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Cognitive Radio Network Duality and Algorithms for Utility Maximization
"... Abstract—We study a utility maximization framework for spectrum sharing among cognitive secondary users and licensed primary users in cognitive radio networks. All the users maximize the network utility by adapting their signaltointerferenceplusnoise ratio (SINR) assignment and transmit power su ..."
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Cited by 8 (5 self)
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Abstract—We study a utility maximization framework for spectrum sharing among cognitive secondary users and licensed primary users in cognitive radio networks. All the users maximize the network utility by adapting their signaltointerferenceplusnoise ratio (SINR) assignment and transmit power subject to power budget constraints and additional interference temperature constraint for the secondary users. The utility maximization problem is challenging to solve optimally in a distributed manner due to the nonconvexity and the tight coupling between the power budget and interference temperature constraint sets. We first study a special case where egalitarian SINR fairness is the utility, and a tuningfree distributed algorithm with a geometric convergence rate is developed to solve it optimally. Then, we answer the general utility maximization question by developing a cognitive radio network duality to decouple the SINR assignment, the transmit power and the interference temperature allocation. This leads to a utility maximization algorithm that leverages the egalitarian fairness power control as a submodule to maintain a desirable separability in the SINR assignment between the secondary and primary users. This algorithm has the advantage that it can be distributively implemented, and the method converges relatively fast. Numerical results are presented to show that our proposed algorithms are theoretically sound and practically implementable. Index Terms—Optimization, network utility maximization, cognitive radio networks, spectrum allocation. I.
Maximizing Sum Rates in Cognitive Radio Networks: Convex Relaxation and Global Optimization Algorithms
"... Abstract—A key challenge in wireless cognitive radio networks is to maximize the total throughput also known as the sum rates of all the users while avoiding the interference of unlicensed band secondary users from overwhelming the licensed band primary users. We study the weighted sum rate maximiza ..."
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Cited by 6 (3 self)
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Abstract—A key challenge in wireless cognitive radio networks is to maximize the total throughput also known as the sum rates of all the users while avoiding the interference of unlicensed band secondary users from overwhelming the licensed band primary users. We study the weighted sum rate maximization problem with both power budget and interference temperature constraints in a cognitive radio network. This problem is nonconvex and generally hard to solve. We propose a reformulationrelaxation technique that leverages nonnegative matrix theory to first obtain a relaxed problem with nonnegative matrix spectral radius constraints. A useful upper bound on the sum rates is then obtained by solving a convex optimization problem over a closed bounded convex set. It also enables the sumrate optimality to be quantified analytically through the spectrum of speciallycrafted nonnegative matrices. Furthermore, we obtain polynomialtime verifiable sufficient conditions that can identify polynomialtime solvable problem instances, which can be solved by a fixedpoint algorithm. As a byproduct, an interesting optimality equivalence between the nonconvex sum rate problem and the convex maxmin rate problem is established. In the general case, we propose a global optimization algorithm by utilizing our convex relaxation and branchandbound to compute an optimal solution. Our technique exploits the nonnegativity of the physical quantities, e.g., channel parameters, powers and rates, that enables key tools in nonnegative matrix theory such as the (linear and nonlinear) PerronFrobenius theorem, quasiinvertibility, FriedlandKarlin inequalities to be employed naturally. Numerical results are presented to show that our proposed algorithms are theoretically sound and have relatively fast convergence time even for largescale problems. Index Terms—Optimization, convex relaxation, cognitive radio networks, nonnegative matrix theory. I.
EnergyInfeasibility Tradeoff in Cognitive Radio Networks: PriceDriven Spectrum Access Algorithms
"... Abstract—We study the feasibility of the total power minimization problem subject to power budget and SignaltoInterferenceplusNoise Ratio (SINR) constraints in cognitive radio networks. As both the primary and the secondary users are allowed to transmit simultaneously on a shared spectrum, unco ..."
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Cited by 1 (1 self)
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Abstract—We study the feasibility of the total power minimization problem subject to power budget and SignaltoInterferenceplusNoise Ratio (SINR) constraints in cognitive radio networks. As both the primary and the secondary users are allowed to transmit simultaneously on a shared spectrum, uncontrolled access of secondary users degrades the performance of primary users and can even lead to system infeasibility. To find the largest feasible set of secondary users (i.e., the system capacity) that can be supported in the network, we formulate a vectorcardinality optimization problem. This nonconvex problem is however hard to solve, and we propose a convex relaxation heuristic based on the sumofinfeasibilities in optimization theory. Our methodology leads to the notion of admission price for spectrum access that can characterize the tradeoff between the total energy consumption and the system capacity. Pricedriven algorithms for joint power and admission control are then proposed that quantify the benefits of energyinfeasibility balance. Numerical results are presented to show that our algorithms are theoretically sound and practically implementable. Index Terms—Optimization, cognitive radio networks, spectrum access control, power and admission control. I.
Subspace Communication
, 2014
"... We are surrounded by electronic devices that take advantage of wireless technologies, from our computer mice, which require little amounts of information, to our cellphones, which demand increasingly higher data rates. Until today, the coexistence of such a variety of services has been guaranteed by ..."
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We are surrounded by electronic devices that take advantage of wireless technologies, from our computer mice, which require little amounts of information, to our cellphones, which demand increasingly higher data rates. Until today, the coexistence of such a variety of services has been guaranteed by a fixed assignment of spectrum resources by regulatory agencies. This has resulted into a blind alley, as current wireless spectrum has become an expensive and a scarce resource. However, recent measurements in dense areas paint a very different picture: there is an actual underutilization of the spectrum by legacy systems. Cognitive radio exhibits a tremendous promise for increasing the spectral efficiency for future wireless systems. Ideally, new secondary users would have a perfect panorama of the spectrum usage, and would opportunistically communicate over the available resources without degrading the primary systems. Yet in practice, monitoring the spectrum resources, detecting available resources for opportunistic communication, and transmitting over the resources are hard tasks. This thesis addresses the tasks of monitoring, de
Centralized Power Control in Cognitive Radio Networks Using Modulation and Coding Classification Feedback
"... AbstractIn this paper, a centralized Power Control (PC) scheme and an interference channel learning method are jointly tackled to allow a Cognitive Radio Network (CRN) access to the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The learn ..."
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AbstractIn this paper, a centralized Power Control (PC) scheme and an interference channel learning method are jointly tackled to allow a Cognitive Radio Network (CRN) access to the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The learning process enabler is a cooperative Modulation and Coding Classification (MCC) technique which estimates the Modulation and Coding scheme (MCS) of the PU. Due to the lack of cooperation between the PU and the CRN, the CRN exploits this multilevel MCC sensing feedback as implicit channel state information (CSI) of the PU link in order to constantly monitor the impact of the aggregated interference it causes. In this paper, an algorithm is developed for maximizing the CRN throughput (the PC optimization objective) and simultaneously learning how to mitigate PU interference (the optimization problem constraint) by using only the MCC information. Ideal approaches for this problem setting with high convergence rate are the cutting plane methods (CPM). Here, we focus on the analytic center cutting plane method (ACCPM) and the center of gravity cutting plane method (CGCPM) whose effectiveness in the proposed simultaneous PC and interference channel learning algorithm is demonstrated through numerical simulations.
Convex Relaxation Algorithms for EnergyInfeasibility Tradeoff in Cognitive Radio Networks
"... AbstractIn cognitive radio networks, uncontrolled access of secondary users degrades the performance of primary users and can even lead to system infeasibility, as the secondary users are allowed to transmit simultaneously on a shared spectrum. We study the feasibility of the total energy consumpt ..."
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AbstractIn cognitive radio networks, uncontrolled access of secondary users degrades the performance of primary users and can even lead to system infeasibility, as the secondary users are allowed to transmit simultaneously on a shared spectrum. We study the feasibility of the total energy consumption minimization problem subjecting to power budget and SignaltoInterferenceplusNoise Ratio (SINR) constraints. Finding the largest set of secondary users (i.e., the system capacity) that can be supported in the system is hard to solve due to the nonconvexity of the cardinality objective. We formulate this problem as a vectorcardinality optimization problem, and propose a convex relaxation that replaces the objective with a continuous and convex function. Motivated by the sumofinfeasibilities heuristic, a joint power and admission control algorithm is proposed to compute the maximum number of secondary users that can be supported. Numerical results are presented to show that our algorithm is theoretically sound and practically implementable.
Wideband Cognitive Radio: . . . Subspace Communication
, 2014
"... We are surrounded by electronic devices that take advantage of wireless technologies, from our computer mice, which require little amounts of information, to our cellphones, which demand increasingly higher data rates. Until today, the coexistence of such a variety of services has been guaranteed by ..."
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We are surrounded by electronic devices that take advantage of wireless technologies, from our computer mice, which require little amounts of information, to our cellphones, which demand increasingly higher data rates. Until today, the coexistence of such a variety of services has been guaranteed by a fixed assignment of spectrum resources by regulatory agencies. This has resulted into a blind alley, as current wireless spectrum has become an expensive and a scarce resource. However, recent measurements in dense areas paint a very different picture: there is an actual underutilization of the spectrum by legacy systems. Cognitive radio exhibits a tremendous promise for increasing the spectral efficiency for future wireless systems. Ideally, new secondary users would have a perfect panorama of the spectrum usage, and would opportunistically communicate over the available resources without degrading the primary systems. Yet in practice, monitoring the spectrum resources, detecting available resources for opportunistic communication, and transmitting over the resources are hard tasks. This thesis addresses the tasks of monitoring, de
REVIEW Open Access
"... Background: Perineural invasion is a common path for cholangiocarcinoma (CCA) metastasis, and it is highly correlated with postoperative recurrence and poor prognosis. It is often an early event in a disease that is commonly diagnosed in advanced stages, and thus it could offer a timely therapeutic ..."
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Background: Perineural invasion is a common path for cholangiocarcinoma (CCA) metastasis, and it is highly correlated with postoperative recurrence and poor prognosis. It is often an early event in a disease that is commonly diagnosed in advanced stages, and thus it could offer a timely therapeutic and diagnostic target if better understood. This article systematically reviews the progress of CCA neural invasionrelated molecules. Methods: Studies were identified by searching MEDLINE and PubMed databases for articles from January 1990 to
IEEE COMMUNICATION SURVEYS & TUTORIALS (ACCEPTED FOR PUBLICATION) 1 Cognitive Radio Techniques under Practical Imperfections: A Survey
"... Abstract — Cognitive Radio (CR) has been considered as a potential candidate for addressing the spectrum scarcity problem of future wireless networks. Since its conception, several researchers, academic institutions, industries, regulatory and standardization bodies have put their significant effor ..."
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Abstract — Cognitive Radio (CR) has been considered as a potential candidate for addressing the spectrum scarcity problem of future wireless networks. Since its conception, several researchers, academic institutions, industries, regulatory and standardization bodies have put their significant efforts towards the realization of CR technology. However, as this technology adapts its transmission based on the surrounding radio environment, several practical issues may need to be considered. In practice, several imperfections such as noise uncertainty, channel/interference uncertainty, transceiver hardware imperfections, signal uncertainty, synchronization issues, etc. may severely deteriorate the performance of a CR system. To this end, the investigation of realistic solutions towards combating various practical imperfections is very important for successful implementation of the cognitive technology. In this direction, first, this survey paper provides an overview of the enabling techniques for CR communications. Subsequently, it discusses the main imperfections that may occur in the most widely used CR paradigms and then reviews the existing approaches towards addressing these imperfections. Finally, it provides some interesting open research issues. Index Terms — Cognitive radio, channel uncertainty, noise uncertainty, spectrum sensing, transceiver imperfections, underlay I.
IEEE JOURNAL OF SELECTED AREAS IN COMMUNICATIONS 1 Beamforming Duality and Algorithms for Weighted Sum Rate Maximization in Cognitive Radio Networks
"... In this paper, we investigate the joint design of transmit beamforming and power control to maximize the weighted sum rate in the multipleinput singleoutput (MISO) cognitive radio network constrained by arbitrary power budgets and interference temperatures. The nonnegativity of the physical quanti ..."
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In this paper, we investigate the joint design of transmit beamforming and power control to maximize the weighted sum rate in the multipleinput singleoutput (MISO) cognitive radio network constrained by arbitrary power budgets and interference temperatures. The nonnegativity of the physical quantities, e.g., channel parameters, powers, and rates, is exploited to enable key tools in nonnegative matrix theory, such as the (linear and nonlinear) PerronFrobenius theory, quasiinvertibility, and FriedlandKarlin inequalities, to tackle this nonconvex problem. Under certain (quasiinvertibility) sufficient condition, we propose a tight convex relaxation technique that relaxes multiple constraints to bound the global optimal value in a systematic way. Then, a singleinput multipleoutput (SIMO)MISO duality is established through a virtual dual SIMO network and Lagrange duality. This SIMOMISO duality is equivalent to the zero Lagrange duality gap condition that connects the optimality conditions of the primal MISO network and the virtual dual SIMO network. Moreover, by exploiting the SIMOMISO duality, an algorithm is developed to solve the sum rate maximization problem optimally. Numerical examples demonstrate the computational efficiency of our algorithm when the number of transmit antennas is large. Index Terms Optimization, convex relaxation, cognitive radio network, nonnegative matrix theory, quasiinvertibility, KarushKuhnTucker conditions, PerronFrobenius theorem. I.