The optimization aspects of QoS.
1. Introduction
Modern optical communication networks are expected to meet a broad range of services with different and variable demands of bit rate, connection (session) duration, frequency of use, and set up time [1]. Thus, it is necessary to build flexible alloptical networks that allow dynamic resources sharing between different users and clients in an efficient way. The alloptical network is able to implement ultrahigh speed transmitting, routing and switching of data in the optical domain, presenting the transparency to data formats and protocols which increases network flexibility and functionality such that future network requirements can be met [2]. Optical code division multiplexing access (OCDMA) based technology has attracted a lot of interests due to its various advantages including asynchronous operation, high network flexibility, protocol transparency, simplified network control and potentially enhanced security [3]. Therefore, recent developments and researches on OCDMA have been experienced an expansion of interest, from shortrange networks, such as access networks, to highcapacity medium/large networks.
The optical network presents two promising scenarios: the transport (backbone) networks with optical code division multiplexing/wavelength division multiplexing (OCDM/WDM) technology and the access network with OCDMA technology. In both, transport OCDM/WDM and access OCDMA networks, each different code defines a specific user or logic channel transmitted in a common channel. In a common channel, the interference that may arise between different user codes is known as multiple access interference (MAI), and it can limit the number of users utilizing the channel simultaneously [3]. In this work we have focus on hybrid OCDM/WDM systems. In this one, data signals in routing network configuration are carried on optical code path (OCP) from a source node to a destination node passing through nodes where the signals are optically routed and switched without regeneration in the electrical domain. Hence, in routing and channel (code/wavelength) assignment (RCA) problem, suitable paths and channels are carefully selected among the many possible choices for the required connections [2].
Establishing OCP with higher optical signaltonoise plus interference ratio (SNIR) allows reducing the number of retransmissions by higher layers, thus increasing network throughput. Therefore, RCA techniques that consider physical layer impairments for the establishment of an OCP, namely Quality of TransmissionAware (QoTaware) RCA, could be much more practical [45]. For a dynamic traffic scenario the objective is to minimize the blocking probability of the connections by routing, assigning channels, and to maintain an acceptable level of optical power and adequate SNIR all over the network [6]. Furthermore, different channels can travel via different optical paths and also have different levels of quality of service (QoS) requirements. The QoS depends on SNIR, dispersion, and nonlinear effects [6]. Therefore, it is desirable to adjust network parameters in an optimal way, based on online decentralized iterative algorithms to accomplish such adjustment [7].
As a result, this dynamic optimization allows an increased network flexibility and capacity [67]. The SNIR optimization problem appears to be a huge challenge, since the MAI introduces the nearfar problem [7]. Furthermore, if the distances between the nodes are quite different, like in real optical networks, the signal power received from various nodes will be significantly distinct. Thus, considering an optical node as the reference, the performance of closer nodes is many orders of magnitude better than that of far ones. Then, an efficient power control is needed to overcome this problem and enhance the performance and throughput of the network; this could be achieved through the SNIR optimization [6]. In this case, which is analogous to the CDMA cellular system, the power control (centralized or distributed) is one of the most important issues, because it has a significant impact on both network performance and capacity. It is the most effective way to avoid the nearfar problem and to increase the SNIR [67].
The optical power control problem has been recently investigated in the context of access networks aiming at solving the nearfar problem [78] and establishing the QoS at the physical layer [911]. In [7], the impact of power control on the random access protocol was investigated. In [8], the effect of nearfar problem and a detailed review of the power control were presented including the use of distributed algorithms. On the other hand, in [912] the concept that users of various classes should transmit at different power levels was applied. Distinct power levels were obtained with power attenuators [10], adjustable encoders/decoders [11], and adjustable transmitters [12]. Furthermore, the optimal selection of the system’s parameters such as the transmitted power and the information rate would improve their performances [9, 1315]. In [13], optical power control and time hopping for multimedia applications using single wavelength was proposed. The approach accommodates various data rates using only one sequence by changing the timehopping rate. However, in order to implement such system an optical selector device that consists of a number of optical hardlimiters is needed [13]. On the other hand, in [14] a multi rate and multi power level scheme using adaptive overlapping pulseposition modulator (OPPM) and optical power controller was proposed. The bit rate varies depending on the number of slots in the optical OPPM system and has the advantage that it is not required to change the code sequence depending on the required user’s information rate. The power level can be achieved by accommodating users with the different transmitted power. The power controller requires only power attenuator, and the difference of the power does not cause the change of the bit rate. In [15] a hybrid power and rate control nonlinear programming algorithm for overlapped optical fast frequency hopping (OFFH) was proposed. The multi rate transmission is achieved by overlapping consecutive bits while coded using fiber Bragg grating (FBG). The intensity of the transmitted optical signal is directly adjusted from the laser source with respect to the transmission data rate. The proposed algorithm provides a joint transmission power and overlapping coefficient allocation strategy, which has been obtained via the solution of a constrained optimization problem, which maximizes the aggregate system throughput subject to a peak laser transmission power constraint. In [9], a control algorithm to solve the unfairness in the resource allocation strategy presented in [10] has been analyzed. Also, a unified framework for allocating and controlling the transmission rate and power in a way that it can be applied for any expression of the system capacity was implemented.
Besides, recently researches have showed the utilization of resource allocation and optimization algorithms such as Local Search, Simulated Annealing, GA, Particle Swarm optimization (PSO), Ant Colony optimization (ACO) and Game Theory to regulate the transmitted power, bit rate variation and the number of active users in order to maximize the aggregate throughput of the optical networks [1617]. However, the complexity and unfairness in the strategies presented are aspects to be improved. On the other hand, resource allocation has not been largely investigated considering energy efficiency aspects. This issue has become paramount since energy consumption is dominated by the access segment due to the large amount of distributed network elements. The related works have showed the utilization of resource allocation and optimization algorithms to optimization of the access network; however, these issues have not been largely investigated considering routed OCDM/WDM networks [6]. In the case of the OCP networks optimization, it is necessary to consider the use of distributed iterative algorithms with high performancecomplexity tradeoffs and the imperfections of physical layer, which constitute a new research area so far, which was investigated under an analytical perspective in [6].
It is worth noting the routed OCDM/WDM networks brings a new combination of challenges with the power control, like amplified spans, multiple links, accumulation, and selfgeneration of the optical spontaneous noise power (ASE) noise, as well as the MAI generated by the OCPs. On the other hand, the dispersive effects, such as chromatic or group velocity dispersion (GVD) and polarization mode dispersion (PMD), are signal degradation mechanisms that significantly affect the overall performance of optical communication systems [6, 1821].
In this chapter, optimization procedures based on PSO are investigated in details, aiming to efficiently solve the optimal resource allocation for SNIR optimization of OCPs from OCDM/WDM networks under QoS restrictions and energy efficiency constraint problem, considering imperfections on physical constraints. Herein, the adopted SNIR model considers the MAI between the OCP based on 2D codes (time/wavelength) [22, 23], ASE at cascaded amplified spans, and GVD and PMD dispersion effects.
The optimization method based on the heuristic PSO approach is attractive due to its performancecomplexity tradeoff and fairness features regarding the optimization methods that deploy matrix inversion, purely numerical procedures and other heuristic approaches [9][17].
The chapter is organized in the following manner: in Section 2 the optical transport (OCDM/WDM) is described, while in Section 3 the SNIR optimization for the OCPs based on particle swarm intelligence is described in order to solve the resource allocation problem. In the network optimization context, figures of merit are presented and the PSO is developed in Section 4, with emphasis on its input parameters optimal choice and the network performance. Afterward, numerical results are discussed for realistic networks operation scenarios. Finally, the main conclusions are offered in Section 5.
2. Network architecture
2.1. OCDM/WDM transport network
The transport network considered in this work is formed by nodes that have optical core routers interconnected by OCDM/WDM links with optical code paths defined by patterns of short pulses in wavelengths, such as shown in Fig. 1. The links are composed by sequences of span and each span consists of optical fiber and optical amplifier. The transmitting and receiving nodes create virtual path based on the code and the total link length is given by
The optical core router consists of code converter routers in parallel forming a twodimensional router node [23] and each group of code converters in parallel is preconnected to a specific output performing routing by selecting a specific code from the incoming broadcasting traffic. This kind of router does not require light sources or opticalelectricaloptical conversion and can be scaled by adding new modules [22]. This code is transmitted and its route in the network is determined by a particular code sequence. For viability characteristics, we consider network equipment, such as codeprocessing devices (encoders and decoders at the transmitter and receiver), star coupler, optical routers could be made using robust, lightweight, and lowcost technology platforms with commercialofftheshelf technologies [2324]. For more details about transport networks the references [19],[25] should be consulted.
2.2. OCDMA codes
The OCDMA can be divided into a) noncoherent unipolar systems, based only on optical power intensity modulation [20], and b) coherent bipolar systems, based on amplitude and phase modulation [26]. As expected, the performance of coherent codes is higher than that of noncoherent ones when analyzing the SNIR [27]. This effect occurs, because the bipolar code is trueorthogonal, and the unipolar code is pseudoorthogonal. However, the main drawback to the coherent OCDMA lies in the technical implementation difficulties, concomitant with the utilization of phaseshifted optical signals [20],[27]. In this work we adopt noncoherent codes because their technological maturity and implementation easiness when compared with coherent codes [28]. The noncoherent codes can be classified into onedimensional (1D) and twodimensional (2D) codes. In the 1D codes, the bits are subdivided in time into many short chips with a designated chip pattern representing a user code. On the other hand, in the 2D codes, the bits are subdivided into individual time chips, and each chip is assigned to an independent wavelength out of a discrete set of wavelengths. The 2D codes have better performance than the 1D codes, and they can significantly enhance the number of active users [29]. Besides, the 2D codes have been applied only in access networks [2]; in this way, recently the utilization of the 2D codes to obtain optical code path routed networks was proposed, which performance evaluated by simulation, considering coding, topology, load condition, and physical impairment [2][6][20][21][22].
The 2D codes can be represented by
The OCDMA 2D encoder creates a combination of two patterns: a wavelengthhopping pattern and a timespreading pattern. The common technology applied for code encoders/decoders fiber Bragg gratings (FBGs), as show Fig. 2(b). The losses associated with the encoders/ decoders are given by
3. SNIR optimization procedures
In the present approach, the SNIR optimization is based on the definition of the minimum power constraint (also called sensitivity level) assuring that the optical signal can be detected by all optical devices. The maximum power constraint guarantees the minimization of nonlinear physical impairments, because it makes the aggregate power on a link to be limited to a maximum value. The power control in optical networks appears to be an optimization problem.
3.1. Problem description
Denoting Γ_{i} the carriertointerference ratio (CIR) at the required decoder input, in order to get a certain maximum bit error rate (BER) tolerated by the
where 1^{T} = [1,..., 1] and
The SNIR and the carrier to interference ratio in eq. (1) are related to the factor
where the average variance of the Hamming aperiodic crosscorrelation amplitude is represented by
3.2. Physical restrictions
The physical impairments are signal degradation mechanisms that significantly affect the overall performance of optical communication systems [6]. For the data that are transmitted through a transparent optical network, degradation effects may accumulate over a large distance. The major linear physical impairments are group velocity dispersion (GVD), polarization mode dispersion (PMD), and amplifier spontaneous emission (ASE) noise [24]. On the other hand, the major nonlinear physical impairments are self phase modulation (SPM), crossphase modulation (XPM), and four wave mixing (FWM), stimulated Brillouin scattering (SBS), and Raman scattering (SRS). The nonlinear physical impairments are excited with high power level [24]. However, the maximum power constraint guarantees the minimization of nonlinear physical impairments, because it makes the aggregate power on a link to be limited to a maximum value [6]. In the currently technology stage, besides GVD, the main linear impairment is the PMD, that must be considered in high capacity optical networks. Differently from GVD, PMD is usually difficult to accurately determine and compensate due to its dynamic nature and its fluctuations induced by external stress/strain applied to the fiber after installation [5][21][22]. As a result, the signals quality in an OCDM/WDM network can be quickly evaluated by analyzing the GVD, PMD and MAI restrictions. PMD impairment establishes an upper bound on the length of the optical segment due to fiber dispersion which causes the temporal spreading of optical pulses. On the other hand, due to the advances in the fiber manufacturing process with a continuous reduction of the PMD parameter, the deleterious effect of PMD will not be an issue for 10 Gbps or lower bit rates, for future small and mediumsized networks [20][21]. In this context, the dominant impairment in SNIR will be given by i) ASE noise accumulation in chains of optical amplifiers for future optical networks [29] and ii) ASE, GVD and PMD for currently stage of optical networks.
The dispersive effects, such as chromatic or group velocity dispersion (GVD) and polarization mode dispersion (PMD) constitute degradation mechanisms of the optical signal that significantly affect the overall performance of optical communication systems [21]. Currently, the PMD effect appears to be the only major physical impairment that must be considered in high capacity optical networks, which can hardly be controlled due to its dynamic and stochastic nature [5][2122]. On the other hand, the GVD causes the temporal spreading of optical pulses that limits the product line rate and link length [630]. The pulse spreading effect due to the combined effects of the GVD and the firstorder PMD for Gaussian pulses can be calculated as [30]:
where
The ASE (
This model considers that the receiver gets the signal from a link with cascading amplifiers, numbered as 1, 2,.., starting from the receiver. The preamplifier can be contemplated as the number 0 cascade amplifier. Let
Calculating recursively the
3.3. Particle swarm optimization
3.3.1. PSO description
Particle Swarm Optimization (PSO) is a populationbased stochastic optimization algorithm for global optimization that was presented first in 1995 [31]. It is based on the behavior of social groups like fish schools or bird flocks and it differs from other wellknown Evolutionary Algorithms (EA). As in EA, a population of potential solutions is used to probe the search space, but no operators, inspired by evolution procedures, are applied on the population to generate new promising solutions [32]. The fact which is recursively exploited is that an improved performance can be gained by interactions between individuals, or more specifically by imitation of successful individuals. In a PSO system, particles fly around in multidimensional search space. During the flight, each particle adjusts its position according to its own experience, and the experience of neighboring particles, making use of the best position encountered by itself and its neighbors. The swarm direction of a particle is defined by the set of particles neighboring the particle and its history experience. Although PSO does not rely on the survival of the fittest principle, it is often classified as an evolutionary algorithm (EA) because the update equations, which control the movement of individuals, are similar to the evolutionary operators used in EAs.
In general, the PSO performance for resource allocation problem can guarantee fast convergence and fairness within fewer iterations regarding the genetic algorithmbased [16]. It is well known in the literature that the PSO performance for resource allocation problem is highly dependent on its control parameters and that recommended parameter settings from the literature often do not lead to reliable and fast convergence behavior for the considered optimization problem [33], [34], [35].
In the PSO process, each particle keeps track of its coordinates in the space of interest, which are associated with the best solution (fitness) it has achieved so far. Another best value tracked by the global version of the particle swarm optimizer is the overall best value, and its location, obtained so far by any particle in the population. At each time iteration step, the PSO concept consists of velocity changes of each particle toward local and global locations. Acceleration is weighted by a random term, with separate random numbers being generated for acceleration toward local and global locations. Let
where
where
From (7) it’s clear that if
In order to elaborate further about the inertia weight it can be noted that a relatively larger value of
where
3.3.2. Optical code path resource allocation optimization
The following maximization cost function could be employed as an alternative to OCP resource allocation optimization [33]. This singleobjective function was modified in order to incorporate the nearfar effect [37], [38]
where
i.e. the more close the normalized received power values are with other (small variance of normalized received power vector), the bigger contribution of the term
where the SNIR for the
The PSO algorithm consists of repeated application of the updating velocity and position, eq. (5) and (6), respectively. The pseudocode for the singleobjective continuous PSO power allocation problem is presented in Algorithm 1.
The quality of solution achieved by any iterative resource allocation procedure could be measured by how close to the optimum solution is the found solution, and can be quantified by the normalized mean squared error (NMSE) when equilibrium is reached. For power allocation problem, the NSE definition is given by,
where
3.3.3. Energy efficiency optimization in OCPs
Recent studies have showed the importance of the consideration of energy consumption in optical communications design [39], considering the transmission infrastructure (transmitters, receivers, fibers and amplifiers) [40] and network infrastructure (switchers and routers) [41] aspects. Researches in a global scale network have indicated that the energy consumption of the switching infrastructure is larger than the energy consumption of the transport infrastructure [3941]. In this context, it is necessary to improving the energy efficiency of switching and optimizing the network design in order to reduce the quantity of switching and overheads. The energy necessary for 1 bit transmission on each OCP can be expressed as [40],
where
where
For each
where
From (16) we observe, for
To satisfy (17) it is necessary that the received node achieves the target SNIR, namely
4. Numerical results
For all simulations, it is considered the transmission over a nonzerodispersion shifted fiber (NFD)ITU G.655 with fiber attenuation (α) of 0.2 dB/km, nonlinear parameter (Γ) of 2 (W.km)^{1}, zerodispersion wavelength (λ_{0}) of 1550 nm, dispersion slope (S_{0}) of 0.07 ps/(nm^{2}.km). The signal is placed at λ_{0} and its peak power is P. Note that the nonlinear length [24] L_{NL}= 1/(ΓP) is limited to 500 km, which is much longer than the considered fiber lengths; besides selfphase modulation (SPM) should not seriously affect the system performance. Furthermore, the threshold power for stimulated Brillouin scattering (SBS) is below a few mW; as a result, SBS should also not interfere in our results. Similarly, for these considerations, the physical impairments, such as stimulated Raman scattering (SRS) should not be relevant [24]. Typical parameter values for the noise power in all optical amplifiers were assumed [21]. So, it was adopted
4.1. PSO parameters optimization for resource allocation problem
For power resource allocation problem, simulation experiments were carried out in order to determine the suitable values for the PSO input parameters, such as acceleration coefficients,
The continuous optimization for resource allocation problem was investigated in [33], [34], it indicates that after an enough number of iterations (
Finally, stopping criterion can be the maximum number of iterations
where typically
where,
The parameter
In power resource allocation problem for access network systems the parameters optimization, mainly acceleration coefficients
Numerical results have shown the solution quality for different values of acceleration coefficient
The algorithm reaches convergence for
It is worth to expand this analysis to other number of OCPs that are generally between 4 and 8 OCPs. For this purpose, Fig 6 shows the NMSE for the number of OCPs regarding two combination of acceleration coefficient: i) optimized herein (
The OCPs increasing affects the solution quality. This effect is directly related to the MAI rising which increases with the number of OCPs, i.e., the MAI effects are strongly influenced by the increase of the active OCPs; an error occurs when crosscorrelational pulses from the (
In conclusion, our numerical results for the power minimization problem have revealed for low system loading that the best acceleration coefficient values lie on
4.2. PSO optimization for OCPs
The solution quality
The results show that NMSE decreases when the number of spans increases until 6 spans, after this number of spans the NMSE alters the tendency and increases. This behavior shows the limitation of PSO convergence when the ASE increases. After 6 spans the PSO algorithm does not reach the total convergence. This fact occurs, directly by the limitations generate with the increase of the ASE. In other sense, the transmitted power needed to reach the target SNIR will overcome the maximum allowed transmitted power. The average number of spans increases slightly as
The convergence quality of the PSO algorithm presents variation with the increase in the number of spans. The figure of merit utilized as tool to this analysis is the rate of convergence (RC), which can be described as the ratio of PSO solution after the
The reader interested in quality of solution metrics, a similar definition for RC and another figure of merit for the PSO, namely success cost, are presented in [35].
Fig. 8 (a) and (b) shows the convergence rate of the sum of power for vector evolution through the 800 iterations for 4 a 8 OCPs, respectively, considering 1 until 6 spans. The results have shown that increasing span number results in a faster convergence. This fact occurs because until 6 spans the increase of the number of span increases the contribution of the amplifier with signal, besides for more than 6 spans the contribution of the amplifier is for the ASE noise. On the other hand, the increase in the number of OCPs results in a slow convergence that results from the MAI between the OCPs.
In summary, our numerical results for the power minimization problem considering different number of spans have revealed the viability of the PSO algorithm deployment to solve a power allocation in OCPs with until 6 spans in order to guarantee the solution quality in terms of NMSE. Furthermore, the numerical results revealed that increasing the number of spans results in a faster convergence. In this context, the PSO algorithm is quite suitable to solve a power allocation in OCPs that presents an average of 4 spans as reported in the literature [22].
In order to evaluate the impact of physical restrictions on the OCM/WDM network, further numerical results presented in Fig. 9 shows the sum power evolution of the PSO algorithm with respect to the number of iterations, considering a) 4 OCPs, and b) 8 OCPs. One span was considered as reference for bit rate of 10 Gbps taking into account two situations: i) with only ASE effects, ii) with ASE, GVD and PMD effects.
The target SNIR established for all the nodes is equal, and if the perfect power balancing with ideal physical layer (no physical impairments) is assumed, it could be demonstrated that the maximum SNIR and the transmitted power are defined by the number of OCPs in the same route. However, when the ASE, GVD and PMD effects are considered, there is a penalty. This penalty represents the received power reduction due to temporal spreading. Fig. 9 shows that when ASE, GVD and PMD effects are considered there is a power penalty of 3 decades compared with the situation where only ASE is considered (fibers with low PMD). Comparing Figs. 9(a) and 9(b), it could be noticed that the convergence velocity depends on the number of OCPs. The increase of OCPs from 4 to 8 affects the convergence velocity, from
In OCDM/WDM networks, the OCPs with various classes of QoS are obtained with transmission of different power levels. Distinct power levels are obtained with adjustable transmitters and it does not cause the change of the bit rate. The intensity of the transmitted optical signal is directly adjusted from the laser source with respect to the target SNIR by PSO algorithm. Table 1 shows the optimization aspects of QoS regarding different levels of SNIR considering sum power and NMSE for 4 and 8 OCPs with 1 span.








17  7.2 × 10^{13}  3.3 × 10^{8}  3.0 × 10^{18}  1.2 × 10^{7}  2.3 × 10^{8} 
20  7.6 × 10^{24}  6.0 × 10^{8}  6.2 × 10^{16}  2.8 × 10^{7}  1.2 × 10^{3} 
22  1.2 × 10^{36}  9.5 × 10^{8}  3.8 × 10^{16}  4.3 × 10^{7}  1.0 × 10^{1} 
The results in Table I show the necessary values for transmitted power, as well as the solution quality evaluation in terms of NMSE. The increase in the target SNIR results in the increase of the transmitted power, which is major for more OCPs. On the other hand, the solution quality (NMSE) decreases with the increase of SNIR target, since the number of the PSO iterations is fixed.
4.3. PSO optimization for energy efficiency in OCPs
An efficient resource allocation algorithm is needed to overcome the problem of energy efficiency and to enhance the performance and QoS of the optical network. This could be achieved via signaltonoise plus interference (SNIR) PSO optimization.
Fig. 10 shows the sum of energy per bit as a function of the rate of convergence of eq. (21) for the PSO optimization with different QoS requirements represented by SNIR target of 17, 20 and 22 dB, considering a) 4 OCPs and b) 8 OCPs, i.e., same scenario presented in the previous subsection. One can see when rate of convergence evolving, the energy per bit solution offered by the PSO algorithm convergences to the best lower values as predicted in (17).
It can be seen from Fig. 10 the impact of the PSO power allocation optimization procedure (in terms of transmitted energy per bit) on the energy efficiency improvement. The deployment of PSO with 100% of rate of convergence results in an enormous saving of energy. Indeed, with very low number of PSO iterations, rate of convergence is poor








2  0.0135  0.0120  0.3145  0.1100 
4  1.0545  1.0108  2.2295  1.8199 
The results show the impact of the number of spans in the transmitted energy per bit for the variation of rate convergence of 0.5 and 1. As expected, the increase in the number of spans and the number of OCPs results in the increase of the transmitted energy per bit. Besides, the sum energy per bit variation, regarding the RC from 0.5 to 1.0, declines with the increase of the number of spans from 2 to 4. This results agree with the previous results illustrated in Fig. 8, meaning the increase of the number of spans accelerate the RC.
5. Conclusions
In this chapter, optimization procedures based on particle swarm intelligence are investigated in details, aiming to efficiently solve the optimal resource allocation for signaltonoise plus interference ratio (SNIR) optimization of optical code paths (OCPs) from OCDM/WDM networks under quality of service (QoS) restrictions and energy efficiency constraint problem, considering imperfections on physical constraints. The SNIR model considers multiple access interference (MAI) between the OCP based on 2D codes (time/wavelength), amplifier spontaneous emission (ASE) at cascaded amplified spans, and group velocity dispersion (GVD) and polarization mode dispersion (PMD) dispersion effects. The characteristic of the particle swarm optimization (PSO) is attractive due their performancecomplexity tradeoff and fairness regarding the optimization methods that use numerical methods, matrix inversion and other heuristics. The resource allocation optimization based on PSO strategy allows the regulation of the transmitted power and the number of active OCPs in order to maximize the aggregate throughput of the OCDM/WDM networks considering QoS and energy efficiency constraint. For the network optimization context, system model was described, figures of merit were presented and a suitable model of PSO was developed, with emphasis in the optimization of input parameters and network performance. Afterward, extensive numerical results for the optimization problem are discussed taking into account realistic networks operation scenarios.
In order determine the suitable values for the PSO input parameters, such as acceleration coefficients,
References
 1.
E. Wong, “NextGeneration Broadband Access Networks and Technologies,” Journal of Lightwave Technology, vol. 30, no. 4, pp. 597 – 608, Feb., 2012  2.
H. Beyranvand and J. Salehi, “Alloptical multiservice path switching in optical code switched GMPLS core network”, Journal of Lightwave Technology, vol. 27, no. 17, pp. 2001 – 2012, Jun. 2009.  3.
H. Yin and D. J. Richardson, Optical code division multiple access communication networks: theory and applications. Berlin: SpringerVerlag and Tsinghua University Press, 2009.  4.
A. Rahbar, “Review of Dynamic ImpairmentAware Routing and Wavelength Assignment Techniques in AllOptical WavelengthRouted Networks”, IEEE Communications Surveys & Tutorials, ACCEPTED FOR PUBLICATION  5.
F. R. Durand, M. Lima and E. Moschim, “Impact of pmd on hybrid wdm/ocdm networks,” IEEE Photonics Technology Letters, vol. 17, no. 12, pp. 2787–2789, December 2005.  6.
F. R. Durand and T. Abrão, “Distributed SNIR Optimization Based on the Verhulst Model in Optical Code Path Routed Networks With Physical Constraints”, Journal of Optical Communications and Networking, vol. 3, no. 9, pp. 683–691, Sep. 2011. doi:10.1364/JOCN.3.000683  7.
F. R. Durand, M. S. Filho and T. Abrão, “The effects of power control on the optical CDMA random access protocol”, Optical Switching and Networking, (In press) doi:10.1016/j.osn.2011.06.002  8.
N. Tarhuni, T. Korhonen, M. Elmusrati and E. Mutafungwa, “Power Control of Optical CDMA Star Networks”, Optics Communications, vol. 259, pp. 655 – 664, Mar. 2006.  9.
E. Inaty, R. Raad, P. Fortier, and H. M. H. Shalaby, “A Fair QoSBased Resource Allocation Scheme For a TimeSlotted Optical OVCDMA Packet Networks: a Unified Approach,” Journal of Lightwave Technology, vol. 26, no. 21, pp. 110, Jan. 2009.  10.
E. Inaty, H. Shalaby, P. Fortie, and L. Rusch, “Optical Fast Frequency Hopping CDMA System Using Power Control”, Journal of Lightwave Tech., vol. 20, n. 2, pp.166 – 177, March 2003.  11.
C. C. Yang, J. F. Huang, and T. C. Hsu, “Differentiated service provision in optical CDMA network using power control,” IEEE Photon. Technol. Lett., vol. 20, no. 20, pp. 1664–1666, 2008.  12.
S. Khaleghi and Mohammad Reza Pakravan, Quality of Service Provisioning in Optical CDMA Packet Networks, Journal of Optical Communications and Networking, vol. 2, no. 5, pp. 283–292, Feb. 2010.  13.
H. Yashima, and T. Kobayashi “Optical CDMA with time hopping and power control for multirate networks,” J. Lightwave Technol., vol. 21, pp. 695702, March 2003.  14.
T. Miyazawa and I. Sasase “Multirate and multiquality transmission scheme using adaptive overlapping pulseposition modulator and power controller in optical network,” IEEE ICON, vol. 1, pp. 127131, November 2004.  15.
R. Raad, E. Inaty, P. Fortier, and H. M. H. Shalaby, “Optimal resource allocation scheme in a multirate overlapped optical CDMA system,” J. of Lightwave Technol., vol. 25, no. 8, pp. 2044 – 2053, August 2007.  16.
M. Tang, C. Long and X. Guan, “Nonconvex Optimization for Power Control in Wireless CDMA Networks,” Wireless Personal Communications, vol. 58, n. 4, pp. 851865, 2011.  17.
Q. Zhu. and L. Pavel, “Enabling Differentiated Services Using Generalized Power Control Model in Optical Networks”, IEEE Transactions on Communications, vol. 57, no 9, p. 1 – 6, Sept. 2009.  18.
R. Ramaswami, K. Sivarajan and G. Sasaki, Optical Networks: A Practical Perspective, Morgan Kaufmann, Boston, MA, 2009.  19.
E. Mutafungwa, “Comparative analysis of the traffic performance of fiberimpairment limited WDM and hybrid OCDM/WDM networks”, Photon Network Commun., vol. 13, pp.53–66, Jan. 2007.  20.
F R. Durand, L. Galdino, L. H. Bonani, F. R. Barbosa1, M. L. F. Abbade and Edson Moschim, “The Effects of Polarization Mode Dispersion on 2D WavelengthHopping Time Spreading Code Routed Networks”, Photonics Network Communications, vol. 20, no. 1, pp. 27 – 32, Aug. 2010. DOI 10.1007/s1110701002426.  21.
F. R. Durand, M. L. F. Abbade, F. R. Barbosa, and E. Moschim, “Design of multirate optical code paths considering polarisation mode dispersion limitations,” IET Communications, vol. 4, no. 2, pp. 234–239, Jan. 2010.  22.
CamilleSophie Brès and Paul R. Prucnal, “CodeEmpowered Lightwave Networks”, J. Lightw. Technol. , vol. 25, n. 10, pp. 2911 – 2921, Oct. 2007.  23.
YueKai Huang, Varghese Baby, Ivan Glesk, CamilleSophie Bres, Christoph M. Greiner, Dmitri Iazikov, Thomas W. Mossberg, and Paul R. Prucnal, Fellow, “Novel MulticodeProcessing Platform for WavelengthHopping TimeSpreading Optical CDMA: A Path to Device Miniaturization and Enhanced Network Functionality”, IEEE Journal of Selected Topics in Quantum Electronics, vol. 13, no. 5, pp. 1471 – 1479, september/october 2007.  24.
G. P. Agrawal, Fiberoptic communication systems, John Wiley & Sons, 2002.  25.
K. Kitayama and M. Murata, “Versatile Optical CodeBased MPLS for Circuit, Burst and Packet Switching”, J. Lightwave Technol, vol. 21, no. 11, pp. 2573 – 2764, Nov. 2003.  26.
S. Huang, K. Baba, M. Murata and K. Kitayama, “Variablebandwidth optical paths: comparison between optical codelabeled path and OCDM path”, J. Lightwave Technol., vol. 24, no. 10, pp. 3563 – 3573, Oct. 2006.  27.
Kerim Fouli e Martin Maier, “OCDMA and Optical Coding: Principles, Applications, and Challenges”, IEEE Communications Magazine, vol. 45, no. 8, pp. 27 – 34, Aug. 2007.  28.
G.C. Yang and W.C. Kwong, Prime codes with applications to CDMA optical and wireless networks, Artech House, Boston, MA, 2002.  29.
G. Pavani, L. Zuliani, H. Waldman and M. Magalhães, “Distributed approaches for impairmentaware routing and wavelength assignment algorithms in GMPLS networks”, Computer Networks, vol. 52, no. 10, pp. 1905–1915, July 2008.  30.
A. L. Sanches, J. V. dos Reis Jr. and B.H. V. Borges, “Analysis of HighSpeed Optical Wavelength/Time CDMA Networks Using PulsePosition Modulation and Forward Error Correction Techniques”, J. Lightwave Technol., vol. 27, no. 22, pp. 5134 – 5144, Nov. 2009.  31.
J. Kennedy and R.C. Eberhart, “Particle swarm optimization”, in Proceedings of IEEE International Conference on Neural Networks, Piscataway, USA, pp. 1942–1948, 1995.  32.
N. Nedjah and L. Mourelle, Swarm Intelligent Systems, Springer, SpringerVerlag Berlin Heidelberg, 2006.  33.
T. Abrão, L. D. Sampaio, M. Proença Jr., B. A. Angélico and Paul Jean E. Jeszensky, Multiple Access Network Optimization Aspects via Swarm Search Algorithms, In: Nashat Mansour. (Org.). Search Algorithms and Applications. 1 ed. Vienna, Austria: InTech, ISBN 9789533071565, 2011, v. 1, p. 261298.  34.
K. Zielinski, P. Weitkemper, R. Laur, and K. Kammeyer, “Optimization of Power Allocation for Interference Cancellation With Particle Swarm Optimization”, IEEE Transactions on Evolutionary Computation, vol. 13, no. 1, pp. 128 – 150, Feb. 2009.  35.
N. Nedjah and L. M. Mourelle. Swarm Intelligent Systems, Springer, SpringerVerlag Berlin Heidelberg, 2006.  36.
A. Chatterjee and P. Siarry, Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization, Computers & Operations Research, vol 33, no. 3, pp. 859–871.  37.
M. Moustafa, I. Habib, and M. Naghshineh, Genetic algorithm for mobiles equilibrium, MILCOM 2000. 21st Century Military Communications Conference Proceedings 2000.  38.
H. Elkamchouchi, H., EIragal and M. Makar, Power control in cdma system using particle swarm optimization, 24th National Radio Science Conference, pp. 1–8. 2007.  39.
S. Yoo, “Energy Efficiency in the Future Internet: the Role of Optical Packet Switching and Optical Label Switching”, IEEE J Selected Topics in Quantum Electronics, vol. 17, no. 2, pp. 406 – 418, MarchApril 2011.  40.
Rodney S. Tucker, "Green Optical Communications  Part I: Energy Limitations in Transport", IEEE J Selected Topics in Quantum Electronics, vol. 17, no. 2, pp. 245 – 260, MarchApril 2011.  41.
Rodney S. Tucker, "Green Optical Communications  Part II: Energy Limitations in Networks", IEEE J Selected Topics in Quantum Electronics, vol. 17, no. 2, pp. 261 – 274, MarchApril 2011.  42.
D. Goodman and Narayan Mandayan “Power control for wireless data”, IEEE Personal Communications, vol. 7, no. 2, pp. 48 – 54, April 2000.