Mobile Edge Computing (MEC) creates new infrastructure at the edges of the mobile networks, thus providing transformative opportunities for applications seeking latency benefits by operating closer to end-users and devices. However, the reduced network distance between the application endpoints of the MEC flows causes pattern shifts in the packet bursts exchanged at the network edges. The longer and denser bursts create a new source of contention that is not considered by current solutions. As a result, naively collocating applications onto the MEC tier can negatively affect latency-critical workloads, resulting in up to 73% packets experiencing as much as 3.8x increased latency. This makes it impossible to support latency-centric SLOs in MEC, obviating its expected benefits from MEC. This paper is the first to describe this new contention point in mobile networks and its potentially crippling impact on the achievable latency benefit from MEC. We propose ShapeShifter, a new component in the MEC architecture which solves the MEC latency contention problem through adaptive latency-centric burst management of MEC flows. ShapeShifter is effective - it eliminates SLO violations for latency-critical applications and improves application performance in multi-tenant scenarios by up to 3.8 x – and practical – it can be deployed with minimal disruption to the current mobile network ecosystem.

Energy harvesting has proved to be a viable solution for energy bottlenecks of wireless networks. This paper considers a single tier cellular network, in which the mobile nodes harvest energy from the aggregate downlink inter-cell interference and latter use it as uplink transmit power. A successful uplink transmission is assumed only when a user harvested enough energy in its battery and the received signal-to-interference ratio (SIR) is above a predefined threshold. We investigate the performance of the proposed system model in both downlink and uplink using stochastic geometry and provide theoretical analysis and numerical evaluation. Denser macro tiers show higher uplink successful transmission probability due to the increased number of available power extraction sources i.e macro base stations.

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.

Due to the fundamental tradeoffs, achieving spectrum efficiency and energy efficiency are two contending design challenges for the future wireless networks. However, applying radio-frequency (RF) energy harvesting (EH) in a cognitive radio system could potentially circumvent this tradeoff, resulting in a secondary system with limitless power supply and meaningful achievable information rates. This paper proposes an online solution for the optimal time allocation (time sharing) between the EH phase and the information transmission (IT) phase in an underlay cognitive radio system, which harvests the RF energy originating from the primary system. The proposed online solution maximizes the average achievable rate of the cognitive radio system, subject to the ε-percentile protection criteria for the primary system. The optimal time sharing achieves significant gains compared to equal time allocation between the EH and IT phases.

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.

The location awareness is useful in many wireless networking solutions, such as cellular, ad hoc, self-organizing, cognitive radio networks, and so on. The RSS-based, non-Bayesian source localization is of particular interest, due to the presence of the RSS measurements in all radio devices, as well as the instantaneous estimations without extensive training and learning phases. However, most RSS-based localization algorithms usually neglect problems arising from inherent network topology uncertainty. The positions of some measuring anchors, usually assumed to be precisely known in advance, are often a subject of previous estimation, which propagates errors in the source localization procedure and results in performance degradation. A joint localization framework, termed source position estimation for anchor position uncertainty reduction (SPEAR), has been recently proposed as a possible solution to the problem of RSS-based localization in the presence of network topology uncertainty. The framework simultaneously estimates the positions of the sources and the uncertain anchors and serves as robust tool that provides improved source localization performance and more reliable anchor position estimates. This paper focuses on the theoretical assessment of SPEAR's performance by deriving its fundamental lower bounds using the Cramér-Rao information inequality. The main contribution of this paper is the detailed information-theoretic interpretation of the obtained results. The interpretation provides valuable insight into the essence of the SPEAR localization problem because it identifies different position information components in the observed data, discovers how they are mutually related, and determines how they contribute toward the resulting position information. This paper also provides geometric interpretation of the obtained results and illustrates the principles of distribution of position information in space. The theoretical results as well as their information-theoretic and geometric interpretations can be used as a benchmark for RSS-based location-aware systems that operate in the presence of network topology uncertainty.

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.

In spectrum bands where spectrum sharing is allowed by national regulators, radio access technology recognition is an important technique for reducing interference and facilitating cooperation among cognitive radios. Unlicensed users (secondaries) need to be able to differentiate between transmissions of licensed users (primaries) and other unlicensed users. Furthermore, secondaries should only free a band when the licensed primary user starts to transmit. In this regard, secondary users' transmission technology classification will have a vital role for coexistence/cooperation purposes in such shared spectrum bands. For the purpose of this work, a practical testbed made up of software defined radio transceivers and a set of computing units was put together. A classification neural network was trained in a supervised learning method. Testbed results demonstrate the efficiency of the classification in differentiating among different radio access transmissions.

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.

Flexible, reliable and robust signal detectors are vital components of the emerging cognitive radio networks. This paper presents a novel Goodness-of-fit Higher-Order-Statistics Testing (GHOST) signal detector, which eliminates the drawbacks of the previous related detectors and offers dominant signal detection performances. The detection is made based on goodness-of-fit testing comparing the skewness and kurtosis of the received power spectrum with empirically estimated system noise statistics. This allows reliable detection of different types of signals with SNR even lower than -32dB. The detector can be easily applied to the existing radio hardware since it adapts to the specific noise characteristics.

Spectrum sensing is a fundamental feature of cognitive radio technologies facilitating detection and localization of spectrum opportunities. Combined with centralized database storage, it fosters reliable spatial spectrum measurements and Radio Environmental Maps (REMs) construction that can provide an accurate insight in the spectrum utilization over time, space and frequency. The spectrum sensing process often combines different devices with different capabilities. This paper introduces novel REM construction platform that integrates several mid- and low-end spectrum sensing devices into a single heterogeneous testbed. The devices of interest are USRP2s, TI RF2500 and Sun SPOTs, which are upgraded with custom developed software for providing versatile spectrum sensing capabilities. The paper evaluates their performances and discusses their integration into a single heterogeneous testbed platform. Additionally, the paper evaluates the realized heterogeneous spectrum sensing testbed in terms of accurate REM derivation and additional statistics gathering for indoor environments.

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.

Policing a wireless network is an evolving and exciting paradigm, specially emphasized with the emergence of cognitive radio networks. The possibility to manage radio resources efficiently and achieve different goals imposed by different stakeholders with dynamically changing policies in a wireless network is the main advantage of the policy based cognitive system. Moreover, the ability to express a variety of policy types for different purposes allows for high system flexibility and adaptability. This paper presents novel policy reasoning architecture for cognitive radio networks and elaborates on its functionalities and applications. Additionally, the paper provides a proof-of-concept testing of spectrum usage management feature of the architecture using USRP2 nodes. The elaborated policy framework is general enough to handle wide range of resource management issues in the wireless domain.

This paper introduces a novel rendezvous protocol (RAC2E) for dynamic control channel provisioning among cognitive radios in a decentralized and cooperative ad-hoc environments. Unlike previous work in the field, RAC2E does not require synchronization in the radio environment. Its main characteristics are lightweight design and scalability. The paper elaborates RAC2E operation, gives a numerical analysis of its performance behavior under different circumstances and demonstrates its functionalities on a testbed platform realized with four Universal Software Radio Peripheral 2 (USRP2) nodes.

Energy efficiency of Wireless Sensor Networks (WSNs) is highly affected by duration of periods when sensors transmit data. In order to reduce the amount of transmitted bits from each sensor's radio, the basic Data Timed Sending (DTS) method was recently proposed by the authors. Basic DTS addresses the packet size reduction, achieved by eliminating the Data Payload from the data packets or by using the acknowledgment packets. The information about measured values is transferred by choosing appropriate moment (time slot and time window) in the time interval for performing the packet transmission. The position of every time slot corresponds to a particular node and the position of every time window corresponds to a certain value of measured phenomenon. In one time interval each node sends data from one measurement. Since the synchronization is crucial for feasibility of this method, this paper examines the impact of synchronization error. The simulation results show that for the realistic levels of noise, the Linear Regression (LR) synchronization technique offers sufficient accuracy.

Wireless IEEE 802.11a systems are expected to become an important cornerstone of future generation heterogeneous networks. Their efficient operation and adaptation to the variable channel conditions is a must. This paper derives a novel analytical model for the throughput performance of IEEE 802.11a systems. Unlike previous work, the model in this paper is based on realistic assumptions, i.e. non-saturation mode of operation, Rayleigh fading channel and contentions in the medium. The model is then used for discussing optimization of the IEEE 802.11a system behavior through optimal frame length and transmission mode selection under various channel conditions and communication scenarios

This paper proposes a QoS-based strategy for call admission in mobile cellular networks, based on the past system's behavior. The algorithm is an extension of the conventional guard scheme (CGC) and demonstrates improvements compared to CGC. It reduces the new call blocking probability, increases the total carried traffic and keeps the forced call termination probability almost unchanged. The proposed policy falls into the class of fractional guard policies. Each cell builds cumulative statistics from its "experience" and accepts a new call only when predetermined handoff failure probability is not exceeded. The generic form of the algorithm is independent of cellular network topology.