Resource allocation and interference management for multi-layer wireless networks in heterogeneous cognitive networks

Meng, Yun; Liu, Xinyi

doi:10.1186/s13638-019-1514-1

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…The neural network approach was used to estimate the bandwidth [14]. To minimize the cross-layer interference of multiple base station cells, a cluster-based cooperative interference management scheme for a multi-interface environment was proposed in [15]. Several conditions for applying ML to wireless communication were discussed in [16].…”

Section: Background and Related Workmentioning

confidence: 99%

Switching of Interfaces and Self-optimization of weights using Backpropagation ANN in WHN Enviornment

Rani

2023

Preprint

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In today’s scenario, mobile communication is facing a healthy competition due to different networks, interfaces, channels, and many more available in wireless heterogeneous environment. The problem arises when customers/users get the availability of many interfaces at the same time. At that time users need an intelligent or smart mechanism to connect them to the best services according to their requirements/preferences. Interface management manages available interfaces and connects the user with the best. In this paper, Interface management with Artificial Neural Network (ANN) allows the smart use of different radio accesses/interfaces. The selection is made with different parameters of different networks. This paper proposed a backpropagation neural network that is used for the switching in between different networks-3G, WLAN, 4G and 5G. The different parameters of a network are used as the selection parameters with assigning proper weights. Weights are initialized with fuzzy AHP and optimized with Back Propagation Neural Network (BPNN). The target value and the actual value is compared and their difference used as the adjusting value for the weights to get the optimum value. The backpropagation is used to train the network. The comparison among the projected algorithm and the existing algorithm shows the valuablity of the new method and the best connectivity of the network.

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Section: Background and Related Workmentioning

confidence: 99%

Switching of Interfaces and Self-optimization of weights using Backpropagation ANN in WHN Enviornment

Rani

2023

Preprint

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“…After a rigorous review process, Xin et al in [8] portrayed a trailer on interference management in wireless communication systems by bringing together 11 papers presenting state-of-art research contributions and practical implementations covering a broad spectrum of research topics including resource allocation, interference channels, relay systems, base station cooperation and multiple-input multiple-output systems. The works in [10,11,14,21] show a coordination between channel assignment and power allocation incorporating interference mitigation in them.…”

Section: Background and Related Workmentioning

confidence: 99%

Interference mitigation and capacity enhancement of cognitive radio networks using modified greedy algorithm/channel assignment and power allocation techniques

2020

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“…However, with higher rate of resources being allocated and amount of interference exceeding, the threshold increase the cross‐layer interference. To address this issue, a joint channel and power allocations were presented in [4] to reduce the interference. Despite minimising the interference, malicious activities are not said to be controlled.…”

Section: Related Workmentioning

confidence: 99%

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Ganesh

Kumar

Kumar³

2020

IET Communications

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Cognitive radio networks (CRN) make use of dynamic spectrum access to communicate opportunistically. Unlicensed users severely affect the spectrum sensing outcomes in CRN. Primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) have become a paramount concern in CRNs. It is especially challenging when both masquerading (in the physical layer) and falsification (in the data link layer) occur by providing false spectrum reports. Existing methods to detect such attacks cannot be utilised in scenarios with multi-hop CRN. In this study, to mitigate attack against PUEA and SSDF, a method called optimised sensing and Levenshtein nearest centroid classification (OS-LNCC) for multi-hop CRN is presented. First, a network model for multi-hop CRN is designed. Next, a probable density optimal logical sensing model is designed to alleviate the problems related to falsification of spectrum reports. Here, the falsification of spectrum reports is overcome by exploiting dual factors, that is, probability for false alarm and probability for detection according to the departure rate of primary user (PU). With these dual factors, optimal logical sensing is made, therefore improving the throughput with minimum delay. Finally, each cognitive radio (CR) user evaluates its current sensing information to existing sensing classes through the Levenshtein distance function. Based on quantitative variables, the prediction function of each sensing class is measured using nearest centroid (NC) classifier and the sensing report is classified into either presence or absence of PU. These predictive classes are then integrated at the fusion centre so that robust mitigation against PUEA and SSDF is made. Computer simulation outputs show that OS-LNCC method performs better than the conventional methods using metrics such as sensing delay by 47%, percentage of error in prediction by 46% and throughput by 45%.

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Resource allocation and interference management for multi-layer wireless networks in heterogeneous cognitive networks

Cited by 8 publications

References 23 publications

Switching of Interfaces and Self-optimization of weights using Backpropagation ANN in WHN Enviornment

Switching of Interfaces and Self-optimization of weights using Backpropagation ANN in WHN Enviornment

Interference mitigation and capacity enhancement of cognitive radio networks using modified greedy algorithm/channel assignment and power allocation techniques

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

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