Distributed Approach for Power and Rate Allocation to Secondary Users in Cognitive Radio Networks

Akter, Lutfa; Natarajan, Balasubramaniam

doi:10.1109/tvt.2011.2132809

Cited by 16 publications

(9 citation statements)

References 32 publications

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“…The water filling algorithm showed a comparable complexity to the proposed algorithm, while the proposed algorithm is more efficient than water filling and uniform loading algorithms specially in low interference threshold or high power budget due to the fact that in these states the proposed algorithm can be allocated more power to each subcarrier while satisfy the constraints in Eqs. (8) and (9). …”

Section: Discussionmentioning

confidence: 99%

“…(8) and (9). In the optimal algorithm, all the L + 1 constraint was considered simultaneously, while in the proposed suboptimal algorithm only one constraint is kept and the transmission power in each subcarrier is determined.…”

Section: Suboptimal Algorithmmentioning

confidence: 99%

“…More powers and bits per symbol must be allocated to the subcarrier with higher channel fading gain, and less powers and bits per symbol to noisy subcarrier [9]. Adaptive modulation and bit loading are efficient techniques for reliable transmission [10].…”

mentioning

confidence: 99%

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Optimal and suboptimal adaptive algorithms for rate and power transmission in OFDM-based Cognitive Radio systems

Khederzadeh

Farrokhi

2015

Computers & Electrical Engineering

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Section: Discussionmentioning

confidence: 99%

Section: Suboptimal Algorithmmentioning

confidence: 99%

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Optimal and suboptimal adaptive algorithms for rate and power transmission in OFDM-based Cognitive Radio systems

Khederzadeh

Farrokhi

2015

Computers & Electrical Engineering

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“…Earlier works have concentrated on bi-objective models. They minimize the power and delay [ 7 ] or minimize the power and maximize the transmission rate [ 1 , 8 , 9 ], but not all of them simultaneously. The authors in [ 1 , 9 ] design a QoS-constrained bi-objective optimization model in order to minimize transmission power and maximize rate.…”

Section: Introductionmentioning

confidence: 99%

A Joint Power, Delay and Rate Optimization Model for Secondary Users in Cognitive Radio Sensor Networks

Muwonge

Pei

Otim

et al. 2020

Sensors

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To maximize the limited spectrum among primary users and cognitive Internet of Things (IoT) users as we save the limited power and energy resources available, there is a need to optimize network resources. Whereas it is quite complex to study the impact of transmission rate, transmission power or transmission delay alone, the complexity is aggravated by the simultaneous consideration of all these three variables jointly in addition to a channel selection variable, since it creates a non-convex problem. Our objective is to jointly optimize the three major variables; transmission power, rate and delay under constraints of Bit Error Rate (BER), interference and other channel limitations. We analyze how total power, rate and delay vary with packet size, network size, BER and interference. The resulting problem is solved using a branch-and-cut polyhedral approach. For simulation of results, we use MATLAB together with the state-of-the-art BARON software. It is observed that an increase in packet size generally leads to an increase in total rate, total power and total transmission delay. It is also observed that increasing the number of secondary users on the channel generally leads to an increased power, delay and rate.

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“…Furthermore, effective rate allocation will also help to minimize end-to-end delay and control message overhead. Various rate allocation methods have been developed for CR networks from different perspectives, including end-to-end delay optimization [17], QoSconstrained bi-objective optimization [18], joint rate and power allocation [19], etc.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Rate Allocation for Multipath Routing under Path Stability and Prioritized Traffic Session Constraints for Cognitive Radio Ad Hoc Networks with Selfish Secondary Users

Zhang¹,

Bai²,

Zhuo³

et al. 2016

JCM

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In this paper, we investigate the dynamic rate allocation for multipath routing with the node-disjoint paths in cognitive radio ad hoc networks composed of selfish secondary users (SUs) which are unwilling to forward the packets. We model the path stability factor of each path under a certain traffic session by distributing available channel in view of the carrier frequency of the available uplink channel on each path. Then the traffic sensing factor is also devised to characterize the priorities of different traffic sessions along the node-disjoint paths. In addition, we propose the differential game model of dynamic rate allocation for multi-path routing by taking into account both the mobility and random nature of the selfish SUs. A set of the non-cooperative optimal solutions to the dynamic rate allocation model is further obtained by deriving an optimization problem. Moreover, the distributed optimal rate update rule is developed to dynamically regulate the rate of traffic session over a certain path. Numerical results confirm that the effectiveness of the proposed dynamic rate allocation model.

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Distributed Approach for Power and Rate Allocation to Secondary Users in Cognitive Radio Networks

Cited by 16 publications

References 32 publications

Optimal and suboptimal adaptive algorithms for rate and power transmission in OFDM-based Cognitive Radio systems

Optimal and suboptimal adaptive algorithms for rate and power transmission in OFDM-based Cognitive Radio systems

A Joint Power, Delay and Rate Optimization Model for Secondary Users in Cognitive Radio Sensor Networks

Dynamic Rate Allocation for Multipath Routing under Path Stability and Prioritized Traffic Session Constraints for Cognitive Radio Ad Hoc Networks with Selfish Secondary Users

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