Design Of Wireless Networks Research Articles

Clock Synchronization in Wireless Networks Using Matrix Completion-Based Maximum Likelihood Estimation

Clock synchronization has been a challenging task in the design of wireless networks because the synchronization accuracy suffers from uncertain transmission delay and/or packet loss due to poor wireless channel conditions. To address this problem, this paper develops robust clock synchronization schemes based on the matrix completion theory to improve the accuracy in presence of random transmission delay and packet loss. We propose a matrix completion-based maximum likelihood estimator (MC-MLE) to estimate the clock offset and clock skew under Gaussian transmission delay model. Thanks to the denoise feature of matrix completion, the proposed schemes outperform the maximum likelihood estimator (MLE) in the presence of packet loss and random transmission delay. That is, the proposed schemes are closer to the Cramer-Rao lower bound (CRLB) than the MLE when the timestamp packets are randomly corrupted. The robustness and accuracy of the proposed schemes are validated by numerical results.

Swarm intelligence–based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks

Wireless sensor network is a hot research topic with massive applications in different domains. Generally, wireless sensor network comprises hundreds to thousands of sensor nodes, which communicate with one another by the use of radio signals. Some of the challenges exist in the design of wireless sensor network are restricted computation power, storage, battery and transmission bandwidth. To resolve these issues, clustering and routing processes have been presented. Clustering and routing processes are considered as an optimization problem in wireless sensor network which can be resolved by the use of swarm intelligence–based approaches. This article presents a novel swarm intelligence–based clustering and multihop routing protocol for wireless sensor network. Initially, improved particle swarm optimization technique is applied for choosing the cluster heads and organizes the clusters proficiently. Then, the grey wolf optimization algorithm–based routing process takes place to select the optimal paths in the network. The presented improved particle swarm optimization–grey wolf optimization approach incorporates the benefits of both the clustering and routing processes which leads to maximum energy efficiency and network lifetime. The proposed model is simulated under an extension set of experimentation, and the results are validated under several measures. The obtained experimental outcome demonstrated the superior characteristics of the improved particle swarm optimization–grey wolf optimization technique under all the test cases.

Spectrum Occupancy and Interference Model Based on Network Experimentation in Hospital

Emerging healthcare radio technologies are designed to operate in the 2.4GHz industrial, scientific and medical (ISM) band. Since both standardized (Bluetooth and Wi-Fi) and non-standardized (proprietary) devices use the same frequency band, the aggregate interference may significantly affect the performance of medical wireless systems. This paper characterizes the spatiotemporal spectrum occupancy and proposes models for the aggregate interference in hospital environments. In particular, time–frequency and cluster-based statistical models for the aggregate interference are developed based on network experimentation. The proposed models enable the design of wireless networks for e-health applications and medical services.

Hybrid Kriging and multilayer perceptron neural network technique for coverage prediction in cellular networks

ABSTRACT Coverage prediction is a crucial issue in wireless network planning and design. Typically, coverage prediction techniques start by model identification given a set of measurements at specified locations. Then, this model is used to predict the signal strength at other locations using some machine-learning like approach. However, when the drive tests (or test-beds) become costly because of some environmental constraints, the performance of the machine learning-based model is questionable due to the insufficiency of the training dataset. In this context, we suggest exploiting the geostatistical interpolation technique named Ordinary Kriging to enrich the training data set. For this purpose, a set of received signal strengths from wireless transmitters has been collected at known locations through cellular network technology, which are then used to generate an enriched dataset according to the Ordinary Kriging interpolation technique. The results show that the hybrid Ordinary Kriging and machine learning model significantly enhances path loss accuracy and offers a new setting for data reproducibility. A database is constructed from received signal strength measurements which is not enough to produce accurate results when it is used to train a feed forward neural network for the task of coverage prediction. A geospatial interpolated technique named Kriging is used in order to produce a big amount of data. The neural network is trained by both of the original and the interpolated datasets. The coverage prediction results more reproducible and accurate than the results without the interpolation stage.

Learning to Communicate and Energize: Modulation, Coding, and Multiple Access Designs for Wireless Information-Power Transmission

The explosion of the number of low-power devices in the next decades calls for a re-thinking of wireless network design, namely, unifying wireless transmission of information and power so as to make the best use of the RF spectrum, radiation, and infrastructure for the dual purpose of communicating and energizing. This article provides a novel learning-based approach towards such wireless network design. To that end, a parametric model of a practical energy harvester, accounting for various sources of nonlinearities, is proposed using a nonlinear regression algorithm applied over collected real data. Relying on the proposed model, the learning problem of modulation design for Simultaneous Wireless Information-Power Transmission (SWIPT) over a point-to-point link is studied. Joint optimization of the transmitter and the receiver is implemented using Neural Network (NN)-based autoencoders. The results reveal that by increasing the receiver power demand, the baseband transmit modulation constellation converges to an On-Off keying signalling. Utilizing the observations obtained via learning, an algorithmic SWIPT modulation design is proposed. It is observed via numerical results that the performance loss of the proposed modulations are negligible compared to the ones obtained from learning. Extension of the studied problem to learning modulation design for multi-user SWIPT scenarios and coded modulation design for point-to-point SWIPT are considered. The major conclusion of this work is to utilize learning-based results to design non learning-based algorithms, which perform as well. In particular, inspired by the results obtained via learning, an algorithmic approach for coded modulation design is proposed, which performs very close to its learning counterparts, and is significantly superior due to its high real-time adaptability to new system design parameters.

Reducing the Energy Budget in WSN Using Time Series Models

Energy conservation is critical in the design of wireless sensor networks since it determines its lifetime. Reducing the frequency of transmission is one way of reducing the cost, but it must not tamper with the reliability of the data received at the sink. In this paper, duty cycling and data-driven approaches have been used together to influence the prediction approach used in reducing data transmission. While duty cycling ensures nodes that are inactive for longer periods to save energy, the data-driven approach ensures features of the data that are used in predicting the data that the network needs during such inactive periods. Using the grey series model, a modified rolling GM(1,1) is proposed to improve the prediction accuracy of the model. Simulations suggest a 150% energy savings while not compromising on the reliability of the data received.

Evolution of wireless sensor network design from technology centric to user centric: An architectural perspective

Wireless sensor networks form the crux of a wide range of automated applications that are gaining popularity with every passing day. The journey of wireless sensor networks has seen simple sensory monitoring to sensing-processing-actuation based end-to-end applications to the design and deployment of highly sophisticated service-oriented use cases. This highly disruptive field has seen rapid transformations to gain insights and assess the relative merits. Demerits of competing design strategies, it very important to know the evolutionary milestones this technology has undergone in achieving the state-of-the-art in this area. In this article, a systematic review is carried out that captures the evolution of architectural designs and developments in wireless sensor network–based applications. This review delves into the relative pros and cons of various epochs in developments and delineates the future areas of research in wireless sensor network design paradigm.

Effect of Spatial and Temporal Traffic Statistics on the Performance of Wireless Networks

The traffic in wireless networks has become diverse and fluctuating both spatially and temporally due to the emergence of new wireless applications and the complexity of scenarios. The purpose of this paper is to quantitatively analyze the impact of the wireless traffic, which fluctuates both spatially and temporally, on the performance of the wireless networks. Specially, we propose to combine the tools from stochastic geometry and queueing theory to model the spatial and temporal fluctuation of traffic, which to our best knowledge has seldom been evaluated analytically. We derive the spatial and temporal statistics, the total arrival rate, the stability of queues and the delay of users by considering two different spatial properties of traffic, i.e., the uniformly and non-uniformly distributed cases. The numerical results indicate that although the fluctuation of traffic (reflected by the variance of total arrival rate) when the users are clustered is much fiercer than that when the users are uniformly distributed, the unstable probability is smaller. Our work provides a useful reference for the design of wireless networks when the complex spatio-temporal fluctuation of the traffic is considered.

Toward Resilient Wireless Sensor Networks: A Virtualized Perspective.

The Internet of Things (IoT) has been one of the main focus areas of the research community in recent years, the requirements of which help network administrators to design and ensure the functionalities and resources of each device. Generally, two types of devices—constrained and unconstrained devices—are typical in the IoT environment. Devices with limited resources—for example, sensors and actuators—are known as constrained devices. Unconstrained devices includes gateways or border routers. Such devices are challenging in terms of their deployment because of their connectivity, channel selection, multiple interfaces, local and global address assignment, address resolution, remote access, mobility, routing, border router scope and security. To deal with these services, the availability of the IoT system ensures that the desired network services are available even in the presence of denial-of-service attacks, and the use of the system has become a difficult but mandatory task for network designers. To this end, we present a novel design for wireless sensor networks (WSNs) to address these challenges by shifting mandatory functionalities from unreliable to reliable and stable domains. The main contribution of our work consists in addressing the core network requirements for IoT systems and pointing out several guidelines for the design of standard virtualized protocols and functions. In addition, we propose a novel architecture which improves IoT systems, lending them more resilience and robustness, together with highlighting and some important open research topics.

Routing design and experimental analysis of wireless sensor monitoring network for mine environment

According to the demands of actual mine production and environment monitoring, this paper proposes a networking scheme that combines optical fibre transmission and wireless sensor monitoring networks. In accordance with the special arrangement of str

Fronthaul Design for Wireless Networks

Cloud Radio Access Network (C-RAN) architectures have arisen as an alternative to traditional wireless network architectures, notably by taking advantage of the functional split between the multiple distributed Remote Radio Heads (RRHs) and the centralized Baseband Units (BBUs), through the creation of a new connectivity segment—the fronthaul. In order to maximize the investment return, it is important to find out, for this C-RAN segment, which technologies provide cost-effective solutions. This paper addresses this issue by evaluating and comparing the performance of Microwave Radio Transmission (MRT), Free Space Optics (FSO), and Fiber Optics (FO) technologies when applied to the fronthaul. First, a methodology is provided to determine the most cost-effective solution for each RRH–BBU link, as well as to compute the required number of BBUs and where they should be positioned in order to minimize the overall network costs. Next, a cost-effectiveness comparison of the aforementioned communication technologies is presented for individual fronthaul segments under different weather conditions, link lengths, and bit rate requirements. Moreover, an assessment is performed regarding the impact of the RRH density on the selection of cost-effective communication technologies for C-RANs. The obtained results allow concluding that fronthaul expenses are significantly affected by the performance of FSO systems, which in turn is affected by weather conditions; this highlights the relevance of having accurate climate statistics and forecasts in order to get the most out of the FSO technology and, consequently, lowering the overall network costs.

New Routing Technique to Enhance Energy Efficiency and Maximize Lifetime of the Network in WSNs

Energy conservation plays a role important in wireless sensor network (WSN) design. The technique of clustering is one of the approaches to save energy of WSNs; several protocols based on the clustering technique are proposed in the context of energy conservation and maximization of network lifetime. This article proposed a new routing technique to maximize the network lifetime and enhance energy efficiency in WSNs, namely optimal selection of cluster head in the grid (OSCH-Gi). This technique divides the network area into a clustered grid. Each clustered grid has a cluster head (CH) it has been chosen based on the residual energy and distance to the base station of each node. Simulation results indicate that the proposed technique is more effective than other clustering algorithms in terms of the network lifetime and energy consumption.

Performance improvement of space diversity technique using space time block coding for time varying channels in wireless environment

Performance improvement of space diversity technique using space time block coding for time varying channels in wireless environment

Design and analysis of spectral and energy efficient wireless communication networks

In this article, we summarize the research conducted for YFRFs of Visvesvaraya PhD Programme. The research work is mainly focused on the design and analysis of wireless communication networks. We also explore the spectral and energy efficiency aspects of future generation networks like smart grid communications. This research is useful for improving the performance of current and future generation of communication systems.

A General Model of Fractional Frequency Reuse: Modelling and Performance Analysis

Fractional Frequency Reuse (FFR) is a promising to improve the spectrum e ciency in the LongTerm Evolution (LTE) cellular network. In the literature, various research works have been conducted to evaluate the performance of FFR. However, the presented analytical approach only dealt with the special cases in which the users are divided into 2 groups and only two power levels are utilised. In this paper, we consider a general case of FFR in which the users are classified intogroups and each group is assigned a serving power level. The mathematical model of the general FFR is presented and analysed through a stochastic geometry approach. The derived analytical results in terms of average coverage probability can covered all the related well-known results in the literature.
 Keywords: 
 Fractional Frequency Reuse, LongTerm Evolution, coverage probability, stochastic geometry
 References
 [1] Cisco, Cisco visual networking index: Global mobile data traffic forecast update, 2015 - 2020, 2016.
 [2] A.S. Hamza, S.S. Khalifa, H.S. Hamza, K. Elsayed, A Survey on Inter-Cell Interference Coordination Techniques in OFDMA-Based Cellular Networks, IEEE Commun, Surveys & Tutorials 15(4) (2013) 1642-1670
 [3] 3GPP TR 36.819 V11.1.0, Coordinated multi-point operation for LTE physical layer aspects, 2011.
 [4] 3GPP Release 10 V0.2.1, LTE-Advanced (3GPP Release 10 and beyond), 2014.
 [5] 3GPP TS 36.211 V14.1.0, E-UTRA Physical Channels and Modulation, 2016.
 [6] R. Ghaffar, R. Knopp, Fractional frequency reuse and interference suppression for ofdma networks, in: 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, 2010, pp. 273-277.
 [7] Y. Kwon, O. Lee, J. Lee, M. Chung, Power Control for Soft Fractional Frequency Reuse in OFDMA System, Vol. 6018 of Lecture Notes in Comput.Science, Springer Berlin Heidelberg, 2010, book section 7 (2010) 63-71.
 [8] Enhancing LTE Cell-Edge Performance via PDCCH ICIC, in: FUJITSU NETWORK COMMUNICATIONS INC., 2011
 [9] A.S. Hamza, S.S. Khalifa, H.S. Hamza, K. Elsayed, A Survey on Inter-Cell Interference Coordination Techniques in OFDMA-Based Cellular Networks, IEEE Commun, Surveys & Tutorials 15(4) (2013) 1642-1670. https://doi.org/10.1109/SURV.2013.013013.00028.
 [10] A. Busson1, I. Lahsen-Cherif2, Impact of resource blocks allocation strategies on downlink interference and sir distributions in lte networks: A stochastic geometry approach, Wireless Communications and Mobile Computing.
 [11] H. ElSawy, E. Hossain, M. Haenggi, Stochastic Geometry for Modeling, Analysis and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey, IEEE Commun, Surveys Tutorials 15(3) (2013) 996-1019. https://doi.org/10.1109/SURV.2013.052213.00000.
 [12] W. Bao, B. Liang, Stochastic Analysis of Uplink Interference in Two-Tier Femtocell Networks: Open Versus Closed Access, IEEE Trans, Wireless Commun. 14(11) (2015) 6200-6215. https://doi.org/10.1109/TWC.2015.2450216 .
 [13] H. Tabassum, Z. Dawy, E. Hossain, M.S. Alouini, Interference Statistics and Capacity Analysis for Uplink Transmission in Two-Tier Small Cell Networks: A Geometric Probability Approach, IEEE Trans, Wireless Commun 13(7) (2014) 3837-3852.
 [14] J.G. Andrews, F. Baccelli, R.K. Ganti, A tractable approach to coverage and rate in cellular networks, IEEE Transactions on Communications 59(11) (2011) 3122-3134.
 [15] Y. Lin, W. Bao, W. Yu, B. Liang, Optimizing User Association and Spectrum Allocation in HetNets: A Utility Perspective, IEEE J. Sel. Areas Commun. 33(6) (2015) 1025-1039. https://doi.org/10.1109/JSAC.2015.2417011.
 [16] M. Haenggi, Stochastic Geometry for Wireless Networks, Cambridge Univ, Press, November 2012.
 [17] H. ElSawy, E. Hossain, M. Haenggi, Stochastic Geometry for Modeling, Analysis and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey, IEEE Commun, Surveys Tutorials 15(3) (2013) 996-1019.
 [18] Huawei, R1-050507: Soft Frequency Reuse Scheme for UTRAN LTE, in: 3GPP TSG RAN WG1 Meeting #41, 2005.
 [19] M.A. Stegun, I.A., Handbook of Mathematical Functions with Formulas, Graphs and Mathematical Tables, 9th Edition, Dover Publications, 1972.

A survey on secure communication techniques for 5G wireless heterogeneous networks

With the increasing number of emerging robust networks, the challenges to design new security protocols and techniques are never ending. With the enlargement of 5G paradigm, there is a remarkable makeshift in how the distributed devices perform to achieve a common goal. The 5G technology has been significantly revolutionized by the advent of the Internet of Things, and its quick and pervasive evolution. However, it remains imperative that all these devices work in a sequential manner to execute a collective and secure operation. Despite computing being focused around cloud infrastructures in the last two decades, the 5G networks and the huge amount of data it generates is presently inverting this trend, shifting computing power back to where data is generated from. To encompass different autonomous and collaborative systems with functions of data sensing and authentication between such systems in a predictive and adaptive manner is a challenging task. Wireless network design in 5G is expected to emphasize broadly on three facets: security, privacy and energy efficiency. Therefore, this paper investigates the role of various data security and privacy techniques for different generation networks, but emphasis is on 5G. It addresses encryption schemes with the focus on the challenges faced such as side channel and ciphertext attacks, inherent key generation, sharing and distribution, key escrow, analysis of various overheads, and potential techniques, and solutions to address such challenges by leveraging 5G network devices. With the proliferation of ubiquitous computing and wide adoption of hybrid encryption techniques, various attacks will be circumvent in an intelligent manner.

Individual Preference Aware Caching Policy Design in Wireless D2D Networks

Cache-aided wireless device-to-device (D2D) networks allow significant throughput increase, depending on the concentration of the popularity distribution of files. Many studies assume that all users have the same preference distribution; however, this may not be true in practice. This work investigates whether and how the information about individual preferences can benefit cache-aided D2D networks. We examine a clustered network and derive a network utility that considers both the user distribution and channel fading effects into the analysis. We also formulate a utility maximization problem for designing caching policies. This maximization problem can be applied to optimize several important quantities, including throughput, energy efficiency (EE), cost, and hit-rate, and to solve different tradeoff problems. We provide a general approach that can solve the proposed problem under the assumption that users coordinate, then prove that the proposed approach can obtain the stationary point under a mild assumption. Using simulations of practical setups, we show that performance can improve significantly with proper exploitation of individual preferences. We also show that different types of tradeoffs exist between different performance metrics and that they can be managed through caching policy and cooperation distance designs.

On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks

A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies.

Critical Analysis of Localization and Time Synchronization Algorithms in Underwater Wireless Sensor Networks: Issues and Challenges

In underwater wireless sensor networks (UWSNs), localization and time synchronization plays a critical role among many underwater applications. However without time synchronization, localization does not provides guarantee to be accurate, because these procedures are depending upon each other and in most of the application in UWSNs needs time accuracy in nodes. Protocols that have been design for wireless sensor networks cannot apply on UWSNs since these algorithms are not design for the calculation of propagation delay, high error rate and mobility of nodes. This study is going to present a comprehensive survey about the algorithm that have been designed for the localization and time synchronization in underwater sensors networks by classifying them in categories and analyze them on the basis of challenges faced by localization and time synchronization in UWSNs such as type of sensors, ranging methodology, communication mode, time interval, coverage area, energy consumption and computational complexity. This study will critically provides the analyses based on these factors and the comparison of the previously developed techniques which will help in concluding better technique for localization and time synchronization. The propose of this detail discussion is to provide a plat form for the upcoming researcher, they can proceed their research by using the information for the localization and time synchronization as this study will also discusses the open research challenges for future direction.

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

The projected rise in wireless communication traffic has necessitated the advancement of energy-efficient (EE) techniques for the design of wireless communication systems, given the high operating costs of conventional wireless cellular networks, and the scarcity of energy resources in low-power applications. The objective of this paper is to examine the paradigm shifts in EE approaches in recent times by reviewing traditional approaches to EE, analyzing recent trends, and identifying future challenges and opportunities. Considering the current energy concerns, nodes in emerging wireless networks range from limited-energy nodes (LENs) to high-energy nodes (HENs) with entirely different constraints in either case. In view of these extremes, this paper examines the principles behind energy-efficient wireless communication network design. We then present a broad taxonomy that tracks the areas of impact of these techniques in the network. We specifically discuss the preponderance of prediction-based energy-efficient techniques and their limits, and then discuss the trends in renewable energy supply systems for future networks. Finally, we recommend more context-specific energy-efficient research efforts and cross-vendor collaborations to push the frontiers of energy efficiency in the design of wireless communication networks.