Optimal power allocation for OFDM-based cooperative relay cognitive radio networks

Huang, Fangjiang; Ge, Mengyao; Wang, Chonggang

doi:10.1109/icc.2012.6363945

Cited by 15 publications

(17 citation statements)

References 13 publications

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“…Latency, however, is equally important and faces stringent constraints in radio networks. A significant amount of work has been done in the literature for service placement considering the parameters such as latency, cost, QoS and others, especially from the context of the core telecom networks [11,15,27,[32][33][34][35][36][37][38][39][40]. The requirements of the CRAN networks, on the contrary, are significantly different compared with those of the core telecom networks [5,21].…”

Section: Redundancy Ms Latencymentioning

confidence: 99%

Efficient virtual network function placement strategies for Cloud Radio Access Networks

Bhamare

Erbad

Jain

et al. 2018

Computer Communications

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The new generation of 5G mobile services place stringent requirements for cellular network operators in terms of latency and costs. The latest trend in radio access networks (RANs) is to pool the baseband units (BBUs) of multiple radio base stations and to install them in a centralized infrastructure, such as a cloud, for statistical multiplexing gains. The technology is known as Cloud Radio Access Network (CRAN). Since cloud computing is gaining significant traction and virtualized data centers are becoming popular as a cost-effective infrastructure in the telecommunication industry, CRAN is being heralded as a candidate technology to meet the expectations of radio access networks for 5G. In CRANs, low energy base stations (BSs) are deployed over a small geographical location and are connected to a cloud via finite capacity backhaul links. Baseband processing unit (BBU) functions are implemented on the virtual machines (VMs) in the cloud over commodity hardware. Such functions, built in software, are termed as virtual functions (VFs). The optimized placement of VFs is necessary to reduce the total delays and minimize the overall costs to operate CRANs. Our study considers the problem of optimal VF placement over distributed virtual resources spread across multiple clouds, creating a centralized BBU cloud. We propose a combinatorial optimization model and the use of two heuristic approaches, which are, branch-and-bound (BnB) and simulated annealing (SA) for the proposed optimal placement. In addition, we propose enhancements to the standard BnB heuristic and compare the results with standard BnB and SA approaches. The proposed enhancements improve the quality of the solution in terms of latency and cost as well as reduce the execution complexity significantly. We also determine the optimal number of clouds, which need to be deployed so that the total links delays, as well as the service migration delays, are minimized, while the total cloud deployment cost is within the acceptable limits.

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Section: Redundancy Ms Latencymentioning

confidence: 99%

Efficient virtual network function placement strategies for Cloud Radio Access Networks

Bhamare

Erbad

Jain

et al. 2018

Computer Communications

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“…Orthogonal Frequency Division Multiplexing (OFDM), as another promising technique for its ability to provide very high data rates in the multi-path fading environment [3], is always integrated with CR [4]. OFDMbased CR technology [5] has obtained much more attention recently because it performs well in improving the spectrum utilization efficiency as a result of its flexibility in assigning resources among CRUs.…”

Section: Introductionmentioning

confidence: 99%

“…), (5) where σ 2 n denotes the AWGN variance and Γ = − ln(5BER)/1.5 is the SNR ratio gap for an uncoded MQAM with a designated bit error rate BER.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Power Loading with Intercarrier and Intersymbol Interference Considerations for Cognitive OFDM Systems

Zhou

Shi

et al. 2015

2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

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This paper investigates the energy-efficient power loading with intercarrier and intersymbol interference considerations for OFDM-based cognitive systems. The objective is to maximize the energy efficiency (EE) as well as to balance the tradeoff between intercarrier interference (ICI) and intersymbol interference (ISI) by jointly optimizing the subcarrier bandwidth and power allocation in a mobile scenario, under the power budget and the interference constraint. First, the primal problem is converted into a convex optimization problem by fractional programming. Then, the Lagrange dual function and the subgradient method are adopted to achieve the optimal power allocation and the golden section method is employed to search for the optimal subcarrier number (i.e, subcarrier bandwidth). Numerical results show that the proposed algorithm can realize ICI control by choosing an optimal subcarrier number, and simultaneously the EE can be significantly improved by 139%.

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“…Many related works in spectrum sharing with cooperative relay technology have been produced over the last decade [7], [8]. In this paper, our interest is to study spectrum sharing with cooperation in cognitive radio networks.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation in OFDMA-Based Cognitive Radio Networks via Cooperation with Primary Users

Tang¹,

Wang²,

Grace³

et al. 2015

JCM

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This paper considers an OFDMA-based cooperative cognitive radio network where the secondary users (SUs) act as the relay of the primary users (PUs) while guaranteeing the quality of service (QoS) of the PUs. In return, the SUs access the spectrum licensed to PUs in both time and frequency without impacting the communication of the PUs. A joint resource allocation problem is formulated to maximize the data rate of the SUs. It is proven that the optimization problem can be decomposed into two coupled subproblems which are power allocation and relay selection. The power allocation adopts the water-filling algorithm using the Lagrangian method, and the optimal relay selection is solved by Hungarian method. Simulation results show that the proposed algorithm can bring revenue to both the PUs and SUs.

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Optimal power allocation for OFDM-based cooperative relay cognitive radio networks

Cited by 15 publications

References 13 publications

Efficient virtual network function placement strategies for Cloud Radio Access Networks

Efficient virtual network function placement strategies for Cloud Radio Access Networks

Energy-Efficient Power Loading with Intercarrier and Intersymbol Interference Considerations for Cognitive OFDM Systems

Resource Allocation in OFDMA-Based Cognitive Radio Networks via Cooperation with Primary Users

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