Radio Wireless Networks Research Articles

The main trends in the development of information and telecommunication technologies when creating high-tech products

The features of the application of information and telecommunication technologies in high-tech enterprises are examined in order to increase the efficiency of production activities. It is determined that the basis of telecommunication technologies is the procedure for information exchange and data transfer. Telecommunication technologies provide remote transmission of all forms of information, including data, voice, video, between computers via various types of communication lines. Analysis of the results of the classification of information technologies and areas for improving information support in the production of high-tech products allowed us to identify trends in the development of modern information technologies. These trends are due to the dynamics of the development of industrial enterprises and the external environment, which leads to functional changes in the management system of a high-tech enterprise and provides significant economic growth in developed countries. The main promising means for data transmission are the Internet, wireless mobile radio networks, laser and fiber-optic communication lines. The most important task of telecommunication technologies in the field of economics is electronic exchange. This indicates the importance of the telecommunications aspect in the functioning of the information system. Telecommunication technologies that provide data transfer, make it possible to implement a continuous exchange of information through global networks. Modern telecommunication technologies are widely used in creating network options for information technology. It was determined that a significant achievement of telecommunication technologies was the possibility of real-time control of financial, banking, trading and other operations regardless of the territorial location of their participants. Telecommunication technologies are becoming the elements that informally unite the vast territory of Russia.

A divide-and-conquer based improved genetic algorithm for network selection in heterogeneous wireless network

Developing and using wireless network is a trend to serve passengers in aircraft cabins. Cognitive radio technology can be used in aircraft cabin wireless environment to solve the spectrum resource scarcity problem. Heterogeneous network access issue should be noticeable in cognitive radio wireless networks. In this paper, an improved genetic algorithm (GA) based on divide-and-conquer is proposed to handle this problem. The proposed approach is compared with original GA, particle swarm optimisation (PSO) algorithm and tabu search (TS) algorithm. The simulation results prove that the proposed method can obtain greater bandwidth with higher efficiency.

Dual‐collaborative DoS/DDoS mitigation approach in information‐centric mobile Internet

SummaryThe amount of wireless traffic is increasing at an overwhelming speed. Information‐centric networking (ICN) has been proposed as a promising Future Internet Architecture, which can reduce network traffic by putting data objects toward the edge. It is expected that in information‐centric mobile Internet (ICMI), the wireless traffic can be significantly reduced. Yet, DoS/DDoS attack becomes a critical issue in ICMI by causing wireless gateway blockade. To tackle the problem, we propose a dual‐collaborative DoS/DDoS mitigation approach (DCMA) and advanced DCMA to protect wireless gateways. In the algorithm, the attackers' visiting information including international mobile equipment identity (IMEI) and data object name (DON) are analyzed jointly to accurately identify potential attackers through the collaboration between the Internet and mobile network. In addition, the attacker's behaviors are analyzed centrally, and security strategies are applied distributively throughout wireless edge through the collaboration between wireless core network (CN) and radio access network (RAN). Extensive simulations are performed to verify the effectiveness of the proposed algorithms. The results demonstrate that advanced DCMA can achieve high DDoS and attacker detection probability and small false positive probability.

Iron and steel ladles tracking management system based on RFID and WLAN

In the steel mills, in order to improve the circulation efficiency of iron ladles and steel ladles, save energy and promote the development of industrial automation, an iron ladles and steel ladles tracking management system based on radio frequency identification (RFID) and wireless local area network (WLAN) is proposed in this study. Meanwhile, to obtain the optimal number of online iron ladles, the theory of queuing is proposed for optimising the logistics of iron ladles. The system uses RFID and readers to obtain relevant location information. WLAN is used to realise communication between the crane terminals and the main control. Two queuing models are analysed and compared, then the optimal solution of online iron ladles is quantitatively calculated. Moreover, in order to intuitively reflect the on-site scheduling situation, a visualised operation interface is designed accordingly. The system has been used in a steel mill in Northeast China and achieved good performance.

Exponential Separations in the Energy Complexity of Leader Election

Energy is often the most constrained resource for battery-powered wireless devices, and most of the energy is often spent on transceiver usage (i.e., transmitting and receiving packets) rather than computation. In this article, we study the energy complexity of fundamental problems in several models of wireless radio networks. It turns out that energy complexity is very sensitive to whether the devices can generate random bits and their ability to detect collisions . We consider four collision detection models: Strong-CD (in which transmitters and listeners detect collisions), Sender-CD (in which only transmitters detect collisions), Receiver-CD (in which only listeners detect collisions), and No-CD (in which no one detects collisions). The take-away message of our results is quite surprising. For randomized algorithms, there is an exponential gap between the energy complexity of Sender-CD and Receiver-CD: Randomized: No-CD = Sender-CD > Receiver-CD = Strong-CD and for deterministic algorithms, there is another exponential gap in energy complexity, but in the reverse direction : Deterministic: No-CD = Receiver-CD > Sender-CD = Strong-CD Precisely, the randomized energy complexity of Leader Election is Θ(log * n ) in Sender-CD but Θ(log(log * n )) in Receiver-CD, where n is the number of devices, which is unknown to the devices at the beginning; the deterministic complexity of Leader Election is Θ(log N ) in Receiver-CD but Θ(log log N ) in Sender-CD, where N is the size of the ID space. There is a tradeoff between time and energy. We provide a new upper bound on the time-energy tradeoff curve for randomized algorithms. A critical component of this algorithm is a new deterministic Leader Election algorithm for dense instances, when n = Θ( N ), with inverse Ackermann energy complexity.

NOn-parametric Bayesian channEls cLustering (NOBEL) Scheme for Wireless Multimedia Cognitive Radio Networks

In wireless multimedia cognitive radio networks (WMCRNs), to optimize multimedia transmissions and scarce wireless spectrum utilization, a multimedia secondary user (MSU) needs to estimate and/or identify the achievable quality of service (QoS)-levels over the available licensed channels. However, due to the lack of signaling information among MSUs and the primary users (PUs) in uncoordinated environments, identification of the achievable QoS-levels on the available licensed channels is a challenging problem and has not yet been fully explored. To address this challenge, we propose a novel NOn-parametric Bayesian channEls cLustering (NOBEL) scheme. In NOBEL, an infinite Gaussian mixture model-based collapsed Gibbs sampler is adopted to identify the achievable QoS-levels over the feature space, i.e., bitrate, packet delay variation, and packet delivery ratio on the PUs’ licensed channels. Real trace-driven evaluation results demonstrate that NOBEL outperforms other baseline clustering techniques and guarantee high accuracy from 98% to 99.5%.

Improved Data Rate 5G C-Node-B Design with 64x64 MIMO

The fast growth of mobile  telecommunication  fast  motivating the  companies  to  plan continuously and work from first generation until fourth generation, many companies in this field planned and started their scenarios toward fifth generation (5G) mobile, this is because of  the  need of  higher  data  rate  transmission  and wireless  system  radio  network,  many challenges expected will be problem during this project, this paper is an attempt to contribute as a proposed C-Node-B base station transceiver for 5G mobile system, 64 64 64 MIMO system has been used with presenting the beam forming technique, MIMO parameters and the main basic parameters in beamforming has been optimized in the proposed design

Analysis and Optimization of Caching in Fog Radio Access Networks

Caching is a promising approach to address the backhaul traffic congestion problem and boost throughput in fog radio access networks. In this correspondence paper, we investigate a proactive probabilistic caching optimization in wireless fog radio access network where multiple users request different files from multiple base stations (BSs). To assess the performance, we first derive the analytical results of successful transmission probability (STP) using tools of stochastic geometry. With the derived closed-form STP expressions, our objective is to optimize the proactive probabilistic caching distribution to maximize the STP. To reduce the computational complexity, we specially discuss the optimization in high signal-to-noise ratio (SNR). We propose a projection gradient method to get a local optimal solution. Simulation results show that the caching placement optimized by our proposed algorithm increases the STP by about $18\%$ over the best existing caching distribution when ${\rm SNR}=10$ dB with user density $\lambda _u=0.09$ , BS density $\lambda _b=0.1$ and file number $N=100$ .

Content Caching and Delivery in Wireless Radio Access Networks

Today's mobile data traffic is dominated by content-oriented traffic. Caching popular contents at the network edge can alleviate network congestion and reduce content delivery latency. This paper provides a comprehensive and unified study of caching and delivery techniques in wireless radio access networks (RANs) with caches at all edge nodes (ENs) and user equipments (UEs). Three cache-aided RAN architectures are considered: RANs without fronthaul, with dedicated fronthaul, and with wireless fronthaul. It first reviews in a tutorial nature how caching facilitates interference management in these networks by enabling interference cancellation (IC), zero-forcing (ZF), and interference alignment (IA). Then, two new delivery schemes are presented. One is for RANs with dedicated fronthaul, which considers centralized cache placement at the ENs but both centralized and decentralized placement at the UEs. This scheme combines IA, ZF, and IC together with soft-transfer fronthauling. The other is for RANs with wireless fronthaul, which considers decentralized cache placement at all nodes. It leverages the broadcast nature of wireless fronthaul to fetch not only uncached but also cached contents to boost transmission cooperation among the ENs. Numerical results show that both schemes outperform existing results for a wide range of system parameters, thanks to the various caching gains obtained opportunistically.

Performance Analysis of a New MAC Protocol for Wireless Cognitive Radio Networks

In this study, network performance analysis of a newly proposed cognitive radio wireless network (CRWN) medium access control (MAC) protocol is investigated in order to improve the performance of wireless cognitive radio networks (CRNs). Several MAC protocols have been used in the literature in order to increase the performance of wireless CRNs. In this context, a cognitive radio based MAC protocol is proposed, which is also a solution to the needs of wireless CRNs with high performance results. We propose an autonomous control strategy that recognizes both the primary network and the CRN environment considering the service quality requirements of the wireless CRN environment. Simulation of the network environment is performed with Riverbed Modeler software for more realistic performance evaluation. By selecting various workloads for secondary users, the analysis of different network performance parameters such as throughput ratios, end-to-end delays, packet loss ratios, bit error rates, and energy consumptions reveal the originality of our study. In addition, Slotted Aloha, TDMA, and CSMA/CA based MAC protocols for CRNs used in different studies in the literature are compared with CRWN. As a result, proposed protocol is given about 40% higher performance than Slotted Aloha in terms of throughput and about 36% higher performance than TDMA in terms of delay. Moreover, the proposed CRWN consumes less energy than CSMA/CA.

Performance analysis of radio spectrum for cognitive radio wireless networks using discrete time Markov chain

Cognitive radio (CR) arises as an important technique for wireless network with the aim to provide better utilization of radio spectrum, which is being utilized sporadically in some areas. IEEE 802.22 wireless regional area network (WRAN) is the first standard for CR technology designed to opportunistically utilize the unused or under-utilized TV bands. A WRAN cell normally consists of a number of customer premises equipments (CPEs)/CR users and a base station (BS) having master/slave architecture. When a CPE is powered on, it first attempts to associate with the BS. In this paper, a discrete time Markov chain model (DTMC) is presented to show the association process of CPEs with the BS. To the best of our knowledge, this paper is the first in which DTMC Model is analyzed and investigated for WRAN. Using this model, various parameters such as association time, expected return time to the respective backoff stage, first passage time, etc., are derived. Finally, the evaluation results are provided to analyze the system's performance.

Hazardous Chemicals Transportation Decision Method Based on Intelligent Logistics System

The logistics transportation optimization scheduling method based on intelligent decision system is the development trend of the future logistics industry. Based on the intelligent decision-making technology of dangerous goods logistics, and also the combination of wireless radio frequency identification (RFID) and wireless sensor network (WSN), this paper constructs a decision-making method for hazardous chemicals transportation based on intelligent logistics system, and for the defects existing in the current transportation process of hazardous chemicals. Then. it proposes the intelligent optimization scheduling model of hazardous chemicals logistics path, which optimizes the information collection methods in traditional hazardous chemicals logistics, realizes the dynamic monitoring of dangerous goods logistics, and effectively reduces the occurrence probability of hazards. Meanwhile, in this paper, the traditional Petri net has been improved to construct a Petri net-based logistics process optimization algorithm with workflow weighting performance. This algorithm can describe and model the whole process of logistics in a hierarchical way, perform multi-dimensional performance analysis on the logistics process, and accurately find the defects in the logistics process, so as to get the optimal dangerous goods logistics distribution strategy. The example verification results show that the proposed model in this study has high practicability, which can provide the optimal transportation decision-making scheme for hazardous chemicals under the complicated conditions of multi-operation nodes, multi-flow direction and multi-transport modes.

Availability Aspects Through Optimization Techniques Based Outlier Detection Mechanism in Wireless and Mobile Networks

Radio Frequency IDentification (RFID) and Wireless Sensor Networks (WSN) are the two most prominent wireless technologies for implementing a complete smart environment for the Internet of Things (IoT). Both RFID and WSN are resource constraint devices, which forces us to go for lightweight cryptography for security purposes. Security in terms of confidentiality, integrity, authentication, authorization, and availability. Key management is one of the major constraints for resource constraint mobile sensor devices. This work is an extension of the work done by Kumar et al. using efficient error prediction and limit of agreement for anomaly score. This work ensures cryptographic property, availability, in RFID-WSN integrated network through outlier detection mechanism for 50 to 5000 nodes network. Through detection ratios and anomaly scores system is tested against outliers. The proposed outlier detection mechanism identifies the inliers and outliers through anomaly score for protection against Denial-of-Service (DoS) attack. Intruders can be detected in few milliseconds without giving any conflict to the access rights. In terms of throughput, a minimum improvement of 6.2% and a maximum of 219.9% is observed for the proposed protocol as compared to Kumar et al. Protocol and in terms of percentage of Packet Delivery Ratio (PDR), a minimum improvement of 8.9% and a maximum of 19.5% is observed for the proposed protocol as compared to Kumar et al. protocol.

Primary user characterization for cognitive radio wireless networks using long short-term memory

Cognitive radio is a paradigm that proposes managing the radio electric spectrum dynamically by integrating the spectrum sensing, decision-making, sharing, and mobility stages. In the decision-making stage, the best available channel is selected for transmitting secondary user data in an opportunistic fashion, and the success of that stage depends on the efficiency of the primary user characterization model. Use of the long short-term memory technique based on the deep learning concept is proposed in order to reduce the forecasting error present in the future estimation of primary users in the GSM and WiFi frequency bands. The results show that long short-term memory has the capacity needed to improve channel use forecasting significantly more than other methods such as multilayer perceptron neural networks, Bayesian networks, and adaptive neuro-fuzzy inference systems (ANFIS-Grid). It is concluded that although long short-term memory exhibits better performance generating forecasts for time series, computing complexity is higher due to the existence of input, forget, and output gates within the neural structure; therefore, implementation is feasible in cognitive radio networks based on centralized network topologies.

Kernelized Generalized Likelihood Ratio Test for Spectrum Sensing in Cognitive Radio

Spectrum sensing in next-generation wireless radio networks is considered a key technology to overcome the problem of spectrum scarcity. Unfortunately, many approaches to spectrum sensing do not work well in low signal-to-noise ratio (SNR) environments. This paper proposes and analyzes a new algorithm named kernelized generalized likelihood ratio test (KGLRT) for spectrum sensing in cognitive radio systems to overcome this problem. Effectively, KGLRT uses a nonlinear kernel to map input data onto a high-dimensional feature space; then, the widely accepted (linear) generalized likelihood ratio test is used for hypothesis testing. This new algorithm gives a gain of 4 dB in SNR over its linear counterpart. A theoretical analysis for this algorithm is given for the first time and is shown analogous to algorithms used in image signal processing. The detection metrics are found to be concentrated random variables; furthermore, the probability distributions of the detection metrics are proved to follow the F -distributions, which agree with the results obtained using the concentration inequality. The analytical thresholds are derived for target false-alarm probabilities. The thresholds are independent of noise power; thus, the proposed algorithm can overcome noise uncertainty issues at very low SNR levels. Simulations validate the theoretical results.

Wireless Multimedia Cognitive Radio Networks: A Comprehensive Survey

Time-critical and delay-sensitive multimedia applications require more spectrum and transmission resources. With the provision of cognitive radios (CRs), the underutilized spectrum resources can be exploited to gain more bandwidth for the bandwidth hungry applications (multimedia applications). Cognitive radio networks (CRNs) also have the flexibility to adjust their transmission parameters according to the needs of multimedia services or applications. For this reason, wireless multimedia cognitive radio networks (WMCRNs) have gained much attentions in today's research domain. In this paper, we present a comprehensive survey of WMCRNs. Various multimedia applications supported by CRNs, and various CR-based wireless networks are surveyed. We highlight the routing and link layer protocols used for WMCRNs. We cover the quality-of-experience design and security requirements for transmitting multimedia content over CRNs. We provide an in-depth study of white space, television white space, and cross-layer designs that have been used for WMCRNs. We also survey the major spectrum sensing approaches used for the communications of bandwidth hungry and time-critical data over CRNs.

Spectrum sensing in cognitive radio wireless networks for orthogonal freqnency division multiplexing in additive white Gaussian noise channel

Cognitive radio (CR) systems are one of the most interesting topics in recent years. They would enable more efficient use of the spectrum. The main problem of CR is how to dectect exactly the spectrum usage of primary users. There are many ways to do this, such as energy detector (ED), Axell’s detector, the sliding window detector, etc. Among them, cyclostationarity (CS) based dection methods attracted much attention because of their better results in low-SNR regimes. This paper will propose a method based on the autocorrelation property of orthogonal frequency division multiplexing (OFDM) signals in additive white Gaussian noise (AWGN).

Primary user characterization for cognitive radio wireless networks using a neural system based on Deep Learning

Cognitive radio is a paradigm that proposes maximizing the utilization of the usable radio-electric spectrum, allowing licensed users (PUs) and non-licensed users (SUs) to simultaneously coexist through the dynamic management and assignment of spectrum resources, by integrating the stages of spectrum sensing, decision, sharing and mobility. Spectrum decision is one of the most important stages, but its optimal operation depends on the characterization sub-stage, which is in charge of efficiently estimating time gaps in which a PU won’t make use of the assigned spectrum, so that it can be used in an opportunistic fashion by SUs. The design and implementation of an algorithm based on the Long Short-Term Memory (LSTM) recurrent neural network is proposed in order to increase the success percentage in the forecasting (presence/absence) of PUs in spectrum channels. The accuracy level exhibited in the results indicates LSTM increases the prediction percentage as compared to the Multilayer Perceptron Neural Network (MLPNN) and the Adaptative Neuro-Fuzzy Inference System (ANFIS) learning models, which means it could be implemented in cognitive networks with centralized physical topologies.

Performance enhancement of steady-state Markov analysis for cognitive radio networks via channel reservation

Cognitive radio wireless networks CRNs have been considered as an efficient communication paradigm to the utilization of scarce spectrum. The main purpose of channel reservation of dynamic spectrum access (DSA) is to access these idle channels intelligently which are specialized for primary users (PUS) to be used by unlicensed users temporarily, which are called secondary users (SUS) without causing critical interference to the licensed user’s activity. In this paper, continuous-time Markov chain paradigm is improved via channel reservation to show the best usage of the radio spectrum bands, and the transition matrix are deduced for the proposed model. Moreover, the probability state vector is proved by performing steady state analysis. The deduced expressions of the suggested model are illustrated in the numerical results section.

Designing a MAC Algorithm for Equitable Spectrum Allocation in Cognitive Radio Wireless Networks

Spectrum sharing is one of the most important stages in cognitive radio wireless networks, responsible for the opportunistic allocation of free channels to unlicensed users (SUs) to be utilized in ...