Deepti Singhal

LTE-Advanced: Handover interruption time analysis for IMT-A Evaluation

High-speed data applications over wireless networks have been growing rapidly in recent years. With this increased use of wireless data, services in wireless networks require performance guarantee. This is, therefore, driving the need for regular innovations in wireless technologies to provide more and more capacity and higher quality of service (QoS). These higher performance requirements have motivated 3rd Generation Partnership Project (3GPP) to work on LTE-Advanced. LTE-Advanced is a technology enhancement to Long Term Evaluation (LTE) that is under evaluation of the requirements of IMT-Advanced. There are a few mobility enhancements in LTE-Advanced to assure good performance at the time of handover. The generic handover procedure of LTE-Advanced builds upon the one developed for LTE and minimizes the handover interruption time. This tutorial article gives an overview of handover procedure of LTE-Advanced and analyzes handover interruption time in Time Division Duplex (TDD) and Frequency Division Duplex (FDD) modes. The analysis shows that the handover interruption time for LTE-Advanced complies with the IMT-Advanced requirement.

Cognitive cross-layer multipath probabilistic routing for cognitive networks

Mobile Ad-hoc NETworks (MANETs) is a set of mobile nodes that can move around arbitrarily, and communicate with others in a multi-hop fashion without any assistance of base stations. With recent advances in Cognitive Radio (CR) technology, it is possible to apply the Dynamic Spectrum Access model in MANETs. This introduces the concept of Cognitive Radio Ad Hoc Networks (CRAHNs). Applying CR techniques provides better throughput, even in congested spectrum along with better propagation characteristics. CRAHN is a kind of intelligent network that is aware of its surrounding environment, and adapts to the transmission or reception parameters to achieve efficient communication without interfering with primary users. Routing in CR environment is a challenging task as the availability of channel is constrained by the presence of primary user. The problem of routing in CRAHNs targets the creation and maintenance of wireless multi-hop paths among cognitive nodes by deciding both the spectrum to be used and the relay nodes of the path. This paper proposes a cognitive cross-layer multipath probabilistic routing for cognitive radio based networks. The proposed solution uses spectrum holes identified by MAC layer, decides the channel to be used and transmit power level for each hop in the path. The proposed solution is implemented in NS2, and performance of the proposed solution is compared with the existing solution from the literature. The paper also shows that the proposed solution outperforms existing solution in terms of packet delivery ratio, average end-to-end delay and energy consumed per data packet.

Doubly Cognitive Architecture Based Cognitive Wireless Sensor Networks

Scarcity of spectrum is increasing not only in cellular communication but also in wireless sensor networks. Adding cognition to the existing wireless sensor network WSN infrastructure has helped. As sensor nodes in WSN are limited with constraints like power, efforts are required to increase the lifetime and other performance measures of the network. In this article, the authors propose Doubly Cognitive WSN, which works by progressively allocating the sensing resources only to the most promising areas of the spectrum and is based on pattern analysis and learning. As the load of sensing resource is reduced significantly, this approach saves the energy of the nodes and reduces the sensing time dramatically. The proposed method can be enhanced by periodic pattern analysis to review the strategy of sensing. Finally the ongoing research work and contribution on cognitive wireless sensor networks in Communication Research Centre IIIT-H is discussed.

Energy efficient cognitive cross-layer MAC protocol

The ever increasing demand for communication bandwidth and inefficient usage of the existing spectrum has led to spectrum scarcity. In this light, spectrum should be managed as a scare resource. For radio communication systems, efficient utilization of spectrum is the key requirement. The inefficient usage of the existing spectrum can be improved through opportunistic access to the licensed bands without interfering with the primary users. This introduces the concept of dynamic spectrum access and cognitive radio. This paper proposes a energy efficient medium access protocol for any cognitive network. For performance evaluation, proposed solution is implemented in NS2 and comparative analysis is also done against existing solution from the literature. The paper also validates that the proposed solution gives better results.

Simple Median based information fusion in wireless sensor network

The accuracy of a system is measured by the deviation of the systems results from the actual results. Information fusion deals with the combination of information from same source or different sources to obtain improved fused estimate with greater quality or greater relevance. As larger amount of sensors are deployed in harsher environment, it is important that sensor fusion techniques are robust and fault-tolerant, so that they can handle uncertainty and faulty sensor readouts. The sensor nodes in Wireless Sensor Network (WSN) are constrained with computation and communication resources, and efforts are required to increase the performance measures of the network. Thus sensor fusion techniques should be simple with less computation complexity. In this paper we propose a novel Median based sensor fusion function named D function. It is shown that the proposed D function satisfies the lipschitz condition. Paper also presents some of the ideas which can open new areas for research in fusion problem.

Network Architecture for Distributed Computation in Wireless Sensor Networks

Wireless Sensor Networks are used to perform distributed sensing in various fields, such as health, military, home etc. Sensing is done in order to have a better understanding of the behavior of the monitored entity or to monitor an environment for the occurrence of a set of possible events, so that proper action may be taken whenever necessary. In, wireless sensor networks, sensor nodes should communicate among themselves and do distributed computation over the sensed values to identify the occurrence of an event. The architecture for distributed computation of primitive recursive functions and median is presented in this paper. This paper assumes the no memory computational model of sensor nodes, in the architecture for primary recursive functions i.e. The sensor nodes only have two registers. This assumption is not made for the computation of median. This paper presents an optimal architecture for the distributed computation in WSN and also claims that this architecture is optimal for the described computation model.

Energy efficient localization of primary users for avoiding interference in cognitive networks

Cognitive Network is an intelligent network that is aware of its surrounding environment, and adapts the transmission or reception parameters of either a network or a wireless node to achieve efficient communication without interfering with primary users. For avoiding interference to primary users, cognitive users must be aware of primary users within the region of interest and disable themselves whenever primary user is active. However, it is not possible to identify the location of the passive listening primary users, and causes hidden node problem in cognitive networks. This paper applies leveling and sectoring based approach for localization, and then disable region is identified considering the communication range of both primary and cognitive user. This will avoid the interference caused by hidden node problem. The cognitive users within the disabled region are sent to sleep mode until a free channel is detected in that area by spectrum sensing module. This approach avoids interference with primary user and also saves energy of cognitive users. As this approach conserve energy of cognitive users as well as simple to implement, it is suitable for CWSN.