Performance analysis of energy detection algorithm in cognitive radio

Rapid growth of wireless applications and services has made it essential to address spectrum scarcity problem. if we were scan a portion of radio spectrum including revenue-rich urban areas, we find that some frequency bands in the spectrum are largely unoccupied most of the time, some other frequency bands are partially occupied and the remaining frequency bands are heavily used. This leads to a underutilization of radio spectrum, Cognitive radio (CR) technology attempts alleviate this problem through improved utilization of radio spectrum. Cognitive radio is a form of wireless communication in which a transceiver can intelligently detect which RF communication channels are in use and which are not, and instantly move into vacant channels while avoiding occupied ones. This optimizes the use of available radio-frequency (RF) spectrum while minimizing interference to other users. There two types of cognitive radio, full cognitive radio and spectrum-sensing cognitive radio. Full cognitive radio takes into account all parameters that a wireless node or network can be aware of. Spectrum-sensing cognitive radio is used to detect channels in the radio frequency spectrum. Spectrum sensing is a fundamental requirement in cognitive radio network. Many signal detection techniques can be used in spectrum sensing so as to enhance the detection probability. In this thesis we analyze the performance of energy detector spectrum sensing algorithm in cognitive radio. By increasing the some parameters, the performance of algorithm can be improved as shown in the simulation results. In cognitive radio systems, secondary users should determine correctly whether the primary user is absent or not in a certain spectrum within a short detection period. Spectrum detection schemes based on fixed threshold are sensitive to noise uncertainty, the energy detection based on dynamic threshold can improve the antagonism of noise uncertainty; get a good performance of detection while without increasing the computer complexity uncertainty and improves detection performance for schemes are sensitive to noise uncertainty in lower signal-to-noise and large noise uncertainty environments

LMPIT-inspired Tests for Detecting a Cyclostationary Signal in Noise with Spatio-Temporal Structure

In spectrum sensing for cognitive radio, the presence of a primary user can be detected by making use of the cyclostationarity property of digital communication signals. For the general scenario of a cyclostationary signal in temporally colored and spatially correlated noise, it has previously been shown that an asymptotic generalized likelihood ratio test (GLRT) and locally most powerful invariant test (LMPIT) exist. In this paper, we derive detectors for the presence of a cyclostationary signal in various scenarios with structured noise. In particular, we consider noise that is temporally white and/or spatially uncorrelated. Detectors that make use of this additional information about the noise process have enhanced performance. We have previously derived GLRTs for these specific scenarios; here, we examine the existence of LMPITs. We show that these exist only for detecting the presence of a cyclostationary signal in spatially uncorrelated noise. For white noise, an LMPIT does not exist. Instead, we propose tests that approximate the LMPIT, and they are shown to perform well in simulations. Finally, if the noise structure is not known in advance, we also present hypothesis tests using our framework

Testing equality of multiple power spectral density matrices

This paper studies the existence of optimal invariant detectors for determining whether P multivariate processes have the same power spectral density. This problem finds application in multiple fields, including physical layer security and cognitive radio. For Gaussian observations, we prove that the optimal invariant detector, i.e., the uniformly most powerful invariant test, does not exist. Additionally, we consider the challenging case of close hypotheses, where we study the existence of the locally most powerful invariant test (LMPIT). The LMPIT is obtained in the closed form only for univariate signals. In the multivariate case, it is shown that the LMPIT does not exist. However, the corresponding proof naturally suggests an LMPIT-inspired detector that outperforms previously proposed detectors.This work was partly supported by the Spanish MINECO grants COMONSENS Network (TEC2015-69648-REDC) and KERMES Network (TEC2016-81900-REDT/AEI); by the Spanish MINECO and the European Commission (ERDF) grants ADVENTURE (TEC2015-69868-C2-1- R), WINTER (TEC2016-76409-C2-2-R), CARMEN (TEC2016-75067-C4-4- R) and CAIMAN (TEC2017-86921-C2-1-R and TEC2017-86921-C2-2-R); by the Comunidad de Madrid grant CASI-CAM-CM (S2013/ICE-2845); by the Xunta de Galicia and ERDF grants GRC2013/009, R2014/037 and ED431G/04 (Agrupacion Estratexica Consolidada de Galicia accred- ´ itation 2016-2019); by the SODERCAN and ERDF grant CAIMAN (12.JU01.64661); and by the Research Council of Norway grant FRIPRO TOPPFORSK (250910/F20). This paper was presented in part at the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing

Spectral Efficiency of MIMO Millimeter-Wave Links with Single-Carrier Modulation for 5G Networks

Future wireless networks will extensively rely upon bandwidths centered on carrier frequencies larger than 10GHz. Indeed, recent research has shown that, despite the large path-loss, millimeter wave (mmWave) frequencies can be successfully exploited to transmit very large data-rates over short distances to slowly moving users. Due to hardware complexity and cost constraints, single-carrier modulation schemes, as opposed to the popular multi-carrier schemes, are being considered for use at mmWave frequencies. This paper presents preliminary studies on the achievable spectral efficiency on a wireless MIMO link operating at mmWave in a typical 5G scenario. Two different single-carrier modem schemes are considered, i.e. a traditional modulation scheme with linear equalization at the receiver, and a single-carrier modulation with cyclic prefix, frequency-domain equalization and FFT-based processing at the receiver. Our results show that the former achieves a larger spectral efficiency than the latter. Results also confirm that the spectral efficiency increases with the dimension of the antenna array, as well as that performance gets severely degraded when the link length exceeds 100 meters and the transmit power falls below 0dBW. Nonetheless, mmWave appear to be very suited for providing very large data-rates over short distances.Comment: 8 pages, 8 figures, to appear in Proc. 20th International ITG Workshop on Smart Antennas (WSA2016

Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201

Testing equality of multiple power spectral density matrices

This paper studies the existence of optimal invariant detectors for determining whether P multivariate processes have the same power spectral density. This problem finds application in multiple fields, including physical layer security and cognitive radio. For Gaussian observations, we prove that the optimal invariant detector, i.e., the uniformly most powerful invariant test, does not exist. Additionally, we consider the challenging case of close hypotheses, where we study the existence of the locally most powerful invariant test (LMPIT). The LMPIT is obtained in the closed form only for univariate signals. In the multivariate case, it is shown that the LMPIT does not exist. However, the corresponding proof naturally suggests an LMPIT-inspired detector that outperforms previously proposed detectors.Comunidad de Madrid | Ref. CASI-CAM-CM (S2013/ICE-2845)Research Council of Norway | Ref. FRIPRO TOPPFORSK (250910/F20)Ministerio de Economía y Competitividad | Ref. TEC2015-69648-REDCMinisterio de Economía y Competitividad | Ref. TEC2016-81900-REDT/AEIMinisterio de Economía y Competitividad | Ref. TEC2015-69868-C2-1-RAgencia Estatal de Investigación | Ref. TEC2016-76409-C2-2-RAgencia Estatal de Investigación | Ref. TEC2016-75067-C4-4-RAgencia Estatal de Investigación | Ref. TEC2017-86921-C2-1-RAgencia Estatal de Investigación | Ref. TEC2017-86921-C2-2-RXunta de Galicia | Ref. GRC2013/009Xunta de Galicia | Ref. GRC2013/009Xunta de Galicia | Ref. ED431G/0

Distributed Detection and Estimation in Wireless Sensor Networks

In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R. Chellapa and S. Theodoridis, Eds., Elsevier, 201