental setup, data management challenges and potential applications, we conclude with the best practices for assuring data quality and reducing loss. The dataset currently comprises 7 months of measurements, collected by smartphone's sensors and a body-worn camera, while the 3 participants used 8 different modes of transportation. It comprises 950 GB of sensor data, which corresponds to 750 hours of labelled data. The obtained data will be useful for a wide range of research questions related to activity recognition, and will be made available to the community1.

The exchange of confidential messages is an inherent problem in wireless communication due to the broadcast nature of the radio channel. In this paper, we enhance standard cryptography with information-theoretic techniques by exploiting relays to increase the confidentiality of wireless communication in the presence of one or more eavesdroppers with low-noise receivers. To achieve this, we present a protocol which makes use of relays in two ways. First, the relays re-transmit disjoint encrypted chunks of a message. Second, the relays utilize cooperative jamming techniques to generate pseudo-random signals in order to increase the interference level in the propagation domain. Chunks and interference levels are allocated over relays in such a way that the message can only be decoded within a critical area around the intended receiver. Our simulation results show that this area can be minimized under realistic assumptions on propagation environment and channel knowledge.

Accurate coverage maps are an important tool for network planning and operation but it is often impossible to obtain these maps completely from measurements. In this paper we describe two new methods that enable operators to minimize the cost for obtaining a complete coverage map at high accuracy. Our first method applies the Singular Value Thresholding (SVT) algorithm to reconstruct a complete map from a sparse matrix of coverage data. We then use the Query by Committee (QbC) rationale to identify the areas where further measurements would maximize accuracy of the completed map. This second method allows operators to plan their drive tests such that a given budget is spent at highest efficiency. Our numerical examples illustrate that our proposed completion technique outperforms relevant state of the art and that QbC further enhances reconstruction accuracy.

We propose a coordination scheme to minimize the interference between users in a cellular network with full-duplex base stations and half-duplex user devices. Our scheme exploits signal attenuation from obstacles between the users by (i) extracting spatially isolated regions from a radio map and (ii) assigning simultaneous co-channel uplink and downlink transmissions to users in these regions such that inter-user interference is minimized. While adding low computational complexity and insignificant signaling overhead to existing deployments, evaluating our solution with real coverage data shows impressive gains compared to conventional half-duplex and full-duplex operation.

In anticipatory networking, channel prediction is used to improve communication performance. This paper describes a new approach for allocating resources to video streaming traffic while accounting for quality of service. The proposed method is based on integrating a model of the user's local play-out buffer into the radio access network. The linearity of this model allows to formulate a Linear Programming problem that optimizes the trade-off between the allocated resources and the stalling time of the media stream. Our simulation results demonstrate the full power of anticipatory optimization in a simple, yet representative, scenario. Compared to instantaneous adaptation, our anticipatory solution shows impressive gains in spectral efficiency and stalling duration at feasible computation time while being robust against prediction errors.

Limited radio resources and increasing user demands make wireless resource allocation a challenging task. As a user moves, his channel quality varies significantly depending on his location. In this paper we show that wireless link quality can be predicted on a large time scale in terms of Path Loss values by using street and coverage maps. The predicted wireless link quality information can be used by the network operators for optimized resource allocation. The feasibility of the proposed approach is demonstrated by extensive simulations advocating this data driven approach for channel prediction.

In this paper, we present a framework for including geographical context information within the process of relay selection, where both the relay and destination can be user equipment (UE) hence enabling relaying via device-to-device (D2D) communication. Geographical information, acquired through positioning systems and radio maps, are used to infer pathloss, link budgets and relay generated interference. Thus, this information becomes available at the core network without the aid of extra signaling and channel sounding. Tools from image processing can be exploited to analyze the radio map and determine so called isolated regions. Then, these regions are used by the proposed framework to effectively select the relay. Three different relay selection policies that exploit geographical context via the knowledge of the spatial isolated regions are introduced. Each of these policies is analyzed via simulation and performance is compared with respect to baseline schemes. The pertinence of adopting geographical information is confirmed by the gain in terms of useful power and reduced interference level generated.

In non-reciprocal wireless channels, the performance of link adaptation heavily deteriorates with outdated measurement reports. To provide an effective solution to this problem, we construct an accurate channel gain predictor by combining the Kalman Filter (KF) with Expectation-Maximization (EM). This joint design minimizes the mismatch between the autoregressive model of the KF and the true channel gain. After deriving the filter parameters in closed form, we compare the accuracy of our solution to two relevant alternatives in a 3GPP-compliant scenario. Impressive gains at low computational complexity make our solution a promising candidate to improve the next generation of link adaptation functions.

Mobile video streaming is a rising business but poses significant challenges for network and service operators. In mobile networks, time-variant wireless channels often violate the rate requirement of the content, which leads to a stalled video stream. Common HTTP Adaptive Streaming techniques cannot effectively react to channel variation by adapting the video bitrate only to the state of the playback buffer. In this paper, we present an optimal solution that adapts to the current and predicted channel state as well. Our optimal formulation is based on Markov Decision Processes and studied with real content and channel traces from vehicular users. The results clearly show the large benefit of complementing buffer-aware by channel-aware adaptation and prediction. Finally, we present a heuristic that closely achieves optimal service quality at practical computational complexity.

In this work, we analyze the effect of outdated channel information on the performance of a non-reciprocal wireless link. We introduce a model of channel information age for a Finite-State Markov Channel. Then, we use this model to analyze the probability of a channel estimation error and to express utility as a function of channel information age and cost of periodic feedback. This utility function allows us to analyze the trade-off between performance and timeliness of the channel information. Applying this general model to the example of Rayleigh fading, provides interesting insight into the performance and design of channel adaptation functions and feedback protocols.

We present a framework to study the effect of outdated Channel State Information (CSI) on the performance of a non-reciprocal wireless link. The proposed framework is based on the new concept of age of information. We adopt the Gilbert-Elliot channel model to obtain analytic results regarding the use of periodic CSI feedback and investigate the effect of the age of CSI when multiple orthogonal resource blocks are assigned to a communication link. A general utility function is defined as performance metric, which accounts for the cost of feedback. Our work improves the comprehension about the age of channel state information and its effect on the performance of communication links, which is fundamental for designing efficient adaptation functions and feedback protocols.

This demonstration shows a new type of HTTP adaptive streaming (HAS) for mobile users. We will present an anticipatory HAS policy that adapts video quality based on a prediction of the wireless channel state. By anticipating a poor channel state and by reducing video quality in advance, this policy maximizes the number of seconds in the user's playout buffer before coverage is lost. Even in difficult coverage situations such as tunnels and elevators, our approach reaches outstanding fluency without decreasing the average video bitrate. With this anticipatory buffering technique users require no channel resources at low channel state, which increases spectral efficiency. Our solution is based on an modified player application and will be demonstrated on off-the-shelf Smartphones in a conventional 4G network.

Minimizing the cost of deploying and operating a wireless sensor network (WSN) involves deciding how to partition a budget between competing expenses such as node hardware, energy, and labor. Most commercial network operators account for interest rates in their budgeting exercises, providing a financial incentive to defer some costs until a later time. In this paper, we propose a net present cost (NPC) model for WSN capital and operating expenses that accounts for interest rates. Our model optimizes the number, size, and spacing between expenditures in order to minimize the NPC required for the network to achieve a desired operational lifetime. In general, this optimization problem is non-convex, but if the spacing between expenditures is linearly proportional to the size of the expenditures, and the number of maintenance cycles is known in advance, the problem becomes convex and can be solved to global optimality. If non-deferrable recurring costs are low, then evenly spacing the expenditures can provide near-optimal results. With the provided models and methods, network operators can now derive a payment schedule to minimize NPC while accounting for various operational parameters. The numerical examples show substantial cost benefits under practical assumptions.

In this paper, a novel approach for optimizing resource allocation in wireless small cell networks (SCNs) with device-to-device (D2D) communication is proposed. The proposed approach allows jointly exploiting the wireless and social context of wireless users for optimizing the overall allocation of resources and improving the traffic offload in SCNs. This context-aware resource allocation problem is formulated as a matching game, in which user equipments (UEs) and resource blocks (RBs) rank one another, based on utility functions that capture both wireless and social metrics. Due to social interrelations, this game is shown to belong to a class of matching games with peer effects. To solve this game, a novel self-organizing algorithm is proposed, using which UEs and RBs can interact to decide on their desired allocation. The proposed algorithm is then proven to converge to a two-sided stable matching between UEs and RBs. The properties of the resulting stable outcome are then studied and assessed. Simulation results using real social data show that clustering of socially connected users allows offloading a substantially larger amount of traffic than the conventional context-unaware approach. These results show that exploiting social context has high practical relevance in saving resources on wireless links and in the backhaul.

Abstract In current cellular networks, schedulers allocate wireless channel resources to users based on instantaneous channel gains and short‐term moving averages of user rates and queue lengths. By using only such short‐term information, schedulers ignore the users' service history in previous cells and, thus, cannot guarantee long‐term quality of service (QoS) when users traverse multiple cells with varying load and capacity. In this paper, we propose a new long‐term lookback scheduling (LLS) framework, which extends conventional short‐term scheduling with long‐term (QoS) information from previously traversed cells. We demonstrate the application of (LLS) for common channel aware, as well as channel and queue‐aware schedulers. The developed long‐term schedulers also provide a controllable trade‐off between emphasizing the immediate user (QoS) or the long‐term measures. Our simulation results show high gains in long‐term (QoS) without sacrificing short‐term user requirements. Therefore, the proposed scheduling approach improves subscriber satisfaction and increases operational efficiency. Copyright © 2014 John Wiley & Sons, Ltd.

Small cell networks are seen as a promising technology for boosting the performance of future wireless networks. In this paper, we propose a novel context-aware user-cell association approach for small cell networks that exploits the information about the velocity and trajectory of the users while also taking into account their quality of service (QoS) requirements. We formulate the problem in the framework of matching theory with externalities in which the agents, namely users and small cell base stations (SCBSs), have strict interdependent preferences over the members of the opposite set. To solve the problem, we propose a novel algorithm that leads to a stable matching among the users and SCBSs. We show that the proposed approach can better balance the traffic among the cells while also satisfying the QoS of the users. Simulation results show that the proposed matching algorithm yields significant performance advantages relative to traditional context-unaware approaches.

The deployment of small cells, overlaid on existing cellular infrastructure, is seen as a key feature in next-generation cellular systems. In this paper, the problem of user association in the downlink of small cell networks (SCNs) is considered. The problem is formulated as a many-to-one matching game in which the users and SCBSs rank one another based on utility functions that account for both the achievable performance, in terms of rate and fairness to cell edge users, as captured by newly proposed priorities. To solve this game, a novel distributed algorithm that can reach a stable matching is proposed. Simulation results show that the proposed approach yields an average utility gain of up to 65% compared to a common association algorithm that is based on received signal strength. Compared to the classical deferred acceptance algorithm, the results also show a 40% utility gain and a more fair utility distribution among the users.

Sharing wireless channel resources among cellular network operators saves costs by efficiently utilizing hardware and spectrum. Following the approach of resource sharing, we introduce a general formulation that covers current fixed and dynamic approaches as special cases. Unlike previous approaches, our formulation maximizes spectral efficiency by occasionally penalizing an operator within defined boundaries. This interesting tradeoff between sharing guarantees and system performance is studied by simulation. Our results clarify the costs of fair sharing and how much spectral efficiency can be gained by relaxing fairness in a controlled manner. This study, thus, provides important insight into the efficient design of shared base stations in future cellular networks.

Sharing wireless channel resources among cellular network operators saves costs by efficiently utilizing hardware and spectrum. Following the approach of Generalized Resource Sharing, this paper provides a mathematical rigorous formulation of multi-operator scheduling. The presented scheduling policy allows to trade off sharing guarantees versus spectral efficiency, covering current fixed and dynamic approaches as special cases. Analyzing this general scheduling policy leads to a profound understanding of its most important parameters and of their effect on rate-dependent utilities. The presented proofs are confirmed by simulation results that hold for insightful scenarios and point to promising future work.

Over its lifetime, a Wireless Sensor Network (WSN) loses connectivity as more and more of its battery-powered nodes fail. In this paper, we study how to repair such a network by the gradual deployment of new sensor nodes. We provide algorithms that not only economically adjust the number of replaced nodes but also improve the network by placing the new nodes in better locations. This new approach substantially increases the lifetime of the repaired WSN compared to replacing all nodes at their old positions.

In current cellular networks, schedulers allocate wireless channel resources to users based on short-term moving averages of the channel gain and of the queuing state. Using only such short-term information, schedulers ignore the user's service history in previous cells and, thus, cannot meet long-term Quality of Service (QoS) guarantees when users traverse cells with varying load and capacity. We propose a new scheduling framework, which extends conventional short-term scheduling with long-term QoS information from previously traversed cells. We demonstrate our scheme for relevant channel-aware as well as for channel and queue-aware schedulers. Our simulation results show high gains in long-term QoS while the average throughput of the network increases. Therefore, the proposed scheduling approach improves subscriber satisfaction while increasing operational efficiency.

Sharing wireless channel resources among cellular network operators saves costs by efficiently utilizing hardware and spectrum. To allocate time slots among the users of multiple operators, we introduce a new scheduling algorithm that is based on General Processor Sharing and a Bin Packing heuristic. Our flexible scheduler design allows each operator to execute its own multi-user scheduling policy while providing the agreed resource share. From a practical point of view, our multi-operator scheduler reaches very high spectral efficiency and fairness at an only insignificant increase in queuing delay.

Sharing wireless channel resources among cellular network operators saves costs by efficiently utilizing hardware and spectrum. We introduce a practical scheduler that (i) allocates time slots among operators in a round robin manner and (ii) allocates resources to the users of each operator. This two-step approach guarantees the agreed resource shares while allowing each operator to execute its own multi-user scheduling policy. Our simulation results demonstrate an insignificant increase in delay at high spectral efficiency and fairness.

In the downlink of Long Term Evolution (LTE) systems, feedback and processing delays cause a mismatch between the current channel state and the Channel Quality Information (CQI) at the base station. This CQI aging leads to inaccurate channel adaptation and can, thus, highly degrade the cell capacity. To compensate for this performance loss, we study several CQI predictors under realistic delay and channel assumptions. Our results on cell throughput show that linear prediction with Stochastic Approximation provides at least the performance gains of the computationally more complex covariance-based linear predictors and Kalman filters. This surprising result points to Stochastic Approximation as a powerful and practical technique to increase downlink performance with limited channel knowledge.

Deploying wireless sensor networks (WSNs) from unmanned aerial vehicles (UAVs) can reduce the cost of large rollouts and make it safer to deploy sensors in disaster areas. However, aerial deployment can lead to nodes with arbitrary orientations and, thus, to misaligned antennas. While this problem was observed during experiments, it has not been systematically studied so far. This paper considers an aerially deployed network that utilizes routing designed to minimize latency and improve reliability. A new antenna model is used to demonstrate the performance degradation caused by aerial deployment. By mitigating a large share of this degradation, cooperative relaying substantially reduces the energy consumption of the network.

To cope with highly loaded networks in diverse traffic situations, scheduling time slots in advance is an efficient approach. However, such Lookahead scheduling relies on future Channel Quality Information (CQI), which has to be accurately predicted. In this paper, we introduce a generic model for channel prediction errors. With this model, we study how the performance of lookahead scheduling depends on channel prediction's accuracy. Finally, we propose a simple channel predictor. This predictor provides high accuracy, is easy to implement and of low complexity. Consequently, it is a large step in making Lookahead scheduling feasible.

Forecasting economic quantities can bring high benefits to business planning and operation. To provide accurate forecasts and to investigate the factors behind the trends, we present a new prediction system in this paper. Applying it to the Number of Mobile Subscribers (NMS) in the United States, China, and Germany, we observe interesting differences between saturated and emerging markets. Moreover, we find that prediction accuracy highly increases when (i) external factors are systematically included in the prediction model, while factors with ambiguous effects are removed and (ii) the time lag between a factor's change and using it for prediction is optimized. By doing both, our system is not only more accurate than common forecasting methods but reveals if and how quickly a factor affects a trend's accuracy.

By supporting advanced multimedia applications and multitasking, recent Smartphone operation systems offer great flexibility to the users but make these devices even more difficult to serve. Now, operators of cellular networks face a situation where many applications and services generate highly heterogeneous and bursty traffic patterns. To cope with this challenge, we introduce Context-Aware Resource Allocation (CARA). This new approach enables a scheduler to exploit new information from the users' environment, thereby, allocating its resources more efficiently. We demonstrate CARA's high performance and feasibility by studying it as network utility maximization problem. Comparing our results to traditional proportional fair scheduling clearly shows that knowing more about a user's context is worth the effort.

Letting nodes cooperate improves the performance of wireless networks. To organize this cooperation, many cooperative transmitters employ channel knowledge that they obtain from the destination. Such Channel State Information (CSI) feedback introduces errors and overhead whose degrading effect on the overall performance has not been consistently studied so far. Capturing this degradation, we provide a new framework to analyze cooperation's outage capacity with limited feedback. Our framework holds for selection relaying and can be easily applied to arbitrary network graphs. Applying it to a simple example shows that selection relaying profits from CSI feedback when the acceptable error rate is high. Surprisingly, no effort for feedback needs to be spend at a strict target error rate.

To achieve multiuser diversity gains, resource allocation relies on accurate and timely feedback of the users' channel states. This feedback suffers from transmission errors and delay which can heavily degrade the sum capacity of a scheduled multiuser downlink. The order of this capacity loss and its interdependency to relevant scenario factors is characterized in this paper. From studying a simple sum capacity-maximizing scheduler, we conclude that in many mobile scenarios the upper bound of the achievable data rate is primarily defined by the feedback delay. This makes reducing feedback delay to one of the most important challenges in designing future wireless systems.

Rate adaptation based on signal-to-noise ratio (SNR) measurements is a common channel adaptation scheme to increase throughput in wireless communication systems. To use rate adaptation efficiently in cooperative wireless networks, an adaptation algorithm must consider multiple channels (source- destination, source-relays, and relays-destination) to select modulation and code rates that maximize throughput. In this paper we analyze the potential gains that combining cooperation with rate adaptation brings in three steps: (1) We derive the theoretical capacity bounds for ideal rate adaptation schemes for typical topologies. (2) We propose an offline heuristic for computing SNR thresholds aimed at reaching the derived bounds. (3) Using this heuristic, we compare rate adaptation for maximal ratio combining (MRC), where links are equally adapted, with soft-bit MRC (SBMRC), where links are individually adapted. We find that adapting the rate per link is superior in terms of throughput.

Interference limits throughput in wireless networks. To protect themselves against interference, many wireless protocols create areas around receivers in which no node is allowed to transmit. If such an exclusion area is small, more transmissions can proceed simultaneously but observe higher interference, creating a tradeoff between network capacity and link capacity.

Multiuser dynamic OFDMA for the downlink has been extensively studied, specifically, in terms of fast close-to- optimal subcarrier allocation heuristics and the efficient representation of signaling information. Although these functions provide the fundament of dynamic OFDMA, a complete multiuser OFDMA system requires more functionality. Focusing on WLAN systems, we discuss such additional functionality required for enabling the IEEE 802.11 link and Medium Access Control (MAC) sublayer to leverage OFDMA advantages. We identify necessary extensions, study the resulting overhead, and introduce a lightweight design for a complete dynamic OFDMA IEEE 802.11a system. Studying its performance shows that with our lightweight integration dynamic OFDMA can improve IEEE 820.11a UDP throughput by up to 154% and UDP latency by up to 63% even if the full overhead is taken into account.

Cooperative relaying has been shown to provide diversity gains which can significantly improve the packet error rate (PER) in wireless transmissions. In ad hoc wireless routing where packets may travel over a number of hops before reaching the destination, hop-wise cooperative relaying may severely reduce network capacity. This approach was mainly addressed in literature so far. In this paper, we efficiently apply cooperative relaying along a complete path and over multiple hops at the same time. We use information from the routing layer to improve the medium access control (MAC) layer's performance. Simulations and testbed implementation show appealing gains through diversity resulting in up to 66% better PER performance and up to 148% goodput increase compared to conventional approaches.

Opportunistic Relaying (OR) and Selection Decode-and-Forward (SDF) cooperation protocols can both substantially improve the performance of wireless networks but fundamentally differ in utilized redundancy, relays, and channel knowledge. To analyze when SDF or OR improves error and data rate, we (1) derive their general outage probability and capacity for arbitrary relay configurations, (2) systematically benchmark both approaches in two-hop configurations, (3) study how often beneficial configurations occur in large networks, and, finally, condition our capacity results by this occurrence probability. Our results clearly show that OR maximizes the outage capacity at high acceptable error rate while SDF succeeds if a low error rate is required. SDF performs best if even the relays can cooperate among themselves, supported frequently in networks with more than three neighbors. Consequently, cooperating relays, adapting between OR and SDF, and joining these two approaches should be the focus of future protocol design. To this end, our paper provides a theoretical basis, adaptation rules, and design guidelines.

We describe and analyze minimum path difference (MPD), a metric to improve threshold-based selection decode-and-forward (SDF) protocols in cooperative wireless networks with channel coding. By observing the decoding process, MPD provides a more frequent channel quality estimation than CRC or SNR and, thus, allows SDF to forward only the parts of a message that are likely to be correct. In this paper, we describe MPDpsilas efficient integration into Viterbi decoders and SDF protocols, analyze its accuracy and threshold selection, and study its end-to-end Bit Error Rate in practical cooperative WLANs where ideal block fading and ideal channel codes cannot be assumed. Our studies validate that, unlike all prior methods, in these practical scenarios, MPD significantly improves SDFpsilas performance and experiences only a marginal performance penalty if suboptimal, constant thresholds are selected.

In this paper a cooperative retransmission scheme for 802.11 WLANs is proposed. The mechanism allows stations with good channel to the access point to retransmit packets on behalf of other stations with worse channel. The cooperative partner selection is carried out in a completely opportunistic and distributed way that is compatible with 802.11. Simulations performed in a realistic WLAN scenario with adaptive modulation show that the system provides considerable gains in terms of goodput and packet error rate in comparison with legacy 802.11.

We propose a practical medium access control (MAC) protocol and transceiver design for mobile cooperative WLANs. Our MAC protocol integrates selection decode-and-forward (SDF) cooperative relaying into IEEE 802.11. Unlike previous approaches, its cooperative signaling cycle allows communication if the direct link fails even for small signaling frames. Further SDF functions are efficiently supported by our ready- to-use transceiver design. We implement this design, including our MAC protocol, on a software defined radio (SDR) resulting in a cooperative IEEE 802.11g prototype. Using this prototype, we validate feasibility and performance of our approaches by extensive field measurements in indoor and railroad scenarios with an actual train moving the cooperating terminals.

For block-fading Rayleigh channels, this work considers a network coding approach to cooperative transmission. The network under consideration consists of two sources, S and P, that transmit to a common destination, D. Using half-duplex channels, S and P transmit their packets to D; they also overhear each other's transmission. Then both sources cooperate by transmitting a network-coded packet, which is used at D as incrementally redundant information. We first analyze outage probability and show that this scheme achieves full diversity when good quality inter-source channels exist; the result is also verified through simulation. Then, using the outage result we investigate the 'optimal' rate and energy allocations that minimize the outage probability. The results show that the outage-based performance is more sensitive to the energy allocation than to the rate allocation.

In wireless networks, cooperative transmission is used as a means to combat channel fading. In this system, a source and a relay transmit each others' messages to a common destination using either amplify-and-forward or decode-and-forward strategies; the protocols in cooperative transmission can also broadly be categorized as static and adaptive. The idea in a network-coding-based cooperative transmission protocol is to allow the source and relay to combine messages by a network coding operation; a modulo-2 sum operation implements this network coding. In this work, the performance of various decode-and-forward-based cooperative transmission protocols without and with additional network coding is investigated. Outage probability is used as a measure of performance, and results for symmetrical source-relay and source/relay-destination channels are presented; also a source-relay-channels-based comparison is made. Based on these outage results, network-coding-based protocols are found to be suitable when the source-relay channels are unreliable; when the source-relay channels are good, protocols without network coding perform better. Moreover, to improve the performance of static protocols, we have introduced a sequence of decoding at the destination, and the corresponding outage results show that these protocols can achieve full diversity.

In future wireless networks, cooperating users may create virtual antenna arrays to profit from user cooperation diversity. Even then, fading can severely reduce the quality of real-time media streams. To improve streaming quality in cooperative networks, we propose, firstly, the asymmetric allocation of diversity branches. Secondly, we employ this method in our traffic-aware asymmetric cooperation diversity (TACD) scheme to prioritize important parts of the media streams. We demonstrate by outage analysis and simulation that this approach significantly increases the quality of real-time media streams without introducing additional delays.

In cooperative diversity systems, single antenna nodes may share their antennas to achieve a performance comparable to multi-antenna systems. Cooperative diversity is efficiently provided by the coded cooperation algorithm (Hunter and Nosratinia, 2002) where users cooperate by mutually transmitting their FEC-coded data. This paper studies the effect of user mobility on the outage probability performance of coded cooperation with two users. For this study the authors assumed that users do not always move and that for moving users, fading channel characteristics depend on the motion speed. Considering these two mobility factors, scenarios were defined for which the outage probabilities were analytically derived. The effects of user speed and the level of cooperation are further investigated by simulation. Finally a simple method for the dynamic selection of partners required for successful cooperation by adapting the cooperation level was proposed and discussed the effect of mobility and transmission power on the performance of this method.