Prediction-Based Spectrum Aggregation with Hardware Limitation in Cognitive Radio Networks

Huang, Furong; Wang, Wei; Luo, Haipeng; Yu, Guanding; Zhang, Zhaoyang

doi:10.1109/vetecs.2010.5494044

Cited by 36 publications

(28 citation statements)

References 7 publications

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“…(19) in case of M-QAM. For the bandwidth reservation, we propose to use MSA algorithm of the spectrum aggregation presented in [24]. The MSA algorithm is utilizing the worst spectrum band which can barely satisfy the bandwidth requirements B SUi of the SU in consideration relating to all unassigned SUs.…”

Section: Central Frequency and Bandwidth Cr Spectrum Assignmentmentioning

confidence: 99%

Cognitive radio frequency assignment with interference weighting and categorization

Tabaković

Grgić

2016

J Wireless Com Network

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Cognitive radio is one of the technologies promoting flexible and efficient use of radio frequency spectrum, thus solving spectrum scarcity problem. Frequency assignment is an integral part of the cognitive radio spectrum management and a critical point of success or failure of the cognitive radio concept. In this paper, cognitive radio frequency assignment with a novel interference weighting and categorization is proposed, as an extension of the solution to the graph coloring problem. In our approach, the edge weights quantifying the interference potential are appended to the conflict graph, co-and adjacent channel interference are treated, and dynamically changing local lists of blocked frequencies are included. We propose the improved cognitive radio saturation metric for the dynamic vertex ordering and to introduce interference categorization which will reduce the communication overhead. Using the proposed model, resource manager can quantify individual interference components, as well as aggregate interference from multiple users, resulting in more knowledgeable frequency decisions. Generalization of the proposed model is suggested. The suggested generalization consists of the selection of the central frequency and optimal bandwidth to be used, according to the user requirements. We have developed interference-sensitive algorithms for minimizing the interference and maximizing the throughput, both in centralized and distributed implementation. The results show significant reduction of the interference, improved spectrum efficiency, and increase in network throughput, comparing to the benchmark algorithms.

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Section: Central Frequency and Bandwidth Cr Spectrum Assignmentmentioning

confidence: 99%

Cognitive radio frequency assignment with interference weighting and categorization

Tabaković

Grgić

2016

J Wireless Com Network

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“…Consequently, we introduce an important metric (m) called belief vector to represent the SU's estimation of the system states based on the past decisions and observations as (12) where…”

Section: Pomdp Frameworkmentioning

confidence: 99%

“…However, these existing works have all focused on the cases that each user uses only a single channel without considering the cases with spectrum aggregation. In [12], we propose a Maximum Satisfaction Algorithm (MSA) for admission control and a Least Channel Switch (LCS) strategy for DSA, but the spectrum sensing and access schemes are not considered jointly. In [13], we provides some preliminary results on a general POMDP framework for cognitive radio networks.…”

Section: Introductionmentioning

confidence: 99%

Joint spectrum sensing and access for stable dynamic spectrum aggregation

Wang¹,

Wu²,

Zhang³

et al. 2015

J Wireless Com Network

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Spectrum aggregation is an emerging technology to satisfy the data rate requirement of broadband services for next-generation wireless communication systems. In dynamic spectrum environment, in which the spectrum availability is time-varying, it is quite challenging to maintain the stability of spectrum aggregation. In this paper, we investigate the spectrum sensing and access schemes to minimize the times of channel switching for achieving stable dynamic spectrum aggregation, taking into consideration the hardware limitations of spectrum sensing and aggregation capability. We develop an analytical framework for the joint spectrum sensing and access problem based on partially observable Markov decision process (POMDP). Especially, we derive the reward function by estimation of the stability of different spectrum sensing and access strategies. Based on the POMDP framework, we propose a rollout-based suboptimal spectrum sensing and access scheme which approximates the value function of POMDP, and propose a differential training method to improve its robustness. It is proved that the rollout policy achieves performance improvement over the basis heuristics. The simulation results show that the proposed POMDP-based spectrum sensing and access scheme improves the system stability significantly and achieves near-optimal performance with a much lower complexity.

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“…Considering the dynamics of spectrum availability, [18] provides the prediction-based spectrum aggregation scheme for decreasing the overhead of re-allocation. One is Maximum…”

Section: Introductionmentioning

confidence: 99%

Spectrum Aggregation: Overview and Challenges

Wang

Zhang

Huang

2010

NPA

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Spectrum aggregation has recently received much attention due to the quick increasing of services. This technique makes it possible for multiple spectrum bands to be utilized by the same user in order to satisfy the large bandwidth demand of the service and achieve better performance. This paper provides an overview on spectrum aggregation. The research progresses on spectrum aggregation for LTE-Advanced and dynamic spectrum access are briefly described respectively. Finally, some research challenges for protocols and algorithms are addressed for future studies.

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Prediction-Based Spectrum Aggregation with Hardware Limitation in Cognitive Radio Networks

Cited by 36 publications

References 7 publications

Cognitive radio frequency assignment with interference weighting and categorization

Cognitive radio frequency assignment with interference weighting and categorization

Joint spectrum sensing and access for stable dynamic spectrum aggregation

Spectrum Aggregation: Overview and Challenges

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