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In machine-type communication (MTC), massive access attempts are generated and the massive MIMO is the key technology to support this demand. To support massive MTC (mMTC), the recent extra-large scale massive multiple-input multiple-output (XL-MIMO) architecture has been seen as a promising technology for providing very high-data rates in high-user density scenarios. Therefore, the large dimension is of the same order as the distances to the user equipment (UE) causing spatial non-stationarities and visibility regions (VRs) to occur across the huge XL array extension. In this work, we investigate the random access (RA) problem in crowded mMTC XL-MIMO scenarios; the proposed grant-based random access (GB-RA) protocol combining the advantage of non-orthogonal multiple access (NOMA) and the strongest user collision resolution in extra-large arrays (SUCRe-XL), namely NOVR-XL scheme can allow access of multiple colliding users in the same XL subarray (SA) selecting the same pilot sequence. The proposed NOVR-XL GB-RA protocol is able to provide a reduction in the number of attempts to access the network, while improving the average sum-rate, as the number of SA increases.

Extra-large multiple-input multiple-output (XL-MIMO) systems reveal themselves as a potential candidate for the sixth generation (6G) of wireless communication systems due to their features. As XL-MIMO has a large antenna array, and, typically, the urban environment is plenty of obstacles and reflectors, the spatial non-stationarities are introduced in the signal received at the base station (BS), which means that only a portion of the antenna array is visible to users; hence, accurately modeling XL-MIMO channels is paramount. Previous works on XL-MIMO channel modeling have adopted the non-stationarities only in a spatial sense, and do not consider spatial-time evolution scenarios. Moreover, simplified models with only one set of clusters between the BS and the user equipment (UE) are usually adopted; hence, there is a lack of understanding regarding such channels modeling in the literature. This work proposes and extensively analyzes a double-scattering XL-MIMO channel model, by admitting two types of scattering clusters, one placed at the BS side, and another one located near the UEs. In addition, two distinct antenna array configurations are included in the analysis, the uniform linear and planar arrays (ULA and UPA). We propose a new double-scattering channel model under UPA arrangements, suitable for modeling spatial non-stationarities scenarios in XL-MIMO dynamic environment subject to BS-cluster and UE-cluster correlation, and birth-death channel clusters and scatterers. Numerical results for signal-to-interference-plus-noise ratio (SINR), condition number (CN), and spectral efficiency (SE) performance metrics considering different XL-MIMO channels and system configurations are analyzed via Monte-Carlo simulations, under linear combiners MRC, ZF, and MMSE. Also, we characterize the impact of the number of visible clusters per user, the birth-death rate growth effect on the channel clusters and scatterers, and the favorable propagation effect according to the size of visibility region (VR) overlap. It can be observed that the birth and death processes have a significant impact on the system performance. Under the proposed clustered double-scattering channel modeling, the analyzed XL-MIMO linear receivers presented an SINR degradation around 3 to 4 dB for the MMSE when the number of UEs substantially increased, while the ZF and MRC receivers present a decrease of 1 to 2 dB, approximately, in SINR for the considered dynamic configuration compared with static scenarios.

Machine-type communications (MTC) should account for half the connections to the internet by 2030. The use case massive MTC (mMTC) allows for applications to connect a massive number of low-power and low-complexity devices, leading to challenges in resource allocation. Not only that, mMTC networks suffer under rigid random access schemes due to mMTC ultra-dense nature resulting in poor performance. In this sense, this paper proposes a $Q$ -Learning-based random access method for massive machine-type communications, with device clustering and non-orthogonal multiple access (NOMA). The traditional NOMA implementation increases spectral efficiency, but at the same time, demands a larger $Q$ -Table, thus slowing down convergence, which is known to be a highly detrimental effect on massive networks. We use pre-clustering through short-range device-to-device technology to mitigate this drawback, allowing devices to operate with a smaller $Q$ -Table. Furthermore, the previous selection of partner devices allows us to implement a full-feedback-based reward mechanism so that clusters avoid time slots already successfully allocated. Additionally, to cope with the negative impact of system overload, we propose an adaptive frame size algorithm to run in the base station (BS). It allows adjusting the frame size to the network load, preventing idle slots in an underloaded scenario, and providing extra slots when the network is overloaded. The results show the great benefits in terms of throughput of the proposed method. In addition, the impact of the use of clustering and the size of the clusters, as well as the frame size adaptation, are analyzed.

In this paper, we investigate the random access (RA) problem in a massive MIMO scenario. To deal with the continuous increase in the number of users, and the difficulty associated with sharing pilots among such users in order to organize channel access, a strongest-user collision resolution (SUCRe) protocol is modified to include a non-orthogonal multiple access (NOMA) approach. The proposed NOMA-RA protocol allows to resolve collisions between users who try to access the medium using the same pilot signal. Numerical results show that the proposed NOMA-RA strategy achieves a better performance in terms of sum-rate with lower average delay in comparison with the traditional SUCRe scheme, even considering imperfect channel estimation and successive interference cancellation. For instance, in a scenario when 10 pilot sequences are available and 20 devices compete to access the channel, comparing with the SUCRe protocol, the average delay is reduced by 30% while the sum-rate is improved by 33% and 22% considering, respectively, perfect and imperfect SIC decoders. In addition, the percentage of users that fail to access the BS in this situation drops from 66.5% with SUCRe to 38.5% with the proposed NOMA-RA scheme.

In this paper, we propose a dynamical power allocation (PA) procedure for elastic optical networks (EONs) based on the evolutionary hurricane search optimization (HSO) algorithm with a chaotic logistic map diversification strategy with the purpose of improving the capability to escape from local optima, namely PA-CHSO. The aiming is the dynamical control of the transmitted optical powers according to the variations of each link state due to traffic fluctuations, channel impairments, as well as other channel-power coupling effects. Such realistic EON scenarios are affected mainly by the channel estimation inaccuracy, channel ageing and power fluctuations. The link state is based on the channel estimation and quality of transmission (QoT) parameters obtained from the optical performance monitors (OPMs). Numerical results have demonstrated the effectiveness of the PA-CHSO to dynamically mitigate the power penalty under real measurement conditions with uncertainties and noise, as well as when perturbations in the optical transmit powers are considered.

Detection in multiple-input-multiple-output (MIMO) wireless communication systems is a crucial procedure in receivers since the multiple access transmission schemes generate interference due to the simultaneous transmission along with the several antennas, unlike single-input-single-output (SISO) transmission schemes. Precoding is a technique in MIMO systems used to mitigate the effects of the channel over the received signal. Hence, it is possible to adjust continuously the transmitted information to reverse the effect of the wireless channel at the receiver side. In this work, linear sub-optimal detectors and precoders for massive MIMO (M-MIMO) systems are implemented, analyzed, and compared in terms of performance-complexity trade-off. It is also being considered numerical results in both channel scenarios: a) receiver and transmitter have perfect channel state information (CSI); b) complex channel coefficients are estimated with different levels of inaccuracy. Monte-Carlo simulations (MCS) reveal that linear zero-forcing (ZF) and minimum mean squared error (MMSE) massive MIMO detectors result in a certain robustness against multi-user interference when operating under low and medium system loading, L = K/M, thanks to the favourable propagation phenomenon arising in massive MIMO systems.

Visible light communication (VLC)-based localization is a potential candidate for a wide range of indoor localization applications. In this paper, we propose a VLC architecture based on orthogonal frequency division multiplexing (OFDM) with multiple functionalities integrated into the same system, i.e., the 3-D receiver location, the control of the room illumination intensity, as well as the data transmission capability. In this paper, we propose an original methodology for LED power discrimination applying spatial optical OFDM (SO-OFDM) structure for position estimation. The hybrid locator initially makes a first estimate using a weighted angle-of-arrival (WAoA)-based locator, which is then used as the starting point of the recursive estimator based on the strength of the received signal. Hence, the first stage is deployed to increase the convergence probability, reducing the root-mean-square error (RMSE) and the number of iterations of the second stage. Also, a performance versus computational complexity comparative analysis is carried out with the parameter variations of these estimators. The numerical results indicate a decade improvement in the RMSE for every two decades of decrement of power noise on the receiver photodiode. The best clipping factor is obtained through the analysis of locator accuracy and transmission capacity for each simulated system. Finally, the numerical results also demonstrate effectiveness, robustness, and efficiency of the proposed architecture.

One of the downsides of the massive multiple-input-multiple-output (M-MIMO) system is its computational complexity. Considering that techniques and different algorithms proposed in the literature applied to conventional MIMO may not be well suited or readily applicable to M-MIMO systems, in this paper, the application of different formulations inside the convex optimization framework is investigated. This paper is divided into two parts. In the first part, linear programming, quadratic programming (QP), and semidefinite programming are explored in an M-MIMO environment with high-order modulation and under realistic channel conditions, i.e., considering spatial correlation, error in the channel estimation, as well as different system loading. The bit error rate is evaluated numerically through Monte Carlo simulations. In the second part, algorithms to solve the QP formulation are explored, and computational complexity in terms of floating-point operations (flops) is compared with linear detectors. Those algorithms have interesting aspects when applied to our specific problem (M-MIMO detection formulated as QP), such as the exploitation of the structure of the problem (simple constraints) and the improvement of the rate of convergence due to the well-conditioned Gram matrix (channel hardening). The number of iterations is higher when the number of users $K$ becomes similar to the number of base station antennas $M$ (i.e., $K\approx M$ ) than the case $K\ll M$ ; the number of iterations increases slowly as the number of users $K$ and base station antennas $M$ increases while keeping a low system loading. The QP with projected algorithms presented better performance than minimum mean square error detector when $K\approx M$ and promising computational complexity for scenarios with increasing $K$ and low system loading.

With the ever-growing data traffic in computer networks nowadays, the management of large-scale networks is a challenge for guaranteeing the quality of the provided services. This is due to the increasingly usage of connected applications, such as Internet of Things and cloud computing environments. Software-defined networking (SDN) is a new paradigm that aims to make this management process easier by centralizing the configuration of all network devices into a single programmable central controller. However, as any centralized service, this architecture is susceptible to security vulnerabilities, such as distributed denial of service (DDoS) and port scan attacks. Thus, security methods are necessary to guarantee the normal operation of SDN's central controller. Furthermore, networks are transporting an increasingly amount of information day by day, which could mean data loss in case of long network unavailability. For this reason, security mechanisms must operate online, with fast-responding countermeasures to mitigate the impact of the detected attacks over the SDN. In this paper, we present a fast SDN defense system against DDoS and port scan attacks, which runs directly into the central controller and uses a game theoretical approach for attack mitigation. For the detection, we compare three different approaches, particle swarm optimization, multi-layer perceptron neural network, and discrete wavelet transform. We test our approach over IP flow data generated over Mininet network emulator, along with floodlight controller, and the presented defense system achieved good outcomes for both detection and mitigation processes.

We investigate the uplink (UL) performance of multicell massive MIMO (maMIMO) communication systems in frequency-selective fading channels, when the channel estimates are corrupted by pilot contamination, and the receiver employs maximum-ratio combining (MRC). Specifically, two fundamental schemes (receivers/waveforms) are considered: the time-reversal MRC (TRMRC) under single-carrier, and the conventional frequency-domain MRC (FDMRC) for orthogonal frequency-division multiplexing. We first derive approximate analytical expressions for the UL signal-to-interference-plus-noise ratio (SINR) performance of maMIMO systems, deploying both TRMRC and FDMRC. Then, lower bounds for the achievable rates of both systems are presented, and the derived expressions are validated by means of Monte–Carlo simulations. The performance of the investigated systems are compared regarding the variation of several parameters, such as the number of base station (BS) antennas $M$ , number of users for each cell $K$ , UL transmit power $\rho _{u}$ , and the number of taps of the time-dispersive channel $L$ . Our results demonstrate that the attained SINR performance of both schemes are very similar, but, in terms of achievable rates, TRMRC in general outperforms FDMRC by not requiring cyclic prefix transmission. On the other hand, our computational complexity analysis demonstrates that the TRMRC scheme demands somewhat higher processing resources ( $\mathcal {O}(LMK)$ ) than FDMRC ( $\mathcal {O}(MK)$ ). Thus, we propose a reduced complexity implementation for the TRMRC receiver, by employing the fast convolution with overlap-and-add technique, which is able to achieve the same result of TRMRC with a complexity of $\mathcal {O}(\log _{2}(2L)MK)$ . For a practical maMIMO setup, the complexity reduction is about 50%. Finally, based on the derived expressions, we propose an iterative way to obtain the number of users that is able to maximize the sum rate of the cell, and investigate its behavior with the variation of different parameters, such as number of antennas, data, and pilot transmit power, as well as different frequency and pilot reuse schemes.

The ever expanding the usage of cloud computing environments, connected applications and Internet of Things-based devices have progressively increased the amount of data that travels through our networks. Software-defined network (SDN) is an emergent paradigm that aims to support next-generation networks through its flexible and powerful management mechanisms. One of the biggest threats faced by these services nowadays is security management. Attacks based on the denial of service (DoS) are particularly efficient against this paradigm due to its centralized control characteristic. Once this controlling system receives a massive amount of malicious requests, the overall performance of the network operation is impaired. Although several researches propose to address this problem, most of them are reactive approaches, detecting the attacks and warning the network administrators, i.e., after the network is already compromised. This paper presents an autonomic DoS/DDoS defensive approach for SDNs called Game Theory (GT)-Holt-Winters for Digital Signature (HWDS), which unites the anomaly detection and identification provided by an HWDS system with an autonomous decision-making model based on GT. Real collected data and simulated attacks are used by the system to measure its effectiveness and efficiency. Furthermore, we also use a heuristic Fuzzy-GADS method for anomaly detection instead of HWDS, aiming to compare the achieved performance and evaluate the behavior of the presented game theoretical approaches a standalone mitigation module.

We propose a spectrally efficient design that guarantees the statistical delay quality-ofservice (QoS) for delay-sensitive traffic in the downlink of orthogonal frequency-division multiple-access (OFDMA) networks. This design is based on the so-called effective capacity (EC) concept, which describes the maximum throughput, a system can achieve under a specific statistical delay-QoS violation probability constraint. We investigate the EC maximization problem, in which, the statistical delay profile of the traffic is characterized by the QoS-exponent θ determining the exponential decay rate of the delay-QoS violation probability. By exploiting the properties of concave programming and Slater's condition, the Lagrangian dual decomposition method is applied and an iterative algorithm that does not depend on the instantaneous channel state information (CSI) is proposed for solving the concave problem formulated. Extensive simulations demonstrate the efficacy and robustness of the proposed iterative algorithm. Furthermore, we show that the system's achievable EC does not depend on the specific choice of the subcarrier allocation, but rather on the number of subcarriers allocated to each user. This is, because, the EC is calculated using the channel's statistics, instead of the instantaneous CSI, implying that the EC is more of a long term channel capacity metric.

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 Wu's 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 Dinkelbach's 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 algorithm's 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.

This article discusses computational implementation aspects and performance of a Bayesian methodology, namely particle filter (PF). The PF channel estimation technique is directly applied to the channel coefficients estimation of DS/CDMA systems. Simulation results for non-line-of-sight (NLOS) Rayleigh fading channel propagation have indicated that the bootstrap PF estimator is capable to provide RMSE in the range of [10-3 ; 10-2] for a wide range of multiple access interference (MAI) levels and signal-noise ratio (SNR), and still be able to offer robustness to near-far ratio (NFR) effect.

The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, grant-free (GF) random access (RA) protocols are seen as a promising solution due to the small amount of data that MTC devices usually transmit. In this paper, we propose a GF RA protocol based on a multi-agent reinforcement learning approach, applied to aid IoT devices in selecting the least congested RA pilots. The rewards obtained by the devices in collision cases resemble the congestion level of the chosen pilot. To enable the operation of the proposed method in a realistic B5G network scenario and aiming to reduce signaling overheads and centralized processing, the rewards in our proposed method are computed by the devices taking advantage of a large number of base station antennas. Numerical results demonstrate the superior performance of the proposed method in terms of latency, network throughput, and per-device throughput compared with other protocols.

Fifth-generation (5G) and beyond networks are expected to serve large numbers of user equipments (UEs). Grant-based random access (RA) protocols are efficient when serving human users, typically with large data volumes to transmit. The strongest user collision resolution (SUCRe) is the first protocol that effectively uses the many antennas at the 5G base station (BS) to improve connectivity performance. In this paper, our proposal involves substituting the retransmission rule of the SUCRe protocol with a neural network (NN) to enhance the identification of the strongest user and resolve collisions in a decentralized manner on the UEs’ side. The proposed NN-based procedure is trained offline, admitting different congestion levels of the system, aiming to obtain a single setup able to operate with different numbers of UEs. The numerical results indicate that our method attains substantial connectivity performance improvements compared to other protocols without requiring additional complexity or overhead. In addition, the proposed approach is robust regarding variations in the number of BS antennas and transmission power while improving energy efficiency by requiring fewer attempts on the RA stage.

Abstract A neighborhood-restricted mixed Gibbs sampling (MGS)-based approach is proposed for low-complexity high-order modulation large-scale multiple-input multiple-output (LS-MIMO) detection. The proposed LS-MIMO detector applies a neighborhood limitation (NL) on the noisy solution from the MGS at a distance d — thus, named d-simplified MGS (d-sMGS) — in order to mitigate its impact, which can be harmful when a high-order modulation is considered. Numerical simulation results considering 64-QAM demonstrated that the proposed detection method can substantially improve the MGS algorithm convergence, whereas no extra computational complexity per iteration is required. The proposed d-sMGS-based detector suitable for high-order modulation LS-MIMO further exhibits improved performance × complexity tradeoff when the system loading is high, i.e., when K N ≥ 0.75 $\frac {K}{N}\geq 0.75$ . Also, with increasing the number of dimensions, i.e., increasing number of antennas and/or modulation order, a smaller restriction of 2-sMGS was shown to be a more interesting choice than 1-sMGS.

This work deals with power and spectrum allocation approaches for massive MIMO (M-MIMO) systems. An analysis is made to verify the efficiency of the solutions provided by schemes used for the spectral and energy efficiency (SE-EE) trade-off problem in massive antenna-based wireless communication systems. We first introduce the Geometry Based Stochastic Model (GBSM) channel model (One-ring model), describing the behavior of a uniform linear array of antennas (ULA) arrangement, revealing how the channel parameters affect the channel capacity. We also show that under this model, the SE still increases without boundary when the massive number of base-station (BS) antennas $M$ increases, provided that pilot contamination is substantially mitigated or eliminated but when the number of users equipment (UEs) $K$ increases with a fixed number of antennas in the BS, there is a increasing limitation from the combiners in mitigating the inter-user interference, making decoding difficult. The downlink (DL) M-MIMO scenario is analyzed, by introducing the generalized power allocation problem and derived the max-min fairness scheme from it. We propose a procedure to solve the max-min problem and

A demanda por maiores taxas de dados pode ser satisfeita pela melhoria da eficiência espectral fornecida pelos sistemas multiple-input multiple-output de larga escala. No entanto, a contaminação piloto continua a ser um problema fundamental para obter a eficiência espectral primordial em tais sistemas. Isto propiciou a pesquisa de diversos métodos para mitigar a contaminação piloto. Um desses procedimentos é baseado na coordenação das células, culminando em propostas com múltiplas fases de treinamento piloto. Este artigo tem como objetivo ampliar os resultados do trabalho original, através do qual os conceitos de grandes fases de treinamento piloto foram oferecidos. A avaliação de tal método foi realizada por meio de resultados numéricos mais compreensíveis, em que um grande número de antenas foi assumido e expressões mais rigorosas de eficiência espectral foram usadas. As abordagens de estimativa de canal baseadas em múltiplas fases de treinamento piloto foram consideradas problemáticas para implementação e uma solução desinteressante para superar a contaminação piloto; contradizendo os resultados apresentados no artigo original.

This work analyzes the performance of implementable detectors for the multiple‐input multiple‐output (MIMO) orthogonal frequency division multiplexing (OFDM) technique under specific and realistic operation system conditions, including antenna correlation and array configuration. A time‐domain channel model was used to evaluate the system performance under realistic communication channel and system scenarios, including different channel correlation, modulation order, and antenna array configurations. Several MIMO‐OFDM detectors were analyzed for the purpose of achieving high performance combined with high capacity systems and manageable computational complexity. Numerical Monte Carlo simulations demonstrate the channel selectivity effect, while the impact of the number of antennas, adoption of linear against heuristic‐based detection schemes, and the spatial correlation effect under linear and planar antenna arrays are analyzed in the MIMO‐OFDM context.

In this work key concepts on coding division multiple access (CDMA) communication systems have been discussed. The sequence selection impact on the performance and capacity of direct sequence CDMA (DS/CDMA) systems under AWGN and increasing system loading, as well as under multiple antennas channels was investigated.

Este trabalho analisa pela primeira vez o sincronismo e o controle caótico aplicados ao circuito eletrônico da partícula em mesa vibratória. Esta análise foi realizada a partir da implementação da técnica de sincronismo por realimentação de erro em sistemas de comunicação caóticos. A robustez do acoplamento entre os dois sistemas foi verificada experimentalmente nas condições de descasamento de parâmetros (sinal senoidal e sinal DC). Resultados experimentais comprovam a robustez destes sistemas em aplicações com requisitos de sincronismo entre transmissor e receptor.

This contribution analyses the performance of multiple-input-multiple-output (MIMO) detectors under correlated channels. Two MIMO detection principles, namely minimum mean squared error (MMSE) detector – with and without ordered successive interference cancellation (OSIC) – and the lattice reduction (LR) technique, are analysed and compared with the maximum likelihood (ML) limit under specific scenarios of interest: (a) increasing spectral efficiency, by increasing number of antennas; (b) increasing correlated fading channel indexes. In this context, the ML MIMO detector performance are used as reference in order to compare how the performances of those sub-optimal detectors are close to the optimum performance.

This paper analyzes the method of power allocation for wireless communication networks, limited by multiple access interference under fading Rayleigh channels. The method takes into account the statistical variation of the desired and interfering signals power and optimally and dynamically allocates the power resources in the system. This allocation is made being taken into account the restrictions imposed by the outage probability for each receiver/transmitter pair. Based on the Perron-Frobenius eigenvalue and geometric programming (GP) theories, some interesting results were found for the optimization problem analyzed. A GP is a special type of optimization problem that can be transformed in a non-linear convex optimization problem simply by the change of variables, and then to be globally solved. Also, an iterative method for fast convergence was shown to find the optimally power allocation seeking to minimize the outage probability in the communication.

This paper analyses and compares relay selection schemes in different topologies, whose goal work is to characterize each method according to its performance. Links in two-way channels are used to increase spectral efficiency of the system, and multiple relays are combined to increase the diversity order. The conclusions are based on simulations in different scenarios, with the trade-off complexity versus performance in all the methods.

This work explores the heuristic optimization algorithm based on ant colonies (ACO), deployed on complex optimization problems, aiming to achieve an iterative and feasible method which is able to solve NP and NP-Hard problems related to wireless networks. Furthermore, the convergence and performance of the Ant Colony Optimization algorithm for continuous domains are addressed through dozens of benchmark functions, which in turn, differ on each other regarding the number of dimensions and the difficulty w.r.t. the optimization (number of local optima). Finally, the applicability of the ACO is depicted in an minimum power control problem for CDMA networks.

The use of Pilates-based exercises for trunk strengthening has been reported in the literature. The objective of this study was to analyze and compare the electrical activity of the rectus abdominis and external oblique muscles during a traditional abdominal exercise program and an exercise program based on the Pilates method using a ball and an elastic band. The sample was composed of 10 healthy women, non-practitioners of Pilates, who performed the traditional abdominal exercise and roll-up with the ball and elastic band. The sign was normalized by the electromyographic peak of the dynamics activity and was adjusted for 2000 samples/s; the filter was set in a frequency band from 20 to 450 Hz. In the comparison between exercises, the external oblique muscle in the concentric phase had a higher recruitment in the roll-up with the ball (P =0.042). In the comparison between muscles in each exercise, the rectus abdominis showed a higher activation in the concentric phase (P = 0.009) and in the eccentric phase (P = 0.05) of the traditional abdominal exercise. Activation percentages ranged from 15% to 22%. The traditional abdominal exercise had the largest activation percentage.

Exercícios baseados no método Pilates são relatados na literatura para serem utilizados como proposta de fortalecimento de tronco. O objetivo deste estudo foi analisar e comparar a atividade elétrica dos músculos reto abdominal e oblíquo externo, durante um exercício de abdominal tradicional e um exercício baseado no método Pilatescom bola e com faixa elástica. A amostra foi composta por 10 mulheres saudáveis não praticantes de Pilates, que realizaram o exercício de abdominal tradicional e Roll-up com bola e faixa elástica. O sinal foi normalizado pelo pico eletromiográfico da atividade dinâmica e foram ajustados para 2000 amostras por segundo e o filtro em uma frequência de passagem de 20 a 450 Hz. Na comparação entre exercícios, o músculo oblíquo externo na fase concêntrica teve maior recrutamento no Roll-up com bola (P = 0,042). Na comparação entre os músculos em cada exercício, o reto abdominal teve maior ativação nas fases concêntrica (P =0,009) e excêntrica (P = 0,05) do abdominal tradicional. As porcentagens de ativação variaram de 15% a 22%. O exercício de abdominal tradicional teve a maior porcentagem de ativação.

In this work, the Verhulst model and the Perron-Frobenius theorem are applied on the power control problem which is a concern in multiple access communication networks due to the multiple access interference. This paper deals with the performance versus complexity tradeoff of both power control algorithm (PCA), as well as highlights the computational cost aspects regarding the implementability of distributed PCA (DPCA) version for both algorithms. As a proof-of-concept the DPCA implementation is carried out deploying a commercial point-floating DSP platform. Numerical results in terms of DSP cycles and computational time as well indicate a) feasibility of implementing the PCA-Verhulst model in 2G and 3G cellular systems; b) high computational cost for the PCA-Perron-Frobenius model.

The aim of this study was to compare the electromyographic indices of fatigue (slope of median frequency) calculated with the fast Fourier transform (FFT) and wavelet transform (WT) in trained and untrained individuals during cycle exercise. A second objective was to compare the variance of the spectral parameters (median frequency - MF) obtained by the FFT and WT during exercise. Twelve cyclists and non-cyclists performed a maximal incremental test to determine the peak power (Wp) and electromyographic activity of the vastus lateralis (VL), rectus femoris (RF), biceps femoris (BF), semitendinous (ST) and tibialis anterior (TA). Mean values of median frequency, determined by the FFT and WT, were used for the spectral analysis of the electromyographic signals of the studied muscles. The analyzed parameters were obtained for each time period corresponding to 0, 25, 50, 75, and 100% of total duration of the maximal incremental test. No statistically significant differences were found in the values of MF and electromyographic indices of fatigue between the two techniques (FT and WT) both in the cyclists and non-cyclists group (P>0.05). Regarding the MF variance, statistically significant differences were found in all analyzed muscles, as well as in different time periods, both in the cyclists and non-cyclists groups when comparing the FFT and WT techniques (P

This work provides a review on the main spatial modulation (SM) schemes, suitable to wireless communication systems. Performance, complexity and diversity gain of the three new spatial SM schemes suitable for multiple-input-multiple-output (MIMO) communication systems are analyzed: a) transmission scheme for spatial modulation (SM) scheme; b) space shift keying (SSK) scheme; c) generalized space shift keying (GSSK) scheme. These three schemes offer low complexity, higher data rate when compared to single-input-single-output (SISO) communication systems, as well as design flexibility, while exploits randomness characteristics of wireless communication channel for data transmission. The paper aims to explore the main features of those three SM schemes and to evaluate the inherent performance-complexity trade-off in order to determine which of those schemes results in a higher energy and spectral efficiencies.

Este trabalho analisa a redução da relação entre a potência de pico e potência média (PAPR) em sistemas com multiplexagem por divisão de frequência ortogonal (OFDM) sob a perspectiva da otimização heurística aplicada à topologia de transmissão por sequência parcial, visando a redução de sua complexidade computacional. Uma elevada PAPR em sinais OFDM acarreta em redução da eficiência energética dos amplificadores de alta potência (HPA) presentes no transmissor que, para evitar distorção no sinal amplificado, devem ter seu ponto de operação deslocado reduzindo a potência do sinal de entrada por um nível denominado input back-off (IBO). A técnica de transmissão por sequência parcial(PTS) para redução dos níveis de PAPR foi analisada e avaliada por meio de otimização heurística denominada enxame de partículas (PSO); os resultados foram comparados aos obtidos com o algoritmo PTS convencional (C-PTS) em termos da relação entre complexidade computacional e redução da PAPR obtida. Os resultados numéricos de simulação mostraram que ao utilizar o algoritmo PSOPTS é possível atingir resultados quase-ótimos em aproximadamente um quarto do tempo utilizado pelo C-PTS, tornando o primeiro mais adequado para utilização comercial, que usualmente envolvem sistemas robustos com um elevado número de subportadoras, o que necessariamente implica em uma alta PAPR.

This work analyzes the most common wireless cooperative network protocols, verifying potentialities and performance relative to the non cooperative methodology, also techniques that can maximize the performance, such as combining signal rules and relay selection. Furthermore, considerations are provided for the computational complexity of topologies, combining techniques and protocols of cooperative networks.