Spreading and power allocation for multiple antenna transmission using decorrelating receivers

We propose a new scheme for multiple antenna transmission in the context of spread-spectrum signaling. The new scheme consists of using shifted Gold sequences to modulate independent information on the multiple antennas. We show that this strategy of using multiphase spreading (MPS) on different antennas greatly improves the throughput over currently known spread-spectrum multiple-antenna methods. We also find the optimal power allocation strategy among multiple transmit antennas for a fixed rate of channel state information, which might be provided via a feedback link, at the transmitter. We demonstrate the differences in optimal power distribution for maximizing capacity and minimizing probability of outage. When the transmission from the two antennas uses orthogonal spreading, we find that optimizing the power does not give much gain over the equal power transmission. However, when the transmissions are not orthogonal as in the case of MPS, then allocating power to maximize throughput gives considerable gain over equal power transmission. We also consider the effect of imperfections in the feedback channel on the optimal power allocation and show that our power allocation scheme is robust to feedback errors.     

A QoS Aware Resource Placement Approach Inspired on the Behavior of the Social Spider Mating Strategy in the Cloud Environment

Wireless Personal Communications - The efficient management of resource sharing plays a crucial role in the cloud execution environment. The constraints such as heterogeneity and dynamic nature of...     

Ultra Wideband Co-planer Microstrip Patch Antenna for Wireless Applications

In this paper, a comparative study of co-planar gap coupled rectangular and elliptical microstrip patch antenna is presented. It is analyzed by using circuit theory concept. The proposed antennas show ultra wideband operations which depend on dimensions of the patches, gap between them as well as air gap thickness. It is observed that the performance of gap coupled elliptical patch antenna is better than that of rectangular patch antenna while the area of rectangular and elliptical patches is same. The bandwidth of proposed rectangular patch antenna is found to be 50.7 % with average gain of 6.5 dBi while for elliptical patch antenna the bandwidth and gain further improves up to 57.6 % and 9.5 dBi respectively. The theoretical results are in good agreement with the simulated results obtained from IE3D simulation software.     

Exploiting WiMAX for covert transmission of secret data over fading channel

Worldwide Interoperability for Microwave Access (WiMAX) is the broadband wireless access technique that provides rapid broadband services to domestic and enterprise users. Owing to the broadcast characteristic of the WiMAX system, transmitted data security is crucial, particularly when the messages are confidential. In this work, exploiting of WiMAX to convey sensitive secret information is presented. In the first phase, we exploited conventional WiMAX (fast Fourier transform [FFT]‐WiMAX), and in the second phase, we propose a wavelet packet transform (WPT)–based WiMAX for covert communication. The quality evaluation of both covert transmission models (FFT‐WiMAX and WPT‐WiMAX) over the fading channel is done in this work. The experimental outcomes of the proposed WPT‐WiMAX covert transmission system reveal a significant enhancement in error rate (bit error rate) and peak signal‐to‐noise ratio for a given signal‐to‐noise ratio.     

Energy-Aware WBAN for Health Monitoring Using Critical Data Routing (CDR)

Wireless Personal Communications - Wireless body area network (WBAN) is the subfield of Wireless Sensor Network, employs in the area of monitoring the health of the patient. WBAN is also known as...     

Enhanced Binary Hexagonal Extrema Pattern (EBHXEP) Descriptor for Iris Liveness Detection

Wireless Personal Communications - Biometric traits are frequently used by security agencies for automatic recognition of a person. There are numerous biometric traits used for person...     

A stable energy efficient clustering protocol for wireless sensor networks

Sensor networks comprise of sensor nodes with limited battery power that are deployed at different geographical locations to monitor physical events. Information gathering is a typical but an important operation in many applications of wireless sensor networks (WSNs). It is necessary to operate the sensor network for longer period of time in an energy efficient manner for gathering information. One of the popular WSN protocol, named low energy adaptive clustering hierarchy (LEACH) and its variants, aim to prolong the network lifetime using energy efficient clustering approach. These protocols increase the network lifetime at the expense of reduced stability period (the time span before the first node dies). The reduction in stability period is because of the high energy variance of nodes. Stability period is an essential aspect to preserve coverage properties of the network. Higher is the stability period, more reliable is the network. Higher energy variance of nodes leads to load unbalancing among nodes and therefore lowers the stability period. Hence, it is perpetually attractive to design clustering algorithms that provides higher stability, lower energy variance and are energy efficient. In this paper to overcome the shortcomings of existing clustering protocols, a protocol named stable energy efficient clustering protocol is proposed. It balances the load among nodes using energy-aware heuristics and hence ensures higher stability period. The results demonstrate that the proposed protocol significantly outperforms LEACH and its variants in terms of energy variance and stability period.     

Intrusion Detection System in Wireless Sensor Network Using Conditional Generative Adversarial Network

Wireless communication networks have much data to sense, process, and transmit. It tends to develop a security mechanism to care for these needs for such modern-day systems. An intrusion detection system (IDS) is a solution that has recently gained the researcher’s attention with the application of deep learning techniques in IDS. In this paper, we propose an IDS model that uses a deep learning algorithm, conditional generative adversarial network (CGAN), enabling unsupervised learning in the model and adding an eXtreme gradient boosting (XGBoost) classifier for faster comparison and visualization of results. The proposed method can reduce the need to deploy extra sensors to generate fake data to fool the intruder 1.2–2.6%, as the proposed system generates this fake data. The parameters were selected to give optimal results to our model without significant alterations and complications. The model learns from its dataset samples with the multiple-layer network for a refined training process. We aimed that the proposed model could improve the accuracy and thus, decrease the false detection rate and obtain good precision in the cases of both the datasets, NSL-KDD and the CICIDS2017, which can be used as a detector for cyber intrusions. The false alarm rate of the proposed model decreases by about 1.827%.

     

Detecting DDoS Attacks in Cloud Computing Using Extreme Learning Machine and Adaptive Differential Evolution

Wireless Personal Communications - Distributed denial of service (DDoS) attacks disrupt the availability of cloud services. The detection of these attacks is a major challenge in the cloud...     

Attack Resilient and Efficient Protocol based on Greedy Perimeter Coordinator Routing—Mobility Awareness for Preventing the Attack in the VANET

Wireless Personal Communications - A Greedy Perimeter Coordinator Routing and Mobility Awareness (GPCR-MA) vehicular routing is a widely accepted routing protocol for VANET (Vehicular Ad hoc...