Paul Jean Etienne Jeszensky

Particle Swarm and Quantum Particle Swarm Optimization Applied to DS/CDMA Multiuser Detection in Flat Rayleigh Channels

The particle swarm and quantum particle swarm optimization (PSO and QPSO) techniques applied to direct sequence/code division multiple access systems (DS/CDMA) with multiuser detection (MuD) are analyzed, evaluated and compared. The swarm techniques efficiency when applied to the DS-CDMA MuD (PSO-MuD and QPSO-MuD) in flat Rayleigh channels is compared through the trade-off between performance and computational complexity. With the same simulation scenario, the comparison is accomplished among the PSO-MuD, QPSO-MuD, genetic algorithm (GA) and evolutionary programming with cloning (EP-C) algorithms. The complexity of these four heuristics-MuD (heur-MuD) is compared, expressing it through the number of computational operations necessary in order to reach the performance obtained through the maximum likelihood detector (ML)

S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

This paper analyzes the complexity-performance trade-off of several heuristic near-optimum multiuser detection (MuD) approaches applied to the uplink of synchronous single/multiple-input multiple-output multicarrier code division multiple access (S/MIMO MC-CDMA) systems. Genetic algorithm (GA), short term tabu search (STTS) and reactive tabu search (RTS), simulated annealing (SA), particle swarm optimization (PSO), and 1-opt local search (1-LS) heuristic multiuser detection algorithms (Heur-MuDs) are analyzed in details, using a single-objective antenna-diversity-aided optimization approach. Monte- Carlo simulations show that, after convergence, the performances reached by all near-optimum Heur-MuDs are similar. However, the computational complexities may differ substantially, depending on the system operation conditions. Their complexities are carefully analyzed in order to obtain a general complexity-performance framework comparison and to show that unitary Hamming distance search MuD (uH-ds) approaches (1-LS, SA, RTS and STTS) reach the best convergence rates, and among them, the 1-LS-MuD provides the best trade-off between implementation complexity and bit error rate (BER) performance.

Hybrid heuristic-waterfilling game theory approach in MC-CDMA resource allocation

Abstract: This paper discusses the power allocation with fixed rate constraint problem in multi-carrier code division multiple access (MC-CDMA) networks, that has been solved through game theoretic perspective by the use of an iterative water-filling algorithm (IWFA). The problem is analyzed under various interference density configurations, and its reliability is studied in terms of solution existence and uniqueness. Moreover, numerical results reveal the approach shortcoming, thus a new method combining swarm intelligence and IWFA is proposed to make practicable the use of game theoretic approaches in realistic MC-CDMA systems scenarios. The contribution of this paper is twofold: (i) provide a complete analysis for the existence and uniqueness of the game solution, from simple to more realist and complex interference scenarios; (ii) propose a hybrid power allocation optimization method combining swarm intelligence, game theory and IWFA. To corroborate the effectiveness of the proposed method, an outage probability analysis in realistic interference scenarios, and a complexity comparison with the classical IWFA are presented.

Evolutionary programming with cloning and adaptive cost function applied to multi-user DS-CDMA systems

A new utilization form for the evolutionary programming (EP) algorithm applied to multi-user DS-CDMA (direct sequence code division multiple access) systems in a synchronous AWGN (additive white Gaussian noise) channel is proposed. In order to find the maximum likelihood (ML) solution, the variance matrix for the generation of new candidates was expressed as a function of the following parameters: E/sub b//N/sub 0/; near-far ratio (NFR); cost function (fitness value). A cloning strategy procedure was introduced where the best vectors with the bit-candidates were cloned, reducing drastically the required time for the algorithm convergence to the single user bound (SuB) performance. An improvement in the convergence process was obtained using a parallel interference canceller with soft decision (SD-PIC) for initial vector generation.

Statistically correct simulation models for the generation of multiple uncorrelated Rayleigh fading waveforms

An ergodic simulation model for the generation of multiple time uncorrelated Rayleigh waveforms with correct ensemble averages is proposed. This model uses the inverse discrete Fourier transform to generate the transmission coefficients with only one random number generator. Two recent modified versions of the Jakes and Smiths models, with correct ensemble averages, are analysed and compared with the proposed model. Analytical and simulation results show that the proposed model is ergodic in mean and autocorrelation, as is the modified Smiths model. Simulation results also show that the modified Jakes model requires the highest computation effort among the three analysed models. As a result, this model become less efficient in simulation.

Energy and spectral efficiencies trade-off with filter optimisation in multiple access interference-aware networks

In this contribution, the optimised deployment of both spectrum and energy resources scarcely available in the mobile multiple access systems has been analysed, with special attention to the impact of the filter design on the energy efficiency EE of code division multiple access networks. Putting into perspective two conflicting metrics, namely throughput maximisation and power consumption minimisation, the distributed EE utility function is formulated. We also show that the best EE versus spectral efficiency trade-off is achievable when each node allocates exactly the power necessary to attain the maximum EE. In order to demonstrate the validity of our analysis, a low-complexity energy-spectral-efficient algorithm based on distributed instantaneous signal-to-interference-plus-noise ratio level is developed, and the impact of single and multi-user detection filters on the EE-spectral efficiency trade-off is extensively analysed by numerical simulation. Copyright

Multiple Access Network Optimization Aspects via Swarm Search Algorithms

This chapter explores the application of particle swarm optimization (PSO) search algorithm in two different optimization aspects of code division multiple access (DS/CDMA) systems, namely multiuser detection and resource allocation problem. In the first problem, a discrete PSO version is considered while in the latter, a continuous PSO algorithm is adopted. In both cases, the input parameters, specially the weight factors (acceleration coefficients), are optimized for each considered scenario. The motivation to use heuristic search algorithms is due to the nature of the NP complexity posed by the wireless network optimization problems. Hence, from the practical engineering point-of-view, the challenge is to obtain suitable performances in solving those hard complexity problem in a polynomial time, allowing the algorithm implementation using modern digital signal processing (DSP) platforms. Previous results indicated that the application of heuristic search algorithm in several wireless optimization problems have been achieved excellent performance-complexity tradeoffs, particularly the use of genetic algorithm (GA), evolutionary program (EP), particle swarm optimization (PSO), and local search (LS). For the heuristic multiuser detection (Heur-MUD) problem, the performance is extensively evaluated and characterized under single input single/multiple output (SISO/SIMO) flat Rayleigh channels, as well as frequency selective (multipath) Rayleigh channels. For this class of problem, single-objective optimization criterion (SOO) and a discrete PSO algorithm are adopted. Extensive simulations are carried out in order to demonstrate that, after convergence, the performance reached by all the analyzed Heur-MUD is much better than the conventional detector (CD), and somewhat close to the single-user bound (SuB), with the advantage of the computational complexities substantially lower than optimum multiuser detection (OMUD) and not excessively higher than CD complexity. For the heuristic resource allocation (Heur-RA) problem, there are several challenging optimization problems associated. The total network power minimization subject to multiclass information rate constraints, as well as the problem of throughput maximization subject to the transmitted power limitation are considered in this chapter. Multirate users associated with different types of traffic are aggregated to distinct classes of users, with the assurance of minimum target rate allocation per user and quality of service (QoS). For this class of problem, the SOO methodology is also carried out using a continuous PSO search algorithm, aiming to achieve promising performance-complexity tradeoffs. The numerical results are promising in terms of sum rate maximization while simultaneously minimizing the total power allocated to the multirate mobile terminals.

Energy Efficient OFDMA Networks Maintaining Statistical QoS Guarantees for Delay-Sensitive Traffic

An energy-efficient design is proposed under specific statistical quality-of-service (QoS) guarantees for delay-sensitive traffic in the downlink orthogonal frequency-division multiple-access networks. This design is based on Wus effective capacity (EC) concept, which characterizes the maximum throughput of a system subject to statistical delay-QoS requirements at the data-link layer. In the particular context considered, our main contributions consist of quantifying the effective energy-efficiency (EEE)-versus-EC tradeoff and characterizing the delay-sensitive traffic as a function of the QoS-exponent θ, which expresses the exponential decay rate of the delay-QoS violation probabilities. Upon exploiting the properties of fractional programming, the originally quasi-concave EEE optimization problem having a fractional form is transformed into a subtractive optimization problem by applying Dinkelbachs method. As a result, an iterative inner-outer loop-based resource allocation algorithm is conceived for efficiently solving the transformed EEE optimization problem. Our simulation results demonstrate that the proposed scheme converges within a few Dinkelbach algorithms iterations to the desired solution accuracy. Furthermore, the impact of the circuitry power, the QoS-exponent, and the power amplifier inefficiency is characterized numerically. These results reveal that the optimally allocated power maximizing the EEE decays exponentially with respect to both the circuitry power and the QoS-exponent, while decaying linearly with respect to the power amplifier inefficiency.

Simplified Local Search Algorithm for Multiuser Detection in Multipath Rayleigh Channels

This work intends to show that the simple 1-optimum local search (LS) technique is enough to solve the multiuser detection problem with an excellent performance-complexity trade-off. Based on the deterministic behavior of the LS algorithm and some simplifications in the cost function calculation, a more efficient algorithm can be obtained and also a new perspective about the multiuser detector implementation. The performance and complexity analysis are evaluated considering a DS/CDMA system under multipath fading channels.

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

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.