Gradient descent (GD) is an iterative optimization method to minimize a differentiable cost function. A main drawback of GD is its use of the entire dataset to perform a parameter update per learning step, which is cost-prohibitive for large-scale problems. Variants of GD such as mini-batch GD and stochastic GD compute the update direction with respect to a randomly sampled selection of the training dataset per iteration. This reduces the computational burden at the cost of adding jitters to the learning direction. Alternatively, we propose an iterative optimization algorithm that performs cheap parameter updates at minimal perturbation of the GD direction. The new method computes a deterministic summary of the training dataset, and then computes the learning direction per iteration with respect to the summary. We provide a convergence analysis for the proposed method and show that the thoroughness of the summary can be tweaked to optimize the trade-off between computational complexity and convergence rate. Simulation results are illustrated numerically for single and multi-agent systems.

Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring systems that demand intensive data collection, for detection, classification, and prediction of future events, from different locations while maintaining a strict privacy constraint. Due to privacy concerns and critical communication bottlenecks, it can become impractical to send the FL updated models to a centralized server. Thus, this paper studies the potential of hierarchical FL in Internet of Things (IoT) heterogeneous systems. In particular, we propose an optimized solution for user assignment and resource allocation over hierarchical FL architecture for IoT heterogeneous systems. This work focuses on a generic class of machine learning models that are trained using gradient-descent-based schemes while considering the practical constraints of non-uniformly distributed data across different users. We evaluate the proposed system using two real-world datasets, and we show that it outperforms state-of-the-art FL solutions. Specifically, our numerical results highlight the effectiveness of our approach and its ability to provide 4–6% increase in the classification accuracy, with respect to hierarchical FL schemes that consider distance-based user assignment. Furthermore, the proposed approach could significantly accelerate FL training and reduce communication overhead by providing 75–85% reduction in the communication rounds between edge nodes and the centralized server, for the same model accuracy.

This article proposes a novel ultra-low power, tunable, and asynchronous baseband architecture for joint radio-frequency (RF) wakeup and charging receivers. The designed system switches between the wakeup and charging operations based on the type of the received RF signal. To our knowledge, the proposed system is the first of a kind that introduces multiple power states to reduce the energy consumption by sequentially activating minimal components required for RF wakeup or charging. The fabricated prototype, using off-the-shelf components, features a sensitivity of −40 dBm, a bit rate of 500 bps, and a current consumption of 225 nA at a bias voltage of 2.6 V in the listening state. Current consumption is estimated at 3.225 and$13.725~\mu \text{A}$while processing the preamble and bit sequence, respectively. The address detector is powered OFF during charging to reduce the system’s current consumption to 150 nA. Our experimental results show that shutting down the address detector during charging reduces charging time and allows charging from received power levels that are as low as −6 dBm. We demonstrate that operating the detector with multiple power modes reduces its current consumption and enhances its noise immunity when compared to conventional address detectors with two power modes of operation.

Visible light communication (VLC) transceivers suffer from optical interference and ambient light saturation, which severely affect the reception of optical signals. In this paper, we discuss various techniques for the suppression of ambient light in electronic, optical, communication and wavelength domains with focus on VLC systems for vehicular communication. Designing transceivers for outdoor VLC systems is challenging due to the dynamic environment and the time-varying nature of interference from different types of ambient light sources. This is a unique study that addresses the problem of optical interference in multiple physical domains and offers qualitative insights in a systematic manner.

Low power requirements, real-time constraints and big data applications present several challenges to digital signal processing (DSP) that span multiple domains including filtering and frequency analysis. Design of digital filters that can efficiently process signals with low computational complexity is desirable and requires innovative approaches. In this paper, we present an efficient two-step design methodology to design low complexity finite-impulse response (FIR) filters. In the first step, the filter design is formulated as an optimization problem to find the minimum number of coefficients, based on K-means clustering with a Euclidean distance metric. In the second step, a mapping approach is proposed to compensate for the mean square error (MSE) that results from the clustering. We compare the proposed method with three different methods from the literature to demonstrate its effectiveness in terms of computational complexity without compromising the filtering performance.

The term Tactile Internet broadly refers to a communication network that is capable of delivering control, touch, and sensing/actuation information in real-time. The Tactile Internet is currently a topic of interest for various standardization bodies. The emerging IEEE P1918.1 standards working group is focusing on defining a framework for the Tactile Internet. The main objective of this work is to present the IEEE P1918.1 reference architecture framework for the Tactile Internet. The paper provides an indepth survey of various architectural aspects including the key entities, the interfaces, the functional capabilities, and the protocol stack. A case study has been presented as a manifestation of the architecture. Performance evaluation demonstrates the impact of functional capabilities and the underlying enablers on user-level utility pertaining to a generic Tactile Internet application.

The vision towards 5G and beyond is to provide remarkable performance enhancements that enable the launch of new services and markets in different industry verticals. Example scenarios of those services include indoor hotspot, broadband access in a crowd, and dense urban; many of which require very high data rates. Traffic offloading and device-to-device cooperation have been leveraged to expand the system capacity and coverage through using heterogeneous network technologies. In this work, we shed the light on the significant gains incurred when predicted short term network performance is factored into network splitting decisions over multiple wireless interfaces, while guaranteeing a desired quality of experience to end users. Accordingly, we allow dynamic use of multiple network interfaces taking into consideration their energy requirement and price models to deliver premium services and minimize the overall energy consumption and total cost. We develop a novel and efficient real-time traffic splitting approach that makes use of predicted bit rate of each network interface in addition to device-to-device cooperation to decide on the amount of video traffic to be delivered on each interface at every time slot. The proposed approach is validated and evaluated under realistic network conditions. Simulation results demonstrate substantial gains in terms of energy consumption, data cost and quality of user experience as compared to multiple alternative solutions.

This paper investigates the bit-error probability of several color shift keying (CSK) constellations in a visible light communications (VLC) scenario with a camera-based receiver. A novel VLC communication constellation set, the extended circular CSK, is presented and analyzed. Its performance is compared to the CSK constellation described in the IEEE 802.15.7 standard, in addition to other constellations that were introduced previously, namely the geometric and circular CSK constellations. The bit-error rate (BER) of the proposed extended circular CSK constellation is studied. Simulation results show that the extended circular CSK significantly outperforms the other constellations and deserves further research attention in the future.

The IEEE “Tactile Internet” (TI) Standards working group (WG), designated the numbering IEEE 1918.1, undertakes pioneering work on the development of standards for the TI. This paper describes the WG, its intentions, and its developing baseline standard and the associated reasoning behind that and touches on a further standard already initiated under its scope: IEEE 1918.1.1 on “Haptic Codecs for the TI.” IEEE 1918.1 and its baseline standard aim to set the framework and act as the foundations for the TI, thereby also serving as a basis for further standards developed on TI within the WG. This paper discusses the aspects of the framework such as its created TI architecture, including the elements, functions, interfaces, and other considerations therein, as well as the novel aspects and differentiating factors compared with, e.g., 5G Ultra-Reliable Low-Latency Communication, where it is noted that the TI will likely operate as an overlay on other networks or combinations of networks. Key foundations of the WG and its baseline standard are also highlighted, including the intended use cases and associated requirements that the standard must serve, and the TI's fundamental definition and assumptions as understood by the WG, among other aspects.

This paper introduces circular color shift keying (CSK) constellations in a visible light communications (VLC) system using a camera-based receiver with applications to vehicular communications, where vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are required to improve the safety of vehicles in high traffic density. The circular CSK constellations are shown to outperform the geometric and IEEE 802.15.7 constellations in terms of bit-error probability. Afterward, the noise at the receiver is investigated and transferred from the spatial domain to an angular noise in the International Commission on Illumination (CIE) color domain, where it is shown to behave similarly to a Gaussian distribution. Based on this noise model, a closed-form expression of the bit-error rate (BER) of circular CSK constellations is derived and shown to be accurate for BER ranges corresponding to VLC applications of practical interest. Diversity techniques are also investigated, and interesting differences are noted during the analysis compared to traditional radio frequency (RF) communication scenarios. Finally, the design of a link adaptation approach maximizing the number of bits per transmitted symbol, while meeting a target BER, is proposed. Hence, this paper presents a detailed design and analysis of a novel VLC modulation technique and demonstrates its efficiency and superiority.

In recent years, there has been a significant growth of context-aware applications, which extract the user's context from multiple embedded sensors in smartphones and wearable sensors. However, running multiple context-aware applications simultaneously causes extensive battery drainage for mobile devices. To alleviate the energy limitation in multi-context setting, we propose EGO: an ontology-based framework for group sensor selection to achieve synergy across applications while trading off energy consumption, accuracy, and delay in context recognition. A new context recognition ontology is designed to capture context recognition models. It captures the alternative groups of sensors for each context and the parameters for context recognition models. EGO includes an adaptive sensor selection mechanism that selects the appropriate sensors based on the current user state, the available resources, and the requested accuracies by running applications. The framework provides an open architecture that allows integration with other sensor optimization modules for sensor scheduling. EGO is validated using a real test-bed implemented on an Android platform that provides accessibility to on-board mobile sensors and external sensors. Results show that EGO provides better trade-off than previous state-of-the-art methods. Furthermore, EGO provides 63 percent energy saving with comparable accuracy when compared to the most accurate group of sensors.

With the advent of wireless EEG headsets and the improved computational capabilities of mobile devices, the ubiquitous monitoring of patients suffering from epilepsy has recently gained notable interest from research and industry under the umbrella of emerging e-health systems. Among others, the problem of seizure prediction plays a central role in reducing the adverse effects of epileptic seizures by trying to foresee the occurrence of an epileptic seizure before its onset. This paper presents a patient-aware seizure prediction approach which combines an optimized spatio-temporal EEG feature extraction algorithm and classifier selection to maximize the prediction accuracy and reduce the false alarm rate. Experimental results demonstrate that the proposed approach leads to promising prediction results when tested on real clinical data from the Freiburg database, with accuracy exceeding 90% for most subjects. The key differentiating aspect of the proposed approach is its flexibility to be efficiently fine-tuned and optimized per subject in order to enhance sensitivity and minimize the false alarm rate.

In today's wireless networks, interference serves as a harmful factor that can notably degrade the overall performance. Nevertheless, interference can also be beneficial when utilized as an additional power source for radio frequency (RF) based energy harvesting in order to charge Internet of Things (IoT) devices. In this paper, we quantify the positive impact of interference for energy harvesting using tools from stochastic geometry; in particular, we derive an expression for a suitable success probability metric and validate its accuracy using Monte-Carlo simulations as a function of various system parameters. Moreover, we design, implement and evaluate an experimental test bed for RF-based energy harvesting using WiFi access points (APs) in order to demonstrate the practical performance gains resulting from interference as a function of the number of access points, their relative locations, and the receivers antenna configuration. The obtained experimental measurement results corroborate the insights extracted from the analytical derivations.

Monitoring context depends on continuous collection of raw data from sensors which are either embedded in smart mobile devices or worn by the user. However, continuous sensing constitutes a major source of energy consumption; on the other hand, lowering the sensing rate may lead to missing the detection of critical contextual events. In this paper, we propose VCAMS: a Viterbi-based Context Aware Mobile Sensing mechanism that adaptively finds an optimized sensing schedule to decide when to trigger the sensors for data collection while trading off the sensing energy and the delay to detect a state change. The sensing schedule is adaptive from two aspects: 1) the decision rules are learned from the user's past behavior, and 2) these rules are updated over real time whenever there is a significant change in the user's behavior. VCAMS is validated using multiple experiments, which include evaluation of model success when considering binary and multi-user states. We also implemented VCAMS on an Android-based device to estimate its computational costs under realistic operational conditions. Test results show that our proposed strategy provides better trade-off than previous state-of-the-art methods under comparable conditions. Furthermore, the method provides 78 percent energy saving when compared to continuous sensing.

In current wireless communication systems, there is a rapid development of location based services, which will play an essential role in the future 5G networks. One key feature in providing the service is the mobile crowdsourcing in which a central cloud node denoted as the principal collects location based data from a large group of users. In this paper, we investigate the problem of how to provide continuous incentives based on user's performances to encourage users' participation in the crowdsourcing, which can be referred to the moral hazard problem in the contract theory. We not only propose the one-dimensional performance-reward related contract, but also extend this basic model into the multi-dimensional contract. First, an incentive contract which rewards users by evaluating their performances from multiple dimensions is proposed. Then, the utility maximization problem of the principal in both one-dimension and multi-dimension are formulated. Furthermore, we detailed the analysis of the multi-dimensional contract to allocate incentives. Finally, we use the numerical results to analyze the optimal reward package, and compare the principal's utility under the different incentive mechanisms. Results demonstrate that by using the proposed incentive mechanism, the principal successfully maximizes the utilities, and the users obtain continuous incentives to participate in the crowdsourcing activity.

We use stochastic geometry to analyze the performance of an energy-efficient joint downlink/uplink (UL) radio-frequency (RF) wake-up solution for Internet of Things (IoT) devices over cellular networks. When the IoT device has no data to transmit, it turns its main circuitry completely OFF and switches to a deep sleep mode. The transition back to the active mode is only achieved upon receiving enough power at the device's front end. After wake up, the device initiates regular UL communication with its serving base station (BS). The device experiences a successful wake-up event when the total received power includes a wake-up signal transmitted from its serving BS, and the UL signal-to-interference-and-noise ratio (SINR) is above a predefined threshold. On the other hand, the device experiences a false wake-up event when the wake up is due to received power from neighboring BSs excluding the serving BS. We derive lower and upper bounds for the success and false wake-up probabilities in addition to closed-form expression for the UL SINR coverage probability after successful wake up. We present performance results as a function of various key design parameters and highlight the effectiveness and tradeoffs of RF wake up for IoT devices.

Device‐to‐device (D2D) offloading has been shown to be a highly effective technique to enhance the performance of wireless networks. Yet, for any two mobile users to share data efficiently and reliably via D2D links, they should be in close proximity for long enough period of time, share similar content interests, and have some level of incentive and trust to cooperate. In this work, we focus on the practical implementation aspects of D2D data sharing taking into account realistic operational conditions. To this end, we design and conduct an experimental study to collect location and neighbor discovery data from 38 mobile users in a university campus over several weeks using our own customized crowdsourcing Android mobile application. The collected data is then processed and utilized to empirically model mobility‐related parameters that include contact frequency, contact duration, and inter‐contact duration. The participating users did also fill a user interest survey in order to correlate mobility and connectivity patterns with content interests and social network relations. The obtained insights are then used to develop a practical implementation framework for designing effective D2D data sharing strategies. To test the proposed ideas under realistic operational constraints, we design and implement a social‐aware D2D data sharing Android mobile application and demonstrate its functionality and effectiveness using an example case study scenario.

We present in this paper a visible light communication transceiver system design for Vehicle-to-Vehicle (V2V) communication over an outdoor wireless channel using a camera as a receiver. We propose a channel model relevant to the nature of the receiver, according to which different modulation schemes and constellation sets are designed and compared through Monte-Carlo simulations. We evaluate performance for various design alternatives and output metrics, and demonstrate that our proposed color shift keying circular constellation set outperforms schemes included in the IEEE 802.15.7 standard in terms of noise resiliency.

Ultra dense networks and device-to-device communications are expected to play a major role in 5G networks to meet tremendous traffic requirements. In our work, we address traffic offloading in dense device-to-device cooperative heterogeneous networks with focus on use cases where a very large number of users request simultaneously common streaming content from a remote server with quality of service guarantees. We formulate an optimization problem to maximize the number of users served and reduce the number of access points deployed while satisfying a set of system constraints. The solution determines the best strategy for downloading the content either over long range connectivity from the access points or short range connectivity from peer mobile devices. Results are presented for various scenarios in a stadium setting to demonstrate the significant gains of optimized traffic offloading in ultra dense wireless networks.

The planning procedure of 802.11 WLAN networks is specific to each type of venue. In the case of large venues with high number and dense Wi-Fi devices, the network planning requires a deep investigation on: available channels, number of devices to associate, throughput per channels, etc. In addition, the WLAN planning of dense Wi-Fi devices becomes more complicated when adding collaboration between devices. The collaboration between devices is achieved by forwarding and sharing data through neighboring devices, named relays. In this paper, we provide guidelines for WLAN planning in high-dense collaborative environments. In addition, we study the network performance gains when content is distributed in cooperative manner through both unicast and multicast mode. Results show that using cooperative content distribution in dense environments leads to notable gain in network performance.

The Wi-Fi Direct technology was proposed by Wi-Fi Alliance in order to facilitate device-to-device communications in Wi-Fi. The Wi-Fi Direct technology offers new possibilities to deploy opportunistic and cooperative networks. The actual state of the Wi-Fi Direct specification lacks optimization for P2P grouping and creating opportunistic networks. In this paper, we present a new P2P group formation method for opportunistic networks and we introduce the concept of nominating a backup group owner that can replace the group owner of a broken P2P group. In addition, we investigate required times to form a P2P group of variable number of nodes and we evaluate the efficiency of the backup group owner. Results show that our proposed P2P group formation method, in addition to the introduction of the backup group owner, can facilitate and accelerate opportunistic P2P grouping.

In the future, all devices that benefit from an Internet connection will be connected. Internet of Things technologies are key enablers of this vision by moving beyond basic connectivity machine-to-machine (M2M) communications brings to more intelligent interconnection of physical things on a massive scale. This anticipated growth is expected to challenge the planning and operation of cellular networks due to new diverse traffic models and high signaling loads. In this paper, we conduct a detailed experimental study using state-of-the-art drive testing equipment in order to measure, quantify, and analyze the signaling overhead of two classes of M2M services that resemble smart metering and vehicular applications. Two practical signaling reduction techniques are proposed and analyzed, with focus on aggregation as an efficient approach to overcome the resulting surge in signaling load. We complement the experimental results with an analytical evaluation to quantify the tradeoffs between M2M data transmission delay and the level of aggregation. Moreover, we present a novel case study to assess the potential negative impact of M2M signaling traffic on network planning and operation in 4G cellular networks.

With the wide adoption of smart mobile devices, there is a rapid development of location-based services. One key feature of supporting a pleasant/excellent service is the access to adequate and comprehensive data, which can be obtained by mobile crowdsourcing. The main challenge in crowdsourcing is how the service provider (principal) incentivizes a large group of mobile users to participate. In this paper, we investigate the problem of designing a crowdsourcing tournament to maximize the principal's utility in crowdsourcing and provide continuous incentives for users by rewarding them based on the rank achieved. First, we model the user's utility of reward from achieving one of the winning ranks in the tournament. Then, the utility maximization problem of the principal is formulated, under the constraint that the user maximizes its own utility by choosing the optimal effort in the crowdsourcing tournament. Finally, we present numerical results to show the parameters' impact on the tournament design and compare the system performance under the different proposed incentive mechanisms. We show that by using the tournament, the principal successfully maximizes the utilities, and users obtain the continuous incentives to participate in the crowdsourcing activity.

In cognitive radio networks (CRNs), spectrum trading is an efficient way for secondary users (SUs) to achieve dynamic spectrum access and to bring economic benefits for the primary users (PUs). Existing methods require full payment from SU, which blocked many potential “buyers,” and thus limited the PU's expected income. To better improve PUs' revenue from spectrum trading in a CRN, we introduce a financing contract, which is similar to a sealed non-cash auction that allows SU to do financing. Unlike previous mechanism designs in CRN, the financing contract allows the SU to only pay part of the total amount when the contract is signed, known as the down payment. Then, after the spectrum is released and utilized, the SU pays the rest of payment, known as the installment payment, from the revenue generated by utilizing the spectrum. The way the financing contract carries out and the sealed non-cash auction works similarly. Thus, contract theory is employed here as the mathematical framework to solve the non-cash auction problem and form mutually beneficial relationships between PUs and SUs. As the PU may not have the full acknowledgment of the SU's transmission status, the problems of adverse selection and moral hazard arise in the two scenarios, respectively. Therefore, a joint adverse selection and moral hazard model is considered here. In particular, we present three situations when either or both adverse selection and moral hazard are present during the trading. Furthermore, both discrete and continuous models are provided in this paper. Through simulations, we show that the adverse selection and moral hazard cases serve as the upper and lower bounds of the general case where both problems are present.

In this paper, a novel framework is proposed for optimizing the operation and performance of a large-scale multi-hop millimeter wave (mmW) backhaul within a wireless small cell network having multiple mobile network operators (MNOs). The proposed framework enables the small base stations to jointly decide on forming the multi-hop, mmW links over backhaul infrastructure that belongs to multiple, independent MNOs, while properly allocating resources across those links. In this regard, the problem is addressed using a novel framework based on matching theory composed of two, highly inter-related stages: a multi-hop network formation stage and a resource management stage. One unique feature of this framework is that it jointly accounts for both wireless channel characteristics and economic factors during both network formation and resource management. The multi-hop network formation stage is formulated as a one-to-many matching game, which is solved using a novel algorithm, that builds on the so-called deferred acceptance algorithm and is shown to yield a stable and Pareto optimal multi-hop mmW backhaul network. Then, a one-to-many matching game is formulated to enable proper resource allocation across the formed multi-hop network. This game is then shown to exhibit peer effects and, as such, a novel algorithm is developed to find a stable and optimal resource management solution that can properly cope with these peer effects. Simulation results show that, with manageable complexity, the proposed framework yields substantial gains, in terms of the average sum rate, reaching up to 27% and 54%, respectively, compared with a non-cooperative scheme in which inter-operator sharing is not allowed and a random allocation approach. The results also show that our framework improves the statistics of the backhaul sum rate and provides insights on how to manage pricing and the cost of the cooperative mmW backhaul network for the MNOs.

The severe power limitations of machine-to-machine (M2M) devices challenge their access connectivity and reliable communication over cellular networks. In this work, we propose a new solution to reduce the power consumption of M2M over cellular by minimizing the power dissipated during inactive intervals. When the M2M device has no data to transmit, it will turn its main circuitry off and switch to a new deep sleep mode. The transition back to the active mode is only achieved upon receiving a radio frequency (RF) wake-up signal from the device's serving base station (BS). We use stochastic geometry to analyze the performance of the proposed wake-up solution. In the proposed model, the M2M device leaves the deep sleep mode when it receives enough power to be activated. The device experiences a successful wake-up event when the total received power includes a wake-up signal transmitted from its serving base station. On the other hand, the device experiences a false wake-up event when it wakes up due to received power from neighboring base station excluding the serving base station. The performance of the proposed model is evaluated in terms of the probabilities of these two events. We use Poisson point processes (PPPs) to derive tractable expressions for the performance metrics, and we present insights for network design and optimization.

Wireless network virtualization has emerged as a promising technology to provide a variety of services and applications for future wireless network as by enabling a more effective exploitation of network resources. In a mobile virtual network (MVN), both infrastructure provider (InP) and service provider (SP) must have a complementary relationship, as their revenues are mutually dependent. The trading of resources and services between the InP and SP is usually a long-term supply contract, and details of trades are left to be specified in the future. Thus, the returns of the InP and SP depend on their bargaining positions, ex post, and investments, ex ante. As a result, the InP and SP may hesitate to have specific investment, since it may put them at a risk of no return. In this paper, the problems of determining how the ownership of the resources affect the InP and SP's incentives to invest and how to choose the most efficient investments in an MVN are studied. First, a general system model is developed in multiple InPs and SPs engaged in a complementary relationship to exchange multiple physical and virtual resources. Subsequently, for this formulated problem, the optimal investments are derived. Furthermore, we give detailed analysis of a special case and shed light on the problem of ownership and investment efficiency by answering the question on whether the ownership of resources should be integrated or operated separately by the SP and InP. Simulation results assess the parameters that affect the efficiency of investment through simulations.

This work proposes a novel patient-aware approach that utilizes an n-gram based pattern recognition algorithm to analyze scalp electroencephalogram (EEG) data and predict epileptic seizures. The method addresses the major challenge of extracting distinctive features from EEG signals through a detection of spatio-temporal signatures related to neurological events. By counting the number of occurrences of amplitude patterns with predefined lengths, the algorithm generates a probabilistic measure (anomalies ratio) that is used as a prediction marker. These extracted ratios are classified using state of the art machine learning algorithms into seizure and non-seizure windows. The efficacy of the prediction model is tested on patient records from the Freiburg database with more than 100 hours of recordings per patient and for a total of 145 seizures. The proposed algorithm is further optimized to obtain the n-gram parameters for enhanced feature extraction. Results demonstrate an average accuracy of 93.83%, sensitivity of 96.12%, and false alarm rate of 8.44%.

The development of a neurologically-oriented mobile health system involves significant challenges in terms of the proper sensing and efficient transmission of electroencephalogram (EEG) signals, and the faithful reconstruction of these signals at the receiving node. EEG compression has been widely used to reduce storage requirements, improve the real time processing of the sensed signals, and provide a better and timely feedback to the concerned patients. The non-stationarity of the EEG signals and the large volumes of data being continuously processed mandate the development of data reduction schemes that provide a good tradeoff between compression performance and the preservation of the signal quality and integrity. To this end, we propose in this work a dynamic and effective compression approach for EEG data that relies on a sequence of compression and decompression phases to optimize the compression rate while maintaining a distortion level below a target threshold. Simulation results using real EEG data segments show that even with stringent quality requirements, a notable compression ratio can be attained with minimal processing overhead.

Modern mobile pervasive applications focus on context awareness that monitors a diverse range of personal domains. In order to infer contextual information, most of these applications require the collection of raw data from sensors which are either embedded in personal smartphones or worn by the user. Critical context-aware applications rely on continuous accurate monitoring of the user's current context. Continuous sensing mechanisms in sensors cost high energy consumption to support accurate contextual detection. Hence, there is a trade-off between the classification accuracy and the energy consumption. In this paper, we exploit the advantages of Deep Neural Network (DNN) with ensemble classification of other complementary machine learning approaches to determine the best sensor sampling frequency for the recognition of a given context. DNN relies on raw data for classification while the other complementary methods (such as Decision Tree and Naïve Bayes) use feature recognition to classify data. Therefore, our approach provides a range of granularity from raw data. We prove the robustness of our approach in experiments which show high accuracy in context recognition. In addition, real experiments demonstrate the energy gains of the proposed algorithm which reach 87% reduction in energy consumption when compared to continuous sensing.

We present a scalable mobile multimedia streaming system with device-to-device cooperation that enables common content distribution in dense wireless networking environments. This is particularly applicable to use cases such as delivering real-time multimedia content to fans watching a soccer game in a stadium or to participants attending a major conference in a large auditorium. The key novel characteristics of our system include seamless neighbor discovery and link quality estimation, intelligent clustering and channel allocation algorithms based on constrained minimum spanning trees, robustness against device mobility, and device centric operation with no changes to existing wireless systems. We demonstrate the functionality of the proposed system on Android devices using heterogeneous networks (cellular/WiFi/WiFi-Direct) and show the formation of multiple clusters to allow for scalable operation. The gained insights will help bridge the gap between theoretical and simulation based research conducted in this area and practical operation taking into account the capabilities and limitations of existing wireless technologies and smartphones/tablets.

Abstract Energy is becoming a main concern nowadays due to the increasing demands on natural energy resources. Base stations (BSs) consume up to 80% of the total energy expenditure in a cellular network. In this paper, we propose and evaluate a green radio network planning approach by jointly optimizing the number of active BSs and the BS on/off switching patterns based on the changing traffic conditions in the network in an effort to reduce the total energy consumption of the BSs. The problem is formulated as an integer optimization problem, which proves to be NP‐complete, and thus it can be efficiently solved for small to medium network sizes. For large network sizes, we propose a heuristic solution with close to optimal performance because the optimal solution becomes computationally complex. Planning is performed based on two approaches: a reactive and a proactive approach. In the proactive approach, planning will be performed starting with the lowest traffic demand until reaching the highest traffic demand, whereas in the reactive approach, the reverse way is considered. Performance results are presented for various case studies and are complemented by testing the proposed approaches using commercial radio network planning tools. Results demonstrate considerable energy savings reaching up to 40% through dynamic adaptation of the number of simultaneously active BSs. Copyright © 2015 John Wiley & Sons, Ltd.

With the wide adoption of smart mobile devices, there is rapid development of location based services. One key feature of supporting a pleasant/excellent service is the access to adequate and comprehensive data, which can be obtained by mobile crowdsourcing. The main challenge in crowdsourcing is how the service provider (principal) incentivize a large group of mobile users to participate. In this paper, we investigate the problem of designing a tournament to provide continuous incentives for users by rewarding them based on the rank achieved in crowdsourcing. First, we model the user's utility of reward from achieving one of the winning ranks in the tournament. Then, the utility maximization problem of the principal is formulated, under the constraint that the user maximizes its own utility by choosing the optimal effort in the crowdsourcing tournament. Furthermore, we show that, the tournament can approximate the optimal contract under full information by step function. Finally, we present numerical results to compare the system performance under the different proposed incentive mechanisms; we show that by using the tournament, the users obtain the continuous incentives to participate in the crowdsourcing activity.

Designing cost-effective and scalable backhaul solutions is one of the main challenges for emerging wireless small cell networks (SCNs). In this regard, millimeter wave (mmW) communication technologies have recently emerged as an attractive solution to realize the vision of a high-speed and reliable wireless small cell backhaul network (SCBN). In this paper, a novel approach is proposed for managing the spectral resources of a heterogeneous SCBN that can exploit simultaneously mmW and conventional frequency bands via carrier aggregation. In particular, a new SCBN model is proposed in which small cell base stations (SCBSs) equipped with broadband fiber backhaul allocate their frequency resources to SCBSs with wireless backhaul, by using aggregated bands. One unique feature of the studied model is that it jointly accounts for both wireless channel characteristics and economic factors during resource allocation. The problem is then formulated as a one-to-many matching game and a distributed algorithm is proposed to find a stable outcome of the game. The convergence of the algorithm is proven and the properties of the resulting matching are studied. Simulation results show that under the constraints of wireless backhauling, the proposed approach achieves substantial performance gains, reaching up to 30% compared to a conventional best-effort approach.

The explosion in the mobile broadband data usage and the advent of applications with different uplink and downlink quality of service requirements has triggered an increased interest in time division duplexing (TDD) systems. Despite the flexibility it offers in dynamically changing the uplink and downlink transmission schedules according to the traffic variations in a given network, the TDD mode of operation introduces increasing complexity in managing the interference generated by the concurrent uplink and downlink tranmission in different cells. In this paper, we present a statistical framework for the modeling of interference in TDD systems and the analysis the impact of dynamic switching point configuration on the TDD-based network operation. The probability density function (pdf) for each type of interference is first derived using moment generating functions. Then, interference maps are generated to analyze the impact of TDD operation in a given network.

With the widely adoption of smart mobile devices, there is a rapidly development of location based services. One key feature in providing the service is the crowdsourcing in which the principal obtains essential data from a large group of users, and inversely sharing the data based service with everyone for free. In this paper, we investigate the problem of how to provide continuous incentives for users to participate in the crowdsourcing activity, which can be referred to the moral hazard problem in the contract theory. First, a performance related incentive mechanism is proposed. Then, the utility maximization problem of the principal is formulated, under the constraint that each user maximizes its own utility by choosing the optimal effort in the crowdsourcing activity. Finally, the numerical results show that by using the proposed incentive mechanism, the users obtains the continuous incentives to participate in the crowdsourcing activity, and the principal successfully maximize the utilities.

Device-to-device (D2D) communication is viewed as one promising technology for boosting the capacity of wireless networks and the efficiency of resource management. D2D communication heavily depends on the participation of users in sharing contents. Thus, it is imperative to introduce new incentive mechanisms to motivate such user involvement. In this paper, a contract-theoretic approach is proposed to solve the problem of providing incentives for D2D communication in cellular networks. First, using the framework of contract theory, the users' preferences toward D2D communication are classified into a finite number of types, and the service trading between the base station and users is properly modeled. Next, necessary and sufficient conditions are derived to provide incentives for users' engagement in D2D communication. Finally, our analysis is extended to the case in which there is a continuum of users. Simulation results show that the contract can effectively incentivize users' participation, and increase capacity of the cellular network than the other mechanisms.

Device-to-device (D2D) communication is seen as a major technology to overcome the imminent wireless capacity crunch and to enable new application services. In this paper, a novel social-aware approach for optimizing D2D communication by exploiting two layers, namely the social network layer and the physical wireless network layer, is proposed. In particular, the physical layer D2D network is captured via the users' encounter histories. Subsequently, an approach, based on the so-called Indian Buffet Process, is proposed to model the distribution of contents in the users' online social networks. Given the social relations collected by the base station, a new algorithm for optimizing the traffic offloading process in D2D communications is developed. In addition, the Chernoff bound and approximated cumulative distribution function (cdf) of the offloaded traffic are derived and the validity of the bound and cdf is proven. Simulation results based on real traces demonstrate the effectiveness of our model and show that the proposed approach can offload the network's traffic successfully.

In this paper, the quality of experience (QoE) of real-time video streaming over long term evolution (LTE) networks is investigated. Network QoE metrics are proposed in order to capture the overall performance of radio resource management algorithms in terms of video quality perceived by the end users. Thus, real time video streaming over both the uplink (UL) and downlink (DL) directions is investigated, and LTE dynamic resource allocation is taken into account. Metrics corresponding to average, geometric mean, and minimum QoE in the network are measured when max C/I, proportional fair, and max-min radio resource management algorithms are implemented. Monte Carlo simulation results show that proportional fair scheduling maximizes the average network QoE both in the uplink and downlink, and that it also leads to more fairness.

Heterogeneous networks are expected to play a major role towards meeting the exploding traffic demand over cellular systems. Particularly, existing WiFi hotspots will be dynamically utilized to offload the traffic of cellular mobile subscribers. This will be further facilitated by forthcoming advances in mobile device capabilities that will include the ability to operate multiple wireless interfaces simultaneously. To this end, we focus in this work on cellular/WiFi heterogeneous networks with traffic splitting where a mobile device can utilize existing cellular and WiFi links simultaneously to achieve various performance gains. We propose a multi-objective approach for traffic splitting that captures the tradeoffs between throughput maximization on one hand and battery energy minimization on the other hand. We evaluate the proposed approach using parameters determined via experimental measurements using Samsung Galaxy SIII mobile devices. Results are presented for various scenarios in order to quantify and analyze the throughput-energy tradeoffs of traffic splitting in cellular/WiFi heterogeneous networks.

In mobile networks, traffic fluctuates heavily over time which rises uncertainty in the cell load for an eNodeB. Conventional radio network planning (RNP) focuses on a static model for the traffic distribution which is usually taken at hours of peak demand. However, a major disadvantage of such a deterministic model is that the locations of the eNodeBs are not optimized for the various traffic distributions that vary across the day which decreases the average network's throughput or the end user's QoS at off-peak hours. The main contributions of this work are the following. First, we present a stochastic approach for LTE RNP that optimizes the eNodeBs locations taking into account various traffic distributions or the uncertainty of the traffic distribution over time. Second, we formulate the LTE RNP under uncertainty as a stochastic optimization problem that is solved to optimality for multiple scenarios. Finally, we apply a dynamic eNodeB switching on/off strategy to reduce the energy consumption. Simulation results show that significant increase in the network throughput can be achieved, if RNP is done taking into account the uncertainty of the traffic distribution compared to a deterministic traffic model. Moreover, switching off eNodeBs during low traffic states leads to a more energy efficient wireless network operation.

In this paper, the joint use of carrier aggregation (CA) and relay stations (RSs) to support machine-to-machine (M2M) traffic in 5G networks is investigated. The focus of the paper is mainly on fixed M2M devices, periodically transmitting fixed amounts of data (e.g. sensor measurements). With this type of devices, knowing the deployment density in a given area allows to plan the cellular network accordingly, by determining the number of needed RSs, and the number of wireless channels that should be used by each RS to schedule M2M transmissions. Thus, after presenting an LTE resource allocation algorithm suitable for the investigated M2M devices, numerical evaluations are presented to determine the number of M2M devices that can be served given the available bandwidth. Then, a detailed analysis is presented to determine the number of RSs required depending on the M2M device density, the use of CA, and frequency reuse.

The increasing demand for large data downloads on cellular networks is increasing congestion which decreases end user quality of service. This work addresses the problem of offloading the cellular network while distributing common content to a group of mobile devices that cooperate during the download process by forming device-to-device communication networks. The base station unicasts different chunks of the content to selected mobile devices that multicast it to each other over local ad hoc networks using multihop cooperation while maintaining fairness constraints on the energy consumption of the mobile devices. The optimal cellular offloading problem is formulated as a mixed integer linear programming problem and the corresponding complexity is analyzed. Then, a dynamic programming approach is proposed to adapt the solution to the dynamics of the network as the mobile devices move. Cellular offloading assuming single hop cooperation among the mobile devices proves to be significantly less computationally complex than cooperation using a higher number of hops; however both problems are NP-complete. Thus, polynomial time greedy algorithms are presented to obtain computationally fast solutions with good performance. Performance results demonstrate that significant cellular offloading gains can be achieved, even if only a very small fraction of the mobile devices' battery levels can be consumed for cooperation.

This paper presents a novel framework to derive the statistics of the interference considering dedicated and shared spectrum access for uplink transmission in two-tier small cell networks such as the macrocell-femtocell networks. The framework exploits the distance distributions from geometric probability theory to characterize the uplink interference while considering a traditional grid-model set-up for macrocells along with the randomly deployed femtocells. The derived expressions capture the impact of path-loss, composite shadowing and fading, uniform and non-uniform traffic loads, spatial distribution of femtocells, and partial and full spectral reuse among femtocells. Considering dedicated spectrum access, first, we derive the statistics of co-tier interference incurred at both femtocell and macrocell base stations (BSs) from a single interferer by approximating generalized-K composite fading distribution with the tractable Gamma distribution. We then derive the distribution of the number of interferers considering partial spectral reuse and moment generating function (MGF) of the cumulative interference for both partial and full spectral reuse scenarios. Next, we derive the statistics of the cross-tier interference at both femtocell and macrocell BSs considering shared spectrum access. Finally, we utilize the derived expressions to analyze the capacity in both dedicated and shared spectrum access scenarios. The derived expressions are validated by the Monte Carlo simulations. Numerical results are generated to assess the feasibility of shared and dedicated spectrum access in femtocells under varying traffic load and spectral reuse scenarios.

The advancement of wireless communications standards and the introduction of new services with tight bidirectional quality of service requirements necessitated the development of novel and intelligent resource allocation algorithms. These include delay-aware scheduling techniques to cope with the tight round trip delay constraints imposed by the standards. In this paper, we present a novel delay-aware joint uplink/downlink resource allocation algorithm that controls the scheduling process in one direction by using delay information from the other direction. The main idea behind this design is the balancing of the transmission delays between the uplink and downlink to avoid resource wastage and service degradation that can result from transmission imbalances. Results show an improved constrained delay behavior of the system. Moreover, using the definition of a satisfaction index based on the well-known Jain fairness index, we show how our proposed scheme outperforms traditional single-link resource allocation schemes in terms of user satisfaction.

In this paper, we derive a statistical model of the co-tier interference in closed access two tier heterogeneous wireless cellular networks with femtocell deployments. The derived model captures the impact of bounded path loss model, wall penetration loss, user distributions, random locations, and density of the femtocells. Firstly, we derive the analytical expressions for the probability density function (PDF) and moment generating function (MGF) of the co-tier interference considering a single femtocell interferer by exploiting the random disc line picking theory from geometric probability. We then derive the MGF of the cumulative interference from all femtocell interferers considering full spectral reuse in each femtocell. Orthogonal spectrum partitioning is assumed between the macrocell and femtocell networks to avoid any cross-tier interference. Finally, the accuracy of the derived expressions is validated through Monte-Carlo simulations and the expressions are shown to be useful in quantifying important network performance metrics such as ergodic capacity.

Intercell interference (ICI) is a primary cause for performance limitation in emerging wireless cellular systems due to its highly indeterministic nature. In this paper, we derive an analytical statistical model for the uplink ICI in a multiuser multi-carrier cellular network considering the impact of various uncoordinated scheduling schemes on the locations and transmit powers of the interferers. The derived model applies to generic composite fading distributions and provides a useful computational tool to evaluate key performance metrics such as the network ergodic capacity. The derived model is extended to incorporate coordinated scheduling schemes. A study is then presented to quantify the potential performance gains of coordinated over uncoordinated scheduling schemes under various base station coordination scenarios. Numerical results demonstrate that different frequency allocation patterns significantly impact the network performance depending on the coordination among neighboring base stations. The accuracy of the derived analytical expressions is verified via Monte-Carlo simulations.

Group testing, also known as pooling, is a common technique used in high-throughput experiments in molecular biology to significantly reduce the number of tests required to identify rare biological interactions while correcting for experimental noise. Central to the group testing problem are 1) a pooling design that lays out how items are grouped together into pools for testing and 2) a decoder that interprets the results of the tested pools, identifying the active compounds. In this work, we take advantage of decoder guarantees from the field of compressed sensing (CS) to address the problem of efficient and reliable detection of biological interaction in noisy high-throughput experiments. We also use efficient combinatorial algorithms from group testing as well as established measurement matrices from CS to create pooling designs. First, we formulate the group testing problem in terms of a Boolean CS framework. We then propose a low-complexity l1-norm decoder to interpret pooling test results and identify active compounds. We demonstrate the robustness of the proposed l1-norm decoder in simulated experiments with false-positive and false-negative error rates typical of high-throughput experiments. When benchmarked against the current state-of-the-art methods, the proposed l1-norm decoder provides superior error correction for the majority of the cases considered while being notably faster computationally. Additionally, we test the performance of the l1-norm decoder against a real experimental data set, where 12,675 prey proteins were screened against 12 bait proteins. Lastly, we study the impact of different sparse pooling design matrices on decoder performance and show that the shifted transversal design (STD) is the most suitable among the pooling designs surveyed for biological applications of CS.

Uplink power control is in essence an interference mitigation technique that aims at minimizing the inter-cell interference (ICI) in cellular networks by reducing the transmit power levels of the mobile users while maintaining their target received signal quality levels at base stations. Power control mechanisms directly impact the interference dynamics and, thus, affect the overall achievable capacity and consumed power in cellular networks. Due to the stochastic nature of wireless channels and mobile users' locations, it is important to derive theoretical models for ICI that can capture the impact of design alternatives related to power control mechanisms. To this end, we derive and verify a novel statistical model for uplink ICI in Generalized-K composite fading environments as a function of various slow and fast power control mechanisms. The derived expressions are then utilized to quantify numerically key network performance metrics that include average resource fairness, average reduction in power consumption, and ergodic capacity. The accuracy of the derived expressions is validated via Monte-Carlo simulations. Results are generated for multiple network scenarios, and insights are extracted to assess various power control mechanisms as a function of system parameters.

This paper presents a novel framework for modeling the uplink intercell interference (ICI) in a multiuser cellular network. The proposed framework assists in quantifying the impact of various fading channel models and state-of-the-art scheduling schemes on the uplink ICI. Firstly, we derive a semi-analytical expression for the distribution of the location of the scheduled user in a given cell considering a wide range of scheduling schemes. Based on this, we derive the distribution and moment generating function (MGF) of the uplink ICI considering a single interfering cell. Consequently, we determine the MGF of the cumulative ICI observed from all interfering cells and derive explicit MGF expressions for three typical fading models. Finally, we utilize the obtained expressions to evaluate important network performance metrics such as the outage probability, ergodic capacity, and average fairness numerically. Monte-Carlo simulation results are provided to demonstrate the efficacy of the derived analytical expressions.

Multicasting provides a scalable solution to deliver a common content to a set of requesting mobile terminals in wireless broadband networks. However, the multicasting rate is limited by the worst channel conditions of the mobile terminals which increases the content download time, and consequently, increases the energy consumed from the mobile terminals' batteries. In this paper, we address the problem of cooperative multicasting where a base station multicasts the content over a cellular network to an optimally selected set of mobile terminals, that in turn multicast the content over multihop connections to other mobile terminals in their vicinity which leads to the formation of cooperative groups. We formulate the joint problem of content distribution, group formation, and interference avoidance as a linear mixed binary optimization problem with the objective of minimizing the total energy consumed by the mobile terminals. Moreover, we propose a simplified multicasting formulation with a close to optimal performance but with a lower computational complexity. Results demonstrate remarkable energy consumption reduction gains in various network scenarios.

The emergence of resource-demanding wireless services such as mobile gaming or multimedia applications motivates the introduction of advanced paradigms for resource allocation in next-generation OFDMA-based wireless networks. In this context, while existing literature mainly focused on schemes in which the uplink and downlink resources are considered independently, this paper proposes a novel subcarrier allocation approach suitable for services that have coupled uplink/downlink (UL/DL) quality-of-service requirements. We model the proposed joint uplink/downlink subcarrier allocation problem as a two-sided stable matching game and we propose a corresponding novel resource allocation algorithm. Results show that the proposed stable matching algorithm enables an efficient joint uplink/downlink subcarrier allocation and yields notable performance gains, in terms of the average utility per user, relative to classical resource allocation schemes. The results also show that our approach leads to an improved fairness in the subcarrier allocation.

This paper deals with the statistical modeling of uplink inter-cell interference (ICI) considering greedy scheduling with power adaptation based on channel conditions. The derived model is implicitly generalized for any kind of shadowing and fading environments. More precisely, we develop a generic model for the distribution of ICI based on the locations of the allocated users and their transmit powers. The derived model is utilized to evaluate important network performance metrics such as ergodic capacity, average fairness and average power preservation numerically. Monte-Carlo simulation details are included to support the analysis and show the accuracy of the derived expressions. In parallel to the literature, we show that greedy scheduling with power adaptation reduces the ICI, average power consumption of users, and enhances the average fairness among users, compared to the case without power adaptation.

This work addresses the problem of optimized content distribution over wireless networks with cooperation among mobile terminals. The mobile terminals are optimally divided into groups where each group internally cooperates by downloading and exchanging a common content from the base station in order to minimize the total energy consumed by the mobile terminals. We assume unicasting transmissions from the base station and multicasting transmissions among the mobile terminals. It is well known that the optimal content multicasting problem that minimizes the mobile terminal's energy consumption is an NP-hard problem. Consequently, several heuristic algorithms have been proposed in the literature that take into account only few network parameters to simplify the energy minimization problem. The main contributions of this work are the following. First, we derive the optimal energy consumption expression in a grid network as a function of the optimal cooperative group size. Second, we study the effect of several important network parameters on energy consumption; these include the transmission rates and the energy consumed per unit time during data transmission and reception. Results show that cooperation leads to notable reduction in energy consumption compared to the non-cooperative case. Besides, we demonstrate that several network parameters have nearly equivalent importance; consequently, energy-aware content distribution protocols should take into account multiple network parameters to obtain more efficient solutions.

For real-time video broadcast where multiple users are interested in the same content, mobile-to-mobile cooperation can be utilized to improve delivery efficiency and reduce network utilization. Under such cooperation, however, real-time video transmission requires end-to-end delay bounds. Due to the inherently stochastic nature of wireless fading channels, deterministic delay bounds are prohibitively difficult to guarantee. For a scalable video structure, an alternative is to provide statistical guarantees using the concept of effective capacity/bandwidth by deriving quality of service exponents for each video layer. Using this concept, we formulate the resource allocation problem for general multihop multicast network flows and derive the optimal solution that minimizes the total energy consumption while guaranteeing a statistical end-to-end delay bound on each network path. A method is described to compute the optimal resource allocation at each node in a distributed fashion. Furthermore, we propose low complexity approximation algorithms for energy-efficient flow selection from the set of directed acyclic graphs forming the candidate network flows. The flow selection and resource allocation process is adapted for each video frame according to the channel conditions on the network links. Considering different network topologies, results demonstrate that the proposed resource allocation and flow selection algorithms provide notable performance gains with small optimality gaps at a low computational cost.

Increasing the capacity of wireless cellular networks is a major concern for network operators. We address the problem of offloading the cellular network via assistance from the mobile terminals to receive an on-demand common content from the base station. The base station unicasts the content to selected mobile terminals that either unicast or multicast it to other mobile terminals over local ad hoc networks. We formulate the resulting problems as linear mixed integer optimization problems that minimize the required number of cellular channels subject to energy consumption constraints at the mobile terminals side. Performance results demonstrate that the achieved gains in terms of reduction of cellular channels depend significantly on the mobile terminals' energy consumption constraints. For one hop ad hoc networks, significant reduction gains reaching up to 90% and 50% for multicasting and unicasting, respectively, can be achieved, if only 5% of the mobile terminals' battery level can be spent on cooperation. More gains can be achieved with multihop ad hoc networks, however, at the cost of a higher computationally complex solution.

The development of the Time Division Duplexing (TDD) mode in next generation OFDMA-based wireless standards has brought some major advantages but at the same time raised several critical design requirements. Among others, the introduced flexibility in splitting resources between the uplink and downlink directions makes of the uplink/downlink switching point configuration a major design problem, especially that it is directly connected to other design aspects such as resource allocation and interference mitigation. In this paper, we propose a dynamic joint uplink/downlink resource allocation and switching point configuration algorithm. The proposed algorithm is channel-aware, traffic-aware, and service-aware. The resource allocation problem is formulated as an optimization problem that captures the different network parameters. Resulting instantaneous uplink and downlink rates are utilized in a dynamic estimation of the uplink and downlink time shares. A fast implementation using sequential quadratic programming is presented. Simulation results demonstrate the efficiency of the proposed approach in responding dynamically to varying network conditions and the improved performance when compared to fixed uplink/downlink switching point configurations.

Uplink resource allocation with intercell interference mitigation in LTE networks is investigated. A non-cooperative probabilistic interference avoidance scheme and a pricing-based cooperative power control scheme are proposed. A scheduling algorithm is presented and used with the proposed interference mitigation schemes. In the absence of power control, scheduling with the probabilistic interference avoidance scheme is shown to lead to considerable enhancements over classical reuse schemes. When cooperative power control is added, the proposed approach is shown to outperform the standard LTE power control approach.

The emergence of new wireless technologies paved the way to the introduction of a new class of symmetric services with joint uplink/downlink quality of service requirements. In this paper, we formulate joint uplink/downlink resource allocation in OFDMA networks as an optimization problem with total sum-rate maximization as an objective function. A regularization term is added to the problem formulation to account for the coupling between the uplink and the downlink directions. This additional term controls the resource allocation via the minimization of the per-user difference between the uplink and downlink rates. We solve the formulated problem using dual optimization techniques. Performance results demonstrate the effectiveness of the proposed joint uplink/downlink allocation scheme compared to traditional single-link schemes.

Resource allocation in orthogonal frequency division multiple access (OFDMA) networks plays an imperative role to guarantee the system performance. However, most of the known resource allocation schemes are focused on maximizing the local throughput of each cell, while ignoring the significant effect of inter-cell interference. This paper investigates the problem of resource allocation (i.e., subcarriers and powers) in the uplink of a multi-cell OFDMA network. The problem has a non-convex combinatorial structure and is known to be NP hard. Firstly, we investigate the upper and lower bounds to the average network throughput due to the inherent complexity of implementing the optimal solution. Later, a centralized sub-optimal resource allocation scheme is developed. We further develop less complex centralized and distributed schemes that are well-suited for practical scenarios. The computational complexity of all schemes has been analyzed and the performance is compared through numerical simulations. Simulation results demonstrate that the distributed scheme achieves comparable performance to the centralized resource allocation scheme in various scenarios.

With the emergence of green communication as a central design issue in wireless networks, the need for energy-efficient transmission protocols has significantly increased. In this paper, we study the problem of cooperative energy-efficient content distribution among a number of mobile terminals that are seeking to receive a content from a common wireless access point, e.g., a base station. We model the problem as a coalitional game among the mobile terminals and we propose a distributed algorithm for coalition formation. Using the proposed algorithm, the mobile terminals can cooperate and self-organize into independent coalitions for sharing content while minimizing the network's energy consumption. Within each coalition, a mobile, designated as coalition head, receives the content from the base station and shares it with its cooperating partners, either by unicasting or multicasting, using a short-range wireless technology. We analyze the resulting coalitional structures and study their properties. Simulation results show that the proposed algorithm presents a significant energy reduction in the network reaching up to 60% relative to the non-cooperative case and 37.5% relative to the case where all requesting mobiles form a grand coalition.

Given the exploding number of mobile subscribers and the quality of service requirements of emerging mobile services specially the multimedia ones, efficient use of the limited network resources has become so important that it is driving development of intelligent resource allocation (RA) algorithms. Whereas link specific RA problems have been widely investigated in the literature targeting either the uplink or downlink of a communication system, we present in this paper, SIRA, a novel joint uplink/downlink RA scenario in an OFDMA system for users running symmetrical services on their mobile devices. The RA process is modeled as a social gaming paradigm where users are considered as players interacting with the base station which takes the role of a principal controlling the RA game. Intelligently evolving from a soft RA and inspired by the model of team development proposed by Bruce Tuckman, SIRA uses penalty based concepts which play a feedback mechanism role from a system theory perspective to finalize the decision on allocated resources. Simulation results show the merit of the proposed technique and motivate follow on research.

A novel reservation Aloha scheme is presented for OFDMA systems. The proposed scheme is based on two dimensional reservation in time and frequency. The proposed approach is compared to other OFDMA extensions of reservation Aloha. The proposed scheme is shown to be superior in terms of increasing sum-rate, reducing the number of users in outage, and reducing the collision probability in the reservation phase.

Cognitive radio networks are receiving a lot of research attention lately. Most user equipment in state-of-the-art wireless communication systems are equipped with two antennas. In this paper, we take advantage of the two transmit antennas in an underlay cognitive radio network scenario. Secondary users use the two transmit antennas in order to insert a null in the direction of the primary base station, thus protecting primary users from interference on their uplink transmissions. Primary users benefit from the antennas in order to perform beam steering in direction of the primary BS, thus enhancing their uplink SINR without being concerned of the presence of the secondary users. This has the additional possible benefit of directing most primary radiation away from the secondary BS, thus reducing the interference on uplink secondary transmissions.

ABSTRACT In this paper, we consider maximizing the sum rate in the uplink of a multi‐cell orthogonal frequency‐division multiple access network. The problem has a non‐convex combinatorial structure and is known to be NP‐hard. Because of the inherent complexity of implementing the optimal solution, firstly, we derive an upper bound (UB) and a lower bound (LB) to the optimal average network throughput. Moreover, we investigate the performance of a near‐optimal single cell resource allocation scheme in the presence of inter‐cell interference, which leads to another easily computable LB. We then develop a centralized sub‐optimal scheme that is composed of a geometric programming‐based power control phase in conjunction with an iterative subcarrier allocation phase. Although the scheme is computationally complex, it provides an effective benchmark for low complexity schemes even without the power control phase. Finally, we propose less complex centralized and distributed schemes that are well suited for practical scenarios. The computational complexity of all schemes is analyzed, and the performance is compared through simulations. Simulation results demonstrate that the proposed low complexity schemes can achieve comparable performance with that of the centralized sub‐optimal scheme in various scenarios. Moreover, comparisons with the UB and LB provide insight on the performance gap between the proposed schemes and the optimal solution. Copyright © 2011 John Wiley & Sons, Ltd.

The weighted ergodic sum-rate maximisation problem subject to per-user power and rate constraints in the orthogonal frequency division multiple access uplink is formulated and solved. The solution is found via dual optimisation techniques, and the duality gap between the primal and dual solutions is analysed. Ergodicity allows taking advantage of the temporal dimension in addition to the frequency and multiuser dimensions. The per-user rate constraints allow an improved fairness level in the system. The achievable rate region of the weighted ergodic sum-rate maximisation problem is determined, and the effect of the per-user rate constraints on this region is studied. Offline and online iterative techniques to obtain the Lagrangian parameters are presented, and the results show that online iterations converge to the offline solution within a limited time.

3G UMTS networks are being widely deployed by several cellular operators worldwide that already have existing GSM networks. In this paper, we model the problem of GSM/UMTS co-siting as a nested mixed integer optimization problem which takes as input an area of interest with existing GSM sites and obtains as output the optimal number and locations of UMTS sites to be deployed (new and co-sited with GSM sites) in order to optimize target objectives. The goal is to minimize the cost of UMTS network deployment by reusing as many existing GSM sites as possible while guaranteeing that every cell is covering a target capacity load which is a percentage of the downlink pole capacity. We propose an algorithm taking into account both integer and continuous variables to solve the formulated optimization problem as a function of the user distribution and the existing fixed GSM site locations. Results for different scenarios show that it is optimal to reuse a subset of the existing GSM sites in addition to deploying new UMTS sites.

Bluetooth wireless technology is being installed in almost any electronic or information device for ad-hoc connectivity among them. Up to eight Bluetooth devices can form a network called a piconet. Interconnecting multiple piconets through gateway devices forms a scatternet. In the Bluetooth specifications, no scatternet topology has been specified. This paper proposes a scatternet formation topology that, unlike the existing literature, considers the non-uniform distribution of Bluetooth devices. Our proposed approach dictates a ring topology formed in the dense area and extended by trees to the other areas. The proposed approach is denoted as BlueHRT: Hybrid Ring Tree Scatternet Formation in Bluetooth Networks. Results are presented to highlight the performance gains of the proposed approach.

Uplink scheduling in LTE systems is considered. Two low complexity heuristic algorithms for suboptimal subcarrier allocation are proposed and compared to other algorithms in the literature. Throughput and fairness analysis of the different algorithms are performed via Monte-Carlo simulations. The proposed algorithms are utility maximizing algorithms that can be used with various utility functions. Simulations show the superiority of the proposed algorithms and that promising results can be achieved by linear complexity algorithms.

Uplink ergodic sum-rate maximization in OFDMA systems is considered. The general problem is formulated as a convex optimization problem and a dual solution is presented. The problem formulation is generalized to the case of utility maximization where the utility can be any function of rate. A suboptimal scheduling algorithm with low complexity is proposed and shown to achieve close performance to the optimal solution.

Uplink scheduling in LTE systems is considered. A game theoretical formulation is derived where the scheduling problem is represented as a Nash bargaining solution. An algorithm to implement the proposed scheduling scheme is presented. Throughput and fairness analysis are performed via simulations. Results show that channel aware scheduling schemes outperform the round-robin scheme, but a tradeoff must be made between the increase of total throughput and fairness towards the different users.

Distributed uplink scheduling in OFDMA systems is considered. In the proposed scenario, mobile users have the responsibility of making their own transmission decisions. Users are grouped into categories according to their channel state on each subcarrier. Users from each category decide on their own transmission by waiting for a random delay then transmitting with a certain probability. The proposed algorithm is completely distributed and does not require scheduling effort from the base station. Although no centralized control is present in the proposed approach, promising results are achieved in terms of throughput and fairness.

Abstract In the communication sector, the optimal objective is to equate quality and cost. The technologies that best serve these objectives are Wireless Access Technologies since they are easily deployed and capable of reaching and serving customers everywhere in a cost effective way. In this paper, we examine the communication options, and account for a country's geography to propose optimal WiMax planning keeping in mind the security concerns that are inherent in wireless communication. To perform WiMax radio network planning, we use a network simulation tool from ATDI called ICS Telecom. Our approach offers all users a minimum bandwidth of 1.4 Mbps as well as a coverage that exceeds 90% for all indoor users in the area under study. We optimize the network by iteratively minimizing the number of base stations required, and equivalently minimizing the cost, while maximizing the coverage for the subscribers. We also analyze the impact of security on the performance of WiMax. More specifically, we use well‐known simulation software called Qualnet to simulate a WiMax environment under different security protocols and encryption scenarios. We then analyze the results to determine the impact of the added security features on the data rates between the base station(s) subscribers. The results of our proposed WiMax planning approach and the conducted security experiments showed that efficient deployment and coverage plans could be achieved for big cities as well as for rural areas. Copyright © 2009 John Wiley & Sons, Ltd.

Multimedia applications are migrating to IP networks imposing high challenges on network planners. Challenges arise due to the stringent quality of service (QoS) requirements of multimedia applications that cannot be met over enterprise IP networks unless advanced techniques and strategies are applied. Example techniques are traffic differentiation, capacity evaluation and reservation, and call admission control. In this work, we assume practical call admission control (CAC) schemes and study by simulations the distribution of traffic inside the network. We show how capacity needs of traffic are affected when various CAC schemes are employed. Finally, we identify a procedure to evaluate the link capacity share for realtime traffic and study the resulting tradeoff between capacity needs and QoS parameters such as packet loss and blocking probability.

The base station transmit power is a common resource shared among multiple users in the downlink of WCDMA cellular systems. The overall user capacity decreases if a certain user consumes a large fraction of the base station power. In this work, we investigate the performance gains of adaptive antenna arrays with a power allocation strategy that limits the maximum fraction of transmit power per user. Using adaptive antenna arrays at base stations is a key technique to improve the downlink capacity of WCDMA cellular systems. We consider two adaptive antenna schemes: fixed beam and steered beam. For each scheme, we study the impact on capacity of the proposed power allocation strategy. Uniform linear arrays, uniform circular arrays, and Chebyshev antenna arrays are used to form the beams. The adopted simulation environment supports various types of adaptive antenna arrays, soft/softer handover, and multiple services. Results demonstrate an increase in capacity due to the power allocation scheme, especially when combined with adaptive antenna arrays. A remarkable superiority of the Chebyshev antenna array configuration is shown.

A new method for multitask learning in a Bayesian network context is presented for multiorganism gene network estimation. When the input datasets are sparse, as is the case in microarray gene expression data, it becomes difficult to separate random correlations from actual edges in the true underlying Bayesian network. Multitask learning takes advantage of the similarity between related tasks, in order to construct a more accurate model of the underlying relationships represented by the Bayesian networks. The proposed method is tested on synthetic data to illustrate its validity. Then it is iteratively applied on real gene expression data to learn the genetic regulatory networks of two organisms with homologous genes (human and yeast).

A promising approach for power reduction in mobile devices is peer-to-peer cooperation. In this paper, we present a testbed system implementation to evaluate the performance of an infrastructure controlled cooperative video streaming architecture. A number of mobile devices in close proximity are connected to the Internet through a wireless access point and are all requesting the same video stream from a dedicated server. Instead of sending the stream independently to each device, the server distributes the video packets among the devices, over a long-range wireless link using WLAN technology; the devices then exchange the received packets among each other over short-range wireless links using Bluetooth technology. The implemented testbed is used to experimentally demonstrate the power reduction gains in mobile devices and to derive an analytical model that relates power consumption to various design and system parameters.

The aim of genetic mapping is to locate the loci responsible for specific traits such as complex diseases. These traits are normally caused by mutations at multiple loci of unknown locations and interactions. In this work, we model the biological system that relates DNA polymorphisms with complex traits as a linear mixing process. Given this model, we propose a new fine-scale genetic mapping method based on independent component analysis. The proposed method outputs both independent associated groups of SNPs in addition to specific associated SNPs with the phenotype. It is applied to a clinical data set for the Schizophrenia disease with 368 individuals and 42 SNPs. It is also applied to a simulation study to investigate in more depth its performance. The obtained results demonstrate the novel characteristics of the proposed method compared to other genetic mapping methods. Finally, we study the robustness of the proposed method with missing genotype values and limited sample sizes.

In this paper, we present the design and implementation of a telecommunication system architecture that is capable of providing a new class of services for personal mobile devices such as cellular mobile phones and PDAs. The idea is to facilitate pervasive communication and information acquisition for mobile devices by taking advantage of their location and inherent Bluetooth capabilities. This includes providing free-of-charge voice and data services to the subscribers of the system. The offered services can be customized to different environments such as universities, hospitals, and companies. An example prototype implementation with specific services will be presented for the campus of the American University of Beirut.

Multihop transmission is expected to play a central role in the design of 4G cellular standards. To evaluate the system level performance of multihop based cellular networks, there is a need to develop simulation tools that support relaying capabilities. In this work, we present the design, development, and capabilities of a new simulator for multihop based cellular networks (SiMBCN) that has research and demonstration modes with a user-friendly graphical user interface. The research mode can be used to test a large set of system parameters and advanced enhancement options (multiple relaying, multihop diversity, directional antennas, etc.) using Monte-Carlo simulations whereas the demonstration mode can be used to visualize the network operation and performance in real time. Example results are presented which show the capabilities of relaying in reducing power consumption and combatting shadowing.

Third generation CDMA networks strive to deliver high speed data services through a shared radio channel with scarce resources. To efficiently utilize the available radio resources, we propose a new scheduling algorithm based on techniques from micro-economics. Unlike existing literature that mainly focuses on maximizing total system and/or individual user utility, this new algorithm aims at ensuring QoS guarantees from end to end for all active connections. Moreover, it considers the time varying channel conditions in both uplink and downlink directions jointly rather than each direction separately. Simulation results show that the proposed algorithm allows several users to simultaneously transmit while providing end-to-end QoS guarantees in terms of frame success rate and end-to-end delay.

Studies performed so far on multihop based cellular networks assume the use of omnidirectional antennas at relay stations. In this paper, we investigate the employment of advanced antenna configurations at relay stations including directional antennas and adaptive antenna arrays. We analyze several directional antenna orientation strategies and the gains achieved relative to other cellular network designs. Results show that directional antenna designs perform better than omnidirectional antenna designs for practical network loads with appropriate antenna orientation strategies. Furthermore, adaptive antenna arrays offer best results even with only two antenna elements.

Relaying has been proven to improve coverage and capacity in wireless cellular networks. In this work, we study the effect of three enhancement techniques on the performance of fixed relays in multihop based cellular networks. First, multiuser detection is used to cancel part of the interference at the relays coming from neighboring nodes. Second, multihop diversity is achieved at the base stations as a result of a cooperation scheme between the relay stations. Finally, multiple relaying is employed whereby a relay station is able to relay multiple users simultaneously. Results show that gains up to 78% could be achieved with respect to conventional cellular networks when such techniques are employed with fixed relaying in comparison to gains of 34% in the case of mobile relaying and 7% in the case of simple fixed relaying.

Determining the model-order of a given data set is an important task in signal analysis. Principal component analysis (PCA) can be used for this purpose if there is a criterion upon which the correct order can be chosen. In this work, we propose a new and simple technique to determine automatically the rank of a PCA model. Tested with simulated data, the algorithm is able to determine the correct model order efficiently. Applied to video sequences, this method is able to estimate the necessary subspaces that capture the motion and illuminance changes within the different frames. This helps in reducing the storage need/requirements of video sequences and improves the efficiency of context based search and retrieval techniques.

Finding the causal genetic regions underlying complex traits is one of the main aims in human genetics. In the context of complex diseases, which are believed to be controlled by multiple contributing loci of largely unknown effect and position, it is especially important to develop general yet sensitive methods for gene mapping. We discuss the use of Shannon's information theory for population-based gene mapping of discrete and quantitative traits and for marker clustering. Various measures of mutual information were employed in order to develop a comprehensive framework for gene mapping analyses. An algorithm aimed at finding so-called relevance chains of causal markers is proposed. Moreover, entropy measures are used in conjunction with multidimensional scaling to visualize clusters of genetic markers. The relevance chain algorithm successfully detected the two causal regions in a simulated scenario. The approach has also been applied to a published clinical study on autoimmune (Graves') disease. Results were consistent with those of standard statistical methods, but identified an additional locus of interest in the promotor region of the associated gene CTLA4. The developed software is freely available at http://www.Int.ei.tum.de/download/InfoGeneMap/.

Identifying the causal genetic markers responsible for complex diseases is a main aim in human genetics. In the context of complex diseases, which are believed to have multiple causal loci of largely unknown effect and position, there is a need to develop advanced methods for gene mapping. In this work, we propose a novel algorithm based on independent component analysis for gene mapping. To apply the algorithm, we model the intra-cellular interactions as a mixing process of multiple sources. Results prove the superiority of the proposed algorithm over conventional statistical based methods, and demonstrate yet another successful application of a well known signal processing technique to an important problem in the field of human genetics.

The increase in the availability of genetic data in the last years is prompting the efforts to use tools from communications engineering for the understanding of genetic information. Processes in molecular biology can be modeled through the use of these tools. A communication theory based model for the process of translation in gene expression is proposed. The model is based on the assumption that the ribosome decodes the mRNA sequences using the 3' end of the 16SrRNA molecule as a one dimensional codebook. The biological consistency of the model is proven in the detection of the Shine-Dalgarno signal and the initiation codon for translation initiation. Furthermore, implications on the role of the 16SrRNA 3' end in the complete process of prokaryotic translation are presented and discussed. Interestingly, the obtained results lay out the possibility of an interaction of this part of the ribosome in the process of translation termination. Finally, results obtained via the proposed model are compared with published experimental results for different mutations of the rRNA molecule. Total agreement between both sets of results proves the validity of the proposed model. By means of simulated mutations in the last 13 bases of the 16SrRNA, a global analysis of this part of the ribosome in the process of translation is established. This work illustrates the relevance of communication theory based models for genetic regulatory systems.

Possibilities of using mutual information for classification and content recognition are exploited. Two different mutual information based distance measures are proposed, one for classification and one for content recognition. The measure proposed for classification is shown to be a metric. The influence of compression based estimation methods on the proposed measures is investigated. Several examples of successful applications in the field of genetics are presented.

In this paper, the distribution of mutual information between two discrete random variables is approximated by means of a second-order Taylor series expansion. Approximative expressions for the distribution of mutual information (MI) between independent random variables, conditional MI between conditionally independent variables, and MI between (weakly) dependent random variables are derived. These distributions are functions of the available sample size and the number of realisations of the random variables only; knowledge of the variables' PMF is not required. The results are verified numerically for various cases. Exemplary application ideas in statistics and communications engineering are proposed.

Transport of compressed video is expected to pervade computer networks in the near future. Video is commonly encoded in variable bit rate traffic to improve video quality and reduce encoding delays and yet to make efficient use of the available capacity using statistical multiplexing. Variable bit rate coded video imposes a real challenge to network planners who aim to evaluate its necessary capacity share. In this paper, we present a dimensioning model tailored especially for variable bit rate video transmission over IP networks taking the network delay into account unlike existing dimensioning models. We investigate this new model in terms of different system parameters.

Advanced antenna arrays at the base stations are one of the key techniques to improve the downlink capacity of WCDMA cellular systems. Typically, uniform linear arrays and uniform circular arrays are used to form the beams. In this work, we propose the use of Chebyshev antenna arrays which are linear arrays with non-uniform input excitations. We consider two adaptive antenna schemes: fixed beam and steered beam. For each scheme, we compare the user capacity per cell among the three different types of antenna arrays (uniform linear, Chebyshev linear, and uniform circular). The adopted simulation environment supports various types of adaptive antennas, soft/softer handover, and multiple services. Results demonstrate a notable superiority of the proposed Chebyshev antenna arrays.

the IEEE 802.11e standard has been introduced recently for providing quality of service (QoS) capabilities in the emerging wireless local area networks. This standard introduces a contention window based enhanced distributed channel access (EDCA) technique that provides a prioritized traffic to guarantee the minimum bandwidth needed for time critical applications. However, the EDCA technique resets statically the contention window of the mobile station after each successful transmission. This static behavior does not adapt to the network state hence reduces the network usage and results in bad performance and poor link utilization whenever the demand for link utilization increases. This paper proposes a new adaptive differentiation technique for IEEE 802.11e wireless local area networks that takes into account the network state before resetting the contention window. The performance of the proposed technique is evaluated compared to the original differentiation techniques of the IEEE 802.11a and IEEE 802.11e standards. Preliminary results show that the proposed adaptive technique enhances the channel utilization and increases throughput.

This paper presents the usability of context tree weighting method (CTW) for applications involving content recognition. The CTW method is a statistics-based universal date compression algorithm that is capable of achieving superior performance compared to Lempel-Ziv based algorithms. The concept of context tree freezing after the reference sequence is encoded to avoid learning the memory structure of the appended test sequence is used to modify CTW for content recognition purposes.

The uplink coverage and capacity of CDMA cellular systems with the conventional single user detector receiver are interference limited. Particularly, during the roll-out phase, the coverage of a CDMA system is uplink limited. Hence, using serial interference cancellation (SIC) at the base station is a low cost option to increase the overall performance. Considering the typical quality of service requirements of mixed services, i.e. voice and data, a new hybrid receiver structure for interference cancellation is proposed. In order to perform system level analysis, the calculation of signal-to-interference ratios is extended to the case of multiple service classes. Given this tooling, the optimum powers of the mobile stations are derived as a function of various system parameters and receiver structures. This enables an accurate calculation of the intracell and intercell interference and a derivation of coverage-capacity tradeoff expressions. Results show significant performance gains in terms of user capacity and cell coverage by using SIC receivers including the proposed hybrid structure that meets the delay and complexity requirements of the different service classes.

IP network planning based on a given traffic demand becomes obsolete as soon as the actual traffic slightly deviates from the given values. It is highly probable that this be the case in IP networks with no deployed resource control mechanisms. Complex and comprehensive resource control mechanisms are still a matter of research; while the most practical and common scenario is to have control units at the ingress of the IP network as pertained by the differentiated services architecture (DiffServ). Based on this practical model, we study the distribution of traffic demand inside the network and introduce the concept of a capacity margin, which is added to the network links in order to account for a certain level of traffic demand variability. Moreover, we analytically calculate the capacity margin and investigate the tradeoff provided between the degree of network robustness and the consequent additional costs in terms of network resources. Finally, we show how such statistical consideration of traffic variability can offer high degrees of network robustness with rather low costs.

Methods used in information theory can be used in genomic analysis to provide a more meaningful insight into the genetic process. Using an information transfer model between certain polymorphisms in the human genome (SNP) and certain diseases (e.g. Alzheimer), Shannon's mutual information can identify the relevant SNP. Several compression algorithms approximating the entropy of DNA sequences are used to distinguish between introns and exons. Certain distance measures derived from DNA sequences allow classification leading to evolutionary trees. They can also be used for content recognition.

We examine a network consisting of a source-destination pair that want to communicate at a fixed transmission rate with the possibility of using an arbitrary number of nodes as relays. This network model forms a building block for cooperative diversity and coding systems. Fixing the transmission rate, we formulate the power allocation problem as a nonlinearly constrained optimization problem for a class of cooperative accessing schemes and we present results for two practically motivated network geometries. The first network geometry is the regular linear model where the relay nodes are allocated equidistantly on the line joining the source to the destination. The second network geometry is the clustered network model where the relay nodes form a cluster of closely packed nodes which act as a virtual antenna array. The presented analysis gives useful insights to the design of practical wireless multihop networks with deterministic properties and finite number of relay nodes.

Cellular operators make use of radio network planning tools to determine the number of base stations required to cover an area of interest with a given traffic profile. These tools are based on a link budget analysis that takes into account the pathloss model in addition to various losses and power margins. The power margins compensate for sources of variation in the network in order to guarantee a bounded probability of outage and, thus, help in reducing the complexity of radio network planning tools. In CDMA cellular systems, the mean of the intercell interference is commonly used for coverage calculations and radio network planning. However, the intercell interference is a random variable that depends on the positions of the users in neighboring cells. In this work, we propose and calculate a new power margin that should be used in link budget analysis to compensate for variation in intercell interference. We show that not using the proposed margin leads to a notable increase in outage probability. The proposed margin is of particular interest to cellular operators and for the development of radio network planning tools.

The uplink coverage and capacity of CDMA cellular systems are intracell and intercell interference limited. In this work, we propose a practical multihop based cellular network design to enhance the uplink performance. The proposed design is based on dividing each cell into two regions where users belonging to an inner region communicate with the base station (BS) using one hop and users belonging to an outer region communicate with the BS using two hops. Each of the two regions is allocated a separate frequency channel to enable practical implementation and reduce interference. Analytic results show that intercell interference in CDMA cellular systems can be reduced by more than 75%. Moreover, a link budget analysis is performed to obtain coverage-capacity results with different receiver structures at the BS. Results show that with the single user detector (SUD) receiver, gains up to 100% in area coverage are achieved with moderate to low user loads. Furthermore, deploying multiuser detection to combat intracell interference, the proposed multihop based design achieves very high gains exceeding 100% in pole capacity and 300% in area coverage per cell.