Optimal resource allocation for opportunistic spectrum access in multiple‐input multiple‐output –orthogonal frequency division multiplexing based cooperative cognitive radio networks

Adian, Mehdi Ghamari; Aghaeinia, Hassan

doi:10.1049/iet-spr.2012.0401

Cited by 20 publications

(24 citation statements)

References 23 publications

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“…One such decomposition method is the Dantzig-Wolfe decomposition, and there are others. Examples of RA problems in the CRN that have employed decomposition in arriving at solutions can be found in references [21,55]. In [55], the authors obtained optimal solution to their RA problem by using a primal-dual decomposition method whereby the overall problem is decomposed into individual power allocation sub-problems and solved for every decision variable pair.…”

Section: Solutions By Studying Problem Structurementioning

confidence: 99%

“…Examples of RA problems in the CRN that have employed decomposition in arriving at solutions can be found in references [21,55]. In [55], the authors obtained optimal solution to their RA problem by using a primal-dual decomposition method whereby the overall problem is decomposed into individual power allocation sub-problems and solved for every decision variable pair. Authors in [21] divided their RA problem (joint spectrum and power allocation for multband CRN) into two stages and used an iterative dual decomposition method to solve it.…”

Section: Solutions By Studying Problem Structurementioning

confidence: 99%

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Solving resource allocation problems in cognitive radio networks: a survey

Awoyemi

Maharaj

Alfa

2016

J Wireless Com Network

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Cognitive radio networks (CRN), in their quest to become the preferred next-generation wireless communication paradigm, will depend heavily on their ability to efficiently manage the limited resources at their disposal in meeting the demands of their numerous users and driving their operations. As a result, a considerable amount of research work has been recently dedicated to investigating and developing resource allocation (RA) models that capture the essentials of CRN. The various ideas put forward by researchers to address RA problems in CRN have been somewhat diverse, and somehow, there seem to be no links that bring cohesion and clarity of purpose and ideas. To address this problem and bridge the gap, in this paper, a comprehensive study on the prevalent techniques developed for addressing RA problems in CRN is carried out, with an intent to put some structure, relevance and meaning to the various solution approaches. The solution models are therefore grouped and/or classified based on certain outstanding criteria, and their strengths and weaknesses highlighted. Open-ended problems are identified, and suggestions for improving solution models are given. The study therefore gives good directions for further investigations on developing RA solutions in CRN.

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Section: Solutions By Studying Problem Structurementioning

confidence: 99%

Section: Solutions By Studying Problem Structurementioning

confidence: 99%

Solving resource allocation problems in cognitive radio networks: a survey

Awoyemi

Maharaj

Alfa

2016

J Wireless Com Network

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“…MIMO-OFDM consolidates the benefits of both MIMO and OFDM highlights [5] [6]. MIMO joins numerous information various yield innovations, which duplicates limit by transmitting diverse signs over different receiving wires, and OFDM (orthogonal recurrence division multiplexing) separates a radio channel into an expansive number of firmly divided sub-channels to give more dependable correspondence at fast [6].…”

Section: Mimo-ofdmamentioning

confidence: 99%

Appropriate Channel Selection for Dynamic Resource Allocation with Priority Scheduling Approach in Multi-hop Cognitive Radio Networks

Santhamurthy¹,

Parasuramane²

2017

IJIES

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Today's wireless communication systems required groundbreaking solution to meet scarcity of radio spectrum. Cognitive radio (CR) is an emerging technology which provides futuristic solution and improving the precious likely resources utilization. Cognitive radio network (CRN) is an intellectual wireless communication system as well as aware of its environment. In CRN, we have sensing problem by multiple nodes with changing computational power, spectrum allocation and range of sensing. The secondary user (SU) plays major role of primary channel sensing and appropriate unused channel selection for communication. In this paper we focused on optimum resource allocations and priority based scheduling for primary user (PU) and secondary user (SU) to exploit the performance of the entire network by reducing the interference at PU and also exploiting the throughput and decreasing the risk of overlying the coverage of CRNs. The effectiveness of dynamic resource allocation priority based scheduling (DRAPBS-CRN) algorithm is compared with dynamic resource allocation (DRA-CRN) algorithm and is found to be 26% more efficient using ns-2 simulation. This proposed DRAPBS-CRN algorithm also will avoid collisions and perform smart data transmission in CRN.

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“…The authors in [10][11][12] analysed RA problems in heterogeneous multiple-input multiple output CRN. In their works, the dual optimisation problems developed focused on optimising the transmit power and transmission time allocation of the SUs.…”

Section: Related Literaturementioning

confidence: 99%

Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks

Awoyemi

Maharaj

Alfa

2017

Wireless Communications and Mobile Computing

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Resources available for operation in cognitive radio networks (CRN) are generally limited, making it imperative for efficient resource allocation (RA) models to be designed for them. However, in most RA designs, a significant limiting factor to the RA's productivity has hitherto been mostly ignored, the fact that different users or user categories do have different delay tolerance profiles. To address this, in this paper, an appropriate RA model for heterogeneous CRN with delay considerations is developed and analysed. In the model, the demands of users are first categorised and then, based on the distances of users from the controlling secondary user base station and with the assumption that the users are mobile, the user demands are placed in different queues having different service capacities and the resulting network is analysed using queueing theory. Furthermore, to achieve optimality in the RA process, an important concept is introduced whereby some demands from one queue are moved to another queue where they have a better chance of enhanced service, thereby giving rise to the possibility of an improvement in the overall performance of the network. The performance results obtained from the analysis, particularly the blocking probability and network throughput, show that the queueing model incorporated into the RA process can help in achieving optimality for the heterogeneous CRN with buffered data.

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Optimal resource allocation for opportunistic spectrum access in multiple‐input multiple‐output –orthogonal frequency division multiplexing based cooperative cognitive radio networks

Cited by 20 publications

References 23 publications

Solving resource allocation problems in cognitive radio networks: a survey

Solving resource allocation problems in cognitive radio networks: a survey

Appropriate Channel Selection for Dynamic Resource Allocation with Priority Scheduling Approach in Multi-hop Cognitive Radio Networks

Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks

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