Channel estimation for WiMaX systems using fuzzy logic cognitive radio

Shatila, Hazem; Khedr, Mohamed; Reed, Jeffrey H.

doi:10.1109/wocn.2009.5010562

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Performance of GA-based CE showed that certain changes were imparted to transmission parameters which changed to different settings [21,26]. Some other methods that were applied to CE are "Rule Based System" [48], "Case Based Learning" [57], "Fuzzy Logic" [59] and "Neural Network" [46]. The CR learning algorithm based on AI can be called as a Radio Environment Map (REM) enabled situation aware learning algorithm.…”

Section: Implementation Of Soft Computing Techniques In Cognitive Radiomentioning

confidence: 99%

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

Eappen

Shankar

2020

SN COMPUT. SCI.

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The need for faster wireless connectivity is increasing rapidly in all the sectors of the technologies. Whether it is a patient monitoring system, military application, entertainment services, streaming services, or global stock markets, there is a tremendous increase in the need for enhanced wireless telecommunication services. The wireless telecommunication consumers rely on bulk data, and massive growth in the number of users has resulted in the spectrum congestion. To avoid such spectrum congestion and to satisfy the data hunger of the wireless telecommunication users, the possible solution is Cognitive Radio Network (CRN). A CRN, therefore, plays a significant role in the field of wireless communication, and an efficient spectrum sensing enhances the effectiveness of the CRN. In this paper, complete research carried out so far in the field of spectrum sensing for CRN is discussed. Different soft computing techniques (GA, PSO, ABC, ACO, FFA, FSS, Cuckoo Search, ANN, FIS, GFIS) are surveyed in this paper, along with a detailed comparative analysis between conventional and soft computing techniques for spectrum sensing. In addition to that, the challenges faced in the implementation of CRN and its requirements is also addressed. Different spectrum sensing elements and requirements are presented and road map of spectrum sensing with soft computing techniques towards 5G is discussed. Furthermore, the paper also suggests the future prospects, research challenges and open issues associated with soft computing techniques for spectrum sensing in CRN.

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Section: Implementation Of Soft Computing Techniques In Cognitive Radiomentioning

confidence: 99%

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

Eappen

Shankar

2020

SN COMPUT. SCI.

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“…13. Use of fuzzy logic as the core of the reasoning engine to determine different parameters used by the WiMAX system is reported in [44]. This work focuses on one of the main functions of the reasoning engine i.e.…”

Section: Application Of Fuzzy Fuzzy-neural and Neuro-fuzzy Systems Imentioning

confidence: 99%

Application of Soft Computing Tools in Wireless Communication—A Review

Sarma

2015

Recent Trends in Intelligent and Emerging Systems

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The proliferation of number of users in a limited wireless spectrum have raised the levels of inter symbol interference (ISI) and have also contributed towards probable degradation of quality of service (QoS). The key challenges faced by upcoming wireless communication systems is to provide high-data-rate wireless access with better QoS. Also, the fast shrinking spectrum for such communication have necessitated the development of methods to increase spectral efficiency. Multiple input multiple output (MIMO) wireless technology is a viable option in such a situation and is likely to be able to meet the demands of these ever-expanding mobile networks. Many researchers have explored this field over a considerable period of time. A sizable portion of the research have been on the application of traditional statistical methods in such areas. Over the years, soft computational tools like artificial neural network (ANN), fuzzy systems and their combinations have received attention in the diverse segments of wireless communication. This is because of the fact that these are learning based systems. These learn from the environment, retain the knowledge and use it subsequently. This paper highlights some of the important application areas in wireless communication which have reported the use of soft computing tools in wireless communication that are in circulation in open literature.

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“…The proposed cognitive radio architecture incorporates the various mechanisms for signal detection, spectrum sensing, tuning of radio parameters, minimizing interference and radio resources allocation. Channel estimation, spectrum sensing and monitoring are conducted using the minimum mean square error for channel estimation, beaconing mechanisms for monitoring and sensing . With the beacon method, normally unlicensed devices will only transmit in the operating band if they receive a control signal (beacon) identifying allocated resources within their service areas.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Our goal is to design a cognitive radio that has learning capability and can take opportunistic decisions to control different radio parameters in wireless communication. For example if we consider a Worldwide Interoperability for Microwave Access (WiMAX) system, we can use parameters like Doppler frequency, delay spread, bit duration, and signal bandwidth as inputs to determine the channel type . Modulation type, code rate, and number of subcarriers can be also used for the determination of the number of pilots that can be used in channel estimation.…”

Section: Dynamic Resource Allocation Algorithmmentioning

confidence: 99%

Opportunistic channel allocation decision making in cognitive radio communications

Shatila

Khedr

Reed

2012

Int J Communication

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SUMMARYThe global spread of wireless devices with mobile Internet access and the increasing demand of multimedia‐based applications are fueling the need for wireless broadband networks. IEEE 802.16 and 802.20 are standards for a broadband wireless access with promising cognitive radio features to support mobile Internet access. However, because of the fast changing radio environment and the demand for dynamic spectrum allocation mechanisms, these standards must continuously readjust different radio parameters. The cognitive radio makes decisions based on its built‐in inference engine, which also in time can adapt itself to different situations through the process of learning from experience. In this paper we present an automated opportunistic decision making and learning process for cognitive radio based on uncertainty reasoning algorithms. This novel approach is well suited in fast changing wireless environments with vague, incomplete, and heterogeneous information. Theory and simulations prove that decision making and learning of the cognitive radio based on the proposed approach cope with the changes in the radio environment. In this work we use fuzzy logic for the learning and decision making of the cognitive radio. Simulation also show that our approach provides accurate and precise decisions on allocating spectrum to mobile Internet users even in fast varying radio conditions. Copyright © 2012 John Wiley & Sons, Ltd.

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Channel estimation for WiMaX systems using fuzzy logic cognitive radio

Cited by 8 publications

References 9 publications

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

Application of Soft Computing Tools in Wireless Communication—A Review

Opportunistic channel allocation decision making in cognitive radio communications

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