Resource Allocation for Heterogeneous Cognitive Radio Networks with Imperfect Spectrum Sensing

Wang, Shaowei; Zhou, Zhi‐Hua; Ge, Mengyao; Wang, Chonggang

doi:10.1109/jsac.2013.130312

Cited by 106 publications

(90 citation statements)

References 38 publications

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“…The channel gain depends on multiple effects and can be defined as follows: The system parameters used in the simulation are [7], [20], [23], [24]: T s = 4 µs, were obtained over 100 simulation times, by randomly generating the locations of CUs and PUs at each time. The six algorithms to be investigated and compared are shown in Table 2.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Resource Allocation Algorithm for Multi‐cell Cognitive Radio Networks with Imperfect Spectrum Sensing and Proportional Fairness

Zhu

Zhou

2016

ETRI Journal

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This paper addresses the resource allocation (RA) problem in multi-cell cognitive radio networks. Besides the interference power threshold to limit the interference on primary users PUs caused by cognitive users CUs, a proportional fairness constraint is used to guarantee fairness among multiple cognitive cells and the impact of imperfect spectrum sensing is taken into account. Additional constraints in typical real communication scenarios are also considered-such as a transmission power constraint of the cognitive base stations, unique subcarrier allocation to at most one CU, and others. The resulting RA problem belongs to the class of NP-hard problems. A computationally efficient optimal algorithm cannot therefore be found. Consequently, we propose a suboptimal RA algorithm composed of two modules: a subcarrier allocation module implemented by the immune algorithm, and a power control module using an improved sub-gradient method. To further enhance algorithm performance, these two modules are executed successively, and the sequence is repeated twice. We conduct extensive simulation experiments, which demonstrate that our proposed algorithm outperforms existing algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

“…In [6], an RA algorithm was proposed for OFDMA-based CRNs, using interference alignment to improve the efficiency of radio resources. In [7], the RA problem in heterogeneous CRNs serving both real-time CUs and non-real-time CUs was studied. In [8], Kim uncertainty by applying robust optimization theory.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Algorithm for Multi‐cell Cognitive Radio Networks with Imperfect Spectrum Sensing and Proportional Fairness

Zhu

Zhou

2016

ETRI Journal

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“…To achieve their objectives, firstly, power allocation to each SU was optimised (on assumption of a constant transmission time), and secondly, optimal scheduling of the SUs was realised. In [13,14], the authors studied RA in CRN, first with cooperative relays and then with imperfect spectrum sensing. The developed problems were nonconvex and nondeterministic polynomial-time-hard (NP-hard).…”

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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“…Resource allocation for a multiuser OFDM-based CR system was investigated in [22][23][24][25][26][27]. The authors in [22] made use of subcarrier allocation to improve the performance of cognitive networks by exploiting the so-called multiuser interference diversity.…”

Section: Introductionmentioning

confidence: 99%

“…In [26], the sum capacity of a multiuser OFDM-based CR system was maximized while satisfying the SUs' proportional rate requirements. The adaptive resource allocation problem in multiuser OFDMbased CR networks with imperfect spectrum sensing was investigated in [27]. The problem was addressed in two steps: subcarrier allocation and then power allocation for subcarriers.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation for OFDM-based multiuser cooperative underlay cognitive systems

Chami

Pischella

Ruyet

2017

J Wireless Com Network

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This paper investigates the resource allocation problem for a multiuser underlay cognitive system where the secondary system is allowed to transmit and cooperate with the primary system. In this scenario, the secondary users are subject to two main constraints in the presence of the primary user: their total power budget and the allowable interference threshold at the primary receiver. Power and subcarrier allocation problems are detailed in order to maximize the system sum rate. In this work, we highlight the benefits of the proposed multiuser adaptive algorithm which encompasses three phases. The first step includes the adaptive selection of the decoding strategy at the secondary receiver which is either treating interference as noise or performing successive interference cancelation or superposition coding. The second step describes the subcarrier allocation among the different users. Finally, the third step details the optimal distribution of the available power budget on the users. The problem is first treated assuming perfect channel state information (CSI). The simulation results show that our proposed scheme achieves higher secondary and sum rates compared to existing approaches with perfect CSI. The robustness of the proposed algorithm when the secondary user has only partial information about channel gains is also derived.

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Resource Allocation for Heterogeneous Cognitive Radio Networks with Imperfect Spectrum Sensing

Cited by 106 publications

References 38 publications

Resource Allocation Algorithm for Multi‐cell Cognitive Radio Networks with Imperfect Spectrum Sensing and Proportional Fairness

Resource Allocation Algorithm for Multi‐cell Cognitive Radio Networks with Imperfect Spectrum Sensing and Proportional Fairness

Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks

Resource allocation for OFDM-based multiuser cooperative underlay cognitive systems

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