Effects of Co-channel Interference in WLAN and Cognitive Radio Based Approach to Minimize It

2010 ◽  
Author(s):  
Prabhat Kumar Tiwary ◽  
Niwas Maskey ◽  
Suman Khakurel ◽  
Gitanjali Sachdeva
Keyword(s):  
Cognitive Radio ◽  
Channel Interference

Age of Information Minimization of Multichannel Allocation Mechanism for Hybrid Cognitive Radio Networks with Energy Harvesting under Collusion Constraint

2021 ◽  
Author(s):  
Hakan Murat Karaca
Keyword(s):  
Cognitive Radio ◽  
Energy Harvesting ◽  
Cognitive Radio Networks ◽  
Channel Allocation ◽  
Radio Networks ◽  
Allocation Mechanism ◽  
Industrial Networks ◽  
Channel Interference ◽  
Promising Solution ◽  
Age Of Information

Abstract Cognitive radio networks (CRNs) with energy harvesting has been a promising solution for wireless industrial networks and seems a promising solution for the spectrum scarcity problem. However, it is critical the wireless spectrum should carefully be managed in order to fullfill the tight requirements on the trustworthy and minimum delayed delivery of information. Motivated by this, in order to represent the freshness of the information, this paper takes into account the metric of age of information (AoI) and multi-channel allocation problem in hybrid cognitive radio networks where secondary transmitters (STs) are able to harvest energy is investigated to maximize allocation performance while minimizing average AOI values. A novel mechanism has been proposed to restrict average AoI values while distributing channels to STs by handling both collusion and energy constraints. STs are prioritized based on a proposed metric and they are able to obtain channels only if the resulting AoI value satisfy proposed conditions. AoI values are computed based on co-channel interference between STs and minimized by decreasing interference levels. For comparison, two algorithms were considered: a greedy mechanism for m-channel allocation of hybrid CRNs with harvesting, but without AOI control and the proposed m-channel allocation schemes based on sorting STs according to the proposed metric and controlling AOI levels based on co-channel interference. The simulations depict that performance of the proposed m-channel allocation method outweighs the greedy algorithm in terms of both AoI minimization and maximization of channel allocation performance, proving the superiority of the proposed algorithm.

scholarly journals Co-channel Interference Monitoring based on Cognitive Radio Node Station

2018 ◽  
Vol 5 (5) ◽  
Author(s):  
Arief Marwanto ◽  
M. Ulin Nuha ◽  
Jenny Putri Hapsari ◽  
Daniel Triswahyudi
Keyword(s):  
Cognitive Radio ◽  
Channel Interference

Cognitive MIMO Radio

2011 ◽  
Vol 5 (2) ◽  
pp. 58-79
Author(s):  
Mingming Li ◽  
Jiaru Lin ◽  
Fazhong Liu ◽  
Dongxu Wang ◽  
Li Guo
Keyword(s):  
Cognitive Radio ◽  
Fading Channels ◽  
Cognitive Radio Network ◽  
Multiple Input Multiple Output ◽  
Primary User ◽  
Base Station ◽  
Radio Network ◽  
First Case ◽  
Channel Interference ◽  
Stable Throughput

The authors consider a cognitive radio network in which a set of cognitive users make opportunistic spectrum access to one primary channel by time-division multiplexing technologies. Multiple Input Multiple Output techniques (MIMO) are similarly considered to enhance the stable throughput for cognitive links while they should guarantee co-channel interference constraints to the primary link. Here, two different cases are considered: one is that cognitive radio network is distributed; the other is centrally-controlled that cognitive radio network has a cognitive base station. In the first case, how to choose one fixed cognitive user and power control for each transmission antenna at the cognitive base station are considered to maximize the cognitive link’s stable throughput. In the second case, a scheme to choose a group of cognitive users and a Zero-Forcing method to pre-white co-channel interference to the primary user, are also proposed in order to maximize cognitive base station’s sum-rate. The algorithm can be employed to realize opportunistic spectrum transmission over the wireless fading channels.

scholarly journals Age of Information Minimization of Multichannel Allocation Mechanism for Hybrid Cognitive Radio Networks with Energy Harvesting under Collusion Constraint

2021 ◽  
Author(s):  
HAKAN MURAT KARACA
Keyword(s):  
Cognitive Radio ◽  
Energy Harvesting ◽  
Cognitive Radio Networks ◽  
Channel Allocation ◽  
Radio Networks ◽  
Allocation Mechanism ◽  
Industrial Networks ◽  
Channel Interference ◽  
Promising Solution ◽  
Age Of Information

Abstract Cognitive radio networks (CRNs) with energy harvesting has been a promising solution for wireless industrial networks and seems a promising solution for the spectrum scarcity problem. However, it is critical the wireless spectrum should carefully be managed in order to fullfill the tight requirements on the trustworthy and minimum delayed delivery of information. Motivated by this, in order to represent the freshness of the information, this paper takes into account the metric of age of information (AoI) and multi-channel allocation problem in hybrid cognitive radio networks where secondary transmitters (STs) are able to harvest energy is investigated in terms of both maximization of allocation performance while minimizing average AOI values. A novel mechanism has been proposed to restrict average AoI values while distributing channels to STs by handling both collusion and energy constraints. STs are prioritized based on a proposed metric and they are able to obtain channels only if the resulting AoI value satisfy proposed conditions. AoI values are computed based on co-channel interference between STs and minimized by decreasing interference levels. For comparison, two algorithms were considered: a greedy mechanism for m-channel allocation of hybrid CRNs with harvesting, but without AOI control and the proposed m-channel allocation schemes based on sorting STs according to the proposed metric and controlling AOI levels based on co-channel interference. The simulations depict that performance of the proposed m-channel allocation method outweighs the greedy algorithm in terms of both AoI minimization and maximization of channel allocation performance, proving the superiority of the proposed algorithm.

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