Genetic algorithm for mobiles equilibrium

Moustafa, Mohamed; Naghshineh, M.

doi:10.1109/milcom.2000.904915

Cited by 14 publications

(20 citation statements)

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“…When it comes to metaheuristics, in [5] and [6] a genetic algorithm approach was used to propose the genetic algorithm for mobiles equilibrium, providing the joint power-rate control in CDMA multiple access networks. In [7], the particle swarm optimization (PSO) metaheuristic was used in order to establish a low-complexity power control algorithm.…”

Section: Related Workmentioning

confidence: 99%

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Ant Colony Optimization for Resource Allocation and Anomaly Detection in Communication Networks

Marques¹,

Adaniya²,

Abrão³

et al. 2013

Search Algorithms for Engineering Optimization

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Section: Related Workmentioning

confidence: 99%

“…In [5,6] a new cost function for power control problem in CDMA systems using genetic algorithms has been proposed. This function was later modified and used with swarm intelligence in [1] to solve the power control problem.…”

Section: Power Allocation Problemmentioning

confidence: 99%

Ant Colony Optimization for Resource Allocation and Anomaly Detection in Communication Networks

Marques¹,

Adaniya²,

Abrão³

et al. 2013

Search Algorithms for Engineering Optimization

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“…Based on the work of Moustafa et al (2000), and further results of Elkamchouchi et al (2007), the above single-objective function could be modified in order to incorporate the near-far aspect; hence the following maximization cost function could be employed as an alternative:…”

Section: Fixed-rate Power Allocation Criterionmentioning

confidence: 99%

“…gives credit to solutions that use minimum power and punishes others using high levels Moustafa et al (2000;2001a;.…”

Section: Fixed-rate Power Allocation Criterionmentioning

confidence: 99%

“…The application of heuristic optimization to the power allocation in CDMA systems was discussed in Elkamchouchi et al (2007); Moustafa et al (2000). In Moustafa et al (2000;2001a), a genetic approach, named genetic algorithm for mobiles equilibrium (GAME), was considered in order to control two main resources in a wireless network: bit rate and corresponding transmitting power level from mobile terminals. The basic idea is that all the mobile terminals have to harmonize their rate and power according to their location, QoS, and density.…”

mentioning

confidence: 99%

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Multiple Access Network Optimization Aspects via Swarm Search Algorithms

Abrão¹,

Proença²,

Angélico³

et al. 2011

Search Algorithms and Applications

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1.IntroductionIn the current telecommunication scenario, companies must deal with spectrum scarcity, power consumption issues, increasing throughput demand, and quality of service (QoS) requirements, including high performance (in terms of bit error rate (BER)) with guarantees of delivering the target information rate per user-class, maximum allowed delay, and so on. In these situations, many optimization problems arise, such as multiuser detection and resource allocation. A lot of innovative algorithms conception and research efforts have been spent in order to satisfy the new services requirements, such as growing capacity, availability, mobility, and multiclass services (i.e., multirate users) with different quality of service (QoS). Hence, inspired by this scenario, a different optimization approach based on heuristic procedures has been investigated. Particle swarm optimization (PSO) was developed after some researchers have analyzed the birds behavior and discerning that the advantage obtained through their group life could be explored as a tool for a heuristic search. Considering this new concept of interaction among individuals, J. Kennedy and R. Eberhart developed a new heuristic search based on a particle swarm Kennedy & Eberhart (1995). The PSO principle is the movement of a group of particles, randomly distributed in the search space, each one with its own position and velocity. The position of each particle is modified by the application of velocity in order to reach a better performance. The interaction among particles is inserted in the calculation of particle velocity. In a multiple access DS/CDMA system, a conventional detector by itself may not provide a desirable performance and quality of service, once the system capacity is strongly affected by multiple access interference (MAI). The capacity of a DS/CDMA system in multipath channels is limited mainly by the MAI, self-interference (SI), near-far effect (NFR) and fading. The conventional receiver for multipath channels (Rake receiver) explores the path diversity in order to reduce fading impairment; however, it is not able to mitigate neither the MAI nor the near-far effect Moshavi (1996);Verdú (1998). In this context, multiuser detection (MUD) emerged as a solution to overcome the MAI. The best performance is acquired by the optimum multiuser detection (OMUD), which is based on the log-likelihood function (LLF), but results

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