Judicious power loading for a cognitive relay scenario

Nadkar, Taskeen; Thumar, Vinay; Desai, U.B.; Merchant, S. N.

doi:10.1109/ispacs.2009.5383836

Cited by 5 publications

(9 citation statements)

References 15 publications

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“…We consider the OA, the proposed SOA, the method proposed in [9] referred to as the individual method (IM), and the uniform power allocation. To provide more clarifications, the transmit covariance matrices in the OA are determined using (8) and (12).…”

Section: Simulation Results and Further Discussionmentioning

confidence: 99%

“…and P rem I D 0. 2) Calculate the optimal solution of (P3) using It is noteworthy that the algorithm presented in [9] tries to solve the problem (P1) by performing two individual optimizations at SU-TX and relay and then try to match the subcarriers in SU-TX and relay in order to satisfy (7) in a single-input and single-output scenario. However, it is not guaranteed to obtain a perfect equality over all subcarriers while optimising individually.…”

Section: Sub-optimal Algorithmmentioning

confidence: 99%

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Low complexity resource allocation in MIMO‒OFDM‒based cooperative cognitive radio networks

Adian

Aghaeinia

2014

Trans Emerging Tel Tech

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A multiple‒input multiple‒output orthogonal frequency division multiplexing based cooperative cognitive radio network is considered in this study, where the cooperation strategy is decode‒and‒forward. The objective is to maximise the capacity of the secondary system while guaranteeing that the interference introduced to the primary system is below the interference temperature limit. The optimal power allocation in the source and the relay is presented and because of the high computational complexity of the optimal approach, a low computational complexity near‒optimal algorithm is further proposed. The joint allocation of the resources in the sub‒optimal algorithm is carried out taking into account the channel qualities, the decode‒and‒forward cooperation strategy and the interference induced to the primary system. The performance of the different approaches and effect of deploying multiple antennas at users are explored through the simulation results. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Simulation Results and Further Discussionmentioning

confidence: 99%

Section: Sub-optimal Algorithmmentioning

confidence: 99%

Low complexity resource allocation in MIMO‒OFDM‒based cooperative cognitive radio networks

Adian

Aghaeinia

2014

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Since the subcarrier pairing is not allowed, optimal algorithm means that the powers are allocated using (20) while proposed means the power allocation algorithm presented in Section IV-B. Individual algorithm applies the method proposed in [7] which allocates the powers in S and R independently. One can observe that the CR system throughput increases with the interference constraint as the CR system become able to use more power on the different subcarriers.…”

Section: Simulations Resultsmentioning

confidence: 99%

“…An overview of the cooperative communications in cognitive scenario has been presented in [6]. In [7], a power allocation algorithm in DF-OFDM based CR system has been proposed. Under the assumption of perfect subcarrier matching in the S-R and R-D links, the authors treated the optimization problem in S and R individually.…”

Section: Introductionmentioning

confidence: 99%

Joint Subcarrier Pairing and Power Allocation for DF-Relayed OFDM Cognitive Systems

Shaat

Bader

2011

2011 IEEE Global Telecommunications Conference - GLOBECOM 2011

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In this paper, the problem of resource allocation in OFDM based decode-and-forward (DF) cognitive radio (CR) system is considered. The objective is to maximize the CR system throughput by optimizing the subcarrier pairs and powers. The interference introduced to the primary system should not exceed the prescribed interference temperature limit. The resources are optimized jointly by applying the dual decomposition technique to get asymptotically optimal solution. In addition, a suboptimal algorithm that performs the resources allocation in two phases is proposed. By the suboptimal algorithm, the subcarrier pairing is first performed considering the channels quality as well as the interference introduced to the primary system. Afterwards, the powers are allocated in the second phase by applying a waterfilling-like algorithm. The suboptimal algorithm reduces significantly the computational complexity with small performance degradation. The simulation results confirm the efficiency of the proposed algorithm.

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“…The algorithms presented in [9] and [10] are dealing with the point-to-point systems. In [11], a power allocation algorithm in decode and forward (DF) OFDM based CR system has been investigated under the interference constraint only. By assuming a perfect subcarrier matching in the S-R and R-D links, the authors treated the optimization problem in S and R individually.…”

Section: Introductionmentioning

confidence: 99%

Asymptotically optimal subcarrier matching and power allocation for cognitive relays with power and interference constraints

Shaat

Bader

2012

2012 IEEE Wireless Communications and Networking Conference (WCNC)

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This paper considers the resources allocation problem in decode-and-forward (DF) relayed OFDM based cognitive radio (CR) system. The subcarrier matching and power allocation are optimized jointly under the individual power constraints in source and relay so that the sum rate is maximized while the interference induced to the primary system is kept below a pre-specified interference temperature limit. Therefore, the dual decomposition technique is adopted to obtain an asymptotically optimal solution. Moreover, a suboptimal scheme is presented to get rid of the high computational complexity of the optimal scheme. The suboptimal algorithm solves the problem in two phases. The subcarriers powers are adjusted in the first phase considering the interference that can be induced to the primary system in addition to the available power budgets. The subcarriers are matched in the second phase based on the fixed powers and the channel qualities. The performance of the different schemes is discussed throughout numerical simulation.

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Judicious power loading for a cognitive relay scenario

Cited by 5 publications

References 15 publications

Low complexity resource allocation in MIMO‒OFDM‒based cooperative cognitive radio networks

Low complexity resource allocation in MIMO‒OFDM‒based cooperative cognitive radio networks

Joint Subcarrier Pairing and Power Allocation for DF-Relayed OFDM Cognitive Systems

Asymptotically optimal subcarrier matching and power allocation for cognitive relays with power and interference constraints

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