Current Trends and Research Challenges in Spectrum-Sensing for Cognitive Radios

Garg, Roopali; Saluja, Nitin

doi:10.14569/ijacsa.2016.070756

Cited by 6 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CR is a self-learning system that detects and analyzes its environment, adapts to changes, and acts to use the radio spectrum efficiently. CR scans the spectrum for licensed users recognition to allow unlicensed users to utilize the assigned spectrum when it is not used as illustrated in Figure 1 [ 24 ]. Spectrum holes are the channels that are available and not in usage at the time and space of sensing [ 25 , 26 ].…”

Section: Cognitive Radio Overviewmentioning

confidence: 99%

LoRaCog: A Protocol for Cognitive Radio-Based LoRa Network

Salika

Nasser

Mroue³

et al. 2022

Sensors

View full text Add to dashboard Cite

In this paper, we propose a new protocol called LoRaCog to introduce the concept of Cognitive Radio (CR) in the LoRa network. LoRaCog will enable access to a wider spectrum than that of LoRaWAN by using the unutilized spectrum and thus has better efficiency without impacting the end devices’ battery consumption. LoRa networks are managed by LoRaWAN protocol and operate on the unlicensed Industrial, Scientific and Medical (ISM) band. LoRaWAN is one of thriving protocols for Low-Power Wide-Area Networks (LPWAN) implemented for the Internet of Things (IoT). With the growing demand for IoT, the unlicensed spectrum is expected to be congested, unlike the licensed spectrum, which is not fully utilized. This can be fairly balanced by applying CR to the LoRa network, where the End Devices (EDs) may change the operating channel opportunistically over the free/available licensed spectrum. Spectrum sensing, channel selection and channel availability relevance become essential features to be respected by the proposed protocol. The main objective of adding CR to LoRaWAN is reducing the congestion and maintaining LoRaWAN’s suitability for battery-operated devices. This is achieved by modifying LoRaWAN components such as the ED receive window RX2 rearrangement, spectrum sensing functionality by gateway (GW) for identifying unused channels, and reaching a decision on the unused channels by network server (NS). These changes will create LoRaCog meeting spectrum efficiency and maintain the same level of battery consumption as in LoRaWAN. Numerical simulations show a significant decrease in the rejected packet rate (more than 50%) with LoRaCog when more EDs use cognitive channels. As the results prove, LoRaWAN can reach above 50% rejected packets for the simulated environment versus 24% rejection for LoRaCog using only one additional channel (means total two channels). This means that the system can eliminate rejected packets almost completely when operating over the possible many channels. As well, these results show the flexibility in the system to utilize the available frequencies in an efficient and fair way. The results also reveal that a lower number of GWs is needed for LoRaCog from LoRaWAN to cover the same area.

show abstract

Section: Cognitive Radio Overviewmentioning

confidence: 99%

LoRaCog: A Protocol for Cognitive Radio-Based LoRa Network

Salika

Nasser

Mroue³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Sensing accuracy is typically evaluated using the Receiver Operating Characteristic (ROC) curves. These curves are the plot of the detection probability versus the false alarm probability or plot of the probability of miss detection against the probability of false alarm [60,61]. These probabilities are defined as: Probability of detection (Pd): It is the probability that the SU declares the presence of PU signal when it is actually present.…”

Section: ) System Model For Spectrum Sensingmentioning

confidence: 99%

“…Moreover, energy detection is unreliable at low SNR values [66], ineffective in detecting spread signals [60], and unable to distinguish between PU signals from other signals.…”

Section: ) Narrowband Sensing Techniquesmentioning

confidence: 99%

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

Kassri¹,

En‐Nouaary²,

Bah³

et al. 2021

IJACSA

View full text Add to dashboard Cite

The inherent scarcity of frequency spectrum, along with the fixed spectrum allocation adopted policy, has led to a dire shortage of this indispensable resource. Furthermore, with the tremendous growth of wireless applications, this problem is intensified as the unlicensed frequency spectrum becomes overcrowded and unable to meet the requirement of emerging radio devices operating at higher data rates. Additionally, the already assigned spectrum is underutilized. That has prompted researchers to look for a way to address spectrum scarcity and enable efficient use of the available spectrum. In this context, Cognitive Radio (CR) technology has been proposed as a potential means to overcome this issue by introducing opportunistic usage to less congested portions of the licensed spectrum. In addition to outlining the fundamentals of Cognitive Radio, including Dynamic Spectrum Access (DSA) paradigms and CR functions, this paper has a three-fold objective: first, providing an overview of Software Defined Radio (SDR), in which the architecture, benefits, and ongoing challenges of SDR are presented; second, giving an extensive review of spectrum sensing, covering sensing types, narrowband and wideband sensing schemes with their pros and cons, Machine Learningbased sensing, and open issues that need to be further addressed in this field; third, exploring the use of Cognitive Radio in the Internet of Things (IoT) while highlighting the crucial contribution of CR in enabling IoT. This Review is elaborated in an informative fashion to help new researchers entering the area of Cognitive Radio Networks (CRN) to easily get involved.

show abstract

“…In spectrum sensing stage, the cognitive-radio sense vacant channels within the spectrum range. Spectrum decision helps to detect best available channels in the spectrum [8]. Spectrum sharing performs the task of maintaining the quality of service throughout the process and avoids collision between secondary-users present in the network [9].…”

Section: Introductionmentioning

confidence: 99%

Modelling of Spectrum Handover Scheme using Particle Swarm Intelligence in Cognitive-radio Networks

Bansal¹,

Garg²

2019

IJRTE

View full text Add to dashboard Cite

Cognitive-radio is a self-adaptive network technology, which helps in detecting idle channels within a spectrum range. Cognitive radio has four functionalities namely Spectrum sensing, decision, sharing and mobility. This research work is in the domain of spectrum mobility. Spectrum mobility deals with motion of unlicensed users in the network. Unlicensed users are the unauthorized users of cognitive radio networks who have lower priority than licensed ones. Major functionality of spectrum mobility is spectrum handover and connection management. In cognitive-radio, the method of switching channels is termed as spectrum handover. Whenever a high priority user appears to occupy its spectrum band, that is already been utilized by a low priority user, spectrum handover takes place. During this process, a lot of handover delay occurs, which results in increasing the total service time of transmission. Total service time of spectrum handover means amount of time required to perform successful handover during spectrum mobility stage in cognitive-radio-networks. To decrease this total service time of spectrum handover we have utilized the concept of Particle Swarm Intelligence and M/G/1 queuing model. The parameters used for the purpose are swarm size, arrival rate, service rate, acceleration coefficients, processing time and channel switching time. Swarm size indicates the number of particles present in a swarm. In this research work, value of swarm size is varied to see its effect on total service time of spectrum handover process. Numerical results demonstrate that by increasing the value of swarm size, total service time decreases.

show abstract

Current Trends and Research Challenges in Spectrum-Sensing for Cognitive Radios

Cited by 6 publications

References 29 publications

LoRaCog: A Protocol for Cognitive Radio-Based LoRa Network

LoRaCog: A Protocol for Cognitive Radio-Based LoRa Network

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

Modelling of Spectrum Handover Scheme using Particle Swarm Intelligence in Cognitive-radio Networks

Contact Info

Product

Resources

About