In the field of intrusion detection systems, nonuniform class distribution in training datasets compromises the machine learning based detection mechanisms. Generative Adversarial Networks (GANs) offer promising solutions to address this problem. This paper employs a tailored Conditional Tabular GAN (CTGAN) to generate new data points aligned with the minority-class distribution. The experiments utilize the NF-v2-Bot-IoT dataset for performance evaluation. The results show that employing this tailored architecture to generate synthetic samples for the minority class of theft significantly increases the detection rate from 42% to impressive 99%. This highlights the efficacy of the tailored architecture in substantially enhancing the detection performance for skewed classes.

There is growing interest in using public cellular networks for specialized communication applications, replacing standalone sector-specific networks. One such application is transitioning from the aging GSM-R railway network to public 4G and 5G networks. Finland is modernizing its railway communication system through the Digirail project, leveraging public cellular networks. To evaluate network performance, a nationwide measurement campaign was conducted in two modes: Best Quality and Packet Replication. However, Best Quality mode introduces artificial delays, making it unsuitable for real-world assessments. In this paper, railway network delays are modeled using machine learning based on measurements from the Packet Replication mode. The best-performing model is then employed to generate a dataset estimating network delays across Finland’s railway network. This dataset provides a more accurate representation of network performance. Machine-learning-based network performance prediction is shown to be feasible, and the results indicate that Finland’s public cellular network can meet the stringent performance requirements of railway network control.

This paper proposes an automatic anomaly detection and root cause analysis model that combines Density-based Spatial Clustering of Applications with Noise (DBSCAN) and Long Short-Term Memory (LSTM) Auto-Encoder (AE). The model is trained and tested using unlabelled real-world 4G network data from a Portuguese operator. The study demonstrates the promise of DBSCAN in separating normal network traffic patterns from abnormal ones, as well as the ability of LSTM-AE to learn daily network Key Performance Indicator (KPI) behaviour and detect anomalies and their probable root causes based on reconstruction errors. Results indicate that anomalies in available network KPIs do not always result in abnormal traffic patterns, and vice versa. Consequently, it can be inferred that relying solely on traffic volumes for DBSCAN is not an ideal method to separate normal network data from abnormal data for detecting network anomalies. Additionally, the results emphasize the importance of high-quality data in terms of sampling rate and the number of KPIs, as well as the significance of data analysis in identifying patterns across different levels of mobile networks.

Unmanned aerial vehicles (UAVs), or drones, are transforming industries due to their affordability, ease of use, and adaptability. This emphasizes the need for reliable communication links, especially in beyond-line-of-sight scenarios. This paper investigates the feasibility of predicting future quality of service (QoS) in UAV payload communication links, with a special focus on 5G cellular technology. Through field tests conducted in a suburban environment, we explore challenges and trade-offs that cellular-connected UAVs face, particularly in the context of frequency band selection. We employed machine learning models to forecast uplink (UL) throughput for UAV payload communication, highlighting the significance of diverse training data for accurate predictions. The results reveal the effect of frequency band selection on UAV UL throughput rates at varying altitudes and the influence of integrating diverse feature sets, including radio, network, and spatial features, on ML model performance. These insights provide a foundation for addressing the complexities in UAV communications and enhancing UAV operations in modern networks.

Exploiting millimeter-wave (mm-wave) for high-capacity multi-user networks is predicated on jointly performing beam management for seamless connectivity and efficient resource sharing among all users. Beam management in 5G-NR actively monitors candidate beam pair links (BPLs) on the serving cell to simply select the user’s best beam, but neglects the multi-user resource sharing problem, potentially leading to severe throughput degradation on overloaded cells. We propose flexRLM, a coordinator-based flexible radio link monitoring (RLM) framework for multi-user downlink mm-wave networks. flexRLM enables flexible configuration of monitored BPLs on the serving and other candidate cells and beam selection jointly considering link quality and resource sharing. flexRLM is fully 5G-NR-compliant and uses the LTE coordinator in non-standalone mode to continuously update the monitored BPLs via measurement reports from periodic downlink control synchronization signals. We implement flexRLM in ns-3 and present full-stack simulations to demonstrate the superior performance of flexRLM over default 5G-NR RLM in multi-user networks. Our results show that flexRLM’s continuous updating of monitored BPLs improves both link quality and stability. By monitoring BPLs on candidate cells other than the serving one, flexRLM also significantly decreases handover decision delays. Importantly, flexRLM’s low-complexity coordinated load-balancing achieves a per-user throughput close to the single-user baseline.

Scalable cell-free massive MIMO (CF-mMIMO) offers great advantages in network throughput compared to traditional cellular networks by leveraging sophisticated signal processing for interference management. However, this entails cooperation among CPUs controlling serving AP clusters, resulting in a high signalling and computational burden, and in turn, increased energy and economic network costs. It is thus not yet clear whether CF-mMIMO is overall an attractive architecture for future networks. To address this question, we present the first comprehensive energy and economic efficiency analysis of scalable CF-mMIMO with different cluster cooperation levels, where we explicitly quantify the power consumption cost of signalling and computation. Considering the trade-off between high throughput and low energy and economic cost, our results show that scalable CF-mMIMO is an attractive architecture for sustainable "green" future networks but only at low cooperation levels.

We study the problem of joint user association and beam pair link (BPL) allocation in millimeter-wave (mm-wave) cellular networks. We propose two interference-aware strategies – a centralized and a distributed one – and evaluate their performance based on site-specific directional channel data and realistic antenna models. Our results show that using idealized sectored antenna models severely underestimates the spatial interference, considering the non-negligible sidelobes of realistic antenna arrays which strongly limit the achievable spatial separation of the allocated BPLs in mm-wave networks using beam codebooks. We also show that intra-cell interference is the dominant interference component for all allocated users, in contrast to assumptions in the prior literature. By exploiting non line-of-sight BPLs, our interference-aware strategies achieve significant performance gains over interference-agnostic 5G-NR default user association to the strongest base station and BPL, as well as outperforming a centralized, load-balancing literature benchmark. Our proposed strategies rely solely on downlink 5G-NR reference signals for channel state information updates, making them attractive for practical codebook-based mm-wave cellular networks.

While scalable cell-free massive MIMO (CF-mMIMO) shows advantages in static conditions, the impact of its changing serving access point (AP) set in a mobile network is not yet addressed. In this paper we first derive the CPU cluster and AP handover rates of scalable CF-mMIMO as exact numerical results and tight closed form approximations. We then use our closed form handover rate result to analyse the mobility-aware throughput. We compare the mobility-aware spectral efficiency (SE) of scalable CF-mMIMO against distributed MIMO with pure network- and UE-centric AP selection, for different AP densities and handover delays. Our results reveal an important trade-off for future dense networks with low control delay: under moderate to high mobility, scalable CF-mMIMO maintains its advantage for the 95th-percentile users but at the cost of degraded median SE.

The use of high-gain directional communications is key to enable the capacity enhancement of millimeter-wave (mm-wave) for 5G-and-beyond networks. However, robust mm-wave coverage using directional beams entails significant beamsteering effort for maintaining precise beam alignment between the base station and the user. Numerous mm-wave beamsteering algorithms have been proposed in the literature, but verified largely using statistical channel models or limited measurements. Importantly this still leaves open the question of whether these studies can be directly translated to real outdoor mm-wave network deployments. In this paper, we present the results of the first extensive comparative evaluation of eight state-of-the-art mm-wave beamsteering algorithms based on outdoor mm-wave measurements using phased antenna arrays. We collect received signal strength data over fine-grained 3D angular orientations for 78 spatially-dense user positions in a European city, comprising an open-source dataset of over 421,000 individual measurements. We then perform an empirical evaluation of the beamsteering algorithms in the context of link establishment for static and link maintenance for mobile users. Overall, our results show that these state-of-the-art mm-wave beamsteering algorithms, when tested on real measurement data, perform far from optimal and worse than originally reported. A key takeaway from our evaluation is that the practical feasibility of beamsteering algorithms strongly depends on the trade-off between the incurred beam training delay and the antenna gains facilitated in the initial beam training stage which limit the established link budget. Our study of link maintenance for mobile users showed significant deviations from the maximum achievable performance for all algorithms, including those that leverage correlation of the mm-wave sparse link opportunities or historical link information for link recovery. This shows that, to enable seamless connectivity in future mm-wave networks, more sophisticated beamsteering algorithms must be designed that intelligently adapt to the site-specific mm-wave channel while taking into account the realistic antenna beams of commercially-viable phased antenna arrays.

To address the end-to-end implications of realistic beam management control operations, in this paper we present a full-stack 5G-NR-compliant ns-3 framework which implements realistic scheduling, transmission, and reception of 5G-NR downlink control signals, namely synchronization signal block (SSB) and channel state information-reference signal (CSI-RS). The framework is based on a non-standalone downlink 5G-NR millimeter wave (mm-wave) network, enabling control signal transmission via an LTE network connection for handover coordination and overcoming radio link failure (RLF). Our framework provides customizable interfaces to a threshold-based beam management operation. Furthermore, we propose three 5G-NR-compliant beam management strategies and analyze their performance using site-specific propagation and pedestrian mobility data. Overall, our results show the end-to-end implications of realistic beam management operations required to maintain high-rate mobile user performance, with frequent signal-to-noise ratio variations reflected as significant drops in the achievable throughput and high delay spikes. This highlights the importance of the presented ns-3 framework compared to the ideal, instantaneous beam scanning supported in existing ns-3 mm-wave modules, for realistic 5G-NR beam management evaluation. To this end, we provide open access to the ns-3 code to facilitate the research efforts of the wireless community.

We propose a fully distributed control system architecture, amenable to in-vehicle implementation, that aims to safely coordinate connected and automated vehicles (CAVs) at road intersections. For control purposes, we build upon a fully distributed model predictive control approach, in which the agents solve a nonconvex optimal control problem (OCP) locally and synchronously, and exchange their optimized trajectories via vehicle-to-vehicle (V2V) communication. To accommodate a fast solution of the nonconvex OCPs, we apply the penalty convex-concave procedure which solves a convexified version of the original OCP. For experimental evaluation, we complement the predictive controller with a localization layer, being in charge of self-localization, and an estimator, which determines joint collision points with other agents. Experimental tests reveal the efficacy of the proposed control system architecture.

The use of high-gain directional communications is key to enable the capacity enhancement of millimeter-wave (mm-wave) spectrum for 5G-and-beyond networks. However, this entails extensive beam management to continuously track the mobile user to maintain precise transmitter (TX)/receiver (RX) beam alignment. Detailed characterization of mm-wave link opportunities in real-world urban environments and corresponding beam management requirements, are key to addressing the great challenge of mobility support for mm-wave urban deployments. In this paper, we present the results of our large-scale outdoor urban mm-wave measurements using phased antenna arrays, where we collected received signal strength data over fine-grained 3D TX/RX orientations for 78 spatially-dense RX positions in three sub-regions in Aachen, totaling over 421,000 individual measurements. Our results show 2–8 spatial link opportunities per RX position, where only up to 40% of all RX orientations result in a feasible mm-wave connection. Our results suggest that the beam management burden would fall heavily on the mobile user-side, and be moderate on the TX-side, for a typical walk in vicinity of the serving cell. For example, the required TX-side beam steering from one RX position to the next to maintain a minimum data rate of 1 Gbps is less than 12° (twice the half power-beamwidth), but up to 88° at the RX-side. Therefore, obtaining spatially-dense and fine-grained angular propagation data is a critical input for the design and evaluation of mm-wave beam management protocols.

In this paper, we discuss how certain radio access network optimization problems can be modelled using the concept of constraint satisfaction problems in artificial intelligence, and solved at scale using a quantum computer. As a case study, we discuss root sequence index (RSI) assignment problem — an important LTE/NR physical random access channel configuration related automation use-case. We formulate RSI assignment as quadratic unconstrained binary optimization (QUBO) problem constructed using data ingested from a commercial mobile network, and solve it using a cloud-based commercially available quantum computing platform. Results show that quantum annealing solver can successfully assign conflict-free RSIs. Comparison with well-known heuristics reveals that some classic algorithms are even more effective in terms of solution quality and computation time. The non-quantum advantage is due to the fact that current implementation is a semi-quantum proof-of-concept algorithm. Also, the results depend on the type of quantum computer used. Nevertheless, the proposed framework is highly flexible and holds tremendous potential for harnessing the power of quantum computing in mobile network automation.

Exploiting multi-antenna technologies for robust beamsteering to overcome the effects of blockage and beam misalignment is the key to providing seamless multi-Gbps connectivity in millimeter-wave (mm-wave) networks. In this paper, we present the first large-scale outdoor mm-wave measurement study using a phased antenna array in a typical European town. We systematically collect fine-grained 3D angle-of-arrival (AoA) and angle-of-departure (AoD) data, totaling over 50,000 received signal strength measurements. We study the impact of phased antenna arrays in terms of number of link opportunities, achievable data rate and robustness under small-scale mobility, and compare this against reference horn antenna measurements. Our results show a limited number of 2-4 distinct spatial link opportunities per receiver location, indicating that the mm-wave multipath richness in a European town is surprisingly similar to that of dense urban metropolises. The results for the phased antenna array reveal that significant losses in estimated data rate occur for beam misalignments in the order of the half-power beamwidth, with significant and irregular variations for larger misalignments. By contrast, the loss for horn antennas is monotonically increasing with the misalignment. Our results strongly suggest that the effect of non-ideal phased antenna arrays must be explicitly considered in the design of agile beamsteering algorithms.

The promise of millimeter-wave (mm-wave) frequencies for high capacity cellular networks depends on precise alignment of the narrow directional beams to either line-of-sight (LOS) or strong non-LOS links. Given this sensitivity of mm-wave communication to the spatial distribution of LOS/NLOS links, site-specific propagation data from ray-tracing simulations is an important tool for mm-wave networking studies. In this paper, we present the first detailed validation of mm-wave ray-tracing against large-scale outdoor measurements. We consider fine-grained angle-of-arrival (AoA), angle-of-departure (AoD) and received signal strength (RSS) data, using both horn and phased array antennas. We show that ray-tracing captures well the distribution of multipath clusters (MPCs) in terms of number of MPCs per receiver location, AoA, AoD and individual MPC structure. Moreover, our results indicate that ray-tracing provides accurate propagation data based on publicly available 3D building models which lack detailed material properties. Overall, our results show that individual propagation paths can be accurately identified in the ray-tracing data, with a median RSS prediction error within 5 dB of the measured RSS for all MPCs. This is an encouraging result which confirms the viability of ray-tracing propagation data as an input for mm-wave networking studies, on e.g. beam management protocols.

Recent technological advancements are making the use of compact, low-cost, low-power mm-wave radars viable for providing environmental awareness in a number of applications, ranging from automotive to indoor mapping and radio resource optimisation. These emerging use-cases pave the road towards networks in which a large number of radar and broadband communications devices coexist, sharing a common spectrum band in a possibly uncoordinated fashion. Although a clear understanding of how mutual interference influences radar and communications performance is key to proper system design, the core tradeoffs that arise in such scenarios are still largely unexplored. In this paper, we provide results that help bridge this gap, obtained by means of an analytical model and extensive simulations. To capture the fundamental interactions between the two systems, we study mm-wave networks where pulsed radars coexist with communications devices that access the channel following an ALOHA policy. We investigate the effect of key parameters on the performance of the coexisting systems, including the network density, fraction of radar and communication nodes in the network, antenna directivity, and packet length. We quantify the effect of mutual interference in the coexistence scenario on radar detection and communication network throughput, highlighting some non-trivial interplays and deriving useful design tradeoffs.

This paper describes a new tool aimed for rapid development and modeling of MAC protocols that is based on hierarchical finite state machines with concurrency models. The developed tool is named Decomposite MAC Description Language (DMDL) and we are going to provide it open source in order to support MAC protocol design, implementation, and exchange of ideas among researchers. Following the modular design philosophy, the atomic elements of DMDL are the elementary MAC function components selected from a large-scale survey on different types of MAC that we have conducted. In DMDL, a MAC protocols is modeled as a directed graph with the MAC function components at the vertices. A token-like controlling unit is defined to pass control signals and data elements among MAC function components via the directed connections. The high decomposability and modularity enables DMDL to be used as a pure graphical language, and the concurrency nature of the synchronous data flow inherently ensures concurrency as a fundamental feature of DMDL. However, the tool supports also low level direct programmability and mixing of different programming languages through a common GNU Radio approach. In order to expands its usability, DMDL is developed on GNU Radio with a number of classic MAC protocols. The performance of the protocols implemented with DMDL is in accordance with the theoretical expectations, which demonstrates that DMDL is an efficient MAC designing and implementing tool with high flexibility and reusability.

Cache-enabled networks have received increasing attention in both wired and wireless settings. A big challenge for the operator of such networks is to solve efficiently the content placement problem, i.e., to decide how many caches to deploy in the network and in which nodes. We study the content placement problem for two classes of network optimisation objectives, the first focusing on the minimisation of the sum of the shortest paths and the second capturing the cost vs. benefit trade-off to deploy a cache. We know from the state-of-the-art that, even in small networks with few caches, it is unrealistic to find the optimal solution in a reasonable timescale for similar optimisation problems. In order to cope with this challenge, we present an approach under the prism of network analysis. We introduce a family of lightweight heuristic algorithms that use graph-theoretic metrics that identify the most important nodes of the network. We evaluate the performance of the heuristics using real network datasets, showing that the best heuristics are based on the metrics of betweenness centrality and degree centrality. Finally, we provide a randomised version of the heuristics noticing that the same metrics present again the best performance across the different datasets. Moreover, we find out that, in general, the deterministic version of each heuristic outperforms its randomised version.

Software-defined networking (SDN) is enabling radically easier deployment of new routing infrastructures in enterprise and operator networks. However, it is not clear how to best exploit this flexibility, when also considering the migration costs. In this paper, we use tools from network economics to study a recent proposal of using information-centric networking (ICN) principles on an SDN infrastructure for improving the delivery of Internet Protocol (IP) services. The key value proposition of this IP-over-ICN approach is to use the native and lightweight multicast service delivery enabled by the ICN technology to reduce network load by removing redundant data. Our analysis shows that for services where IP multicast delivery is technically feasible, IP-over-ICN deployments are economically sensible if only few users will access the given service simultaneously. However, for services where native IP multicast is not a technically feasible option, such as for dynamically generated or personalized content, IP-over-ICN significantly outperforms IP.

Cellular deployments in the spectrum-rich mm-wave bands are anticipated to provide multi-Gbps connectivity in future 5G networks. Although measurements have demonstrated the feasibility of outdoor mm-wave links, the network-level opportunities and challenges of mm-wave cellular deployments are yet to be fully understood. In this paper we present a study of mm-wave urban network coverage and robustness - i.e. handover and beamsteering opportunities for supporting user mobility and resolving blockage of directional mm-wave links by moving obstacles. We use real 3D building data with ray tracing and investigate the impact of base station placement and density, building materials, and antenna directivity. Our results show that mm-wave networks can provide multi-Gbps connectivity locally, but that over 25% of the network area remains in outage even with dense pico-cellular deployments and that supporting mobility is very challenging. Moreover, primary link blockage can be resolved via handover or beamsteering to a secondary link at only up to 40% and 25% of high-throughput locations, respectively. Given these networking challenges, we argue that multi-frequency heterogeneous networks are the key to exploiting the great promise of mm-wave for future 5G deployments, i.e. mm-wave as a hotspot capacity booster, rather than a solution for comprehensive cellular coverage.

Improved propagation prediction has become important due to emerging 5G and ultra-dense wireless networks. One important aspect in this domain is to understand and model better the spatial correlations of the underlying shadowing fields. In this work, we investigate the sources of uncertainty on the predictions made by employing the optimal linear predictor, namely \emph{kriging}. We show that kriging is robust to the estimation errors of the spatial correlation structure. Moreover, we show that the amount of the training data (known data points) has great impact on the prediction error. Our results help to quantify the trade off between number of data points collected and accuracy that can be reached by interpolation. The results especially help to design and optimize expensive measurement and test drive campaigns. Another contribution of the paper is to explicitly show the capabilities of kriging for coverage prediction.

Wi-Fi is the dominant wireless indoor broadband solution and thus key for meeting the exponential traffic growth. The recent IEEE 802.11ac amendment enables PHY data rates exceeding 1 Gbps. However, it is not clear how this increased per-link performance, achieved especially via wider channels, translates to network-level performance. The latter is crucial for understanding the true potential of emerging Wi-Fi, as massive densification of network infrastructure is needed for keeping up with capacity demands. In this paper we present results from an extensive measurement study of the performance of IEEE 802.11ac Wi-Fi in a large 24-node indoor testbed. We investigate in detail the impact of channel width, network deployment density, and type/volume of traffic on the achieved network performance in dense indoor deployments. Our results show that using wide 80 MHz channels is beneficial only in dense networks with extremely high traffic loads, owing to strong adjacent channel interference (ACI) effects with narrower channels. We show that ACI not only reduces aggregate network throughput but causes severe unfairness among nodes, where up to half the nodes may experience starvation. Starvation occurs due to frequency flow-in-the-middle effects and the heterogeneous interference coupling among pairs of nodes typical of indoor deployments. However, our results also demonstrate that for bursty TCP traffic with loads typical of modern residential Wi-Fi deployments, there is no harm from using narrower 20 or 40 MHz channels. Finally, we study the scaling of IEEE 802.11ac deployments with increasing traffic load, which is highly relevant for the dimensioning of future networks. We show that ACI effects only become evident at very high loads which are beyond the per-node traffic demand expected in the foreseeable future. Therefore, in practice there is no network-level benefit from employing the wider channels enabled by IEEE 802.11ac, even in emerging highly dense indoor deployments.

Due to the fact that Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocols are extensively used in commercial wireless networks, the analysis of this family of medium access control protocols has been a topic of great interest for the wireless networking community. In this paper, we present a mean field analysis for the throughput of a slotted CSMA/CA scheme in single-hop networks. The concept of mean field approximation originates in statistical mechanics and it is a widely used approximation method employed to address systems of many interacting particles that cannot be solved exactly. The idea is to simplify the treatment of a many-body system by describing the interaction of one particle with the other interacting particles by an average potential, the so-called mean field. In our analysis, the nodes of a wireless network constitute a set of interacting particles; the interaction of a certain node with the other nodes in the network is reduced to an averaged channel condition. We estimate the amount of busy slots and the network throughput with respect to the offered traffic. Our analysis is of a recursive form, in which at each step the amount of traffic is increased by a specific amount. Also, our model takes into accounts retransmissions of collided packets.

We consider a multichannel Cognitive Radio Network (CRN), where secondary users sequentially sense channels for opportunistic spectrum access. In this scenario, the Channel Selection Algorithm (CSA) allows secondary users to find a vacant channel with the minimal number of channel switches. Most of the existing CSA literature assumes exponential ON-OFF time distribution for primary user's (PU) channel occupancy pattern. This exponential assumption might be helpful to get performance bounds; but not useful to evaluate the performance of CSA under realistic conditions. An in-depth analysis of independent spectrum measurement traces reveals that wireless channels have typically heavy-tailed PU OFF times. In this paper, we propose an extension to the Predictive CSA framework and its generalization for heavy tailed PU OFF time distribution, which represents realistic scenarios. In particular, we calculate the probability of channel being idle for hyper-exponential OFF times to use in CSA. We implement our proposed CSA framework in a wireless test-bed and comprehensively evaluate its performance by recreating the realistic PU channel occupancy patterns. The proposed CSA shows significant reduction in channel switches and energy consumption as compared to Predictive CSA which always assumes exponential PU ON-OFF times. Through our work, we show the impact of the PU channel occupancy pattern on the performance of CSA in multichannel CRN.

In-band full-duplex is emerging as a promising solution to enhance throughput in wireless networks. Allowing nodes to simultaneously send and receive data over the same bandwidth can potentially double the system capacity, and a good degree of maturity has been reached for physical layer design, with practical demonstrations in simple topologies. However, the true potential of full-duplex at a system level is yet to be fully understood. In this paper, we introduce an analytical framework based on stochastic geometry that captures the behavior of large full-duplex networks implementing an asynchronous random access policy based on Aloha. Via exact expressions, we discuss the key tradeoffs that characterize these systems, exploring among the rest the role of transmission duration, imperfect self-interference cancellation, and fraction of full-duplex nodes in the network. We also provide protocol design principles, and our comparison with slotted systems sheds light on the performance loss induced by the lack of synchronism.

In this article, we assess the viability of underlay sensor networks in frequencies used by an incumbent digital TV broadcasting system, that is, in the so-called TV grayspaces (TVGS). Grayspace operations are particularly interesting when other unlicensed bands are overcrowded, for example, due to high-volume WiFi operations. We simulate the operational characteristics of the recent IEEE 802.15.4m standard for low-rate wireless personal area networks to evaluate the performance degradation of an incumbent Digital Video Broadcasting - Terrestrial (DVB-T) system if a secondary network of low-power low-rate devices are co-deployed in the same frequency bands. Our results show that short sensor messages will not disrupt the DVB-T service due to the existing error-correction capabilities. Furthermore, if sufficient separation distances to primary transmitters are maintained, transmit powers are sufficient to achieve reasonable connectivity levels of the secondary network. In order to obtain realistic figures on the predicted feasibility of grayspace sensor networks, we study the deployment constraints of a hypothetical secondary network co-located with the TV broadcasting network of Germany. Our analysis shows that if we aim to support a minimum sensor-sensor distance, no universal coverage can be maintained in this country. While our quantitative results are specific to Germany, we deem them indicative for the expected results also in other potential deployments. We found that while a secondary wireless sensor network in TVGS is technically possible, the necessary constraints on operational parameters and service levels for TVGS co-existence will significantly limit its practical viability.

The non-orthogonal multiple access (NOMA) has been investigated recently as a candidate radio access technology for the next generation mobile networks. It realizes powerdomain user multiplexing by employing the successive interference cancellation (SIC). This paper focuses on the power allocation and user set selection problems in multi-user downlink NOMA systems with the aim of providing proportional fairness. The theoretical optimal power allocation solution for a candidate user set is derived in closed form. We propose a tree searching-based user set selection scheme in which the redundant user sets are removed by a delicately designed pruning method. The transmission performance of the proposed power allocation and user set selection schemes is evaluated by system-level simulations and verified to be close to the optimal value. The searching complexity for user set selection is significantly reduced compared to the exhaustive searching method. Finally, the performance gains brought by the multi-user NOMA are analyzed considering the practical application.

Designing distributed protocols with limited requirements for exchange of feedback information between the nodes is a key element in the development of cognitive and self-organizing ad hoc networks. We argue that the Minority Game (MG) and its variations can constitute useful tools towards this direction. In particular, the MG formulation is well suited for addressing a variety of resource allocation problems in self-organizing wireless networks. In this paper we illustrate the employment of MGs by presenting a paradigm of an MG-based model for dynamic channel allocation.

In this paper we present our results from our ongoing measurement campaign, which is targeting to provide information on the current radio noise levels. Our focus is to understand if the increasing number of users and devices with inbuilt transceivers has increased the noise levels considerably. In the literature it is almost universally assumed that the radio noise is a white Gaussian stochastic process and we are testing also how often deviations from this baseline are found. Our measurement approach is able to capture frequency and time domain data at very high accuracy. In this paper we report the first results from several different spatially separated measurement areas, enabling the study of noise levels at diverse indoor and outdoor locations. Our results show that the noise levels have indeed been increasing and we present a specific example on how these increased noise levels can affect today's telecommunications systems.

In recent years, novel spectrum access schemes have increased the need to further explore and exploit radio propagation dynamics. One proposed approach is to use spectrum sensing nodes for propagation environment estimation, and particularly to allow shadowing field extraction. In general there is a tendency to develop techniques that allow the automated and localized estimation of the spatial correlation structure of the shadowing field. Key issue when using such data is to take into account inherent localization errors and the impact that it has on these estimates. In this paper we use real measurement data acquired with extremely high localization accuracy to demonstrate and study the impact of localization errors. We present results for the propagation estimation, the extracted shadow field, and estimated shadow correlations. In particular, we show that spatial correlation metrics such as semivariograms are robust against localization errors higher than arising from the typical embedded GPS chipsets, up to approximately 20 m. Moreover, we have considered various probability distributions of localization errors, exploring their impact into our analysis. The reported results are highly relevant to development of measurement-based coverage estimation techniques and planning of drive tests or understanding limits of crowd-sourced data from user devices.

The beamforming techniques have been recently studied as possible enablers for underlay spectrum sharing. The existing beamforming techniques have several common limitations: they are usually system model specific, cannot operate with arbitrary number of transmit/receive antennas, and cannot serve arbitrary number of users. Moreover, the beamforming techniques for underlay spectrum sharing do not consider the interference originating from the incumbent primary system. This paper extends the common underlay sharing model by incorporating the interference originating from the incumbent system into generic combined beamforming design that can be applied on interference, broadcast, or multiple access channels. This paper proposes two novel multiuser beamforming algorithms for user fairness and sum rate maximization, utilizing newly derived convex optimization problems for transmit and receive beamformers calculation in a recursive optimization. Both beamforming algorithms provide efficient operation for the interference, broadcast, and multiple access channels, as well as for arbitrary number of antennas and secondary users in the system. Furthermore, this paper proposes a successive transmit/receive optimization approach that reduces the computational complexity of the proposed recursive algorithms. The results show that the proposed complexity reduction significantly improves the convergence rates and can facilitate their operation in scenarios, which require agile beamformer computation.

In order to cope with the increasing complexity of wireless networks, dynamic spectrum access, and varying application needs, fast and flexible reconfiguration of protocol stack is desired. Since Medium Access Control (MAC) layer plays a pivotal role in providing efficient spectrum sharing and utilization, rapid on-the-fly reconfigurability of MAC protocols is highly important. We have designed and implemented TRUMP: a Toolchain for RUntiMe Protocol realization. Based on the component-oriented design approach, TRUMP allows runtime realization, reconfiguration, and optimization of MAC layer according to the varying application requirements, spectral environmental, and network conditions. TRUMP, with a platform-independent MAC meta-language, also enables rapid MAC prototyping. In this article, we carry out a detailed performance evaluation of MAC schemes realized through TRUMP on WARP SDR platform. Our results indicate that TRUMP allows reconfiguration of MAC schemes in the order of a few microseconds, thus meeting the strict timeliness requirements of MAC processing. We also present application examples to highlight capabilities of TRUMP.

In this paper, we provide a first comprehensive comparison on the performance of two spatial estimation approaches used for propagation estimation. The first estimates the shadowing distribution with considerably low number of measurements and the second estimates the received power with no information of the transmitter location. We discuss in details the relative advantages of these techniques and study particularly their performance when using different number of measurement points. Our study is based on data from a very carefully conducted large-scale measurement campaign with a high spatial density. The robustness of our results is verified in different propagation environments and different frequencies. In particular, we show that for very small number of samples, prediction based on shadowing results in better performance. However, with small increase in the number of available measurements, this more complex approach becomes unnecessary and direct prediction of the received power yields very good results.

Software-defined networking provides new tools and offers more opportunities to actively manage large-scale network setups during runtime. However, controlling such installations in a unified and adaptive manner is inherently complex due to the remarkable diversity of management tools. In this paper we propose ULLA-X, a novel middleware that unifies network monitoring and on-demand reconfiguration through a universal network programming language. We introduce ULLA-X architecture and usage concept, which aims at bridging sophisticated network planning with practical network maintenance tools, resembling an “autonomic nervous system” for communication networks. Through its modular design, ULLA-X can be easily integrated into current network setups. While this paper is focused on introducing the ULLA-X concept and framework, we also present initial results from a prototype implementation for National Instruments wireless networked devices.

The non-orthogonal multiple access (NOMA) has been investigated recently as a candidate radio access technology in future mobile networks. It is able to implement power-domain user multiplexing based on successive interference cancellation (SIC). This paper focuses on the user pairing and power allocation problem in the 2-user NOMA system. The optimal solution in closed-form is derived with the proportional fairness objective and is used for the design of the user pair power allocation scheme. The prerequisites for user pairing are also formulated in order to avoid unnecessary comparison of candidate user pairs. The performance of the proposed scheme is evaluated by system-level simulations and results in higher gains than the search-based transmission power allocation. On the other hand, the computational complexity is reduced by the proposed scheme effectively.

The large contiguous bandwidths available in the mm-wave frequency bands have recently started to gain significant attention due to the growing belief that it can alleviate the spectrum shortage at lower frequencies and meet the increasing high data rate demands. While some commercially available mm-wave transceivers in the 60 GHz and 70 GHz bands have been developed, only a few studies exist on the achievable data rates in realistic deployment scenarios. Moreover, practical issues such as interference and coexistence of heterogeneous applications have not been investigated. In this paper, we present our empirical study on the bit error ratio and throughput for realistic indoor application scenarios in 60 GHz and 70GHz using orthogonal frequency division multiplexing based physical layer on various commercially available transceivers. Furthermore, we analyze the effects of interference on the performance characteristics of the mm-wave frequency links in different deployment setups.

We study the statistical characteristics of radio environment noise, in particular focusing on spurious received power arising from adjacent channel interference, harmonics and wideband man-made interference. Unlike the majority of existing studies, our measurements enable a comprehensive study on frequencies ranging from 80MHz to 2.64 GHz instead of being focused on the properties of an application-specific narrow band. We also apply a comprehensive statistical methodology to study how closely the measured noise can be modeled using a traditional white Gaussian stochastic process. In particular, we do not only focus on the parametric modeling of the marginal power distribution, but seek deviations from the whiteness of the noise through detailed time domain characterization, and apply a rigorous hypothesis testing on deviations from normality. Our results show that while the measured noise is often close to being white and nearly Gaussian, these properties are not as universal as often assumed in the literature. In particular, impulsive noise causes deviations both from normality of the marginals, as well as from the whiteness of the spectrum at levels that depend on the environment.

In the recent years there has been an effort for the wireless systems to become more efficient in terms of power consumption and coverage planning. One aspect of this effort was the proposal of using spatial estimation approaches, that exploit the spatial correlation of the shadowing and received power, to improve propagation prediction. In this demonstration, we present a software tool that implements these spatial estimation approaches and can be a part of a general network planning tool. Among others, it can provide a valuable insight on which of the approaches provide better results in a given situation, or information about the needed drive test measurements. Our demonstration is based on real, high spatially granular measurement data in various environments and frequencies, but it can be easily extended though to real-time data acquisition from portable spectrum measurement devices, smartphones or drive test measurements.

Directional antennas in wireless networks can provide substantial performance gain and opportunities. In the case of high-frequency systems, such as emerging 60 GHz networks, they are inherent feature of the system design. In contrast to the traditional omni-directional antennas, directional antennas are capable of reducing the interference level, increasing the communication range, and improving spatial reuse. However, new challenges need to be solved, such as the deafness problem and the asymmetric-in-gain. In this paper, we propose Cooperative-Directional MAC (Coop-DMAC) protocol to address the new challenges. The Coop-DMAC is completely distributed and relies on limited cooperation between networked nodes. In Coop-DMAC, nodes are supposed to exchange local information including the angle of arrival of their neighboring nodes and the duration of data transmission to improve their spatial reuse. In this paper, we analyze and evaluate the performance of Coop-DMAC, and pay particular attention to the performance and network topology. Simulation results validate our theoretical analysis and show that the proposed Coop-DMAC protocol outperforms the standard IEEE 802.11ad MAC protocol in terms of higher goodput in most common network topologies considered in the paper.

Most cooperative spectrum sensing research activities focus on maximizing the primary user detection and neglect the achieved secondary system performance. This paper proposes a novel capacity-aware cooperative spectrum sensing optimization method, capable of computing the optimal values for the cooperative sensing parameters (e.g., the number of sensed samples, the number of cooperating devices, control channel bandwidth) so that the secondary system capacity is maximized. The proposed method utilizes energy detection with estimated noise power (ENP). We derive and experimentally validate the analytical models for cooperative spectrum sensing based on the ENP method. Additionally, we evaluate the performance of the proposed capacity-aware spectrum sensing described in this paper. We show that the proposed capacity-aware cooperative spectrum sensing based on noise power estimation achieves enhanced secondary system capacity compared to the previous commonly proposed sensing processes that maximize the detection performance. Moreover, the results give insight into the optimal behavior of the proposed capacity-aware cooperative spectrum sensing based on noise power estimation regarding the common cooperative spectrum sensing parameters.

Recent rulings by US and UK regulators allowing access to unused portions of TV spectrum have elicited high hopes for the practical value of these TV whitespaces (TVWS) for secondary exploitation. However, this optimism has been largely fueled by rather simple early studies, which do not consider all the system aspects in sufficient detail; the few early experimental works reported are proprietary and consider only a small number of nodes, whereas the existing more sophisticated theoretical studies focus on singular aspects of secondary spectrum access in isolation from the overall system interactions. In this paper we study quantitatively the deployment of secondary networks in TVWS by considering two archetypal candidate systems: LTE-like cellular and Wi-Fi-like networks. We develop a systematic framework for the performance evaluation of secondary networks, which we then use to obtain realistic estimates of the performance of our example systems. The secondary network performance assessment methodology demonstrated in this paper can be directly applied for other regions and systems. Our work explicitly takes into account limitations arising from aggregate interference, user density, and specific secondary transceiver characteristics. We argue that a systematic analysis, jointly considering all of these aspects, is key for obtaining realistic and robust results on the estimated value of whitespace spectrum. Our detailed system-level approach reveals a much more conservative picture of the realistic benefit of TVWS deployments than what has been commonly assumed thus far. We find that cellular TVWS networks have limited capabilities, but that a macro-cellular-only network may be a viable option for traffic offloading. Our results also show that Wi-Fi-like secondary deployments in TVWS, although increasing coverage range, lead to increased congestion, which limits the system capacity.

Traffic load balance is among the most crucial issues in heterogeneous cellular networks (HCNs), which can lead to much higher throughput, better user fairness and larger coverage for HCNs. In this paper, we consider the user association problem in HCNs and design a handover scheme for traffic load balancing among macro and femto cells. Three types of handover factors are delicately designed with different objectives focusing on throughput or fairness or trying to balance the both. Correspondingly, three typical resource schedulers are adopted to improve the desired performance. The performance of the proposed scheme is evaluated by simulations and reveals considerable improvement in comparison with the maximum biased-received-power association scheme, especially the data rates of the low-rate users. The impact of different base station and user distributions on the balancing results is also analyzed.

In this paper we describe the effects of the radio propagation model on the spatial characteristics of the radio environment. For this study we have conducted a dedicated propagation measurement campaign focusing on the three different frequency bands of 485 MHz, 1700 MHz and 2600 MHz in order to capture the impact of the used frequency on the radio propagation. The presented results are coming from a high number of samples of very high spatial resolution. We quantify how much the propagation modelling affects the calculation of spatial statistics of radio propagation and in particular shadow fading for the three different frequency bands. Our results show that while the used propagation model does play a role in both the marginal distribution and the spatial correlation characteristics derived, the impact of the precise propagation model chosen is small. This indicates that correlation structure of shadow fading can be accurately derived also using predefined propagation models not directly fitted to the data and radio environment at hand.

A better understanding of the spatial structure of existing opportunistic wireless network deployments will offer new insights for future deployments, e.g. dense femtocell networks. In this paper we present a performance analysis for three different localization algorithms that support such efforts by finding the location of IEEE 802.11 Wi-Fi access points (APs) from distributed signal strength measurements. These algorithms extend prior works on omnidirectional single-point measurements and includes angular information into the estimation process. Using a custom-made measurement setup, we have collected data for typical indoor and outdoor scenarios which are likely to be encountered in large measurement campaigns. We quantify and reason on the localization error for each scenario and algorithm, and propose a weight spread estimate for determining the robustness of the localization approaches. Through variations in the used sample data set we further discuss relevant observations on the stability of the different algorithmic estimation approaches. Our results show that overall propagation-centric algorithms perform best, but that algorithms that build a stochastic model of angular signal strength distribution are more robust if only few measurements are available.

Localization errors can play an important role in measurements of radio propagation or spectrum utilization, especially in urban environment where GPS localization accuracy is highly varying. In this paper we present a low cost and simple method for mitigating the impact of localization errors, based on multiple GPS receivers. The presented results are based primarily on performance evaluation of multiple GPS receivers based method under realistic conditions. We show how to reduce the time needed to estimate the statistics of the spatial radio environment by combining the input of multiple GPS sensors. We also present a simple scenario for small cell size networks based on real measurements in three distinct environments, where the localization errors are the determining factor of the performance of such systems.

We report results from extensive measurements based study to understand the shadow fading correlations. We have particularly studied and analyzed common assumptions done in literature for shadow fading, which is typically modeled as a spatially correlated Gaussian random process in the logarithmic scale. We show that these assumptions, starting from the Gaussianity of the radio propagation environment, do not always hold. We then discuss the assumptions regarding spatial correlations, usually taken to depend only on the distance between the points and assuming that these correlations remain the same throughout the area under consideration. This is done by comparing received power measurements for two different propagation environments, an urban and a sub-urban one, against simulated values based on typical theoretical assumptions. We conclude by explaining how these assumptions should be carefully taken into account and how they can affect simulation results and the performance of applications.

With an increasing demand for monitoring energy consumption at granularity levels down to single household appliances, it is necessary to develop new means to collect sensor measurements in a robust and cost-efficient manner. The smart grid paradigm foresees using wireless links for data transfer, albeit no dedicated spectrum bands have been designated for this purpose. In this paper we study the feasibility of opportunistic spectrum access for smart grids, and focus on underlay spectrum sharing over occupied TV channels. These frequency bands, which are commonly denoted as TV gray spaces, provide superior propagation characteristics, but are locally used by high-power (mostly DTV) broadcasting transmitters. For selected reference geometries of intra-meter and meter-to-operator communications, we study the smart meter performance (in terms of achievable throughput and transmission range), and the necessary power limits. We compare our results from a small-scale measurement campaigns against existing wireless technologies for low-power communications in other adjacent bands. Our results show that wall shielding and fading in indoor to outdoor propagation channels sufficiently protects the primary system from the interference introduced by gray space meter-to-meter communications, but that the required transmit powers to send operations data from indoor meters to outdoor collection point severely limit the applicability of TV gray spaces for such network topologies.

Femtocells are a promising approach to provide high data rates through autonomous configuration in indoor environments. However, due to the random and uncontrolled deployment of femtocells within users premises, interference between femtocells themselves and with macrocell base stations is a major issue. In this work, we look into the interference management problem and work towards the development of an interference mitigation algorithm based on the localization of randomly positioned femtocells using radio environmental information. In particular, we show that based on building floorplans and basic information on the urban landscape, femtocells can accurately localize themselves using macrocellular base stations as anchor nodes. Based on the localized femtocell positions, various channel allocation schemes are employed to mitigate interference.

In an opportunistic spectrum access (OSA) of cognitive radio wireless networks (CRNs), unlicensed secondary users (cognitive radios, CRs) have only partial knowledge of channel parameters (or other users information) and asymmetric spectral opportunities due to time and space varying channel availabilities. Due to the inherent asymmetry and uncertainty of traffic patterns, CRs can have difficulties to determine properly the real usability of unoccupied channels. In this paper we propose a channel ranking algorithm exploiting fuzzy logic (FL) theory. Using current and prior knowledge of channel availability each CR user builds a channel ranking list that serves as an input entry for an asynchronous neighborhood discovery protocol. Since the rendezvous protocol exploits the channel prioritization, and gives more opportunities to better-quality channels, FL-based channel ranking leads to more efficient communications that traditional crisp value based approaches.

A neighborhood discovery (ND), also referred to as a rendezvous (RDV), is a challenging and fundamental phase in opportunistic Cognitive Radio Networks (CRNs) due to changing network topology, the time and location varying spectrum availability and different channel qualities. Simplified solutions, such as, a central controller or common control channel are not feasible in opportunistic CRNs. Therefore, in order to be able to initiate a communication, there is a need of on-demand searching for a control traffic channel by cognitive radio (CR) users, usually working in an asynchronous manner. To meet these challenges we propose an asynchronous neighborhood discovery (AND) for opportunistic CRNs, which takes into account the asynchronous character of CRs, different channel ranks, as well as heterogeneous channel availability.

Like physical systems can exist in different phases, the concept of different phases (or states) can be also attributed to other types of systems, such as wireless networks. In this paper we employ the notion of phase diagrams to characterize a wireless network by means of a reduced number of simulation points. By reducing the amount of simulations - compared to fine-grained simulation-based analyses - we decrease the accuracy of the result, however, not in an arbitrary way. The idea is to derive the so-called phase diagram that fully characterizes the system with respect to the points of state changes. Typically, the different states of a system correspond to distinguishable macroscopic properties, therefore the phase transition points are in a qualitative sense points of particular significance for a system under study. We show how phase diagrams can be estimated from a limited set of simulation points by means of Support Vector Machines.

We report initial results from 48 hour spectrum occupancy measurement campaign that was done in time-correlated fashion in seven different European locations. We give a description of the measurement campaign and provide results from our preliminary analysis of overall duty cycle in the frequency range 110-3000 MHz. The paper particularly focuses on traffic and duty cycle patterns in GSM 900 and GSM 1800 bands. For the sake of completeness, we also discuss ISMband utilization in two of our measurement sites. We show that spectrum utilization is generally low, but that variance between frequency bands and locations is significant, which means that any statistical claims on occupancy statistics need to be done carefully before regulatory claims are made.

Estimating the coverage of a wireless network is one of the key problems in network planning and management. In outdoor environments this is usually done using modern network planning tools combined with intensive drive tests. However, in indoor environments the problem is much more difficult. Solutions based on propagation modeling require precise building information for accuracy, and even then their performance is highly varying. Refining such predictions using measurements from mobile terminals is a promising possibility, but is not straightforward due to the noisy and unreliable measurement quality. In this paper we study the performance of spatial statistics techniques for coverage prediction in indoor environments. Using data collected in an indoor testbed with 60 low cost radio receivers, we show that such techniques can yield accurate coverage predictions provided suitable preprocessing and filtering of the data is performed. Further, a simple optimization approach enables high prediction accuracy to be achieved using only a small subset of the available measurement devices. These results are also highly relevant to the minimization of drive tests (MDT) approach currently being developed in 3GPP to enable mobile terminals carry out coverage measurements for wireless networks.

An understanding of the underlying spatial structure of user-deployed wireless networks is invaluable for the design and optimization of opportunistic small-cell technologies in emerging multi-tier architectures. IEEE 802.11 Wi-Fi networks provide a real-world example of such a large-scale random network structure. In this paper we report on a high-resolution measurement survey of Wi-Fi deployments in Germany as a step towards deriving more sophisticated spatial models for transmitter distributions in emerging small-cell networks, which are the core of offloading strategies for future wireless Internet. Using a custom setup, our measurement campaign covered a range of urbanization and land use scenarios representative of Wi-Fi deployments in developed countries. Our data indicates a 14-fold increase in Wi-Fi density in urban residential areas over the last decade, consistent with increased broadband penetration. This supports our hypothesis that household density has become a strong predictor of access point (AP) density. We infer AP locations using a novel localization technique suitable for large measurement campaigns using off-the-shelf equipment. From this data, we derive pertinent spatial statistics to characterize the topologies of opportunistic networks. We derive nearest-neighbour distributions showing that realistic inter-neighbour distances are higher than predicted by a homogeneous Poisson Point Process, underlining the need for more refined spatial models than the random node location models which are currently prevalent. We also derive the two-pair correlation functions for the AP locations, showing that AP clustering is apparent over a large range of distances in our data set, which has implications for routing and interference mitigation.

We present results from an 8-day spectrum measurement campaign in the centre of the London metropolis. We analyze results from one of the densest central areas in our campaign from the perspective of spectrum measurements at a very exposed location at the roof of a high building. We present the main results of this measurement campaign in terms of spectrum utilization, powers received, and evolution of the activity among different days. The results presented in this paper here are very close to an upper bound of what one might expect to find in any given radio environment in terms of utilization and received power measurements. In addition to producing valuable measurement data on its own, special attention was given on acquiring comparable datasets with previous measurements campaigns we have conducted to allow direct comparisons with other European cities we have studied. In particular, we show a detailed comparison between the downtown London results against measurements carried out in other cities, quantifying the significantly higher levels of spectrum occupancy in London compared to other locations.

Due to the changing network topology and the time and location varying spectrum availability in Cognitive Radio Networks (CRNs), there is a need of on-demand searching for a control traffic channel by CR users in order to be able to initiate a communication. This neighborhood discovery phase, also referred to as a rendezvous (RDV) phase, is challenging due to the dynamics of CRNs. There are a number of different proposed solutions (pseudo-random or systematic approaches such as quorum systems) to tackle this issue, however, not so many elaborate on the asynchronous character of CRNs, and even less taking into account channel heterogeneity in terms of quality. In this study, on the one hand, we propose a novel quorum system-based neighborhood discovery protocol, which operates on channels having different priorities, on the other hand, we perform an analysis of the effect of asynchronism on the proposed protocol. We analyze the behavior of the protocol in terms of rendezvous opportunities, time-to-rendezvous, and the measured error margins for both, for different asynchronous offsets. We show that an asynchronism can bring noticeable profits over the slot synchronized case.

Recent years have witnessed a number of new and fairly complex MAC schemes for cognitive radios and other reconfigurable networks. These emerging MAC protocols impose increasing demands for flexibility, real-time response and a closer interaction between the MAC- and the physical layers. Different designs for Software Defined Radio (SDR) platforms have been investigated to realize these protocols. In this context, hardware acceleration and flexibility through hardware-software partitioning emerge as the major design considerations for prototyping and over-the-air evaluation. Following the hardware-software partitioning philosophy, this demonstration will show how MAC schemes can be decomposed into constituent functional components and how different MAC functionalities, when possible, are scheduled in a parallel fashion to enhance the execution efficiency and flexibility. In particular, we have enabled a dual-processor interrupt driven hardware architecture and supported a customized real-time Operating System (OS) kernel on the commercially available and widely used WARP SDR platform [1]. Moreover, we have adapted our framework for composing MAC protocols based on their elementary functionalities [2] to the dual-processor, OS-supported architecture on WARP boards. Our demonstration will highlight the benefits of parallelization and efficient scheduling of MAC processes in embedded realizations on SDR platforms in order to achieve a high degree of flexibility while satisfying the hard real-time constraints.

Correlations in shadow fading play a key role in the performance of wireless networks. However, despite of this importance, relatively few comprehensive measurement studies have been made on shadow correlations, and the methodology followed in the existing studies is often poorly documented. In this paper we report results from an extensive measurement campaign on shadow correlation in the 485 MHz band in a suburban environment. In particular, we study carefully the influence of fast fading and different data processing choices on the results, as well as describe in detail the design and verification of the measurement set up. Our results show that shadow correlations decay exponentially as a function of distance also on this band, but that the details of the arising correlation model are highly dependent on the steps taken to process the measurement data. In particular, we show that unless fast fading is properly averaged over, highly incorrect conclusions would be drawn with respect to both marginal variance and the correlation distance of shadow fading.

Cooperative spectrum sensing generally consists of two consecutive stages: sensing and reporting, where sensors measure channels at the sensing stage and then use a multiple access protocol to report their local measurement results which we call the reporting MAC protocol. In this paper, we examine the impact of reporting MAC protocols on centralized cooperative sensing in multiband cognitive networks. Specifically, we study the applicability of TDMA and IEEE 802.11 CSMA/CA protocols for cooperative spectrum sensing reporting. A general mathematical model has been developed to quantify the achievable bandwidth utilization within a secondary system, which takes into account different combinations of sensor assignment strategies, reporting MAC, fusion rules, and regulatory constraints. The model allows the cross-layer optimization on the PHY and MAC layers of cooperative detection. The results show that the reporting control signaling can dominate the sensing overhead and result in significant capacity loss in some scenarios, which emphasizes the importance of using an efficient reporting MAC in cooperative sensing.

Various emerging applications enabled by wireless sensor networks demand new and versatile MAC algorithms. These MAC protocols should be able to adapt to varying application requirements and deployment conditions. In this paper, we present a component based scheme for enabling fast prototyping of MAC protocols using a lightweight tool chain. Our tool chain allows run-time MAC protocol reconfiguration by controlling the data path and the execution flow of the constituent components. Our approach of composing protocols based on common MAC components aims at reducing the protocol prototyping efforts. Moreover, our toolchain implemented for various sensor nodes provides a platform independent MAC meta-language which simplifies the protocol designing, testing and debugging processes. Empirical tests conducted on commercially available sensor nodes show that our approach enables run-time reconfiguration for MAC protocols with affordable memory consumption and execution overhead.

The prospect of increasing wireless capacity via secondary access to spatio-temporally underutilized chunks of spectrum, so-called whitespaces, has been proposed as a central aspect of emerging radio systems in response to the imminent spectrum scarcity problem. In this demonstration we present a novel software tool that helps researchers, industry, and regulators in assessing the feasibility and value of secondary spectrum access beyond simple whitespace availability calculation. Whereas existing software applications merely provide visualization of estimated secondary spectrum over a geographic area, our tool uniquely enables a holistic evaluation of the realistic potential of whitespace technologies, by modelling the performance of entire secondary systems in the envisioned eco-system of dynamic spectrum access policy and technology. Our tool provides a unified and flexible software framework and assessment methodology to conduct such studies, and is composed of an extensive primary spectrum usage database, a graphical interface for user interaction, and an interface to an extensible MATLAB backend for numerical calculations. We showcase the deployment scenarios of cellular and Wi-Fi-like secondary networks in TVWS (TV whitespaces). We also compare the impact of employing FCC-type of regulatory rules (with a fixed power/no-talk distance configuration) against European WG-SE43 regulatory proposals (with probabilistic access and power control). The case studies we will demonstrate are based on real network configuration data of European and US TV networks.

Wireless network deployments are becoming increasingly complex in order to cope with the exponential growth in mobile data traffic. Multi-tier technologies capable of supporting spatially dense networks enable high data rates, but require timely and accurate models of the radio environment, particularly for scenarios that incorporate dynamic access to spectrum. However, network planners, algorithm designers, and policy makers are often forced to rely on a small set of radio propagation scenarios, often assuming fixed receiver antenna heights (RAHs). In this paper we show that the RAH in fact plays a key role not only in terms of reception quality but also in determining the interference originating from other transmitters. Our extensive simulation studies show a large variability in key performance metrics of realistic wireless network topologies as RAH is varied, which are not captured through the current practice of selecting few effectively two-dimensional scenarios and drawing general conclusions from them. These results underline the importance of adopting more realistic assumptions on RAH for design, assessment, and policy making for wireless communication systems, and the need for new propagation models valid for a wider range of RAH values than models currently in use.

We present results from the comparison of measurements between dedicated embedded spectrum sensing chip targeting low-cost and low-power applications and a high-end spectrum analyzer. We use different signal types, including actual spectrum usage measured simultaneously with the two devices. We analyze the typical problems such devices suffer from. Finally we also study how to estimate the spectrum sensing results once the device type is taken into account.

In this paper we investigate the possibility of using measurement-based propagation models in the framework of Dynamic Spectrum Access (DSA), where secondary users can opportunistically access the licensed spectrum of a primary system. In principle this will provide a precise representation of the radio environment since it takes into account all environmental details at a given period of time. However the accuracy of the estimated models will depend on sensor density, propagation factors, and any external error. We study the impact of these factors on the accuracy of the estimated model and the satisfaction of primary and secondary networks.We also propose a guideline to overcome these problems in DSA scenarios by using a power margin.

In this paper we analyze the prospects of deploying a cellular network in the TV whitespaces. We study the challenges of planning for a large-scale transmitter network that is constrained by an interference-to-primary regulation. For this purpose we derive a novel methodology to maximize the downlink capacity at the cell edge of base stations that makes use of a heuristic algorithm for per-channel power allocation. A discretized formulation as a multiple-choice nested knapsack problem is presented and scenario-specific algorithmic improvements are discussed. The methodology is assessed for the case of Germany which, due to the specific challenges of the geographic region, can be considered indicative for similar deployments in other countries.

In this work we analyze topology discovery and the procedure for joining the network in the information-centric context. We develop and evaluate such a network attachment procedure in an information-centric network utilizing the publish-subscribe paradigm for the data exchange. In our work the publish-subscribe concept is not only used as a communication means, but we aim at fully exploiting its characteristics for native merging of fine-grained network operations such as topology management and network connectivity establishment. Such an integration adds not only to the simplicity of the network and efficiency of the network information gathering, but includes the means for handling mobility issues. We examine the performance characteristics of the proposed solution particularly focusing on complexity and introduced message overhead. The evaluation results obtained from the testbed experiments show the outstanding performance in terms of delay, while the signaling overhead remains at a very low level.

The cross-correlation between shadowing components in wireless communications has important implications for wireless systems. The new cooperative and cognitive communications methods can exploit these correlations, if the statistical properties of shadowing correlations were known in particular radio environment under the consideration. The statistical large scale models have higher uncertainty compared to locally provided models. In this paper we study the question how much uncertainty for shadow models is generated by sparsely sampling shadowing coefficients in a locality to build Radio Environment Maps that include shadowing models. The spatial correlations induce redundancy in these "shadowing maps", and potentially enable them to be reconstructed based on small number of measurements. We quantify this redundancy for commonly used models of correlated shadowing by applying compressive sensing techniques. We show that shadowing maps are indeed highly compressible, and can be potentially predicted by taking only a small number of random samples. We also present a small case study in which these techniques are used to estimate the coverage area of a wireless transmission system.

The recent decisions by regulators in the USA and UK to open up unused portions of UHF spectrum for secondary use have been met with keen interest in using these TV white spaces (TVWS) for providing broadband services through Wi-Fi-like connectivity. Amid the ensuing media hype about “Wi-Fi on steroids”, there is a widespread perception that Wi-Fi operating in TVWS will provide much longer range, superior speeds, and more reliable connections than traditional Wi-Fi at 2.4 GHz. In this paper, we present a quantitative analysis of the performance of a network of Wi-Fi-like access points (APs) operating in TVWS in order to obtain a realistic estimate of the achievable range and downlink rate of such a secondary system. Unlike previous studies, we explicitly consider the effects of inter-AP interference and congestion and use real TVWS channel availability estimates from an example region of Germany. We confirm the favourable properties of the lower TVWS frequency range, of enabling better propagation through walls and a larger coverage range for the same power budget. Our results show that operating Wi-Fi hotspots in TVWS might be technologically attractive for outdoor rural areas where user demand is low. However, the extended coverage range in TVWS leads to increased congestion which rapidly limits the system capacity for an outdoor urban deployment with high user density. Therefore, a combined technological and economical analysis is essential before any final judgement can be reached about the viability of large-scale Wi-Fi deployments in TVWS.

No abstract available.

Graphs are routinely used to approximate the structure of wireless networks especially when studying connectivity or various aspects of network performance. Although not as detailed as geometrical and spatial models dealing with individual node locations, graphs can nevertheless be used to capture many of critical interactions between the nodes. In this paper we explore the use of graphs to approximate network dynamics in addition to static network structure. We show that a simple linearization of network performance functionals results in natural correlation metrics for influence of parameter changes on performance. Further, based on extensive simulations we demonstrate that these correlation metrics have a high degree of robustness against perturbations in node locations. We also discuss the relationships between the arising graph approximations of network dynamics and the commonly applied connectivity and conflict graphs. Our results indicate that graphs formed by approximating dynamics combined with connectivity structures can shed considerable insight on "gray zone" behavior commonly encountered in wireless networks. They are promising candidates for autonomous context identification.

We study the performance characteristics of cognitive wireless networks under different dynamic spectrum access scenarios. Our focus is especially on the influence of spatial structures of the primary and secondary user networks on the achievable performance of the secondary network. We adopt techniques from spatial statistics to develop stochastic models for the structure and interaction of these networks. The chosen models are based on Gaussian random fields and Gibbs point processes, and are firmly grounded on empirical data. We then apply extensive Monte Carlo simulations to study the behavior of these models and their related performance properties. The models and the applied techniques are applicable also to a wider variety of networking problems. Our results provide first quantitative assessments on the influence of the spatial structure, and especially correlation properties, of the involved networks on the expected performance and thus on the utility of dynamic spectrum access based systems.

The design of a cooperative spectrum sensing strategy has to take two aspects into consideration: from the physical layer perspective, a number of secondary users cooperatively measuring the channels to improve the detection performance, and from the MAC layer perspective, the users efficiently reporting their measurement results to a decision entity. Both operations are time critical and required for efficient spectrum opportunity exploitation by the secondary system. However, most of the existing cooperative solutions in the literature ignore the reporting process or over simplify it by assuming a perfect timeslotted multiple access. In this paper, we consider a CSMA/CA based reporting process. We show that fairness no longer plays a role in multiple access of the reporting process. Instead, fastness becomes the dominant measure of the MAC protocol. We propose a Fast Cooperative Spectrum Sensing (FCSS) scheme over multiband cognitive networks by exploiting concurrent occurrence of the spectrum sensing activities and the reporting process with an improved CSMA/CA based MAC protocol. The proposed scheme incorporates a joint static and random sensor assignment algorithm over multiple channels. A mathematical model has been developed for evaluating the performance of FCSS and serving as a basis to select appropriate parameters.

During the last decade there has been a great interest in applying game theory to several problems of wireless networking. The main focus of the existing research so far has been to ensure and prove that the game shall eventually reach a Nash equilibrium solution. In this paper our aim is to question whether a Nash equilibrium must be used as an allpurpose treatment without taking into consideration additional factors. Specifically, by exploiting the concept of equilibrium phase transitions from statistical mechanics we argue that in the case of a dynamic wireless environment - where the system might be constantly drifting from the Nash equilibrium due to external factors - the Nash equilibrium might be a particularly unstable outcome in terms of the overall performance and hence not a desirable outcome.

A growing demand for bandwidth in wireless data networks has motivated policy makers to consider opening underused frequencies, e.g. in the TV broadcasting bands, to opportunistic secondary access. To support secondary network operations in these TV whitespaces (TVWS), centralized databases are envisioned that employ radio propagation models to derive the spectrum usage of the primary system. Precise estimation of the actual spectrum usage is highly important in this scenario, because maladjusted models can cause primary outage or hamper secondary capacity. The requirements for accurate propagation estimation have hence further increased. Using the example of a single TV transmitter, we study in this poster the prospects of maximum likelihood estimation for generic power law propagation models. Transmitter characteristics are considered known, but environment-specific parameters of the model are derived through fitting measurement results, obtained, e.g., through drive tests. We derive for the case of uncorrelated shadow fading the error distributions and their parameters for two parameters, namely pathloss coefficient and shadow variance. Our results are applicable to other large-scale scenarios where precise signal strength estimation is required. They can help to minimize the number of required measurements and to determine necessary protection policies.

Future wireless networks will be able to exploit unused spectrum whitespaces in the currently underutilized terrestrial TV bands. In order to protect incumbent broadcasting systems from extensive interference from whitespace devices, accurate signal power estimations are needed. Reliable and robust models for predicting the radio propagation in those bands are thus a key enabler of secondary operations. Depending on their level of abstraction and the complexity of the environment, these models can perform quite well, but they require a significant amount of auxiliary information and fine-tuning. Additionally, they usually consider transmitters in one frequency band to have uncorrelated signals, an uncommon scenario in today's digital TV networks where the same geographical area is often served by multiple towers that form a single-frequency network (SFN). Using data from a measurement campaign carried out in a mid-sized Central European city we assess the accuracy of three widely used propagation models in predicting TV signal strengths for configurations of multiple high-power transmitters. We empirically show that the standard deviation of the estimation error is up to 4 dB higher in a multi-transmitter scenarios with correlated signals than in the single-transmitter case. We put the results in contrast to the prediction capabilities of a generic propagation model that estimates pathloss coefficients and individual correction factors from measurements and combines them with information on transmitter locations, power emissions and antenna patterns. Finally, we study the use of spatial statistics based techniques for directly estimating the coverage characteristics without relying on explicit transmitter information. We show that with only a small subset of measurement locations accuracy beyond standard pathloss models can be achieved.

A rapid reconfiguration of medium access scheme is required in order to achieve runtime performance optimization for dynamic spectrum access and fulfilling varying Quality of Service (QoS) demands. We have developed TRUMP, a toolchain which allows composing MAC solutions at runtime. In this demonstration, we will show how MAC reconfiguration can be achieved efficiently using TRUMP. Inspired by the optimum route calculation method used in car navigation systems, the compiler toolchain in TRUMP realizes an appropriate MAC solution at runtime. TRUMP allows expressing various types of constraints and options such as speed, energy consumption and packet delivery rate which leads to different MAC compositions. The live demonstration of MAC reconfiguration will be carried out on WARP SDR platform.

The paper investigates the use of Radio Environment Maps (REMs) as a tool for Interference Management (IM) in two-tier cellular networks comprising macro- and femto-cells. The REMs are databases that provide, through different instances distributed over network elements, a variety of network- and user-related context information for improving IM and Radio Resource Management (RRM) procedures. In this context, the focus in this paper is to present the benefit of using REM information on practical power control schemes for the Femtocell downlink transmission in co-channel two-tier deployment.

Cognitive radio (CR) technology is the key technology that enables the Next Generation (xG) communication network, also called Dynamic Spectrum Access Networks (DSANs), to use spectrum in a dynamic manner. Due to the dynamic network topology and the time and location varying spectrum availability in Cognitive Radio Ad Hoc Networks (CRANs), there is a need of on-demand searching for a control traffic channel by CR users in order to be able to initiate a communication. A rendezvous (RDV) is therefore a new challenge, not encountered in single channel networks. Hence, methods guaranteeing that all nodes meet periodically in reasonable periods of time should be advocated, i.e., instead of using pseudo-random solutions, systematic approaches should be considered, e.g, quorum systems (QSs) popular especially in power saving protocols. In this study, we focus on RDV guarantee on all available common channels with the heterogeneous channel availability, while satisfying the heterogeneous Rotation Closure Property through complying with the heterogeneous difference (map) pair properties. We enhance a torus QS and Difference Set based rendezvous protocol, designed in our previous work, such that we can guarantee a small and bounded maximum Time-to-Rendezvous, and RDV on all channels in a hopping sequence period.

The radio interface is the bottleneck of most wireless communication systems. All techniques used to enhance this interface require certain knowledge of the radio environment. This knowledge usually concerns either short-term, or long-term models and statistical characteristics of the environment, depending on the technique. Short-term models are suitable especially for signal processing applications, whereas long-term models are mostly used by radio resource management techniques. Most of the existing work is either focusing on outdoor propagation or short-term models. In this paper we present first results on developing long-term indoor propagation models based on extensive measurements. The results show that fast fading in indoor propagation models cannot be modeled with one distribution alone and thus dynamic models are required. Moreover, slow fading and its characteristics change very often and cannot be modeled with fixed distribution over time.

In this paper, we study the availability of TV white spaces in Europe. Specifically, we focus on the 470-790 MHz UHF band, which will predominantly remain in use for TV broadcasting after the analog-to-digital switch-over and the assignment of the 800 MHz band to licensed services have been completed. The expected number of unused, available TV channels in any location of the 11 countries we studied is 56 percent when we adopt the statistical channel model of the ITU-R. Similarly, a person residing in these countries can expect to enjoy 49 percent unused TV channels. If, in addition, restrictions apply to the use of adjacent TV channels, these numbers reduce to 25 and 18 percent, respectively. These figures are significantly smaller than those recently reported for the United States. We also study how these results change when we use the Longley-Rice irregular terrain model instead. We show that while the overall expected availability of white spaces is essentially the same, the local variability of the available spectrum shows significant changes. This underlines the importance of using appropriate system models before making far-reaching conclusions.

This special issue includes three highest ranked papers selected by the TPC chairs and members from the technical track of the 2011 DySPAN conference on dynamic spectrum access.

The demand for higher data rates, capacity and better quality-of-service is constantly growing for home networks. Therefore, there is a pressing need for efficient use of wireless network resources. In this context, the application of cognitive radio principles that enable network nodes to characterize their environment and control their resources based on the acquired knowledge, is the prominent solution for next generation home networks. In this paper we present an architecture and a prototype implementation based on these principles. The proposed system is able to autonomously optimize the performance of network nodes in a dynamic environment according to the goals, restrictions and policy regulations formulated by network stakeholders. The obtained results show the momentous and suitability of the cognitive framework for home networking.

In this paper we discuss our ongoing work towards developing a hands-on course on network science. Unlike most of the existing courses on this emerging topic, our focus is on data analysis and modeling, with strong emphasis on model validation. Our objective is to develop a course that will not only give students a broad outlook on this emerging new field, but also equips them with practical skills useful in later professional and research careers. We discuss the planned structure for the course, especially focusing on the topics and methodologies covered. We also outline the structure of the proposed hands-on components, including tools used as foundation for these sessions.

In this paper we propose a general model to estimate the maximum allowed transmit power of a secondary node based on the measured signal from the primary transmitter that is willing to share its downlink spectrum. The transmit power should satisfy primary constraints related to the minimum required Signal to Interference and Noise Ratio (SINR) of its users. The model can be used by secondary nodes whether or not they have full knowledge about the correlation between the different path losses. Simulation results have shown the high impact of the level of correlation on the performance of both primary and secondary users.

In this paper we introduce a utility-based optimization approach for wireless communication based on developed Common Application Requirement Interface (CAPRI). The proposed system allows applications to register their utility functions to a radio resource controller at run-time and in automatic way. This enables the implementation of utility-based optimization and run-time reconfiguration of applications. Specifically, in contrast to traditional utility-based optimization approaches that usually aim at optimizing a single application deployed for the entire system, our approach aims to generalize and express the requirements of multiple applications. To validate our proposed system, we have implemented a prototype of CAPRI using Windows platform. The implementation has been made publicly available under an open source license. We show that the overhead induced by the adoption of the CAPRI framework in terms of memory and processing requirements is small, and additional latencies induced by the framework are negligible. Further, a practical case study of multi-application utility-based optimization has been addressed and the results have been analyzed.

In this contribution a layered radio environment map (REM) architecture is designed and applied in the framework of radio-access network optimization for heterogeneous LTE systems that comprise both macrocells and femtocells. We define layer as the hierarchical representation of a geographical area. In each layer, different instances of the same architectural block will have different spatial/temporal granularities, associated with network functionalities. The effectiveness of the proposed architecture to support LTE functions like automatic neighbor relation (ANR) and minimization of drive tests (MDT) is discussed. In addition, we present the benefits of using such architecture in the implementation of these functions along with its potential to bring performance gains.

We empirically study problems of online estimation and modeling of radio activity (ON/OFF) patterns and their duty cycles. We consider performance of two algorithms that analyze power spectrum samples in time domain as sensed by WARP SDR boards placed at different indoor locations. The first algorithm utilizes density function of the marginal distribution of the received readings to perform clustering of the power samples into the classes that correspond to ON/OFF activity states. The second algorithm uses hidden Semi-Markov processes and the Viterbi algorithm to model and estimate the radio activity patterns of signal sources. Unlike the first algorithm it can also be used for both signal classification and estimation of the state duration that corresponds to the certain power level. We study signals obtained from one or two transmitters. Our results indicate the first technique can often provide accurate estimations. However, if sensor nodes receive low RSSI values or modeling of the temporal structure of the activity patterns is required then the second technique is preferable. This method also has a potential to benefit from cooperation between nodes.

In this paper we investigate a novel distributed channel allocation method for cognitive radio networks (CRNs), which is based on a grid quorum system. The objective of this paper is twofold. First, the paper studies a grid-based quorum arrangement, also used by power saving protocols. Second, it proposes two channel mapping methods based on a grid quorum and sub-grids. Each node visits channels according to its channel map (sequence) and without exchange of any information, which reduces signaling overhead. The study shows that nodes do not need to be dependent on the existence of a common control channel (CCC), synchronization or a central controller, as it is frequently assumed in the current CR literature.

Cognitive radios are becoming the technological foundation for efficiently managing the scarcity of wireless spectrum, fulfilling various QoS demands and allowing different networks to coexist. Cognition and spectrum agility in MAC protocols require adaptability and close PHY-MAC interaction. Classically, MAC protocols have been implemented in hardware, which gives a limited possibility for reconfiguration and customization. Recently, software based MAC implementations have emerged although a close hardware-software co-design is typically required to keep the time critical operations in ASICs or FPGAs. We introduce a MAC development framework for enabling fast composition of MAC protocols, which are best fitted to the application requirements, communication capabilities of the radio, and current spectrum regulations and policies. We decompose MAC protocols into their basic functionalities which are perceived as building blocks and are partitioned across hardware/software. Our framework allows on-the-fly realization of the envisioned MAC protocol through wiring of these fundamental components. By exposing extended metadata and hardware functionalities for the MAC implementation through our granular components, together with the support for run-time re-configuration, spectrum agile and cognitive MAC solutions can be easily realized. In this paper, we describe the design rationale and implementation details of our framework on WARP boards. We show through experimental evaluation that the framework provides flexible means for prototyping different reconfigurable cognitive and spectrum agile MAC protocols.

In this paper we show how to quantify dependency between the node distributions of wireless networks and the underlying population densities. Furthermore, we show that a quantitative analysis of this relation can be beneficial for understanding and generating realistic network models. Towards this direction we argue that spatial statistics is an appropriate method for this purpose. As a case study we analyze the correlations of GSM-900 and UMTS base stations, with the underlying population densities of Germany. We find that there is a significant statistical similarity between the locations of GSM-900 base stations and population, due to the immense penetration of the network, and a less tight relation in the case of UMTS network. We consider the problem of covering a given population pattern and use the concept of fractal dimension to show how the number of required base stations changes when their maximum coverage range decreases, according to the population pattern.

A common concept to mitigate the effects of spectrum scarcity is to allow opportunistic dynamic spectrum access (DSA) to licensed frequency bands. In this context, the license holder may experience a decrease in the performance of its network. For cellular networks this may lead to unwanted coverage holes at locations where the secondary-induced interference is too strong. In this paper we propose a planning tool and a channel assignment mechanism for cellular OFDMA-based networks that takes the coverage requirements of the primary system into account. By keeping a dynamic reserve of channels for those nodes that experience the highest interference, we can maximize spectrum utilization while guaranteeing a predefined service reliability. Our simulations show that only a small fraction of channels will need exclusive reservation, hence large parts of the spectrum may be employed in secondary systems.

The use of beamforming to enable higher data rates in telecommunications is widely appreciated, but performance gains are typically calculated assuming delay-free feedback from the receiver and neglecting processing time. This paper introduces a mathematical framework based on outage probability that measures the extent to which current channel state information is accurate. Performance gains from beamforming can then be evaluated as a function of the currency of system state. Results are provided for Multiple Input Single Output (MISO) and for Multiuser Multiple Input Multiple Output (MU-MIMO) systems. Outage probabilities and effective diversity orders are calculated for widely used methods of beamforming such as Transmit Antenna Selection as a function of the speed of channel variation.

In this paper we explore the problems of storing and reasoning about data collected from very large-scale wireless sensor networks (WSNs). Potential worldwide deployment of WSNs for, e.g., environmental monitoring purposes could yield data in amounts of petabytes each year. Distributed database solutions such as BigTable and Hadoop are capable of dealing with storage of such amounts of data. However, it is far from clear whether the associated MapReduce programming model is suitable for processing of sensor data. This is because typical applications MapReduce is used for, currently are relational in nature, whereas for sensing data one is usually interested in spatial structure of data instead. We show that MapReduce can indeed be used to develop such applications, and also describe in detail a general architecture for service platform for storing and processing of data obtained from massive WSNs.

We have studied the performance of IEEE 802.11n in extensive measurement campaigns carried out in both interference-controlled and typical office environments. The results show that in a typical office environment significant performance improvement compared to earlier IEEE 802.11 technologies can be expected, but theoretically achievable bitrates were not reached. We also studied the sensitivity of IEEE 802.11n performance to interference from other technologies used in the 2.4 GHz ISM band, and found that 802.11n in general tolerates interference well. However, the results also show that a powerful enough narrowband interferer can have a significant negative impact and legacy 802.11 technologies operating in same region will cause performance degradation. The results on interference susceptibility and key role of channel bonding in the increase of throughput indicate that in new IEEE 802.11n deployments the 5 GHz ISM band should be preferred to the 2.4 GHz ISM band.

A wide-spread integration of Wireless Sensor Networks (WSNs) into daily life applications demands modular and flexible service oriented architectures. Discovering nodes and their services is imperative to any large-scale sensor network deployment. In this paper, we describe nanoSD, a lightweight service discovery protocol, designed for highly dynamic, mobile and heterogeneous sensor networks. We demonstrate through a fully functional implementation and its performance evaluation that nanoSD supports heterogeneous architectures and is scalable. It is able to efficiently cope network dynamics present in real WSN deployments. It uses features like delta service advertisements, message compression and piggy-backing techniques, which results in low network overhead. Furthermore, nanoSD protocol is easily integratable to web-service based backend systems using very limited resources.

In this paper we describe an LTE based demonstrator of the Universal Link Layer API (ULLA) and Cognitive Resource Manager (CRM) modules that are developed in ARAGORN project. The demonstrated LTE system comprises one LTE TDD eNode B and one User Equipment (UE). We first introduce ULLA and CRM framework and then demonstrate their suitability to be implemented with the existing LTE equipments. We show how, through ULLA, CRM is able to obtain PHY/MAC status information of the link between the eNode B and UE, and in turn change system parameters to achieve better resource utilization and transmission efficiency. The control logic can be implemented with simple adaptation or policy-based intelligent methods. The platform clearly shows the feasibility to use ULLA/CRM architecture for radio resource management in a LTE network. It also shows the neutrality of ULLA/CRM mechanisms towards PHY/MAC characteristics of LTE technology platform; hence the platform is viable to flexibly switch between technology platforms (e.g. between LTE access and WiFi access) under the control of ULLA/CRM.

In this paper we describe the design, implementation and performance evaluation of a low-power spectrum agile medium access control protocol for wireless sensor networks. With the ever increasing popularity of wireless embedded devices and networks, spectrum is getting congested, which in turn leads to performance degradation. Since protocols are designed in isolation of each other without appropriate consideration for potential interferences and mechanisms for symbiotic coexistence, they fail to achieve the desired performance characteristics in realistic interfering environments. The performance degradation is more significant for low-power embedded networks as they remain handicapped when competing with less resource constrained networks. We design a protocol that allows sensor nodes to dynamically select an interference minimal channel for data communication. It does not pose any synchronization restrictions on the nodes and effectively handles the dynamics of the network such as new nodes joining and old nodes leaving the network. We describe the various energy efficient spectrum sensing features of the protocol on which the dynamic channel selection is based. Our experiments suggest that even in highly crowded spectrum and environments with random interferences, sensor nodes are able to communicate in a reliable and energy efficient manner.

In this poster, we focus on the cross-layer design of a low-power traffic aware MAC protocol and a content and context based routing protocol. We describe the cross-layering aspects of the MAC-Routing framework and present the performance results on real sensor node testbed.

Wireless Sensor Networks (WSNs) have been deployed in a wide range of applications with different sensing and communication requirements. As a consequence, various MAC solutions have been proposed suiting to different application characteristics [1]. However, MAC protocols are typically implemented in a monolithic fashion -- specific to a particular hardware platform. This hinders the possibility of code re-use across different platforms, which makes both implementation and porting cumbersome. It also imposes difficulties in protocol redesign when the application requirements change since the entire radio stack needs to be modified.

We present the estimation and evaluation of deployment models for sensor networks that exploit different amounts of a-priori information. Topologies generated from the models exhibit minimum energy consumption at maximum sensing coverage. The applied energy model makes use of measurements obtained from real experiments. We consider general sets of interesting points in the phenomena since those exhibit most of the information content. The estimated models lead to significantly optimized initial network deployments which can be subsequently used with other existing optimization techniques. Overall the energy-efficiency, sensing coverage and operational lifetime of Wireless Sensor Networks (WSNs) are strongly improved by our suggested deployment model. Furthermore, we derive a novel metric from our analysis enabling the estimation of the performance of WSNs. This metric makes use of both measures describing the similarity of probability distributions and measures characterizing spatial correlations.

We present the derivation of a novel metric for the performance prediction of Wireless Sensor Networks (WSNs). In particular, the metric can be applied in, for example, monitoring and surveillance applications where WSNs are used. Those networks execute protocols or techniques that are often sensitive to spatial correlation. The proposed metric is based on the correlation in the sensed phenomenon and the correlation in the location of the sensor nodes. The main application area of the performance prediction metric lies in the design and optimisation of WSNs prior to the costly deployment phase. Since extensive simulations can be completely avoided it serves as a rapid and lightweight evaluation tool for comparative analysis of WSNs. The concept is applicable and also extensible through merging of selected performance criteria.

In this paper we analyze the energy-efficiency of distributed compression and its dependence on the node deployment strategies for Wireless Sensor Networks (WSNs). Reduced energy and bandwidth usage can be achieved by Distributed Source Coding (DSC) which is a lossless compression technique used in, for example, data gathering applications. We evaluate the performance of clustered WSNs applying DSC under various topologies and different cluster head selection schemes. The topologies considered are obtained from three different node deployment models which are fundamentally based on random point processes. Those models cover the extreme deployment cases and allow direct comparison between various scenarios. The evaluation takes into account the packet header costs and the signal processing costs associated with DSC. Additionally, the applied energy model makes use of measurements obtained from real experiments. The simulations results show very good performance in terms of energy-efficiency. Furthermore, significant average energy savings and average lifetime extensions are possible using DSC under different deployment strategies. This study also leads to the identification of the best fitting deployment strategy in relation to the characteristics of different sensed phenomena. Hence, the proposed approach is particularly suited to support the optimization of networks in realistic environments.

In this paper we analyze performance of network coding focusing on two specific network coding schemes: XOR and random linear network coding. We have simulated different topologies and traffic patterns in order to provide better understanding of network coding behavior and its possible bottlenecks. As a part of our performance evaluation we address computational complexity of coding and decoding operations and benefits of network coding. In particular, we indicate potential drawbacks and trade-offs of network coding when applied on specific topologies. The conclusion is that even applied over robust networks, network coding is not a magic bullet for a better performance.

In this paper we study the problem of quantifying the value of spectrum opportunities to secondary users in Dynamic Spectrum Access (DSA) systems. We especially focus on estimating the impact of key channel parameters, namely the activity patterns of the primary users on the expected quality of experience for secondary users accessing video streams over a DSA system. In our study we consider three basic types of video representing typical video content categorized according to scene change rate ranging from low-activity newscast to high-activity sports video. Through extensive simulations we show that given some information on the expected level of activity in the video, the duty cycle of the primary user alone can yield good predictors for the expected quality of experience of secondary users. Knowledge of the precise distributions of the primary user ON and OFF periods can be used to further enhance the precision of the prediction, but at least for exponential and log-normal channel access patterns the differences are rather small. Finally, we study the problem of determining the channel statistics that are needed to apply the predictor in an optimization setting. Our simulations show that high accuracy can be achieved in matter of minutes of estimation time, which is more than enough for practical deployments in typical urban environments.

In this paper we discuss the design of optimization algorithms for cognitive wireless networks (CWNs). Maximizing the perceived network performance towards applications by selecting appropriate protocols and carrying out cross-layer optimization on the resulting stack is a key functionality of any CWN. We take a "black box" approach to the problem and study the use of simulated annealing for solving it. To improve the convergence rate of the basic algorithm we apply machine learning techniques to construct graphical models on the perceived relations between network stack parameters and application-specific network utilities. We test our optimizer design both in a simulation environment as well as on a network testbed with low-power radios. Our results show that even basic simulated annealing works well, but simple graphical models can further increase the convergence rate. However, use of sophisticated models such as Bayesian networks does not always lead to substantially better performance. The results indicate that enhanced simulated annealing indeed appears to be a promising foundation for future cognitive engine designs.

Network flow models serve as a popular mathematical framework for the analysis and optimization of multi-hop wireless networks. They also serve to provide the understanding necessary to derive effective distributed protocols. However, the high computational complexity of realistic models restrict the translation of theoretical insights into distributed protocols. In this paper, we consider an NP-hard, mixed integer linear programming based routing model that computes single-path routes in a wireless network. We propose an efficient, polynomial time algorithm that applies domain specific heuristics to reduce the complexity. We employ a decomposition based approach to break the monolithic problem into several sub-problems that cooperate to find near-optimal routes. The sub-problem structure is chosen such that it captures the optimal route discovery process between a source and destination; this is a design principle that can be directly used in distributed routing protocols. We show that the resulting formulation achieves orders of magnitude improvement in the run-time. Simulation results show that the routes derived from the model are effective even in practical wireless networks with commonly used protocol stack.

Several measurement studies have found a large amount of underutilized radio spectrum. More flexible regulation employing dynamic spectrum access (DSA) has been proposed as solution to this problem. The analysis of several aspects of DSA systems, e.g., cooperative sensing, requires good spatial models of spectrum usage. However, only very focused models such as propagation or shadowing correlation models exist. In this paper we apply techniques developed by the spatial statistics community to the modelling of spectrum. In more detail, we use random fields and the semivariogram to describe the spatial correlation of spectrum usage. We extract parameters from extensive real-life measurements for multiple wireless technologies. These parameter sets enable other researchers to use the model for different tasks ranging from theoretical to simulation-based studies.

Topology control of ad hoc and mesh networks specifies how to assign per-node transmission parameters (such as power level, frequency etc.) so as to achieve energy efficiency, while maintaining certain desirable properties such as connectivity. In autonomous networks, nodes may act in their self- interest and improve their performance, perhaps at the expense of other nodes', or even the overall network's, performance. Besides, nodes must also contend with limited information about the network operating state during their decision-making. We analyze the above problem using non-cooperative game theory and quantify the impact of partial network state knowledge that nodes possess on the network optimality. We develop a local topology control algorithm that uses the idea of maintaining connectivity of 1-hop neighborhoods. This algorithm is first shown to converge and be stable. We then examine the trade-off between network performance (energy efficiency) and the cost of having knowledge (by exchanging control messages): more information exchange makes the nodes more network-aware, and hence leads to more efficient networks, but exchange of control information itself is costly. Taking the cost of obtaining knowledge into account, we observe that when nodes can operate along the continuum of knowledge, from 1-hop to omniscience, the network consumes least energy when nodes have significantly less connectivity information.

We propose to use multiscale entropy analysis in characterisation of network traffic and spectrum usage. We show that with such analysis one can quantify complexity and predictability of measured traces in widely varying timescales. We also explicitly compare the results from entropy analysis to classical characterisations of scaling and self-similarity in time series by means of fractal dimension and the Hurst parameter. Our results show that the used entropy analysis indeed complements these measures, being able to uncover new information from traffic traces and time series models. We illustrate the application of these techniques both on time series models and on measured traffic traces of different types. As potential applications of entropy analysis in the networking area, we highlight and discuss anomaly detection and validation of traffic models. In particular, we show that anomalous network traffic can have significantly lower complexity than ordinary traffic, and that commonly used traffic and time series models have different entropy structures compared to the studied traffic traces. We also show that the entropy metrics can be applied to the analysis of wireless communication and networks. We point out that entropy metrics can improve the understanding of how spectrum usage changes over time and can be used to enhance the efficiency of dynamic spectrum access networks.

Estimation of the available capacity in a multiple hop route in cognitive radio networks is challenging. Several complex problems like channel assignment, interference estimation and routing effects needs to be addressed. In this demonstration, we exhibit a capacity estimation engine that determines the available capacity of the routes in a dynamic cognitive radio network using USRP and WARP software-defined radios. In addition to the available capacity, we show the various types of MAC interference and its effect in the network. The tool demonstrates a fundamental capacity estimation block, which provides valuable input to higher level applications like routing, network provisioning and monitoring. A scheduling aware routing algorithm that exploits the capacity estimation engine and scheduling interactions is shown as an example application.

In autonomous ad hoc networks, nodes may act in their self-interest to improve their performance, perhaps at the expense of other nodes', or even the overall network's, performance. Besides, nodes are often mobile and must contend with limited and obsolete information about the operating state of network during their decision-making. We examine topology control of ad hoc networks and analyze the impact of mobility, selfishness, and partial information on energy efficiency and network connectivity using tools from game theory. We develop a local topology control algorithm that uses the idea of maintaining connectivity of 1-hop neighborhoods. By examining the trade-off between network performance (energy efficiency) and the cost of cooperation (by exchanging control messages) it turns out that mobility and selfishness are, in some sense, synergistic features in information-constrained networks. Such networks consume least energy when nodes are more mobile and have significantly less connectivity information.

In this paper we verify and study through implementation our earlier game theory algorithm for load balanced dynamic channel assignment. In our algorithm every cognitive radio is initially assigned a channel in a random fashion. The radios periodically evaluate a cost function related to a Quality of Service (QoS) parameter and may change the transmission channel with probability proportional to the excess of the current cost over a cost threshold set by the user application. The actions of the cognitive radios are concurrent and the decision to switch the channel is only based on the current local link quality, which means that neither network state information exchange nor history keeping is required. The earlier theoretical work has shown that the balls-and-bins game theory algorithm has attractive convergence properties and low complexity. In this paper we evaluate the convergence properties of our dynamic channel assignment protocol over real channel conditions for different number of channels and traffic distributions. Our testbed results show that the optimal channel allocations are found in most of the cases within five to eight iterations depending on the complexity of the test case. The proposed algorithm is a good candidate for resource allocation using only local information.

We study the influence of the configuration and distribution of transmitters on radio environment maps. We adopt a statistical approach, treating total received power as a random field characterised with second-order statistics. Using extensive simulations we characterise the effects of changing node counts, distribution of node locations, transmit powers and propagation environments on the studied statistics. We also comment and discuss applications of these results and the methods employed especially from the dynamic spectrum access point of view.

We study the behaviour of packet delays and inter-forwarding times in vehicular networks utilizing opportunistic communication protocols. Our results are obtained through an extensive simulation campaign employing realistic models of vehicular mobility and scenarios derived from real urban environments. We also explore the impact of different forwarding strategies and traffic types on the results. Fitting of probability distributions is carried out for all results, clearly demonstrating the heavy-tailed behaviour of packet delays, inter-forwarding times as well as inter-contact times. In particular, exponential distribution occasionally assumed in analytical studies is strongly ruled out even as a viable approximation for inter-contact times.

We present the estimation and evaluation of deployment models for sensor networks that exploit different amounts of a-priori information. Topologies generated from the models exhibit minimum energy consumption at maximum sensing coverage. The applied energy model makes use of measurements obtained from real experiments. We consider general sets of interesting points in the phenomena since those exhibit most of the information content. The estimated models lead to significantly optimized initial network deployments which can be subsequently used with other existing optimization techniques. Overall the energy-efficiency, sensing coverage and operational lifetime of WSNs are strongly improved by our suggested deployment model.

In this paper we present a mathematical approach to evaluate the area throughput and the energy consumption of a multi-sink Wireless Sensor Network (WSN). The WSN is organised into clusters, with one sink per cluster collecting data from sensors. A small variation of the Thomas point process is used to model sensors and sinks positions in the target area. We denote as area throughput the amount of samples per second successfully transmitted to the sinks. Both area throughput and energy consumption are strictly related to connectivity and MAC issues. The aim of this work is to devise a mathematical model that takes MAC and connectivity issues into account, under a common framework. We study the behavior of these two performance metrics when varying the target rate, defined as the maximum number of samples the network was deployed to deliver. Results show that a tradeoff between the area throughput and the energy consumption must be found. Finally, the impact of different sensors and sinks distributions on the area throughput is evaluated.

We study the problem of optimal parameter selection for Bloom filters in content-based routing. In constrained environments, such as sensor network, optimizing lengths of the filters used and the number of hash functions employed can lead to significant reduction of memory overhead and false positives. We demonstrate by analytical calculations and extensive simulations that the commonly used heuristics for choosing these parameters are suboptimal especially in networks storing replicas of the data for redundancy. We show specifically how network topology and use of replication can be taken into account when selecting the parameter values used. Our results show that memory consumption can be reduced by up to 50% compared to usual approaches, and that the occurrence of false positives can be significantly reduced as well.

Chains or multi-hop paths are the fundamental communication structure in multi-hop wireless networks. Understanding chain behavior is critical in order to build effective higher layer protocols. This paper examines the problem of how MAC level interactions influence chain behavior in a general multi-hop wireless network where multiple chains coexist. We first classify chains based on the MAC interactions observed between its hops when there is no external traffic. Then we identify the interactions across two interfering chains for the most common categories of chains. We study the probability of occurrence, and estimate the effect of MAC interactions on the performance of the chains. We also show that different chains exhibit different transmission patterns; this is an effect that is necessary for accurately estimating chain performance. We observe that destructive interactions arise more frequently among two interfering chains than they do within a single chain. Moreover, chains that have hidden terminals due to self-interference are more prone to have cross-chain hidden terminals. Thus, both intra-chain as well as cross-chain interactions, ultimately provide significant insight into how chains interact.

We study the impact of the distribution of node locations on key properties of wireless networks. In particular, using Monte Carlo simulations we study in detail the behavior of average path length for different distributions and parameters thereof. We also show that distribution of client locations has significant impact on network planning, in particular on combination of base station placement and range assignment. In both cases our results indicate that the underlying point distributions of nodes have clear effects on the wireless network properties. We also discuss the implications of our results and the role of Poisson point process as an approximation of other distributions.

In this paper, we analyze three opportunistic multichannel MAC protocols that associate a newcoming terminal station to one of the access points available within its transmission range: 1) by selecting the access point with best signal-to-noise-ratio transmission link, the protocol "max SNR" achieves selection diversity gain, 2) by selecting the access point with lowest load, the protocol "min load" achieves "load balancing" gain, 3) by selecting the access point which provides the best throughput among all access points, the protocol "max throughput" achieves the optimum trade-off between selection diversity gain and occupancy. All three protocols achieve significant gain against the random association scheme in an opportunistic and distributed manner. Our calculations based on the bins and balls analysis shows that protocols "max SNR" and "min load" are near-optimal at low SNR and high SNR, respectively.

Sensor network applications are generally characterized by long idle durations and intermittent communication patterns. The traffic loads are typically so low that overall the idle duration energy consumption dominates. Low duty cycle MAC protocols are used to reduce the idle duration energy consumption. However, lowering down the duty cycle in favour of energy consumption results in increased latency, which makes it undesirable for many applications. In this paper, we propose radio triggered wake-up with addressing capabilities (RTWAC) that allows suppressing the idle duration current consumption. Our solution consists of an external low-cost hardware wakeup circuit attached to the microcontroller of a sensor node. The sensor node stays in the sleep mode with its normal communication radio turned off. In order to communicate with a sensor node, a special kind of out-of-band modulated wakeup signal is transmitted. The modulated signal contains data that enables to distinguish between differently addressed nodes in order to avoid undesired node wake-ups. Furthermore, we augment this solution to a MAC protocol running on the normal radio on the sensor node in an advantageous way to achieve high energy gains and low latency for data communication.

Most publications on the capacity and performance of wireless ad hoc networks share the underlying assumption of a uniform random distribution of nodes. In this paper, we study the effects of different node distributions on the local throughput of the slotted ALOHA MAC protocol. The throughput achieved in a network where the nodes are distributed according to a Poisson point process is used as a baseline performance measure for the comparison with other point distributions. Our simulations show that non-uniform random node distributions have a strong impact on the local throughput which is related to the network capacity and performance. This means that the node topology should be taken into account in more detailed analyses and simulations of ad hoc, sensor, and mesh networks.

Currently there is no widely accepted formalized description or metadata that represents networks, their components and interrelations between them. There is also no de facto classification of network parameters, models and services. The network community lacks a metadata specification similar to WSDL that represents networks in clear and unified manner and includes software, hardware and networking parts of the system description. This limits the advance of the frameworks aiming to assist the development of complex non-established network systems, such as wireless sensor, cognitive and heterogeneous networks. Additionally a standard metadata would enhance the interoperability and acceptance of various middleware, service discovery schemes and cross-layer optimization solutions. In this paper we suggest a simple, but comprehensive extensible metadata specification and a corresponding metamodel that allows to describe a wide range of network components and network configurations. We describe metadata and metamodel on the example of wireless sensor networks. In this field the need for standard metadata is particularly pressing due to the high number of available protocol solutions and lack of standardization.

There is need to develop better models and characterization methods for spectrum usage and radio environments of cognitive radios. Currently different theoretical and simulation based approaches towards enabling dynamic spectrum access would greatly benefit from the possibility to generate synthetic data for testing purposes. Such Radio Environment Maps must statistically exhibit the characteristics of realistic environments. Previous and on-going spectrum measurement campaigns are generating a vast amount of such data. In this paper we provide a partial answer to the spectrum modelling problem by showing how one can characterize and model spectrum maps with spatial statistics and random fields. We present the basic mathematical premises for building models and also through examples outline how one can generate useful statistics from real measurement data.

We present the design and implementation of UDAE, a universal data access engine for wireless sensor networks. The UDAE allows developers to access data both locally and over the network from a variety of sources, such as sensors, communications links and platform components in a unified manner. It is also fully extensible, allowing data sources and users to be added flexibly at run-time. This enables capabilities for cross-component optimization and run-time configuration. In order to validate our proposed system, we have implemented UDAE for two operating systems (Contiki and TinyOS). While the TinyOS implementation has been done as native code, the Contiki implementation is based on a reconfigurable component-oriented middleware enabling developers to fully benefit from the flexibility offered by the UDAE. Both of these implementations have been made publicly available under an open source license. A detailed performance evaluation carried out with these implementations shows that the overhead induced by the adoption of UDAE in terms of memory, processing requirements and energy consumption is very small and the additional latencies induced by the framework negligible. We also discuss various different application scenarios for UDAE in some detail, particularly cross-component optimization and run-time configuration. Furthermore, a practical case study of cross-component optimization selected from the TinyOS domain is addressed.

Car-to-car communication (C2CC) enables cars to exchange information that can be used to, e.g., improve the drivers' safety or comfort. Recently, WLANs based on the IEEE 802.11 family of standards have been identified as promising communication technology for C2CC. Often, the information exchange between vehicles is of interest for all nearby vehicles so that the communication should be broadcast-based. However, such use cases have not been considered when the IEEE 802.11 systems have been initially designed and a significant scalability problem exists in broadcast scenarios. We present the reasons for this problem and propose several algorithms for dynamic protocol parameter adaptations. Our simulation-based evaluation shows that the suggested enhancements clearly improve the scalability of IEEE 802.11-based systems in vehicular scenarios without changing the main protocol characteristics.

We present a practical distributed source coding scheme for deployable wireless sensor networks. To the best of our knowledge it is the first-of-its-kind practical scheme implemented and evaluated in time-varying environments. The solution developed is particularly appropriate for monitoring and surveillance applications in which energy consumption and bandwidth usage are of paramount importance. We also report on the entropy tracking algorithm based on varying spatial correlations and adaptive code rate assignments. The performance results obtained from the experiments show the promises of practical distributed source coding in the sensor network context.

We present the implementation architecture and performance evaluation of the nanoIP protocol stack. The stack consists of miniaturized versions of UDP, TCP, SLP and HTTP protocols with reduced header sizes and complexity to make the protocols usable in wireless sensor networks. Similarity to the TCP/IP stack facilitates the development of gateways towards IP-based networks and makes the use of the stack easier for developers accustomed to network programming. Our implementation work and experiments show that the footprint of the stack is acceptable even for the most resource constrained sensor nodes.

We present a system-level approach to localizing and tracking users on a basis of different sources of location information. We have applied a combination of the Cricket system and inertial navigation sensors to improve the coverage and accuracy of our framework when used indoors. The system can be applied outdoors as well using, for example, GPS as the source of location data. The overall system architecture is modular and extendible, allowing for creation of location and context aware services and inclusion of additional sources of localization data. Extensive performance evaluation presented shows that the system is both accurate as well as scalable.

This paper presents new approaches for dynamically integrating sensors into a surrounding computing infrastructure. Our work is based on small programs - called Senslets - that are stored on sensor nodes. Senslets are written using a programming model based on a high-level programming language such as C# or Java, but they do not assume a virtual execution environment on the sensors. Instead, they are downloaded to a mobile device or stationary computer, where they are hosted and executed. In this paper, we present (1) a programming framework for developing Senslets, (2) the tools necessary for deploying Senslets on resource-constrained sensor nodes, and (3) a hosting environment for executing Senslets on mobile phones. The paper also contains an evaluation and a description of example applications.

At present wireless devices are able to select their working frequency only to a limited extend although several measurements have shown that the current spectrum regulations are inefficient. Dynamic spectrum access is seen as a promising approach that might solve this inefficiency. Spectrum sensing is one of the main tasks involved. We compare in this paper four methods to efficiently sense the current spectrum based on the spectrum occupancy information statistics. The parameters of all methods are extracted from spectrum occupancy data gathered during an extensive measurement campaign. We show that the usage of historical information considerably improves the spectrum sensing process. We also show that the modelling of the periodic behaviour of the licensed signals leads to negligible performance enhancements because only very few periods shorter than several minutes can be found within 20 MHz-6 GHz.

At present multiband wireless devices are able to select their working frequency only to a limited extent because of the strict, current spectrum regulation. Dynamic spectrum access is a promising approach that might solve this inefficiency. The authors focus on spectrum sensing, one of the main tasks involved. First, three strategies are compared to efficiently sense the current spectrum based on the spectrum occupancy information statistics. In contrast to the simulation-based studies, the authors evaluate the performance of those strategies on real spectrum occupancy data gathered during an extensive measurement campaign. The authors show that the usage of historical information considerably improves the spectrum sensing process and also that the modelling of the periodic behaviour of the licensed signals leads to negligible performance enhancements because only very few periods shorter than several minutes can be found within 20 MHz–6 GHz. Secondly, the authors unveil the fundamental tradeoff between the required bandwidth for the transmission and the total bandwidth that has to be sensed in order to guarantee that the required bandwidth is available. All the results are provided in terms of outage probability that can be viewed as an approximation of the packet loss rate.

The 17 papers in this special issue focus on the theory and applications of cognitive radio.

We investigate the impact of network topology on connectivity of wireless mesh and ad hoc networks. In particular we study the transmission range required to establish connectivity with some fixed proportion of network nodes. Our analysis shows that in practice it can be useful to relax the requirement of a full 100 % connectivity in ad hoc networks. We show that high network connectivity can be reached with much lower power consumption and careful node distribution strategies compared to establishment of full connectivity. Unlike most previous works we focus on non-uniform distributions of nodes. We introduce and apply modified Thomas point processes and power-law based Stoyan model for generating the locations of nodes. We show also that simple one-dimensional studies, although important, should be extended also to higher dimensional cases in order to achieve reliable results for practical network optimization purposes. Furthermore, we introduce a link between spatial correlations and the number of neighbours. This information can be used in practical topology estimation. Finally, we comment on the realism of our topology claims.

The cognitive radio and cognitive wireless networks paradigms are interesting candidates for the next generation self-organizing and self-reconfigurable wireless networks. In this paper we argue that the concept of cognitive resource manager as an evolution of the classical radio resource manager in the cellular networks is a promising concept to enable dynamic and distributed cross-layer in the future cognitive wireless networks. We further elaborate on the fundamental and implementation challenges which need to be solved to make the cognitive networks reality. Especially we point out the value of perfect information (VPI) should be taken in account in the real implementations. We also describe shortly, how different APIs need to support information reliability estimation.

Considerable amount of research work has recently been performed in the area of wireless sensor networks. In this paper we address the diversity of programming interfaces for link layer information as well as sensor measurement data as one of the few critical problems that have not in our opinion been addressed thoroughly enough. We propose the use of the unified link-layer API (ULLA) for wireless sensor networks. It offers a common interface to retrieve link layer information independently of the deployed radio technology considerably simplifying development process of link-aware protocols and applications. The ULLA can be used not only by applications running locally on sensor nodes but also remotely by applications running in an end-user device. Additionally, ULLA allows to retrieve sensor readings via the same convenient interface. The interface offers a powerful notification mechanism, which can be used to monitor the sensor network and trigger certain actions when either the communication environment changed, for instance an ad hoc route disappeared, or the sensor readings pass a threshold, e.g. usable for fire-alarm. These features essentially generalize the approach taken by TinyDB to include link-layer information as well, and further extend the abstraction of the sensor network as a database. A prototype implementation of ULLA on Telos B motes has been made to demonstrate the feasibility of the ULLA approach. The footprint of the implementation is very acceptable, and performance offered sufficient even for protocols taking per-packet decisions based on link conditions. The ULLA implementation is available from http://tinyos.cvs.sourceforge.net/tinyos/tinyos-1.x/contrib/rwth/mobnets/.

Distributed data storage is an important component of wireless sensor networks, which protects the mission critical information from unexpected node failures or malicious destruction of parts of the network. In this paper we present DISC, a protocol for distributed information storage and collection. The two major mechanisms in DISC which make our solution distinct from the related approaches are probabilistic choice of storing nodes and a search engine based on the usage of Bloom filters. In comparison to the deterministic choice of the backup node, the random selection strategy makes it virtually impossible for an attacker to determine and destroy the exact node keeping a particular piece of information. The usage of Bloom filters in the information search engine makes the navigation to a specific data fast and efficient. We show that with DISC the amount of recovered information is more than two times higher than that in deterministic storage schemes.

We describe the design, implementation and performance evaluation of a wireless sensor network based localization and tracking system, which can provide localization services in many of the pervasive computing applications. Our system uses non-linear Bayesian filtering to obtain accurate tracking results from noisy localization data. This allows high-accuracy tracking of non-linear motion even in the presence of non- Gaussian measurement noise, which is a significant improvement in generality compared to many state-of-the-art sensor network localization systems. Our prototype uses a combination of radio and ultrasound signals to obtain distance estimates necessary for the filtering process, although in principle any distance estimation technique can be used. We also describe improved outlier detection and post-calibration methods for enhancing the quality of the distance estimates obtained compared to earlier systems.

In this paper we discuss on the dynamic spectrum allocation policies and architectures related to cognitive radios. First, we point out that so called flash crowd effect should be considered as a possible phenomenon in the case of flexible spectrum allocations and cognitive radios. We outline some simple technological and business models approaches that could be used to solve besides of using only traditional MAC-layers. Second, we discuss also on financial and business modeling grounds, whether the dynamic spectrum should be seen in the context of auctions or larger financial markets. Our conclusion is that spectrum auction based valuation might have only limited use to allocate spectrum within short time-scales, but could have high potential in long-term allocation, if hierarchial spectrum management approach is enabled.

The future mobile networks will have to support increasing number of mobile users and provide efficient services that are context sensitive. Because of the limited amount of wireless resources, the better utilization of system resources is required. Lately there has been an increasing interest to develop predictive algorithms for user movements in order to provide better optimization. In this paper we suggest an algorithm for the implementation of the selective reservation concept for handover resources. We propose a next-move prediction scheme using the history of the user's movement directions and a backpropagation neural network algorithm. We show that for certain types of movement patterns the accuracy of 100% can be achieved. The developed algorithm is light-weight and does not require excessive computing power, and could be into terminals.

In this demonstration paper, we present a multi-radio MAC protocol and a prototype sensor node platform which supports dual frequency bands of operation. The multi-radio MAC protocol combines the advantages of both high and low frequency bands to give an energy efficient performance with high throughput and low latency in several applications. Our prototype supports spectrum agility, which is becoming important due to ever crowding spectrum. Besides demonstrating the improved performance metrics and spectrum agility support, we will also present the flexibility provided by our MAC protocol.

This paper describes the design, implementation and performance evaluation of a wireless sensor network based scalable outdoor vehicular tracking system. The system is highly flexible and configurable both from software and hardware architecture point of views and enables it to adapt to a wide range of vehicle tracking applications. We also present some intermediate results and the rationale behind our design approach. The system was tested for a network of 100 nodes and is scalable to a few thousand node setup. We believe that the vehicle localization and tracking results from our large scale deployment of sensor nodes and system design experience are useful to the community

Communication between cars can be exploited for various applications. Security enhancements as well as interactive games played by occupants of different cars are only two examples. The IEEE 802.11p standard will specify physical and MAC-layer for such scenarios. As one of the design goals is to partially reuse existing WLAN technology we investigate in this paper the performance of IEEE 802.11a, 802.11b and 802.11g devices in car-to-car (C2C) as well as car-to-roadside (C2R) scenarios. The determined performance is mostly independent of the car velocity up to the maximum measured speed of 180 km/h. The major impact factors found are the C2C distance, the availability of line-of-sight between sender and receiver and the rate adaptation algorithm.

The increasing need of ubiquitous wireless connectivity is pushing towards the emergence of hybrid wireless technologies and networks merging the advantages of the different wireless standards. In correspondence to the new hybrid environment, new handoff (HO) algorithms are needed for maximizing the efficiency of the HO decision-making process and guaranteeing the best possible connectivity and service quality. Apart of considering pure connectivity parameters for HO, in the future it is also important to take in account user preferences. In this paper we propose two vertical HO (VHO) decision-making schemes, based on fuzzy logics and neural networks. The mechanism for interworking among the different wireless technologies is taken for granted. We will show that multi-parametic HO decision-making turns out to be a useful and necessary step in the development of hybrid wireless environments.

We compare the performance of various dynamic query abolishment mechanisms in different unstructured overlay network topologies such as found in several P2P systems. We specifically focus on techniques based on iterative deepening and checking. Both unintelligent and intelligent variants of the methods are used in the study. Additionally, we propose a new mechanism called the chasing wave based on the use of increasing delays for search packets on the forwarding nodes. We show that the proposed chasing wave algorithm trades effectively the increase in propagation delay to substantially lower overhead. The performance of the methods are compared in several network configurations and using several metrics. We make concrete proposals on the suitability of the specific dynamic query abolishment methods for different search algorithms.

This paper describes the implementation architecture and performance analysis of nanoMAC, a CSMA/CA based medium access control protocol, which is specifically designed for high density wireless sensor networks. We empirically show that nanoMAC performs with high reliability in a variety of network traffic conditions in single and multihop scenarios. For energy efficient operation and minimizing idle-overhearing, nanoMAC uses a specialized sleep algorithmWe also show results from a comparative study of nanoMAC with B-MAC in terms of performance measures.

We define a notification hierarchy including a set of unified link-layer triggers to be used for cross-layer optimization in heterogeneous wireless networks. Unlike previous suggestions, our approach is not specific to IEEE 802-technologies, and can be used by applications as well as network and transport protocol entities. We also present full testbed implementation of our system together with the corresponding software architecture focussing on IEEE 802.11 WLAN networks. As a test case, we illustrate the benefits of our approach with a video streaming application using the retransmission ratio as a link quality indicator.

Wavelet packet modulation has been proposed as an interesting alternative to DFT-based multicarrier modulation schemes. Some issues still remain to be addressed for this modulation to be used in practice. This article focuses on the design of a symbol timing tracking algorithm derived from Mueller and Müller work.

Present mobile devices often support multiple communication technologies. If software developers want to control the configuration or monitor the characteristics of active links their applications will have to use different programming interfaces for each technology and supported platform. We propose an architecture for a unified link-layer API (ULLA) to abstract common metrics and introduce a single interface for retrieving link-layer information. ULLA offers a flexible query interface and a powerful notification mechanism that enable applications to become link-aware. Additionally, special attention was paid to keep the framework extendible for emerging networking technologies. We have implemented prototypes for a Linux notebook and a Windows CL PDA in order to prove the feasibility of the approach. Memory footprint, query duration, and power consumption tests show that ULLA can also be deployed on mobile, resource limited devices

We present the design and implementation of graph colouring -based channel assignment mechanism for infrastructure mode IEEE 802.11 networks. Several enhancements to earlier designs as well as results from a thorough performance evaluation in a WLAN testbed are given. The performance of the colouring scheme is compared with random and "fixed default" channel assignments, as these are the ones commercially available access points most often use. It is shown that the colouring approach leads to improved utilization of wireless resources, and considerably enhances the performance of the networks.

In this paper we present three context transfer mechanisms to support seamless (inter-system) handover: L2 tunnel, SDU reconstruction and SDU context transfer. The hand-over schemes are evaluated for services based on TCP SACK. The influence of different system parameters like the link layer round trip time and block error rate are investigated. It is shown that there can be large differences in handover performance caused by packet loss or packet duplication, which interact with TCP congestion control. L2 tunnel always performs best but has the highest complexity. It is followed by SDU reconstruction and then SDU context transfer respectively. All schemes are always better in performance than when no context transfer is used

It is desirable that future networks make the use of services and other resources simple for everyone. In that sense, service discovery comes to fill a gap in the networking field, since it eliminates most of the configuration and maintenance tasks. The Universal Plug and Play (UPnP) architecture does so by dividing the communication session in six different phases, and by providing the user with a friendly interface to the services. UPnP was mainly designed with wired or fixed wireless infrastructure networks in mind, and thus is not the optimum solution for the future mobile ad hoc networks (MANETs), where devices are usually battery operated and mobile. In this paper, we present an extension for the UPnP architecture which is designed specifically for wireless ad hoc networks. We made a performance analysis, and show that it has very promising performance characteristics. These extensions were carefully designed to be minimal ones, in order to make it compatible with the existing UPnP framework. The paper shows that UPnP is a promising alternative as service discovery protocol for heterogeneous networks.

In this demonstration paper we show and describe a flexible hardware and software platform for tracking applications. The architecture presented is extendible both on hardware and software sides, allowing for easy inclusion of sensors and signal processing algorithms of different types. During the demonstrations, examples of the software and hardware blocks developed within this generic architecture will be shown, and insight to the design choices and development issues will be given. Both outdoor and table-top versions of the possible demonstrations will be described.

IEEE 802.15.4 was developed to meet the needs for simple, low-power and low-cost wireless communication. In the past couple of years it has become a popular technology for wireless sensor networks. It operates primarily in the 2.4 GHz ISM band, which makes the technology easily applicable and worldwide available. However, IEEE 802.15.4 is potentially vulnerable to interference by other wireless technologies working in this band such as IEEE 802.11 and Bluetooth. This paper gives a short overview of the IEEE 802.15.4 and carefully analyzes the properties and performance of IEEE 802.15.4 through measurement of the RSSI, PER and run lengths distribution using real off-the-shelf hardware. Furthermore we present simulation results from the evaluation of the IEEE 802.15.4 MAC protocol. Finally, we address the coexistence between IEEE 802.11 and IEEE 802.15.4 and measure the impact these two wireless technologies have on each other when operating concurrently and in range

With the booming of multimedia services, the 3/sup rd/ generation partnership project (3GPP) has introduced the multimedia multicast/broadcast service (MBMS) feature for UMTS systems. The main objective of MBMS is to introduce real broadcast distribution capabilities into 3G systems. The MBMS service layer toolbox supports two distinct delivery methods: download and streaming. This paper focuses on the MBMS download delivery method. We have a particular focus on the file repair procedure, because the distribution of binary data via MBMS must result in 100% transmission error free downloaded files. In order to achieve this goal, MBMS offers a forward error correction (FEC) mechanism to protect the actual MBMS transmission and an extra file repair procedure to be used after the actual MBMS transmission. This paper discusses the trade-off between FEC protection and successive file repair, and then proposed a novel file repair scheme. The new scheme combines point-to-multipoint (PTM) file repair transmission with a point-to-point (PTP) file repair procedure. Using only a point-to-multipoint file repair scheme is not efficient, since the sender does not know the amount of needed repair data. Our simulations and analysis show that the new scheme achieves better performance than a PTP-only file repair procedure. Further, the two schemes are compared with respect to the radio resource consumption.

With network operators migrating from circuit-switched networks to IP networks, the need for a concept to provide operator grade trunk signaling over IP arises. A solution to enable signaling transport over IP is defined by the IETF SIGTRAN framework that allows for interworking with the Signaling System No. 7 (SS7). SIGTRAN is based on the stream control transmission protocol (SCTP) that also has a built-in redundancy mechanism, which allows switching to an alternative destination address (failover). Existing work showed that a failover performance comparable to SS7 can be achieved by aggressively setting the SCTP protocol parameters. However, this brings about possible stability issues because these parameter settings seriously impair the protocol mechanisms responsible for reacting to delay variations and congestion situations. We propose a set of network redundancy mechanisms that resolve most network failures transparently to SCTP. In order to eliminate possible stability issues, we use the default SCTP protocol parameters. Simulation results confirm that the proposed architecture can noticeably improve the robustness to concurrent network failures and achieve a similar delay behavior without jeopardizing the protocol stability

As proven by the success of OFDM, multicarrier modulation has been recognized as an efficient solution for wireless communications. Waveform bases other than sine functions could similarly be used for multicarrier systems in order to provide an alternative to OFDM. In this paper, we study the performance of wavelet packet transform modulation (WPM) for transmission over wireless channels. This scheme is shown to be overall quite similar to OFDM but with some interesting additional features and improved characteristics. A detailed analysis of the system's implementation complexity as well as an evaluation of the influence of implementation-related impairments are also reported. Copyright © 2004 John Wiley & Sons, Ltd.

We are arguing that some new interesting trends in making are emerging. We try to identify some aspects that could provide hard problems with potentially high payout for the future research. We also show how some of the topics are shared between both wireless and fixed network domain. We propose as a hypothesis that the key issue in the future is to understand and lower the increasing complexity (increasing abstraction) of communications systems. There are several different ways towards this goal.

Classical graph colouring and its generalisations have been used to model various frequency and channel allocation processes in different radio and wireless network contexts for some time now. However, most of this work has targeted cellular networks and graphs with a relatively large number of nodes. In this article we demonstrate how graph colouring can be used as a theoretical basis for a protocol to effectively assign channels to WLAN access points. We also give the outline of the protocol operation, and show its effectiveness with real-life wireless networks.

This paper reports on the development of an embedded networking protocol stack, which is for pervasive embedded devices. Taking into account requirements for sensors and low-price consumer devices. Instead of applying TCP/IP (designed to support routing and end-to-end connectivity) to embedded networking, our boundary conditions are quite different. The protocols presented here specifically provide embedded networking within local subnets. No routing is performed; instead a gateway can make the embedded devices visible to the global Internet. This configuration provides better scalability, RAM/ROM utilization, and lower power consumption. The developed single subnet architecture and additional protocols supporting pervasive applications are presented and shortly analyzed.

The aim of this paper is to characterize the behavior of the UDP transport protocol, mainly in terms of throughput and average delay, over an IEEE 802.11b compliant wireless local area network (WLAN) from an experimental approach. Although performance evaluation of these kinds of LAN is being extensively discussed in the current literature, most of these studies are analytical or simulation-based and they do not consider the influence of channel impairments or link quality over network throughput. After an exhaustive measurement campaign, different performance parameters such as throughput, goodput, effective goodput, frame error rate, IT loss rate, delay and jitter have been obtained. This could be a preliminary analysis of the behavior of real-time traffic over WLAN infrastructures, such as streaming audio and voice transmission, services that are expected to undergo substantial growth in the coming years. All these studies and results have been developed in the context of the WINE (Wireless Internet Networks) project belonging to the framework of the Information Society Technologies (IST) programme, addressed to enhancing the performance of TCP/IP protocol over WLAN such as IEEE 802.11, Bluetooth and HIPERLAN/2.

This paper gives some perspectives on likely trends for the wireless Internet. The treatment is an exploration of some technologies, and due to the nature of the presentation it has not been possible to go into any technical details. This is not a comprehensive prognosis, but it should serve as a starting point for discussion of what major research requirements we should be addressing. We end the analysis by pondering some issues concerning the wireless information society.

This paper gives some perspectives on likely trends for the wireless Internet. The treatment is an exploration of some technologies, and due to the nature of the presentation it has not been possible to go into any technical details. This is not a comprehensive prognosis, but it should serve as a starting point for discussion of what major research requirements we should be addressing. We end the analysis by pondering some issues concerning the wireless information society.

Heterogeneous networks require mechanisms for transparent protocol boosting in legacy systems and adaptive support for seamless interoperability between different wireless access methods. In this paper, we present an open performance enhancement protocol architecture that is developed to provide a generic protocol enhancing proxy (PEP) service for wireless access points and terminals. First, we describe the basic architecture and philosophy of our approach. Then some illustrative protocol boosting modules are presented with results. Finally, we describe the future work that is required in order to make the so-called wireless adaptation layer (WAL) useful in production quality environments.

The Wind-Flex project has developed a wireless indoor high bit rate OFDM modem, operating at 17 GHz. This modem is a packet-radio based system with a highly dynamic bit-rate range between 64 Kbit/s and 100 Mbit/s. Simulations for implementation have been done to evaluate the performance of the Wind-Flex modem using a concatenated coding scheme in combination with a TDMA-based block repetition mechanism. A simple ARQ-like delay reduction procedure is also presented. We show that TDMA-based repetition can significantly enhance the performance of the packet-oriented concatenated codec by trading off data rate for robustness. This does not lead to increased complexity in the modem implementation.

Vertical handoff means mobility between heterogeneous wireless access network technologies. Hierarchical radio access networks with overlapping cells gives an opportunity to handoff between local area high bandwidth networks and wide coverage cellular data. Vertical mobility combines the capacity of local area networks and the coverage of wide area cellular networks. Resource allocation in this kind of environment has not been yet fully addressed in the current research work. Clearly, additional bandwidth offered by wide-band local area wireless networks opens a new area for network performance optimization. For that, it is needed to understand the processes that have an effect on the system performance. In particular, vertical mobility is usually characterized by handoffs between systems having different data rates. The most critical area for system performance is at the very edge of the cell, where the received signal strength (RSS) varies around the sensitivity threshold of the receiver. Simulation results show the estimates of the delay and general performance in a multiple radio network environment. Generalization of handoff performance between multiple radio networks with different data rates is given with analysis.

Abstract Despite the growing interest for LMDS systems there have been only a few commercial implementations until now especially outside of the U.S.A. The use of hierarchical structure through two‐layer networking has been even rarer. In many cases LMDS systems have strong advantages against their competitors to cover the last mile. In this article, we review and analyze the standards currently available and describe the European two‐layer trial system developed in 1996–2000. We show why further development towards IP based LMDS is useful in the future. Most of our recommendations are based on results derived from the European Union supported research project CABSINET. It had the aim of demonstrating the viability of a 40 GHz cellular digital television system with a return channel to offer interactive services. Two systems were tested: a line of sight link using QPSK, and a non‐line of sight with COFDM modulation scheme. In the RF‐subsystems, the greatest difficulty of any viable LMDS system is to obtain a moderately low price for the user receiver, while fulfilling the hard OFDM requirements in terms of phase noise, stability and spectrum restrictions. Several options have been studied in order to design the subsystems with the smallest cost. This paper will present the architectures of the transmitters, nomadic terminals, and the design of the IF/RF subsystems for both types of modulations. The discussion is focused on system engineering and selections required in order to build a full two‐layer LMDS system. Copyright © 2002 John Wiley & Sons, Ltd.