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Joint sensing and resource allocation for underlay cognitive radios

DOI:10.1109/ICASSP.2014.6855014
  • Conference: ICASSP 2014
Authors:
Luis M. Lopez-Ramos at Universitetet i Agder, Grimstad, Norway
Luis M. Lopez-Ramos
  • Universitetet i Agder, Grimstad, Norway
Antonio G. Marques
Antonio G. Marques
Antonio G. Marques
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Javier Ramos at King Juan Carlos University
Javier Ramos
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This work optimizes the (traditionally separated) tasks of sensing and radio resource allocation jointly for an underlay CR paradigm. The formulation considers that secondary users adapt their power and rate based on the available imperfect channel state information, while taking into account the cost associated with acquiring such an information. The objective of the optimization is twofold: maximize the (sum-rate) performance of the CR and protect the primary users through an average interference constraint. Designing the sensing in our underlay paradigm amounts to decide what channel/frequency slots are sensed at every time instant.
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Supplementary resource (1)

Joint Sensing and Resource Allocation in Underlay CRs
Data
March 2017
Luis M. Lopez-Ramos · Antonio G. Marques · Javier Ramos
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Optimal Resource Allocation for Underlay Cognitive Radio Networks: 4th International Conference, ICCCS 2018, Haikou, China, June 8–10, 2018, Revised Selected Papers, Part IV
Chapter
  • Sep 2018
In order to improve the effective utilization of available resources in the traditional wireless network, this paper studies the optimization of resource allocation (RA) in the underlay cognitive radio network (CRN). Our goal is to maximize the sum rate of the whole system (e.i., primary users (PUs) and secondary users (SUs)), taking into account the constraints of interference temperature (IT) and minimum rate, and the Quality of Service (QoS) guarantees. A heuristic algorithm is proposed to solve the non-convex non-linear programming optimization problem. Theoretical analysis and simulation results show that this algorithm can effectively reduce the power interference to the PUs, maximize the transmission rate of PUs and SUs, and improve resource utilization of the CRN.
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D2D Communications Underlaying UAV-Assisted Access Networks
Article
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Unmanned aerial vehicles (UAVs)-enabled base stations (BSs) can boost the system performance of the terrestrial networks with device-to-device (D2D) communication in the scenarios that fixed BSs in the ground are not available. However, the serious interference among UAVs and multiple D2D pairs is more challenging than the terrestrial case because UAVs are changing the networks topology frequently over time. In this paper, the power control optimization is investigated for D2D communications underlaying UAV-assisted access systems, where a UAV-enabled BS serves multiple users, and the remaining users communicate with each other with the assistance of the UAV, also referred to as D2D pairs. With the aim of throughput maximization, we need to address a non-convex optimization. To this end, D.C. (difference of two convex functions) programming is invoked to solve the formulated optimization, which can obtain suboptimal solutions. Considering the UAV’s limited energy and low computational capability, we further design a low-complexity power control algorithm by exploiting the Hessian matrix’s structure. Simulation results show that the proposed algorithms perform quite well for all considered scenarios. Both of them can improve the system throughput dramatically. Moreover, the low-complexity algorithm produces almost the same throughput as the D.C. programming method with much lower computation burden. OAPA
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Soft-decision sequential sensing for optimization of interweave Cognitive Radio networks
Conference Paper
Full-text available
  • Jun 2013
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Jointly Optimal Sensing and Resource Allocation for Multiuser Overlay Cognitive Radios
Conference Paper
Full-text available
  • Nov 2012
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Article
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Joint Soft-Decision Cooperative Spectrum Sensing and Power Control in Multiband Cognitive Radios
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We consider joint optimization of cooperative spectrum sensing, channel access and power allocation in an overlay multiband cognitive radio network. A soft-decision cooperative spectrum sensing concept using continuous-valued sensing test statistics is considered, instead of making hard binary decisions as in traditional hypothesis testing spectrum sensing schemes. The channel access decision about whether to access the channel or not is relaxed into allowing the secondary user to access channels with some probability. The sensing decision is made at the secondary base station based on the sensing statistics received from all or a subset of secondary users. This joint optimization problem is aimed at maximizing the secondary users' sum instantaneous throughput while keeping the interference to primary users under a specified threshold. The problem is shown to be a convex optimization problem and the Lagrangian dual method is employed to obtain the optimal solution. Two heuristic algorithms are also proposed to reduce computational complexity. We also discuss an alternative formulation where additionally interference to individual PUs is also constrained. Simulation results show that our soft sensing based algorithm significantly outperforms a traditional hard decision sensing algorithm.
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