Multiple Access Network Optimization Aspects via Swarm Search Algorithms

Abstract

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

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

“…It is well known that spectrum and power are valuable resources due to their scarcity, the first one is a natural non renewable resource and the second one is limited by the battery and device size. Therefore, proposing new techniques and algorithms that can allocate this resources in a simple 1 and optimized manner is pretty important.…”

“…It is worthy to note that since this is a theoretical bound a gap can be included, thus, the Shannon equation can be rewritten as [1]:…”

“…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. Due to the good results obtained in [1] that cost function was used with the ACO algorithm and reproduced hereafter for convenience:…”

“…Due to the paramount importance of (wireless) communication systems and computer networks, in the last decade both resource allocation (RA) and anomaly detection (AD) problems addressed herein have been intensively studied and numerous solutions have been proposed in the specialized literature [1]. The RA specially in wireless multiple access networks and the AD in computer networks are problems of complex nature and an engineering compromise solution has much appeal in terms of practical and effective deployment.…”

Multidimensional Optimization-Based Heuristics Applied to Wireless Communication Systems

“…In the last decade, the literature has been collecting sub-optimal solutions proposals based on iterative algorithms and heuristics, particularly evolutionary and local search, applied to inherent multiple access communication systems problems, among which we could cite the following heuristic solutions: optimal multiuser detection [12][13][14][15][16][17][18][19]; spreading sequences selection [20,21]; parameter estimation, particularly the channel coefficients estimation, delay and power users [19,22,23]; power control problem [24,25]; and resource allocation optimization [26][27][28].…”