QoS aware and fault tolerant handovers in software defined LTE networks

Wireless Networks - In order to handle a huge number of mobile users connections and their requirements like higher throughput, lower delay and seamless mobility, telecom operators have started...     

A Review of Transmission Rate over Wireless Fading Channels: Classifications,Applications, and Challenges

To address the explosive traffic demands, the capacity of the fading channel is increasingly becoming a prime concern in the designing of the wireless communication system. The channel capacity is an extremely important quantity, since it allows the transmission of the data through the channel with an arbitrarily small probability of error. In other words, capacity dictates the maximum rate of information transmission, called as ‘capacity’ of channel, determined by the intrinsic properties of the channel and is independent of the content of the transmitted information. In this paper, we present a comprehensive survey of the existing work related to the channel capacity model over various fading channels. With an elaborated explanation of the theory of channel capacity, definitions of channel capacity based on the channel state information are reviewed. To compliment this, review of the technique to enhance the channel capacity is discussed and reviewed. An effective capacity model to overcome the channel capacity limitation is also explained. Furthermore, as the secure transmission of data is of utmost importance, to address this physical layer security model is also reviewed. We also summarize the work related to channel capacity in various types of wireless networks. We finally cover the future research directions, including less explored aspects of the channel capacity that can be studied to design efficient communication systems.

     

Performance analysis of a novel matched filter detector with reduced out-of-band response for narrowband signals

Reduced spectral response in adjacent spectral bins is very desirable for detectors since it provides the capability to detect narrowband signals in the presence of unknown interference and colored noise. Recently, a new method of detecting narrowband signals which uses a close approximation to the matched filter is proposed by Raja Kumar. The impulse response of this filter turns out to be longer than that of the matched filter. In order to support this extra length of filter response, it is proposed that the signal be extrapolated by prediction before the filtering operation. In this paper, the characteristics of this detector are mathematically analyzed and the performance of the detector in the presence of additive white Gaussian noise (AWGN) is studied. The detection method uses predicted signals that are obtained typically from short time data. Hence, in this paper, a method of analyzing the statistics of predicted signals from short time data is presented, and it is applied for the performance analysis of the present detector. The accuracy of the present detection analysis is demonstrated through theoretical and simulation studies. Also the analysis and performance study is extended to fluctuating targets, and it is shown that the proposed method yields near-optimum performance for Swerling's Case-1 and Case-3 targets while giving the excellent advantage of low out-of-bin response.     

A novel bulk-input low voltage and low power four quadrant analog multiplier in weak inversion

A new four quadrant voltage mode bulk input analog multiplier is presented .The proposed multiplier is designed to operate in weak inversion. Multiplication is done by driving the bulk terminals of the MOS devices which offers linear dynamic range of ±80 mV. The simulation shows, it has a linearity error of 5.6 %, THD of nearly 5 % and ?3 dB band width of 221 kHz. Total power consumption is very low i.e. 714 nW. The circuit operates at a supply voltage of 0.5 V and is designed using 180 nm CMOS technology. It is suitable for low power bioelectronics and neural applications.     

Performance evaluation of an IEEE 802.15.4 sensor network with a star topology

One class of applications envisaged for the IEEE 802.15.4 LR-WPAN (low data rate—wireless personal area network) standard is wireless sensor networks for monitoring and control applications. In this paper we provide an analytical performance model for a network in which the sensors are at the tips of a star topology, and the sensors need to transmit their measurements to the hub node so that certain objectives for packet delay and packet discard are met. We first carry out a saturation throughput analysis of the system; i.e., it is assumed that each sensor has an infinite backlog of packets and the throughput of the system is sought. After a careful analysis of the CSMA/CA MAC that is employed in the standard, and after making a certain decoupling approximation, we identify an embedded Markov renewal process, whose analysis yields a fixed point equation, from whose solution the saturation throughput can be calculated. We validate our model against ns2 simulations (using an IEEE 802.15.4 module developed by Zheng [14]). We find that with the default back-off parameters the saturation throughput decreases sharply with increasing number of nodes. We use our analytical model to study the problem and we propose alternative back-off parameters that prevent the drop in throughput. We then show how the saturation analysis can be used to obtain an analytical model for the finite arrival rate case. This finite load model captures very well the qualitative behavior of the system, and also provides a good approximation to the packet discard probability, and the throughput. For the default parameters, the finite load throughput is found to first increase and then decrease with increasing load. We find that for typical performance objectives (mean delay and packet discard) the packet discard probability would constrain the system capacity. Finally, we show how to derive a node lifetime analysis using various rates and probabilities obtained from our performance analysis model.

Semi-supervised annotation of faces in image collection

The objective of this work is to correctly detect and recognize faces in an image collection using a database of known faces. This has applications in photo-tagging, video indexing, surveillance and recognition in wearable computers. We propose a two-stage approach for both detection and recognition tasks. In the first stage, we generate a seed set from the given image collection using off-the-shelf face detection and recognition algorithms. In the second stage, the obtained seed set is used to improve the performance of these algorithms by adapting them to the domain at hand. We propose an exemplar-based semi-supervised framework for improving the detections. For recognition of images, we use sparse representation classifier and generate seed images based on a confidence measure. The labels of the seed set are then propagated to other faces using label propagation framework by imposing appropriate constraints. Unlike traditional approaches, our approach exploits the similarities among the faces in collection to obtain improved performance. We conduct extensive experiments on two real-world photo-album and video collections. Our approach consistently provides an improvement of \({\sim } 4\)% for detection and \(5{-}9\)% for recognition on all these datasets.     

Investigation on effects of system parameters on transmission depth in underwater wireless optical communication

Photonic Network Communications - Performance of underwater wireless optical communication (UWOC) with different vertical water channel conditions is experimentally analyzed. Experiment has been...     

A study of drilling performances with minimum quantity of lubricant using fuzzy logic rules

Nowadays the increasing interest to perform machining operations is in dry/near-dry environments. The reason includes health and safety of operator, cost, ease of chip recyclability, etc. However one important process, which is difficult to perform in dry, is drilling. Without coolant, drilling leads to excessive thermal distortion and poor tool life. In order to tackle these conflicting requirements, the essentiality of study on machining performances with minimum quantity lubricant (MQL) becomes important.Fuzzy logic rules, which are derived based on fuzzy set theory, are used to develop fuzzy rule based model (FRBM). The performance of FRBM depends on two different aspects: structures of fuzzy rules and the associated fuzzy sets (membership function distributions, MFDs). The aim of this study is to investigate the performances of FRBMs based on Mamdani and TSK-types of fuzzy logic rules with different shapes of MFDs for prediction and performance analysis of machining with MQL in drilling of aluminum alloy. A comparison of the model predictions with experimental results and those published in the literature shows that FRBM with TSK-type fuzzy rules describes excellent trade-off with experimental measurements.     

Mitigating Performance Degradation in Congested Sensor Networks

Data generated in wireless sensor networks may not all be alike: some data may be more important than others and hence may have different delivery requirements. In this paper, we address differentiated data delivery in the presence of congestion in wireless sensor networks. We propose a class of algorithms that enforce differentiated routing based on the congested areas of a network and data priority. The basic protocol, called congestion-aware routing (CAR), discovers the congested zone of the network that exists between high-priority data sources and the data sink and, using simple forwarding rules, dedicates this portion of the network to forwarding primarily high-priority traffic. Since CAR requires some overhead for establishing the high-priority routing zone, it is unsuitable for highly mobile data sources. To accommodate these, we define MAC-enhanced CAR (MCAR), which includes MAC-layer enhancements and a protocol for forming high-priority paths on the fly for each burst of data. MCAR effectively handles the mobility of high-priority data sources, at the expense of degrading the performance of low-priority traffic. We present extensive simulation results for CAR and MCAR, and an implementation of MCAR on a 48-node testbed.