A Review paper based on spectrum sensing techniques in Cognitive Radio Networks

Natural frequency spectrum is scared resource; the efficient use of it can only accommodate the need of future computing world. But efficient use of it is not possible within the existing system, where the allocation of spectrum is done based on fixed spectrum access (FSA) policy. Many survey shows that it leads to under use of spectrum. For efficient utilization of spectrum innovative techniques is needed. using Dynamic spectrum access (DSA) policy we can exploiting the available spectrum, For given purpose Cognitive radio arises to be a tempting solution to the spectral congestion problem by introducing opportunistic usage of the frequency bands that are not heavily occupied by licensed users. This paper presents the study of different spectrum sensing techniques of cognitive radio networks. As we know Cognitive radio is a form of wireless communication where radio transceiver intelligently detects which spectrums are free which are not. After this it occupies the vacant one while avoiding busy one spectrum. Cognitive radios promote open spectrum allocation which is a clear departure from traditional command and control allocation schemes for radio spectrum usage. In short, they allow the formation of “infrastructure-less” collaborative network clusters—cognitive radio networks. However, how to detect free spectrum we have to use the spectrum sensing techniques, here we are describing all the spectrum sensing techniques and Finally concluded that cooperative sensing is better than Non-Cooperative sensing for primary user (PU) signal with  low SNR value. Keywords- CRN, FSA, PU, DSA, SU, SNR.

Algorithmic Framework and Implementation of Spectrum Holes Detection for Cognitive Radios

The ability to dynamically discover portions of unused radio spectrum (spectrum holes) is an important ability of cognitive radio systems. Spectrum holes present a potential opportunity for wireless communication. Detection of holes and signals allows cognitive radios to dynamically access and share the spectrum with minimal interference. This work steps through the design, implementation, and analysis of a spectrum holes detector for cognitive radios. Energy detection and cyclostationary detection algorithms for detecting spectrum holes are compared through computer simulations. Ultimately an energy detection algorithm is proposed which performs better than the cyclostationary detection algorithm and requires no a-priori knowledge of noise power. The energy detection algorithm is implemented on the bladeRF x115 software-defined radio for wideband detection, leveraging on-board FPGA hardware and field-programmable analog hardware to scan a gigahertz-order range of frequencies and discover spectrum holes in real time. Resource utilization and requirements of the implementation are analyzed, and a utilization of 8.8% of the FPGA\u27s logic resources is reported. Experiments are performed on the implementation to measure its detection performance and demonstrate its ability to detect holes over a wide bandwidth with reasonable latency

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

Spectrum measurement, sensing, analysis and simulation in the context of cognitive radio

The radio frequency (RF) spectrum is a scarce natural resource, currently regulated locally by national agencies. Spectrum has been assigned to different services and it is very difficult for emerging wireless technologies to gain access due to rigid spectmm policy and heavy opportunity cost. Current spectrum management by licensing causes artificial spectrum scarcity. Spectrum monitoring shows that many frequencies and times are unused. Dynamic spectrum access (DSA) is a potential solution to low spectrum efficiency. In DSA, an unlicensed user opportunistically uses vacant licensed spectrum with the help of cognitive radio. Cognitive radio is a key enabling technology for DSA. In a cognitive radio system, an unlicensed Secondary User (SU) identifies vacant licensed spectrum allocated to a Primary User (PU) and uses it without harmful interference to the PU. Cognitive radio increases spectrum usage efficiency while protecting legacy-licensed systems. The purpose of this thesis is to bring together a group of CR concepts and explore how we can make the transition from conventional radio to cognitive radio. Specific goals of the thesis are firstly the measurement of the radio spectrum to understand the current spectrum usage in the Humber region, UK in the context of cognitive radio. Secondly, to characterise the performance of cyclostationary feature detectors through theoretical analysis, hardware implementation, and real-time performance measurements. Thirdly, to mitigate the effect of degradation due to multipath fading and shadowing, the use of -wideband cooperative sensing techniques using adaptive sensing technique and multi-bit soft decision is proposed, which it is believed will introduce more spectral opportunities over wider frequency ranges and achieve higher opportunistic aggregate throughput.Understanding spectrum usage is the first step toward the future deployment of cognitive radio systems. Several spectrum usage measurement campaigns have been performed, mainly in the USA and Europe. These studies show locality and time dependence. In the first part of this thesis a spectrum usage measurement campaign in the Humber region, is reported. Spectrum usage patterns are identified and noise is characterised. A significant amount of spectrum was shown to be underutilized and available for the secondary use. The second part addresses the question: how can you tell if a spectrum channel is being used? Two spectrum sensing techniques are evaluated: Energy Detection and Cyclostationary Feature Detection. The performance of these techniques is compared using the measurements performed in the second part of the thesis. Cyclostationary feature detection is shown to be more robust to noise. The final part of the thesis considers the identification of vacant channels by combining spectrum measurements from multiple locations, known as cooperative sensing. Wideband cooperative sensing is proposed using multi resolution spectrum sensing (MRSS) with a multi-bit decision technique. Next, a two-stage adaptive system with cooperative wideband sensing is proposed based on the combination of energy detection and cyclostationary feature detection. Simulations using the system above indicate that the two-stage adaptive sensing cooperative wideband outperforms single site detection in terms of detection success and mean detection time in the context of wideband cooperative sensing

A NOISE ESTIMATION SCHEME FOR BLIND SPECTRUM SENSING USING EMD

The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. One solution is to use underutilized spectrum. Cognitive Radio (CR) technologies identify transmission opportunities in unused channels and avoid interfering with primary users. The key enabling technology is the Spectrum Sensing (SS). Different SS techniques exist, but techniques that do not require knowledge of the signals (non-coherent) are preferred. Noise estimation plays an essential role in enhancing the performance of non-coherent spectrum sensors such as energy detectors. In this thesis, we present an energy detector based on the behavior of Empirical Mode Decomposition (EMD) towards vacant channels (noise-dominant). The energy trend from the EMD processed signal is used to determine the occupancy of a given band of interest. The performance of the proposed EMD-based detector is evaluated for different noise levels and sample sizes. Further, a comparison is carried out with conventional spectrum sensing techniques to validate the efficacy of the proposed detector and the results revealed that it outperforms the other sensing methods

An Efficient Security Mechanisms for Different Sort of Attacks in CWSN

One of the major important aspects of wireless sensor networks WSNs is their capability to collect and process huge amounts of information in parallel with the help of small power limited devices enabling their use in observation target detection and various other monitoring applications Recently new ideas have been proposed to develop cognitive WSNs CWSNs to enhance awareness about the network and environment and make adaptive decisions based on the application goals A CWSN is a special network which has many constraints compared to traditional wireless network But the major problem is security In this paper discovering various security threats in these networks and various defense mechanisms to counter these vulnerabilities Various types of attacks on CWSNs are categorized under different classes based on their natures and targets and corresponding to each attack class appropriate security mechanisms are discusse

Comparison among Cognitive Radio Architectures for Spectrum Sensing

Recently, the growing success of new wireless applications and services has led to overcrowded licensed bands, inducing the governmental regulatory agencies to consider more flexible strategies to improve the utilization of the radio spectrum. To this end, cognitive radio represents a promising technology since it allows to exploit the unused radio resources. In this context, the spectrum sensing task is one of the most challenging issues faced by a cognitive radio. It consists of an analysis of the radio environment to detect unused resources which can be exploited by cognitive radios. In this paper, three different cognitive radio architectures, namely, stand-alone single antenna, cooperative and multiple antennas, are proposed for spectrum sensing purposes. These architectures implement a relatively fast and reliable signal processing algorithm, based on a feature detection technique and support vector machines, for identifying the transmissions in a given environment. Such architectures are compared in terms of detection and classification performances for two transmission standards, IEEE 802.11a and IEEE 802.16e. A set of numerical simulations have been carried out in a challenging scenario, and the advantages and disadvantages of the proposed architectures are discussed