Enriched the Spectrum Sensing Performance of Estimated SNR based Detector in Cognitive Radio Networks

Bagwari, Ashish; Tomar, Geetam Singh

doi:10.14257/ijhit.2015.8.9.15

Cited by 16 publications

(3 citation statements)

References 25 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this perspective, a routing protocol named SEARCH is developed on the basis of geographic routing be avoiding the region of PU's activity during route formation [7]. The routing solution for multi-hop CRN is proposed in [18] based on the capacity of user to assign the channel for the high mobility of CR for IoT network. A routing scheme for resource-aware is developed in [19] to enhance the routing on the basis of energy usage of a user.…”

Section: Related Workmentioning

confidence: 99%

“…The routing solutions presented in [15][16][17][18][19][20][21][22][23][24][25] and [26][27][28][29][30][31][32][33] do not consider the concept of multi-agent in taking the routing decisions and used a single agent assumption. Also, the concept of non-cooperative game theory is applied only in [28] and [29] for the routing decisions of SUs.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Non-Cooperative Learning Based Routing for 6G-IoT Cognitive Radio Network

Malik¹,

Malik²,

Sanaullah³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Cognitive Radio Network (CRN) has turn up to solve the issue of spectrum congestion occurred due to the wide spread usage of wireless applications for 6G based Internet of Things (IoT) network. The Secondary Users (SUs) are allowed to access dynamically the frequency channels owned by the Primary Users (PUs). In this paper, we focus the matter of contention of routing in multi hops setup by the SUs for a known destination in the presence of PUs. The traffic model for routing is generated on the basis of Poison Process of Markov Model. Every SU requires to reduce the end-to-end delay and packet loss of its transmission simultaneously to improve the data rate for the Quality of Service (QoS) of the Secondary Users. The issue of routing is formulated as stochastic learning process of non-cooperative games for the transformation of routing decisions of SUs. We propose a distributed non-cooperated reinforcement learning based solution for solving the issue of dynamic routing that can avert user interferences and channel interferences between the competing Sus in 6G-IoT network. The proposed solution combines and simulate the results to show the effectiveness and working of the proposed solution in decreasing the end-to-end delay, packet loss while meeting the average data rate requirement of QoS for SUs.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Non-Cooperative Learning Based Routing for 6G-IoT Cognitive Radio Network

Malik¹,

Malik²,

Sanaullah³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Spectrum use is additionally one of the upsides of OFDM it permits simple channel estimation and leveling, through known settled images (pilot images). It is only backings parcel exchanging progressively [14]. One of the prevalent methods is MIMO frameworks which can be fundamental to alleviate impedance in OFDM.…”

Section: Ofdm Configurationmentioning

confidence: 99%