User activity detection (UAD) is a critical procedure in massive machine-type communications (mMTC) massive MIMO systems (M-MIMO). Physical phenomena are continuously monitored by sensors in the real world, leading to well-defined activity patterns in the data. This work investigates adjustments in regularization weight parameter$\lambda$of sparse group regularizer GLASSO under activity correlation scenarios. By adjusting the input parameters in such regularizer, one can improve the non-Bayesian UAD performance, where pilot hopping ($\mathbf{(H H}$) sequences are deployed by$\mathbf{m M T C}$sensors. Several input factors influence UAD performance, specifically the square cell side$Q$, the number of sensor terminals (UT)$U$, and path loss coefficient$\xi$. Our work identifies how changes in these factors can be incorporated into the regularization parameter to improve the UAD performance. We present an analytical framework that links the optimal$\lambda_{\text{opt}}$to the key system parameters$\xi, Q$, and$U$through power-law functions. We fit results curves and derive expressions that effectively quantify the relationships for three parameters, achieving high coefficients of determination and low mean squared errors in all three fits with high accuracy. Furthermore, we propose a joint fit expression for the optimal$\lambda_{\text{opt}}$, enabling automatic parameter tuning based on system and channel configuration. This advancement enhances the efficiency and robustness of GLASSO-based UAD systems across various operational conditions and channel scenarios.

In this paper, we solve the energy efficiency (EE) optimization problem in active reconfigurable intelligent surface (RIS)-assisted massive multiple-input multiple-output (mMIMO) communication systems. By exploring different optimization techniques, we derive closed-form expressions for both the amplitude and phase of each RIS element, proposing a novel flexible iterative algorithm for active RIS scenarios. The numerical results reveal how the proposed analytical amplitude/phase beamforming design can be promising since it has proven to be of low complexity, providing up to ≈ 42% higher EE compared to the completely passive RIS with a lower running time of ≈ 93.6% lower compared to the semidefinite relaxation (SDR) technique. The paper contributes to the design and optimization of active RIS in mMIMO systems, highlighting the importance of both EE and computational feasibility in future wireless communication technologies.

The rapid growth of Internet of Things (IoT) applications, such as smart cities and industrial automation, necessitates efficient massive machine-type communication (mMTC) solutions for sixth-generation (6G) networks. Traditional access protocols struggle to accommodate many devices with sporadic activity and low data volumes, leading to increased latency and collisions. This paper proposes a novel grant-free random access (RA) protocol that leverages Reconfigurable Intelligent Surfaces (RIS) to enhance connectivity and channel conditions in mMTC scenarios. The protocol consists of two stages: first, the base station transmits downlink (DL) pilots while the RIS sweeps its reflection configurations, enabling devices to identify optimal transmission opportunities. In the second stage, devices utilize these opportunities to transmit data, minimizing collisions and improving throughput. By employing a predefined codebook of reflection configurations, previously optimized to thoroughly scan the covered space in a few rounds of multiple narrow beams, the protocol reduces overhead and meets a maximum latency constraint of 20 ms under certain reliability constraints, demonstrating significant performance improvements over existing methods. This approach enhances network efficiency while supporting a vast number of devices and encourages the deployment of RIS technology in future wireless communication systems.

Channel state information (CSI) estimation is a critical issue in the design of modern massive multiple-input multiple-output (mMIMO) networks. With the increasing number of users, assigning orthogonal pilots to everyone incurs a large overhead that strongly penalizes the spectral efficiency (SE) of the system. It becomes thus necessary to reuse pilots, giving rise to pilot contamination, a vital performance bottleneck of mMIMO networks. Reusing pilots among the users of the same cell is a very desirable operation condition from the perspective of reducing training overheads; however, the intra-cell pilot contamination might become even worse due to the users’ proximity. Reconfigurable intelligent surfaces (RISs), which are capable of smartly controlling the wireless channel, can be leveraged to achieve intra-cell pilot reuse. In this paper, our main contribution is an RIS-aided approach for intra-cell pilot reuse and the corresponding channel estimation method. Relying upon the knowledge of only statistical CSI, we then optimize the RIS phase-shifts based on a manifold optimization framework and the RIS positioning based on a deterministic approach. The extensive numerical results highlight the remarkable performance improvements achieved by the proposed scheme (for both uplink and downlink transmissions) compared to other alternatives.

Reconfigurable Intelligent Surface (RIS) is considered one of the most promising technologies for the next generation of wireless communication networks. Despite its great potential, RIS faces new challenges in integrating efficiently into wireless networks, including reflection optimization, channel estimation (CE), and optimization of deployment locations. This paper presents and analyzes a CE solution in RIS-assisted systems using compressive sensing techniques. The steering vector and the complex channel gains of the base station (BS)-RIS/user equipment (UE)-RIS links are estimated separately by using thematching pursuitwith phase rotation (MP-PR) by deploying a few active elements at the RIS panel. The performance and complexity of the proposed method are comprehensively analyzed in different scenarios and compared against those of several other related methods in the literature. Numerical results show the effectiveness of the proposed method, which achieves better (or at least very similar) performance compared to other relevant techniques but with a significant decrease in computational complexity.

Intelligent metasurfaces are one of the favorite technologies for integrating sixth-generation (6G) networks, especially the reconfigurable intelligent surface (RIS), which has been extensively researched in various applications. Although many applications and studies of electromagnetic manipulation under the linear RIS topology were performed, applying coding sequences to the element switching enables the space-frequency scattering feature, referred to as Space-Time-Coding metasurface (STCM) topology. This type of topology causes impairments to the established communication methods by generating undesirable interference both in frequency and space, which is worsened when using wideband signals. Nevertheless, it can potentially bring forward useful features for sensing and localization. This work exploits STCM sensing capabilities in target detection, localization, and classification using narrowband downlink pilot signals at the base station (BS). The results of this novel approach reveal the ability to retrieve scattering points (SP) localization within the sub-centimeter and sub-decimeter accuracy depending on the SPs positions in space. We also analyze the associated detection and classification probabilities, which show reliable performance for both in the whole analyzed environment, especially when using the STCM information. We conclude that this method presents a promising approach for future integrated sensing and communications (ISAC) protocols by providing a tool to perform sensing and localization services using legacy communication signals.

The extra-large multiple-input multiple-output (XL-MIMO) architecture has been recognized as a technology for giving support for the massive MTC (mMTC), providing very high-data rates in high-user density scenarios. However, the large dimension of the array increases the Rayleigh distance (dRayl), in addition to obstacles and scatters causing spatial non-stationarities and distinct visibility regions (VRs) across the XL array extension. We investigate the random access (RA) problem in crowded XL-MIMO scenarios; the proposed grant-based random access (GB-RA) protocol combining the advantage of non-orthogonal multiple access (NOMA) and strongest user collision resolutions in extra-large arrays (SUCRe-XL) named NOMA-XL can allow access of two or three colliding users in the same XL sub-array (SA) selecting the same pilot sequence. The received signal processing in a SA basis changes the dRayl, enabling the far-field planar wavefront propagation condition, while improving the system performance. The proposed NOMA-XL GB-RA protocol is able to provide a reduction in the number of attempts to access the mMTC network while improving the average sum rate, as the number of SA increases.

We investigate the downlink outage performance of double-RIS-aided non-orthogonal multiple access (NOMA), where a near-BS and a near-users RISs setup are deployed. To extend the coverage to 360 degrees, we deploy a simultaneously transmitting and reflecting RIS (STAR-RIS) structure aiming to improve communication reliability for both indoor and outdoor users. New channel statistics for the end-to-end channel with Nakagami-m considering both the conventional-RIS and the STAR-RIS antenna elements features are derived using the moment-matching (MM) technique. The numerical results reveal that the double-RIS setup can outperform the single-RIS setups when the number of elements of STAR-RIS (RS) and conventional RIS (RC) is suitably adjusted. Moreover, when the link between the base station and the near-user RIS is in good condition, the double-RIS setup is outperformed by the single-RIS setup. Finally, the proposed analytical equations reveal to be very accurate under different channel and system configurations.

In this paper, we have derived a closed-form expression for the signal-to-interference-and-noise ratio (SINR) in extra-large multiple-input multiple-output (XL-MIMO) systems with spatially correlated multi-state line-of-sight (LoS)/ non- LoS(NLoS) channels, which is valid for a sufficiently large number of base station (BS) antennas, when least-squares (LS) channel estimation is used and under the absence of pilot reuse among the scheduled user equipments (UEs). The derived SINR expression can be computed based on the channel statistics instead of the instantaneous channel realizations, which makes it valid for hundreds of channel coherence blocks. We also have proposed an expeditious user scheduling (US) algorithm for crowded XL-MIMO systems, which aims to maximize the sum spectral efficiency (SE) with the constraint of assigning mutually orthogonal pilots to all the scheduled UEs, such that their channel estimation accuracy is very high. The numerical results reveal that the proposed SINR expression is very accurate for a relatively large number of BS antennas, and that the proposed US algorithm improves considerably the sum SE in crowded scenarios.

In a cell-free massive multiple-input–multiple-output (CF-mMIMO) network, multiple access points (APs) actively cooperate to serve users’ equipment (UEs). We consider how the random access (RA) problem can be addressed by such a network under the occurrence of pilot collisions. To find a solution, we embrace the user-centric perspective, which basically dictates that only a preferred set of APs needs to serve a UE. Due to the success of the strongest-user collision resolution (SUCRe) protocol for cellular (Ce) mMIMO, we extend it by considering the new setting. Besides, we establish that the user-centric perspective naturally equips a CF network with robust fundamentals for resolving collisions. We refer to this foundation as spatial separability, which enables multiple colliding UEs to access the network simultaneously. We then propose two novel RA protocols for CF-mMIMO: 1) the baseline cell-free (BCF) that resolves collisions with the concept of spatial separability alone and 2) the cell-free sucre (CF-SUCRe) that combines SUCRe and the spatial separability principle to resolve collisions. We evaluate our proposed RA protocols against the Ce-SUCRe. Respectively, the BCF and CF-SUCRe can support$7\times $and$4\times $more UEs’ access on average compared to the Ce-SUCRe with an average energy efficiency gain based on the total power consumed (TPC) by the network per access attempt of$52\times $and$340\times $. Among our procedures, even with a higher overhead, the CF-SUCRe is superior to BCF regarding TPC per access attempt. This is because the combination of methods for collision resolution allows many APs to be disconnected from the RA process without sacrificing much the performance. Finally, our numerical results can be reproduced using the code package available on:github.com/victorcroisfelt/cf-ra-spatial-separability.

The recent extra-large scale massive multiple-input multiple-output (XL-MIMO) systems are seen as a promising technology for providing very high data rates in high user-density scenarios. Spatial non-stationarities and visibility regions (VRs) appear across the XL-MIMO array, since its large dimension is of the same order of the distances to the user-equipments (UEs). Due to the increased density of UEs in typical applications of XL-MIMO systems and the scarcity of pilots, the design of random access (RA) protocols and scheduling algorithms become challenging. In this paper, we propose a joint RA and scheduling protocol, namely non-overlapping VR XL-MIMO (NOVR-XL) RA protocol, which takes advantage of the different VRs of the UEs for improving RA performance, besides of seeking UEs with non-overlapping VRs to be scheduled in the same payload data pilot (PDP) resource. Our results reveal that the proposed scheme achieves significant gains in terms of sum-rate compared with traditional RA schemes, as well as reducing access latency and improving connectivity performance as a whole.

This study proposes the deployment of the Hopfield neural network (H‐NN) approach to optimally assign power in optical code division multiple access (OCDMA) systems. Figures of merit such as the feasibility of solutions and complexity are compared with the classical power allocation methods found in the literature, such as sequential quadratic programming (SQP) and augmented Lagrangian method. The analysed methods are used to solve constrained non‐linear optimisation problems in the context of resource allocation for optical networks, especially to deal with energy efficiency in OCDMA networks. The promising performance‐complexity trade‐off of the modified H‐NN is demonstrated through numerical results performed in comparison with classic methods for general problems in non‐linear programming. The evaluation is carried out considering challenging OCDMA networks in which different levels of service quality required were considered for large numbers of optical users. The numerical results demonstrated that the three power allocation methods attain suitable convergence for different network sizes, while both the mH‐NN and SQP methods achieve suitable equilibrium with less complexity.

In this study, the authors consider the application of machine learning (ML) models in cooperative spectrum sensing of cognitive radio networks (CRNs). Based on a statistical analysis of the classic energy detection scheme, the probability of detection and false alarm is derived, which depends solely on the number of samples and signal‐to‐noise ratio of the secondary users. The channel occupancy detection obtained from the established analytical techniques such as maximum ratio combining and AND/OR rules is compared to different ML techniques, including multilayer perceptron (MLP), support vector machine, and Naive Bayes, based on receiver operating characteristic and area under the curve metrics. By using standard profiling tools, they obtain the computational performance of the analysed models during the training phase, a critical step for operating in CRNs. Ultimately, the results demonstrate that the MLP ML technique presents a better trade‐off between training time and channel detection performance.

Directivity pattern is a paramount performance measure in antenna array configurations. Applications such as heterogeneous MIMO wireless cellular communication can greatly benefit in exploring antenna directivity improvement. The problem of directivity optimization, which leads to a highly increasing in directivity gain on a certain desired angle of arrive/departure (AoA/AoD), is considered in this work. A new formulation on the volumetric array directivity problem is proposed using the rectangular coordinates to describe each antenna element, and the desired angle in spherical coordinates (θ0, φ0), with a general element pattern given by sinuθ cosvθ. To solve the associated optimization problem, the omnidirectional (cos θ) configuration was selected; besides, generalized scenarios, i.e ∀u, v ≠ 0, can easily be considered deploying a similar formulation. A well-defined genetic algorithm (GA) was selected to expeditiously solve the resultant non-convex optimization problem. Numerical results comparing achieved directivity for N=6,7 and 8 antenna-elements demonstrate gains of ~ 5 dB compared to the regular beamforming technique, using steering vector for uniform linear arrays (ULA) and uniform circular arrays (UCA).

The recently proposed strongest-user collision resolution (SUCRe) protocol resolves the majority of pilot collisions in crowded machine-type massive MIMO scenarios. We show in this paper that the random access (RA) performance of SUCRe can be improved by replacing the hard decision retransmission rule by a soft decision one, so that each user decides to retransmit or not its pilot sequence with a suitable probability. First, we propose to obtain this probability by mapping the ratio between the signal gain of each UE and the sum of the contenders' signal gains with the aid of a sigmoid function. Then, we propose to determine the retransmission probability of each UE according to the probability of this UE being the strongest contender, while proposing also an approximated probability expression for this event. Our results demonstrate an improvement in terms of a lower number of access attempts and a lower fraction of failed access attempts compared to the SUCRe protocol, while maintaining its features of being decentralized and uncoordinated.

In order to allow the operation of cheap and simple computational nodes, the reduction of the complexity related to the computation of signal processing techniques in massive multiple-input multiple-output (M-MIMO) is a desirable property to be obtained. With this in mind, several methods have been proposed to reduce the complexity of the classical combining/precoding schemes, being one of these based on the Kaczmarz algorithm (KA). Recent works demonstrated that KA-based approaches can suitably estimate the signals received and transmitted in the uplink and downlink phases; simultaneously, the procedure seemed to result in a very low computational complexity. Motivated by such context, this paper proposes a modification in the primary KA-based scheme for M-MIMO, striving to improve its rate of convergence, reducing complexity while holds the performance. Such improvement underlies the effects of pathloss and shadowing over the KA's rate of convergence, as well as in the spectral efficiency. Numerical results are given supporting that the proposed method outclasses the original proposal.

Here, the authors develop a prototype filter design for filter bank multi‐carrier (FBMC) systems focusing on short‐length pulses with near‐perfect reconstruction features, favouring operational conditions for FBMC systems. The proposed design is formulated as the optimisation of the weights of a discrete prototype filter that takes into account high symbol reconstruction rates and desirable spectrum features. Despite not being convex, the formulated optimisation problem is relaxed via semi‐definite programming (SDP), enabling solutions that can over‐perform other popular prototype filters. Numerical results show that the proposed design considering overlapping factors can deliver prototype filters with a good signal‐to‐interference ratio (SIR) versus spectrum performance trade‐off. Indeed, for , the proposed methodology delivered prototype filters with the highest SIR levels among the available options. Furthermore, the proposed methodology can be extended and combined with other filter optimisation criteria, enabling to comply with different FBMC design requirements.

This work proposes a low‐complexity detector for medium‐ and high‐order modulation large‐scale multiple‐input multiple‐output (LS‐MIMO) systems based on the set of Markov chain Monte‐Carlo techniques. Such efficient signal detection algorithm is based on the mixed Gibbs sampling with multiple restarts (MGS‐MR) strategy with sample‐averaged approach during the coordinate updating process, named averaged MGS (aMGS). The proposed strategy applies multiple samples average procedure to restrict the range of the random solution, which comes from the mixture proposed by the original MGS. Numerical simulation results considering higher‐order M ‐QAM demonstrated that the proposed detection method can substantially improve the convergence of the MGS‐MR algorithm, while no extra computational complexity is required. The proposed aMGS‐based detector suitable for medium‐ and high‐order modulation LS‐MIMO further exhibits improved performance when the system loading is high, i.e. when ( K / N ) ≥ 0.75. In addition, the proposed numerical simulation analyses have shown that the optimal value of the mixing ratio parameter can vary regarding system and channel configuration scenarios, resulting somewhat different from the 1/2 K value disseminated in the literature.

This work analyses the discrete solution of Hughes‐Hartogs (HH) for the transmission rate maximisation problem with power constraint in the orthogonal frequency division multiplexing access (OFDMA) systems and explores mechanisms to reduce the computational complexity of greedy algorithms. In addition to the solution characterisation, a computational complexity analysis is developed, considering the number of executed operations for running time purpose. Moreover, the authors have compared the system capacity via the throughput obtained with the HH solution, and its variants combined with three complexity reduction mechanisms. These tools consist of an initial allocation bit vector calculated by rounding the results of the water‐filling (WF) solution, the multiple subchannels per iteration updating and the adoption of a subchannel grouping procedure. Their findings indicate that the update of multiple subchannels and the subcarriers grouping techniques reduce the number of iterations required for convergence of the original HH, with some throughput degradation. Also, the bit‐allocation mechanism based on the WF is deployed as an alternative to overcome the HH solution, increasing the computational complexity.

This paper presents a novel non-cooperative game theoretic based distributed resource block (RB) and power allocation (PA) algorithm using different interference temperature metrics for the uplink of large scale multiple-input-multiple- output (LS-MIMO) multi-cell multi-user frequency division multiple access (OFDMA) networks operating through time division duplexing (TDD). The Nash equilibrium (NE) existence and the algorithms complexity are briefly discussed and simulations are conducted aiming to verify the effectiveness, in terms of both achieved system spectral efficiency (SE) and energy efficiency (EE), as well as to corroborate the robustness of the proposed method in terms of NE convergence and outage probability (OP). We also verify the impact of different system parameters choice on the algorithms' performance, such as system loading and the path-loss (PL) exponent.

In this study, the effects of pilot asynchronism on channel estimation of multi‐user multiple‐input multiple‐output systems are analysed. Classical linear sequences such as Gold, Walsh–Hadamard and quaternary were deployed as pilots in large‐scale MIMO context aiming to trace their behaviour in asynchronous scenarios. Through this study, it was derived a closed form expression for the channel estimate error, suggesting that asynchronism on channel estimation may introduce deviations from the actual channel and spatial correlation, which decreases the overall performance. Numerical results also demonstrated performance degradation of the single‐cell massive MIMO system as asynchronism increases, besides of highlighting the effect of the bit‐error‐rate floor as the number of base stations antennas increases in such scenarios.

A message passing detector based on belief propagation (BP) algorithm for Markov random fields (MRF‐BP) and factor graph (FG‐BP) graphical models is analysed under different large‐scale (LS) multiple‐input multiple‐output (MIMO) scenarios, including system parameters, such as damping factor (DF), number of users and number of antennas, from to antennas. Specifically, the DF variation under different number of antennas configuration and signal‐to‐noise ratio (SNR) regions is extensively evaluated; bit error rate (BER) performance and computational complexity are assessed over different scenarios. Numerical results lead to a great performance gain with damped MRF‐BP approach, overcoming FG‐BP scheme in specific scenarios, with no extra computational complexity. Also, message damping (MD) method resulted in faster convergence of MRF‐BP algorithm in LS scenarios, evidencing that, besides the performance gain, MD technique can lead to a computational complexity reduction. Specifically under low number of transmit antennas scenarios, the DF value needs to be carefully chosen. Furthermore, based on the proposed analysis, optimal value for the DF is determined considering wide LS antennas scenarios and SNR regions.

In this study, important aspects concerning the stability of the QR decomposition (QRD) through the modified Gram‐Schmidt (GS) orthogonalisation procedure with application in multiple‐input–multiple‐output (MIMO) detection are investigated. In particular, the numerical stability of GS‐QRD is analysed through the condition number, considering a matrix with Gaussian entries, which is a very special class of matrix, especially for telecommunication systems in general and for MIMO system in particular. The condition number is analysed in the average sense, aided by random processes theory, including in special the central limit theorem, random variable transformation and moment generating functions. An analytical bound for the condition number is found and corroborated by numerical simulations.

In this paper, we investigate computationally efficient strategies for energy-efficiency (EE) maximization in the uplink of a single-cell orthogonal frequency-division multiple-access system. We realize EE maximization by allocating power and subcarriers for each mobile station, and it has exponential complexity to achieve optimality. The Dinkelbach algorithm and Lagrange dual decomposition are well-established frameworks to solve the EE-maximization problem, but the definition of initial values and parameters remains an unresolved issue that is generally solved by empirical methods. To do this, we develop suitable procedures to efficiently calculate such values. We show that these procedures provide a very high EE, such that they can be systematized in two sub-optimal algorithms to efficiently allocate power and subcarriers without reliance on parameter-optimization problems.

Multiuser transmission (MuT) can be carried out at the base station of a multiple‐input–multiple‐output system for mitigating the downlink multiuser interference, achieving a near‐single‐user performance at the mobile terminals. These MuT systems can be designed for optimising different performance metrics, such as the bit‐error‐rate (BER), which is the focus of this study. In order to mitigate the MuT's complexity, a convex formulation for the minimum BER (MinBER) optimisation problem is proposed, by employing some slight modifications on its original formulation. The authors have shown that the novel convex formulation makes the application of some optimisation algorithms very efficient, leading to a reduced complexity and enabling the system operation in higher dimensions. Indeed, the authors also show that in these conditions the MinBER–MuT system presents the most noticeable gains in performance. The results demonstrate that, among the investigated techniques, the proposed Line Search Quasi‐Newton algorithm relying on the penalty function approach results in the best performance versus complexity trade‐off in the context of high capacity multiuser systems.

The energy efficiency (EE) maximization in multi-carrier code division multiple access (MC-CDMA)1cooperative wireless networks is a NP-hard optimization problem of great interest for future networks systems as well as wireless sensor networks. This paper presents a game theoretic approach for EE maximization in MC-CDMA wireless cooperative networks considering receiver single-user or multi-user design, as well as distributed implementation approach in order to solve the EE design problem. Moreover, the iterative water-filling algorithm (IWFA) and the Verhulst distributed power control algorithm (V-DPCA) are employed to solve the inner loop of the proposed EE maximization algorithm. A study over the quasi-concavity of the utility function is presented while numerical results are offered to corroborate the mathematical model, as well as to verify the better performance of the IWFA over the V-DPCA. Indeed, the superiority of the IWFA over V-DPCA in terms of both EE and SE is evident. This may be explained through the fact that the IWFA optimizes the power allocation of each user through all sub-carriers at the same time, while Verhulst-based DPCA performs the power control on each sub-channel considering all users.

Combined analytical and fuzzy techniques are proposed for improving the battery lifetime, performance, as well as energy efficiency of wireless sensor networks (WSNs) with the aid of efficient relay selection methods. We determine the best relay selection method by striking an appealing performance versus network lifetime tradeoff. Furthermore, the beneficial regions of cooperation are determined considering asymmetric traffic scenarios, where relaying provides energy saving.

In this work heuristic ant colony optimization (ACO) procedure is deployed in conjunction with lattice reduction (LR) technique aiming to improve the performance-complexity tradeoff of detection schemes in MIMO communication. A hybrid LR-ACO MIMO detector using the linear minimum mean squared error (MMSE) criterion as initial guess is proposed and compared with other traditional (non)linear MIMO detector, as well as heuristic MIMO detection approaches from the literature in terms of both performance and complexity. Numerical results show that the proposed LR-ACO outperforms the traditional ACO MIMO detector, as well as the proposed ACO detector with the MMSE solution as initial guess, with a significant complexity reduction.

The networks are becoming an essential part of society life and anomalies may represent a loss in network performance. Modeling the traffic behavior pattern is possible to predict the behavior expected and characterize an anomaly. We proposed a hybrid clustering algorithm, Firefly Harmonic Clustering Algorithm (FHCA), for network volume anomaly detection by the combined forces of the algorithms K-Harmonic means (KHM) and Firefly Algorithm (FA). Processing the Digital Signature of Network Segment (DSNS) data and real traffic data, it is possible to detect and point intervals considered anomalous with a trade-off between the 80% true-positive rate and 20% false-positive rate.

The study proposes a constrained least square (CLS) pre-distortion scheme for multiple-input single-output (MISO) multiple access ultra-wideband (UWB) systems. In such a scheme, a simple objective function is defined, which can be efficiently solved by a gradient-based algorithm. For the performance evaluation, scenarios CM1 and CM3 of the IEEE 802.15.3a channel model are considered. Results show that the CLS algorithm has a fast convergence and a good trade-off between intersymbol interference (ISI) and multiple access interference (MAI) reduction and signal-to-noise ratio (SNR) preservation, performing better than time-reversal (TR) pre-distortion.

Abstract This paper deals with a quasi‐synchronous code division multiple access (QS‐CDMA) system with a multiple processing gain (MPG) variable data rate scheme in the uplink direction and subject to multipath fading channels. An analytic expression is obtained for the signal‐to‐noise plus interference ratio (SNIR) at the Rake receiver output (finger). In order to maximize the SNIR, three combinatorial optimization methods applied to sequences selection are compared. Then, sequence selection methodology for this class of system is evaluated, comparing the local search optimization results with unitary Hamming distance (1‐opt LS), evolutionary programming with cloning (EP‐C), and simulated annealing (SA) ones. The contribution of this work consists of considering the self‐interference (SI) effect, besides the multiple access interference (MAI), in the description of the proposed objective function for sequence selection and a compared analysis of three heuristic algorithms maximizing that SNIR. Also, numeric results have demonstrated the effectiveness of the proposed method for the QS‐CDMA sequences selection. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract This work aims at proposing the use of the evolutionary computation methodology in order to jointly solve the multi‐user channel estimation (MuChE) and detection problems at its maximum‐likelihood, both related to the direct sequence code division multiple access (DS/CDMA). The effectiveness of the proposed heuristic approach is proven by comparing performance and complexity merit figures with that obtained by traditional methods found in literature. Simulation results considering genetic algorithm (GA) applied to multipath, DS/CDMA and MuChE and multi‐user detection (MuD) show that the proposed genetic algorithm multi‐user channel estimation (GAMuChE) yields a normalized mean square error estimation (nMSE) inferior to 11%, under slowly varying multipath fading channels, large range of Doppler frequencies and medium system load, it exhibits lower complexity when compared to both maximum likelihood multi‐user channel estimation (MLMuChE) and gradient descent method (GrdDsc). A near‐optimum multi‐user detector (MuD) based on the genetic algorithm (GAMuD), also proposed in this work, provides a significant reduction in the computational complexity when compared to the optimum multi‐user detector (OMuD). In addition, the complexity of the GAMuChE and GAMuD algorithms were (jointly) analyzed in terms of number of operations necessary to reach the convergence, and compared to other jointly MuChE and MuD strategies. The joint GAMuChE–GAMuD scheme can be regarded as a promising alternative for implementing third‐generation (3G) and fourth‐generation (4G) wireless systems in the near future. Copyright © 2010 John Wiley & Sons, Ltd.

This paper presents an anomaly detection method using Digital Signature of Network Segment (DSNS) and Particle Swarm Optimization-based clustering (PSO-Cls). The PSO algorithm is an evolutionary computation technique whose main characteristics include low computational complexity, ability to escape from local optima, and small number of input parameters dependence, when compared to other evolutionary algorithms, e.g. genetic algorithms (GA). In the PSO-Cls algorithm, swarm intelligence is combined with K-means clustering, in order to achieve high convergence rates. On the other hand, DSNS consists of normal network traffic behavior profiles, generated by the application of Baseline for Automatic Backbone Management (BLGBA) model in SNMP historical network data set. The proposed approach identifies and classifies data clusters from DSNS and real traffic, using swarm intelligence. Anomalous behaviors can be easily identified by comparing real traffic and cluster centroids. Tests were performed in the network of State University of Londrina and the obtained detection and false alarm rates are promising.

This paper extends the discrete Verhulst power equilibrium method, previously suggested in to the power-rate optimal allocation problem. Multirate users associated with different types of traffic are aggregated to distinct user' classes, with the assurance of target rate allocation per user and QoS. Therein, single-rate Verhulst power allocation algorithm was adapted to the multirate DS/CDMA power control problem. The analysis was carried out taking into account the convergence time (number of iterations) and quality of solution in terms of the normalized square error (NSE), considering static and dynamic realistic mobile channels. Computational complexity was addressed and compared to the analytical solution based on interference matrix inverse and the Foschini solution as well.

This paper analyzes the complexity-performance trade-off of three heuristic approaches applied to synchronous multicarrier multiuser detection (MUD) of single/multiple transmit antennas and multiple receive antennas code division multiple access (S/MIMO MC-CDMA) systems. Weighting particle swarm optimization (WOPSO) and unitary Hamming distance search-based strategies, specifically 1-opt local search (1-LS) and simulation annealing (SA) multiuser detection algorithms, were analyzed in details using a single-objective antenna-diversity-aided optimization approach. Monte-Carlo simulations show that, after convergence, the performances reached by the three heuristic MUD (HEUR-MUD) S/MIMO MC-CDMA algorithms are identical, with computational complexities remarkably smaller than the optimum multiuser detector (OMUD). However, the computational complexities could differ substantially depending on the operation system conditions. The complexities of the HEUR-MUDs were carefully analyzed in order to demonstrate that 1-LS scheme provides the best trade-off between implementation complexity aspects and bit error rate (BER) performance when applied to multiuser detection of S/MIMO MC-CDMA systems with low order modulation.

This paper analyzes the particle swarm optimization multiuser detector (PSO-MUD) under high-order modulation schemes, (particularly for M-QAM), in DS/CDMA systems single-input-single-output (SISO) multipath channels. In order to avoid the computation of complex-valued variables in high-order squared modulation, the optimization problem is reformulated as a real-valued problem. Considering previous results on literature for low-order modulation formats (usually binary/quadrature phase shift keying - BPSK/QPSK), a performancetimescomplexity trade-off comparison is carried out between PSO-MUD and local search multiuser detector (LS-MUD). Performance is evaluated by the symbol error rate (SER), and complexity by necessary number of cost function calculations for convergence. If the background for BPSK heuristic multiuser detection (HEURMUD) problem indicates that the 1-opt local search method is enough to achieve excellent performancetimescomplexity trade-offs, our Monte-Carlo simulation results and analysis show herein indicate the LS-MUD presents a lack of search diversity under high-order modulation formats, while the PSO-MUD is efficient to solve the MUD problem for high-order modulation schemes.

The multi-code (MC) multirate DS-CDMA (direct sequence-code division multiple access) transmission system's performance is analyzed, associated to a multistage parallel interference cancellation (MPIC) structure, MC-MPIC, with hyperbolic tangent decision device in the intermediate stages. Extensive Monte Carlo simulations (MCS) considering total and partial cancellation in additive white Gaussian noise (AWGN) and flat Rayleigh channels indicated great performance gain of the MC-MPIC structure when compared to the conventional MC detection. The results show the feasibility of the proposed structure for future third generation (3G) systems.

The performance of multirate structures, implemented with the multiple codes (MC) scheme, and associated with multiuser detection (MuD) of successive interference cancellation (SIC) for group of users (GSIC), considering a hyperbolic tangent (named hereafter as tanh) decision device in the intermediate stages, is analyzed. Extensive Monte Carlo simulations (MCS) were accomplished for the MC-GSIC tanh structure's performance characterization considering total and partial cancellation (with cancellation factor /spl xi/ < 1) in additive white Gaussian noise (AWGN) and flat Rayleigh channels.