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We consider multi-constrained power, rate and channelallocation crafted for low power consumption, delaytolerant traffic, and under interfering link conditions thatmay be used in a cognitive radio system. Specifically, aniterative distributed algorithm, based on a sum-powerconstrained sum-rate maximization with upper (andlower) per user and channel power and rate constraints,as well as upper per user sum-power and sum-rateconstraints is developed. The feasibility and performanceof the algorithm is demonstrated by simulation in acellular system. Simulations show that the multipleconstraints are handled while improving the sum-rate vs. sum-power relative an “equal power adaptive rate” RRM approach., © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Qc 20120203

We consider multi-constrained power, rate and channelallocation crafted for low power consumption, delaytolerant traffic, and under interfering link conditions thatmay be used in a cognitive radio system. Specifically, aniterative distributed algorithm, based on a sum-powerconstrained sum-rate maximization with upper (andlower) per user and channel power and rate constraints,as well as upper per user sum-power and sum-rateconstraints is developed. The feasibility and performanceof the algorithm is demonstrated by simulation in acellular system. Simulations show that the multipleconstraints are handled while improving the sum-rate vs. sum-power relative an “equal power adaptive rate” RRM approach., © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Qc 20120203

We consider multi-constrained power, rate and channelallocation crafted for low power consumption, delaytolerant traffic, and under interfering link conditions thatmay be used in a cognitive radio system. Specifically, aniterative distributed algorithm, based on a sum-powerconstrained sum-rate maximization with upper (andlower) per user and channel power and rate constraints,as well as upper per user sum-power and sum-rateconstraints is developed. The feasibility and performanceof the algorithm is demonstrated by simulation in acellular system. Simulations show that the multipleconstraints are handled while improving the sum-rate vs. sum-power relative an “equal power adaptive rate” RRM approach., © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Qc 20120203

We consider multi-constrained power, rate and channelallocation crafted for low power consumption, delaytolerant traffic, and under interfering link conditions thatmay be used in a cognitive radio system. Specifically, aniterative distributed algorithm, based on a sum-powerconstrained sum-rate maximization with upper (andlower) per user and channel power and rate constraints,as well as upper per user sum-power and sum-rateconstraints is developed. The feasibility and performanceof the algorithm is demonstrated by simulation in acellular system. Simulations show that the multipleconstraints are handled while improving the sum-rate vs. sum-power relative an “equal power adaptive rate” RRM approach., © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Qc 20120203

We consider multi-constrained power, rate and channelallocation crafted for low power consumption, delaytolerant traffic, and under interfering link conditions thatmay be used in a cognitive radio system. Specifically, aniterative distributed algorithm, based on a sum-powerconstrained sum-rate maximization with upper (andlower) per user and channel power and rate constraints,as well as upper per user sum-power and sum-rateconstraints is developed. The feasibility and performanceof the algorithm is demonstrated by simulation in acellular system. Simulations show that the multipleconstraints are handled while improving the sum-rate vs. sum-power relative an “equal power adaptive rate” RRM approach., © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Qc 20120203