Secure Robust Ergodic Uplink Resource Allocation in Relay-Assisted Cognitive Radio Networks

Mokari, Nader; Parsaeefard, Saeedeh; Saeedi, Hamid; Azmi, Paeiz; Hossain, Ekram

doi:10.1109/tsp.2014.2370949

Cited by 59 publications

(27 citation statements)

References 28 publications

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“…where ∧ denotes the stochastic estimation, compared with (18). Equations (29) and (18) correspond to the primary and averaged systems, respectively.…”

Section: Stochastic Joint Time Slot and Power Allocation Algorithmmentioning

confidence: 99%

“…In [17], instantaneous and ergodic resource allocation problems were investigated in CRNs with guaranteed secrecy rate of the primary users. Secure robust resource allocation was taken into account for the relay-assisted CRNs in [18]. Different from the above two literatures, [19] proposed the power allocation policy for the physical-layer security in cognitive relay networks from the perspective of auction theory.…”

Section: Introductionmentioning

confidence: 99%

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Fair Resource Allocation with QoS Guarantee in Secure Multiuser TDMA Networks

Bai

Wang

et al. 2018

Wireless Communications and Mobile Computing

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We investigate a secure multiuser time division multiple access (TDMA) system with statistical delay quality of service (QoS) guarantee in terms of secure effective capacity. An optimal resource allocation policy is proposed to minimize the -fair cost function of the average user power under the individual QoS constraint, which also balances the energy efficiency and fairness among the users. First, convex optimization problems associated with the resource allocation policy are formulated. Then, a subgradient iteration algorithm based on the Lagrangian duality theory and the dual decomposition theory is employed to approach the global optimal solutions. Furthermore, considering the practical channel conditions, we develop a stochastic subgradient iteration algorithm which is capable of dynamically learning the intended wireless channels and acquiring the global optimal solution. It is shown that the proposed optimal resource allocation policy depends on the delay QoS requirement and the channel conditions. The optimal policy can save more power and achieve the balance of the energy efficiency and the fairness compared with the other resource allocation policies.

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“…where ∧ denotes the stochastic estimation, compared with (18). Equations (29) and (18) correspond to the primary and averaged systems, respectively.…”

Section: Stochastic Joint Time Slot and Power Allocation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fair Resource Allocation with QoS Guarantee in Secure Multiuser TDMA Networks

Bai

Wang

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…The optimal or suboptimal power allocation policies in the underlay mode have been already investigated widely [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. A heuristic algorithm in CR systems under OFDMA has been reported in [4].…”

Section: Introductionmentioning

confidence: 99%

“…However, the CSI between SC and PU is usually difficult to be estimated accurately in actual situations mainly due to the lack of signaling cooperation between them. Therefore, power allocation with channel uncertainty should be considered in CRN [12][13][14][15][16][17][18][19][20]. A worst-case formulation is utilized in [12] for beamforming design, in which an ellipsoidal channel uncertainty region is assumed.…”

Section: Introductionmentioning

confidence: 99%

“…Dall' Anese et al [13] considers the probabilistic interference constraints for a CR power control problem, where uncertainty in composite fading channels comprising shadowing and Nakagami fading is accounted for. Power allocation problems for generic OFDMA systems with channel uncertainty have also been investigated extensively [14][15][16]. Ma et al [17] shows the availability of imperfect CSI that includes probabilistic uncertainty and considers its worst effect when performing resource allocation.…”

Section: Introductionmentioning

confidence: 99%

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Robust power allocation for massive connections underlaying cognitive radio networks with channel uncertainty

Ren

Chen

et al. 2016

J Wireless Com Network

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This paper investigates a robust power allocation scheme for a cognitive radio network (CRN) with channel uncertainty, where a large number of secondary connections (SCs) share the same frequency spectrum with a primary user (PU). Specifically, considering the fact that the channel gain estimates from the secondary transmitters (S-Tx) to the primary receiver (P-Rx) are typically uncertain and cannot be perfectly known in practice, this paper advocates a robust interference constraint. Meanwhile, we take the S-Tx's transmit power limitation and interference between SCs into account. The optimization problem is a non-convex and non-linear program (NCNLP) with an outage probability constraint. Both the original objective function and the outage probability constraint are converted into manageable forms via the mathematical transformation. Then, we introduce the convex optimization theory to solve the intractable problem. Additionally, we develop an efficient algorithm to obtain the near-optimal solution based on the interior point method, where we take advantage of the Newton algorithm to make the search of the feasible solution simple and effective. Finally, we conduct the in-depth simulations under various parameter configurations, which demonstrate that our proposed robust power allocation scheme can achieve higher performance in comparison to the existing scheme in the literature.

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A new feature extraction and attacking node classification framework using PDCFE‐HPSMM model in cognitive radio networks

Ramasamy¹

2016

Int J Communication

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SUMMARYThe increasing demand of wireless communication introduces a challenge to an effective spectrum utilization. In wireless communication systems, the Cognitive Radio (CR) has emerged as a new key technology to address this issue, which allows an opportunistic access to the spectrum. The CRs form a CRN by extending the radio link features to network layer functions. The existing CR based wireless communication techniques has some drawbacks such as, inability to differentiate interference or noise from primary signals, it does not distinguish the different types of noise, low performance, not effective in spread spectrum detection, long observation time and high computational cost. To overwhelm these disadvantages, a novel method, namely, partial distribution based cyclostationary feature extraction-hidden probability state Markov model (PDCFE-HPSMM) is proposed in this work. The main intention of this technique is to extract the node features, classify the attacker nodes and predict the licensed and unlicensed bandwidth. At first, the CRN is formed with some set of CR nodes, and the communication links between the nodes are estimated. Then, the node features are extracted by employing the PDCFE technique. After that, the attacking nodes are classified based on those features with the help of a HPSMM classification technique. If the attacker is present in the network, the link between the attacking nodes to other nodes is disconnected; otherwise, the communication link is updated. In this work, the Rayleigh channels are used to predict the licensed and unlicensed bandwidth. Again, the HPSMM model is employed to optimally select the channel, and the scheduling technique is implemented to check the status of the channel for message transmission. If the channel is not busy, the message signals are multiplexed and transmitted to the receiver via the selected channel. The novel concept of this paper is, the proposed PDCFE technique extracts the features of the nodes in CRN; based on these features, the attacker nodes in the network are classified with the help of HPSMM technique. Moreover, the HPSMM is used to select the optimal channel for message transmission. The experimental results evaluate the performance of the proposed system in terms of bit error rate (BER), error rate, relay, false detection rate (FDR) and cumulative distribution function (CDF

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Secure Robust Ergodic Uplink Resource Allocation in Relay-Assisted Cognitive Radio Networks

Cited by 59 publications

References 28 publications

Fair Resource Allocation with QoS Guarantee in Secure Multiuser TDMA Networks

Fair Resource Allocation with QoS Guarantee in Secure Multiuser TDMA Networks

Robust power allocation for massive connections underlaying cognitive radio networks with channel uncertainty

A new feature extraction and attacking node classification framework using PDCFE‐HPSMM model in cognitive radio networks

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