Efficient Resource Allocation for Multicell Heterogeneous Cognitive Networks With Varying Spectrum Availability

Salameh, Haythem Bany

doi:10.1109/tvt.2015.2477432

Cited by 21 publications

(7 citation statements)

References 21 publications

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“…In our simulations, the status of each PR channel i is given by a Markov renewal process alternating between BUSY and IDLE periods in which the IDLE period is exponentially distributed random variable with mean

T_{I}^{false(i false)}

. This appropriateness of this model in capturing the PR activities has been demonstrated in several previous works (eg, see other works). We set the busy probabilities of the different PR channels to P B .…”

Section: Performance Evaluationmentioning

confidence: 71%

“…Since the objective function is linear, b is integer, and the linear‐constraint matrix A is unimodular, the BLP in meets the unimodularity requirement (ie, unimodular BLP). Given the fact that the relaxed LP problem of a unimodular BLP has an integral optimal solution (if the problem is feasible), the optimal solution of our unimodular BLP problem can be optimally determined by solving its corresponding LP relaxation using standard LP methods. Since standard LP methods have polynomial‐time complexity, our problem in can be optimally solved in polynomial time (the BLP in has polynomial‐time complexity).…”

Section: The Proposed Spectrum Access Protocolmentioning

confidence: 99%

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Batch‐based security‐aware spectrum sharing with simultaneous assignment decisions in time‐critical IoT networks with cognitive radio capabilities

Almajali

Ayyash

Elgala

2018

Trans Emerging Tel Tech

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Cognitive radio (CR) is considered as a key enabling communication technology that offers efficient wireless connectivity to Internet of Things (IoT) devices. Its integration with the future 5G architecture is expected to advance the IoT paradigm. Security attacks can severely degrade the performance of such CR‐based IoT (CRIoT) networks. The effect of security attacks can be reduced by implementing defensive approaches. However, such solutions come at the expense of degrading spectrum efficiency and consuming more network resources. In this paper, we investigate the spectrum sharing and access problem in a multidevice single‐transceiver CRIoT network with time‐critical applications under jamming attacks. Our main goal is to maximize the number of simultaneously served IoT devices over all available idle channels while ensuring delay requirement, hardware, link quality, security attacks, and spectrum utilization constraints. This problem is formulated as a total unimodular binary linear programming, which is shown to be solvable in polynomial time. Unlike most of previous security‐aware channel assignment solutions that conduct the channel assignment sequentially, our solution simultaneously provides secured distributed channel‐assignment decisions for multiple CRIoT links (batching method). Batching can be realized through an admission control stage for CR IoT devices to announce their control packets. Simulation results reveal that our solution significantly improves network performance compared to previous security‐aware schemes.

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T_{I}^{false(i false)}

Section: Performance Evaluationmentioning

confidence: 71%

Section: The Proposed Spectrum Access Protocolmentioning

confidence: 99%

Batch‐based security‐aware spectrum sharing with simultaneous assignment decisions in time‐critical IoT networks with cognitive radio capabilities

Almajali

Ayyash

Elgala

2018

Trans Emerging Tel Tech

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“…The maximization problem can be established as a two-stage stochastic binary linear program. It is worth mentioning that the interference is a continuous random variable that is highly-correlated over time [1,[10][11][12][13][14][15]. Thus, our optimization problem has an infinite realizations.…”

Section: (B) Contributionsmentioning

confidence: 99%

Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework

Alnabelsi

Albataineh

2020

IJECE

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Several wireless technologies have recently emerged to enable efficient and scalable internet-of-things (IoT) networking. Cognitive radio (CR) technology, enabled by software-defined radios, is considered one of the main IoT-enabling technologies that can provide opportunistic wireless access to a large number of connected IoT devices. An important challenge in this domain is how to dynamically enable IoT transmissions while achieving efficient spectrum usage with a minimum total power consumption under interference and traffic demand uncertainty. Toward this end, we propose a dynamic bandwidth/channel/power allocation algorithm that aims at maximizing the overall network’s throughput while selecting the set of power resulting in the minimum total transmission power. This problem can be formulated as a two-stage binary linear stochastic programming. Because the interference over different channels is a continuous random variable and noting that the interference statistics are highly correlated, a suboptimal sampling solution is proposed. Our proposed algorithm is an adaptive algorithm that is to be periodically conducted over time to consider the changes of the channel and interference conditions. Numerical results indicate that our proposed algorithm significantly increases the number of simultaneous IoT transmissions compared to a typical algorithm, and hence, the achieved throughput is improved.

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“…By placing redundant nodes at appropriate locations in the existing network, the availability of wireless links is improved. The availability of a multi-cell CRN under time varying channels was studied in [14] and accordingly an efficient spectrum allocation mechanism was proposed. In the proposed network model therein, the average number of available channels is determined based on busy probabilities of channels.…”

Section: Related Workmentioning

confidence: 99%

System Times and Channel Availability for Secondary Transmissions in CRNs: A Dependability-Theory-Based Analysis

Balapuwaduge

Pla

2017

IEEE Trans. Veh. Technol.

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Reliability is of fundamental importance for the performance of secondary networks in cognitive radio networks (CRNs). To date, most studies have focused on predicting reliability parameters based on prior statistics of traffic patterns from user behavior. In this paper, we define a few reliability metrics for channel access in multi-channel CRNs which are analogous to the concepts of reliability and availability in the classical dependability theory. Continuous time Markov chains (CTMC) are employed to model channel available and unavailable time intervals based on channel occupancy status. The impact on user access opportunities based on channel availability is investigated by analyzing the steady state channel availability and several system times such as mean channel available time and mean time to first channel unavailability. Moreover, the complementary cumulative distribution function for channel availability is derived by applying the uniformization method and it is evaluated as a measure of guaranteed availability for channel access by secondary users. The preciseness and the correctness of the derived analytical models are validated through discreteevent based simulations. We believe that the reliability metric definitions and the analytical models proposed in this paper have their significance for reliability and availability analysis in CRNs.

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Efficient Resource Allocation for Multicell Heterogeneous Cognitive Networks With Varying Spectrum Availability

Cited by 21 publications

References 21 publications

Batch‐based security‐aware spectrum sharing with simultaneous assignment decisions in time‐critical IoT networks with cognitive radio capabilities

Batch‐based security‐aware spectrum sharing with simultaneous assignment decisions in time‐critical IoT networks with cognitive radio capabilities

Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework

System Times and Channel Availability for Secondary Transmissions in CRNs: A Dependability-Theory-Based Analysis

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