Prabhat Thakur

Performance analysis of high-traffic cognitive radio communication system using hybrid spectrum access, prediction and monitoring techniques

In this paper, the hybrid spectrum access and prediction techniques are exploited simultaneously in the high-traffic cognitive radio communication system, in order to enhance the throughput and overcome the problem of waiting states. The hybrid spectrum access is responsible for throughput enhancement by escaping the waiting states whereas the spectrum prediction alleviates the sensing errors in the high-traffic communication environment. The closed-form expression for the throughput of cognitive user (CU) communication is derived and validated the proposed approach with the reported literature. Moreover, a new framework is proposed to conquer the sharing issues of conventional and proposed approaches. In addition to this, the performance metrics of proposed framework such as the data-loss, energy-loss of the CU and interference at the PU have been analyzed.

Advanced Frame Structures for Hybrid Spectrum Access Strategy in Cognitive Radio Communication Systems

In this letter, we have exploited a novel hybrid-cum-improved spectrum access technique for significant improvement in the throughput and data-loss rate. This proposed technique comprises of hybrid spectrum access and improved frame structure strategies of the cognitive radio communication system. Also, the closed-form expressions for throughput and data-loss of these approaches are derived. Moreover, the proposed approaches are validated numerically as well as with reported literature.

Spectrum mobility in cognitive radio network using spectrum prediction and monitoring techniques

The spectrum mobility during data transmission is an integral part of the cognitive radio network (CRN) which is conventionally two types for instance reactive and proactive. In the reactive approach, the cognitive user (CU) switches its communication after the emergence of the primary user (PU), where the detection of emergence of PU relies either on spectrum sensing and/or monitoring. Due to certain limitations of the reactive approach such as: (1) loss at least one packet on the emergence of PU and (2) resource (bandwidth) wastage if the periodic sensing is used for mobility, the researchers have introduced the concept of proactive spectrum mobility. In this approach, the emergence of PU is predicted on the bases of pre-available spectrum information, and switching is performed before true emergence of the PU, in order to avoid even the single packet loss. However, the imperfect spectrum prediction is a major milestone for the proactive spectrum mobility. Recently, due to introduction of the spectrum monitoring simultaneous to the data transmission, the reactive approach has come into lime-light again, however, it suffers from the single packet loss and imperfect spectrum monitoring issues. Therefore in this paper, we have exploited the spectrum monitoring and prediction techniques, simultaneously for the spectrum mobility, in order to enhance the performance of cognitive radio network (CRN). In the proposed strategy, the decision results of the spectrum prediction and monitoring techniques are fused using AND and OR fusion rules, for the detection of emergence of PU during the data transmission. Further, the closed-form expressions of the resource wastage, achieved throughput, interference power at PU and data-loss for the proposed approaches as well as for the prediction and monitoring approaches are derived. Moreover, the simulation results for the proposed approaches are presented and validation is performed by comparing the results with prediction and monitoring approach. In a special case, when the prediction error is zero, the graphs of all metric values overlies the spectrum monitoring approach, which further validates the proposed approach.

Frame structures for hybrid spectrum accessing strategy in cognitive radio communication system

In this paper, we have exploited the novel hybrid-cum-improved spectrum access strategy to significant improvement in the throughput and data-loss rate. This proposed strategy is comprise of hybrid spectrum access and improved frame structure strategies for the cognitive radio communication system. In addition to this, the mathematical expressions of throughput for these approaches are also presented. Moreover, the proposed approaches are validated numerically as well as with reported literature.

Performance improvement of cognitive radio network using spectrum prediction and monitoring techniques for spectrum mobility

In this paper, we have exploited the spectrum monitoring and prediction techniques, simultaneously for the spectrum mobility, in order to enhance the performance of the cognitive radio network. In the proposed strategy, the decision results of the spectrum prediction and monitoring techniques are fused using AND and OR fusion rules, for the detection of emergence of the PU during data transmission. Further, the closed-form expressions of the resource wastage, achieved throughput, interference power at PU and data-loss for the proposed and prediction approaches are derived. Moreover, the simulation results for the proposed approaches are presented and validation is performed by comparing the results with prediction approach.

Error Rate Analysis of Precoded-OSTBC MIMO System Over Generalized-K Fading Channel

The orthogonal space–time block codes (OSTBCs) increase the diversity order of a multiple-input multiple-output (MIMO) system at a reasonable cost of complexity. However, the precoding technique in which complete channel knowledge is available at the base transceiver station has come into existence with OSTBC for improving the system performance. Moreover, a composite generalized-K channel model has become the key for analyzing both the effects of multipath fading and shadowing. Therefore, this chapter has exploited the precoded OSTBC to improve the bit error rate (BER) performance of MIMO system over a generalized-K fading channel.

Downlink Spectral Efficiency of ZF Precoding Based Multi-user MIMO System Over Weibull Fading Channel

Frequency division duplex (FDD) and time-division duplex (TDD) are two different ways of executing the downlink and uplink transmission over a given frequency band. In which, TDD is the superior mode due to less pilots’ requirement than FDD and its highly scalable quality, where the pilot length is independent of the number of base station (BS) antennas. The linear precoding scheme i.e., zero forcing (ZF) is simple and easy to implement. Therefore, this paper uses ZF for the performance comparison of multi-user multiple-input multiple-output (MU MIMO) system on the basis of downlink spectral efficiency (SE) obtained with perfect channel state information (CSI) and with CSI estimated through pilot sequences of a specified length. The SE is illustrated as a function of the number of BS antennas using TDD and FDD mode over a Weibull fading channel which considers favorable propagation case. The SE increases even after decreasing the signal to noise ratio (SNR). It is practically viable to organize FDD systems with many antennas, mainly for slow-varying channels where we can take a large pilot overhead, but TDD is preferable in this situation.

Analysis of high-traffic cognitive radio network with imperfect spectrum monitoring technique

Abstract This paper introduces the concept of imperfect spectrum monitoring for realistic wireless communication environments with high-traffic cognitive radio networks (HTCRN). The effect of imperfect spectrum monitoring in the HTCRN is analyzed in terms of the probability of data-loss (in the form of packets) and interference at primary user (PU). Further, two algorithms are proposed to compute the data-loss and interference-at-PU considering different scenarios of the traffic-intensity and spectrum monitoring error. Moreover, the closed-form expressions for the ratio of achieved-throughput to the data-loss, power-wastage, interference-efficiency as well as energy-efficiency are derived, and numerically simulated results are presented to support the proposed work. Furthermore, the randomness in emergence of PU is considered and the Monte-Carlo simulations are exploited to validate the numerically simulated results.

Framework of Compressive Sampling with Its Applications to One- and Two-Dimensional Signals

Compressive sampling emerged as a very useful random protocol and has become an active research area for almost a decade. Compressive sampling allows us to sample a signal below Shannon Nyquist rate and assures its successful reconstruction with some limitations on signal, that is, signal should be sparse in some domain. In this paper, we have used compressive sampling for an arbitrary one-dimensional signal and two-dimensional image signal compression and successfully reconstructed them by solving L1-norm optimization problems. We also have showed that compressive sampling can be implemented if a signal is sparse and incoherent through simulations. Further, we have analyzed the effect of noise on the recovery.