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The concept of relay has been introduced in LTE-Advanced as a promising approach to enhance network performance. Because of the diversity introduced by deploying relays to a purely macro network, the conventional cell selection schemes do not perform well, leading to noticeable throughput degradation for some of the users. An algorithm attempting to address the issue is the use of data rate mapped from Reference Signal Received Power (RSRP) as the cell selection criterion. This algorithm, however, introduces an extra interference problem. We therefore describe an additional interference avoidance (IA) mechanism to enhance it. Performance evaluation and analysis are carried out to gain insights on the enhanced algorithm and two additional algorithms, both developed in this paper, to demonstrate their effectiveness in improving the throughput. Simulation results show that for relay-enabled networks the new algorithms based on data rates obtained directly from RSRP and the Reference Signal Received Quality (RSRQ) outperform the algorithm based on RSRP with offset. Moreover, the interference issue is well dealt with via the implementation of additional IA.

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