Results 1-20 of 28.
((uid:50034845))

Bookmark and Share    
Full Text
Peer Reviewed
See detailMulti-Antenna Data-Driven Eavesdropping Attacks and Symbol-Level Precoding Countermeasures
Mayouche, Abderrahmane UL; Alves Martins, Wallace UL; Tsinos, Christos UL et al

in IEEE Open Journal of Vehicular Technology (2021)

In this work, we consider secure communications in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve), who is a ... [more ▼]

In this work, we consider secure communications in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve), who is a legit user trying to eavesdrop other users. In this setting, we exploit machine learning (ML) tools to design soft and hard decoding schemes by using precoded pilot symbols as training data. The proposed ML frameworks allow an Eve to determine the transmitted message with high accuracy. We thereby show that MU-MISO systems are vulnerable to such eavesdropping attacks even when relatively secure transmission techniques are employed, such as symbol-level precoding (SLP). To counteract this attack, we propose two novel SLP-based schemes that increase the bit-error rate at Eve by impeding the learning process. We design these two security-enhanced schemes to meet different requirements regarding runtime, security, and power consumption. Simulation results validate both the ML-based eavesdropping attacks as well as the countermeasures, and show that the gain in security is achieved without affecting the decoding performance at the intended users. [less ▲]

Detailed reference viewed: 40 (2 UL)
Full Text
Peer Reviewed
See detailSymbol-Level Precoding with Constellation Rotation in the Finite Block Length Regime
Kisseleff, Steven UL; Alves Martins, Wallace UL; Chatzinotas, Symeon UL et al

in IEEE Communications Letters (2021)

This paper tackles the problem of optimizing the parameters of a symbol-level precoder for downlink multiantenna multi-user systems in the finite block length regime. Symbol-level precoding (SLP) is a non ... [more ▼]

This paper tackles the problem of optimizing the parameters of a symbol-level precoder for downlink multiantenna multi-user systems in the finite block length regime. Symbol-level precoding (SLP) is a non-linear technique for multiuser wireless networks, which exploits constructive interference among co-channel links. Current SLP designs, however, implicitly assume asymptotically infinite blocks, since they do not take into account that the design rules for finite and especially short blocks might significantly differ. This paper fills this gap by introducing a novel SLP design based on discrete constellation rotations. The rotations are the added degree of freedom that can be optimized for every block to be transmitted, for instance, to save transmit power. Numerical evaluations of the proposed method indicate substantial power savings, which might be over 99% compared to the traditional SLP, at the expense of a single additional pilot symbol per block for constellation de-rotation. [less ▲]

Detailed reference viewed: 69 (4 UL)
Full Text
Peer Reviewed
See detailData-driven Precoded MIMO Detection Robust to Channel Estimation Errors
Mayouche, Abderrahmane UL; Alves Martins, Wallace UL; Chatzinotas, Symeon UL et al

in IEEE Open Journal of the Communications Society (2021)

We study the problem of symbol detection in downlink coded multiple-input multiple-output (MIMO) systems with precoding and without the explicit knowledge of the channel-state information (CSI) at the ... [more ▼]

We study the problem of symbol detection in downlink coded multiple-input multiple-output (MIMO) systems with precoding and without the explicit knowledge of the channel-state information (CSI) at the receiver. In this context, we investigate the impact of imperfect CSI at the transmitter (CSIT) on the detection performance. We first model the CSIT degradation based on channel estimation errors to investigate its impact on the detection performance at the receiver. To mitigate the effect of CSIT deterioration at the latter, we propose learning based techniques for hard and soft detection that use downlink precoded pilot symbols as training data. We note that these pilots are originally intended for signal-to-interference-plus-noise ratio (SINR) estimation. We validate the approach by proposing a lightweight implementation that is suitable for online training using several state-of-the-art classifiers. We compare the bit error rate (BER) and the runtime complexity of the proposed approaches where we achieve superior detection performance in harsh channel conditions while maintaining low computational requirements. Specifically, numerical results show that severe CSIT degradation impedes the correct detection when a conventional detector is used. However, the proposed learning-based detectors can achieve good detection performance even under severe CSIT deterioration, and can yield 4-8 dB power gain for BER values lower than 10-4 when compared to the classic linear minimum mean square error (MMSE) detector. [less ▲]

Detailed reference viewed: 62 (7 UL)
Full Text
Peer Reviewed
See detailDME Interference Mitigation for GNSS Receivers via Nonnegative Matrix Factorization
Silva, Felipe B.; Cetin, Ediz; Alves Martins, Wallace UL

in URSI GASS 2021, Rome 28 August - 4 September 2021 (2021)

In this work a nonnegative matrix factorization based approach is proposed to mitigate the impact of interference due to distance measurement equipment (DME) signals in global navigation satellite system ... [more ▼]

In this work a nonnegative matrix factorization based approach is proposed to mitigate the impact of interference due to distance measurement equipment (DME) signals in global navigation satellite system (GNSS) receivers. The proposed approach operates by separating the DME and GNSS signals, and the results show that it outperforms the traditional pulse-blanking based techniques in terms of acquisition and carrier-to-noise ratio metrics without discarding any of the received signal samples. [less ▲]

Detailed reference viewed: 45 (1 UL)
Full Text
Peer Reviewed
See detailADS-B Signal Detection via Time-Frequency Analysis for Radio Astronomy Applications
Silva, Felipe B.; Cetin, Ediz; Alves Martins, Wallace UL

in IEEE International Symposium on Circuits and Systems (ISCAS), Daegu 22-28 May 2021 (2021)

This paper proposes a time-frequency (TF) domain technique for detecting the presence of automatic dependent surveillance-broadcast (ADS-B) interference signals in radio astronomy applications. The ... [more ▼]

This paper proposes a time-frequency (TF) domain technique for detecting the presence of automatic dependent surveillance-broadcast (ADS-B) interference signals in radio astronomy applications. The proposed technique uses a priori knowledge about the ADS-B signal’s frequency information and compares it with the received signal’s spectrogram time slices via the cosine similarity function. In the presence of ADS-B signals, the similarity levels are higher, whereas in their absence the levels are lower. Hence, the proposed approach exploits this to detect the presence of such signals. Simulation results using signals from the Parkes radio telescope show the efficacy of the proposed method in detecting the presence of ADS-B signals when compared with other classic detectors. [less ▲]

Detailed reference viewed: 29 (0 UL)
Full Text
Peer Reviewed
See detailA Novel Learning-based Hard Decoding Scheme and Symbol-Level Precoding Countermeasures
Mayouche, Abderrahmane UL; Alves Martins, Wallace UL; Tsinos, Christos G. et al

in IEEE Wireless Communications and Networking Conference (WCNC), Najing 29 March to 01 April 2021 (2021)

In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In ... [more ▼]

In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design a hard decoding scheme by using precoded pilot symbols as training data. Within this, we propose an ML framework for a multi-antenna hard decoder that allows an Eve to decode the transmitted message with decent accuracy. We show that MU-MISO systems are vulnerable to such an attack when conventional block-level precoding is used. To counteract this attack, we propose a novel symbol-level precoding scheme that increases the bit-error rate at Eve by obstructing the learning process. Simulation results validate both the ML-based attack as well as the countermeasure, and show that the gain in security is achieved without affecting the performance at the intended users. [less ▲]

Detailed reference viewed: 87 (0 UL)
Full Text
Peer Reviewed
See detailKernel Regression on Graphs in Random Fourier Features Space
Elias, Vitor R. M.; Gogenini, Vinay C.; Alves Martins, Wallace UL et al

in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2021), Toronto 6-11 June 2021 (2021)

This work proposes an efficient batch-based implementation for kernel regression on graphs (KRG) using random Fourier features (RFF) and a low-complexity online implementation. Kernel regression has ... [more ▼]

This work proposes an efficient batch-based implementation for kernel regression on graphs (KRG) using random Fourier features (RFF) and a low-complexity online implementation. Kernel regression has proven to be an efficient learning tool in the graph signal processing framework. However, it suffers from poor scalability inherent to kernel methods. We employ RFF to overcome this issue and derive a batch-based KRG whose model size is independent of the training sample size. We then combine it with a stochastic gradient-descent approach to propose an online algorithm for KRG, namely the stochastic-gradient KRG (SGKRG). We also derive sufficient conditions for convergence in the mean sense of the online algorithms. We validate the performance of the proposed algorithms through numerical experiments using both synthesized and real data. Results show that the proposed batch-based implementation can match the performance of conventional KRG while having reduced complexity. Moreover, the online implementations effectively learn the target model and achieve competitive performance compared to the batch implementations. [less ▲]

Detailed reference viewed: 58 (3 UL)
Full Text
Peer Reviewed
See detailRobust Passive Coherent Location via Nonlinearly Constrained Least Squares
Nicolalde-Rodríguez, Daniel P.; Apolinário Jr., José A.; Alves Martins, Wallace UL

in 12th IEEE Latin America Symposium on Circuits and System (LASCAS), Arequipa 21-24 February 2021 (2021)

This paper addresses the problem of target location by means of a passive radar. Existing approaches based on time difference-of-arrival (TDOA) measurements, namely spherical interpolation and spherical ... [more ▼]

This paper addresses the problem of target location by means of a passive radar. Existing approaches based on time difference-of-arrival (TDOA) measurements, namely spherical interpolation and spherical intersection, are revisited for the case of single transmitter and multiple receivers. The mathematical formulations of these state-of-the-art approaches do not take into account possible TDOA estimation errors, which degrade the target location performance. We extend those formulations by incorporating a nonlinear constraint into the underlying least squares problem, thus conferring robustness to the location technique against TDOA estimation errors, as corroborated by extensive numerical experiments. [less ▲]

Detailed reference viewed: 45 (1 UL)
Full Text
Peer Reviewed
See detailUser Selection based on Inter-channel Interference for Massive MIMO under Line-of-sight Propagation
Chaves, Rafael S.; Cetin, Ediz; Lima, Markus V. S. et al

in URSI GASS 2021, Rome 28 August - 4 September 2021 (2021)

Massive multiple-input multiple-output (MIMO) is seen as a key enabler for next-generation wireless communication systems. Increased throughput afforded by massive MIMO, however, may severely degrade when ... [more ▼]

Massive multiple-input multiple-output (MIMO) is seen as a key enabler for next-generation wireless communication systems. Increased throughput afforded by massive MIMO, however, may severely degrade when the number of users served by a single base station increases, calling for user scheduling algorithms. To deal with this problem, a new user selection algorithm, called inter-channel interference-based selection (ICIBS), is proposed. ICIBS drops those users that induce the highest interference to the remaining users. Simulations show that selecting users with ICIBS significantly improves the throughput, outperforming state-of-the-art user selection algorithms. [less ▲]

Detailed reference viewed: 54 (7 UL)
Full Text
Peer Reviewed
See detailA fault detector/classifier for closed-ring power generators using machine learning
Quintanilha, Igor M.; Elias, Vitor R. M.; Silva, Felipe B. et al

in Reliability Engineering and System Safety (2021)

Condition-based monitoring of power-generation systems is naturally becoming a standard approach in industry due to its inherent capability of fast fault detection, thus improving system efficiency and ... [more ▼]

Condition-based monitoring of power-generation systems is naturally becoming a standard approach in industry due to its inherent capability of fast fault detection, thus improving system efficiency and reducing operational costs. Most such systems employ expertise-reliant rule-based methods. This work proposes a different framework, in which machine-learning algorithms are used for detecting and classifying several fault types in a power-generation system of dynamically positioned vessels. First, principal component analysis is used to extract relevant information from labeled data. A random-forest algorithm then learns hidden patterns from faulty behavior in order to infer fault detection from unlabeled data. Results on fault detection and classification for the proposed approach show significant improvement on accuracy and speed when compared to results from rule-based methods over a comprehensive database. [less ▲]

Detailed reference viewed: 27 (0 UL)
Full Text
Peer Reviewed
See detailOversampled DFT-Modulated Biorthogonal Filter Banks: Perfect Reconstruction Designs and Multiplierless Approximations
Alves Martins, Wallace UL; Shankar, Bhavani UL; Ottersten, Björn UL

in IEEE Transactions on Circuits and Systems. II, Express Briefs (2020), 67(11), 2777-2781

We propose a novel methodology for designing oversampled discrete Fourier transform-modulated uniform filter banks. The analysis prototype is designed as a Nyquist filter, whereas the synthesis prototype ... [more ▼]

We propose a novel methodology for designing oversampled discrete Fourier transform-modulated uniform filter banks. The analysis prototype is designed as a Nyquist filter, whereas the synthesis prototype is designed to guarantee perfect reconstruction (PR) considering oversampling. The resulting optimization problem fits into the disciplined convex programming framework, as long as some convex objective function is employed, as the minimization of either the stop-band energy or the maximum deviation from a desired response. The methodology also accounts for near-PR multiplierless approximations of the prototype analysis and synthesis filters, whose coefficients are obtained in the sum-of-power-of-two (SOPOT) space. The quantized coefficients are computed using successive approximation of vectors, which is able to yield filters with a reduced number of SOPOT coefficients in a computationally efficient manner. The proposed methodology is especially appealing for supporting actual hardware deployments, such as modern digital transparent processors to be used in next-generation satellite payloads. [less ▲]

Detailed reference viewed: 239 (25 UL)
Full Text
Peer Reviewed
See detailDiffusion-based Virtual Graph Adjacency for Fourier Analysis of Network Signals
Elias, Vitor R. M.; Alves Martins, Wallace UL; Werner, Stefan

in XXXVIII SIMPÓSIO BRASILEIRO DE TELECOMUNICAÇÕES E PROCESSAMENTO DE SINAIS, Florianópolis 22-25 November 2020 (2020, November)

This work proposes a graph model for networks where node collaborations can be described by the Markov property. The proposed model augments an initial graph adjacency using diffusion distances. The ... [more ▼]

This work proposes a graph model for networks where node collaborations can be described by the Markov property. The proposed model augments an initial graph adjacency using diffusion distances. The resulting virtual adjacency depends on a diffusion-scale parameter, which leads to a controlled shift in the graph-Fourier-transform spectrum. This enables a frequency analysis tailored to the actual network collaboration, revealing more information on the graph signal when compared to traditional approaches. The proposed model is employed for anomaly detection in real and synthetic networks, and results confirm that using the proposed virtual adjacency yields better classification than the initial adjacency. [less ▲]

Detailed reference viewed: 89 (5 UL)
Full Text
Peer Reviewed
See detailGraph Diffusion Kernel LMS using Random Fourier Features
Gogineni, Vinay; Elias, Vitor R. M.; Alves Martins, Wallace UL et al

in 2020 54th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 1-5 November 2020 (2020, November)

This work introduces kernel adaptive graph filters that operate in the reproducing kernel Hilbert space. We propose a centralized graph kernel least mean squares (GKLMS) approach for identifying the ... [more ▼]

This work introduces kernel adaptive graph filters that operate in the reproducing kernel Hilbert space. We propose a centralized graph kernel least mean squares (GKLMS) approach for identifying the nonlinear graph filters. The principles of coherence-check and random Fourier features (RFF) are used to reduce the dictionary size. Additionally, we leverage on the graph structure to derive the graph diffusion KLMS (GDKLMS). The proposed GDKLMS requires only single-hop communication during successive time instants, making it viable for real-time network-based applications. In the distributed implementation, usage of RFF avoids the requirement of a centralized pretrained dictionary in the case of coherence-check. Finally, the performance of the proposed algorithms is demonstrated in modeling a nonlinear graph filter via numerical examples. The results show that centralized and distributed implementations effectively model the nonlinear graph filters, whereas the random feature-based solutions is shown to outperform coherence-check based solutions. [less ▲]

Detailed reference viewed: 110 (6 UL)
Full Text
Peer Reviewed
See detail'Faster-than-Nyquist Signaling via Spatiotemporal Symbol-Level Precoding for Multi-User MISO Redundant Transmissions
Alves Martins, Wallace UL; Spano, Danilo UL; Chatzinotas, Symeon UL et al

in International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2020), Barcelona 4-8 May 2020 (2020, May)

This paper tackles the problem of both multi-user and intersymbol interference stemming from co-channel users transmitting at a faster-than-Nyquist (FTN) rate in multi-antenna downlink transmissions. We ... [more ▼]

This paper tackles the problem of both multi-user and intersymbol interference stemming from co-channel users transmitting at a faster-than-Nyquist (FTN) rate in multi-antenna downlink transmissions. We propose a framework for redundant block-based symbol-level precoders enabling the trade-off between constructive and destructive multi-user and interblock interference (IBI) effects at the single-antenna user terminals. Redundant elements are added as guard interval to handle IBI destructive effects. It is shown that, within this framework, accelerating the transmissions via FTN signaling improves the error-free spectral efficiency, up to a certain acceleration factor beyond which the transmitted information cannot be perfectly recovered by linear filtering followed by sampling. Simulation results corroborate that the proposed spatiotemporal symbol-level precoding can change the amount of added redundancy from zero (full IBI) to half (IBI-free) the equivalent channel order, so as to achieve a target balance between spectral and energy efficiencies. [less ▲]

Detailed reference viewed: 110 (7 UL)
Full Text
Peer Reviewed
See detailMultichannel Source Separation Using Time-Deconvolutive CNMF
Dias, Thadeu; Alves Martins, Wallace UL; Biscainho, Luiz Wagner

in Journal of Communication and Information Systems (2020), 35(1), 103-112

This paper addresses the separation of audio sources from convolutive mixtures captured by a microphone array. We approach the problem using complex-valued non-negative matrix factorization (CNMF), and ... [more ▼]

This paper addresses the separation of audio sources from convolutive mixtures captured by a microphone array. We approach the problem using complex-valued non-negative matrix factorization (CNMF), and extend previous works by tailoring advanced (single-channel) NMF models, such as the deconvolutive NMF, to the multichannel factorization setup. Further, a sparsity-promoting scheme is proposed so that the underlying estimated parameters better fit the time-frequency properties inherent in some audio sources. The proposed parameter estimation framework is compatible with previous related works, and can be thought of as a step toward a more general method. We evaluate the resulting separation accuracy using a simulated acoustic scenario, and the tests confirm that the proposed algorithm provides superior separation quality when compared to a state-of-the-art benchmark. Finally, an analysis of the effects of the introduced regularization term shows that the solution is in fact steered toward a sparser representation. [less ▲]

Detailed reference viewed: 44 (2 UL)
Full Text
Peer Reviewed
See detailJoint Forecasting and Interpolation of Time-Varying Graph Signals Using Deep Learning
Lewenfus, Gabriela; Alves Martins, Wallace UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Signal and Information Processing over Networks (2020)

We tackle the problem of forecasting network-signal snapshots using past signal measurements acquired by a subset of network nodes. This task can be seen as a combination of multivariate time-series ... [more ▼]

We tackle the problem of forecasting network-signal snapshots using past signal measurements acquired by a subset of network nodes. This task can be seen as a combination of multivariate time-series forecasting (temporal prediction) and graph signal interpolation (spatial prediction). This is a fundamental problem for many applications wherein deploying a high granularity network is impractical. Our solution combines recurrent neural networks with frequency-analysis tools from graph signal processing, and assumes that data is sufficiently smooth with respect to the underlying graph. The proposed learning model outperforms state-of-the-art deep learning techniques, especially when predictions are made using a small subset of network nodes, considering two distinct real world datasets: temperatures in the US and speed flow in Seattle. The results also indicate that our method can handle noisy signals and missing data, making it suitable to many practical applications. [less ▲]

Detailed reference viewed: 50 (3 UL)
Full Text
Peer Reviewed
See detailReconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities
Kisseleff, Steven UL; Alves Martins, Wallace UL; Al-Hraishawi, Hayder UL et al

in IEEE Open Journal of the Communications Society (2020)

The concept of Smart Cities has been introduced as a way to benefit from the digitization of various ecosystems at a city level. To support this concept, future communication networks need to be carefully ... [more ▼]

The concept of Smart Cities has been introduced as a way to benefit from the digitization of various ecosystems at a city level. To support this concept, future communication networks need to be carefully designed with respect to the city infrastructure and utilization of resources. Recently, the idea of 'smart' environment, which takes advantage of the infrastructure in order to enable better performance of wireless networks, has been proposed. This idea is aligned with the recent advances in design of reconfigurable intelligent surfaces (RISs), which are planar structures with the capability to reflect impinging electromagnetic waves toward preferred directions. Thus, RISs are expected to provide the necessary flexibility for the design of the ‘smart’ communication environment, which can be optimally shaped to enable cost- and energy-efficient signal transmissions where needed. Upon deployment of RISs, the ecosystem of the Smart Cities would become even more controllable and adaptable, which would subsequently ease the implementation of future communication networks in urban areas and boost the interconnection among private households and public services. In this article, we provide our vision on RIS integration into future Smart Cities by pointing out some forward looking new application scenarios and use cases and by highlighting the potential advantages of RIS deployment. To this end, we identify the most promising research directions and opportunities. The respective design problems are formulated mathematically. Moreover, we focus the discussion on the key enabling aspects for RIS-assisted Smart Cities, which require substantial research efforts such as pilot decontamination, precoding for large multiuser networks, distributed operation and control of RISs. These contributions pave the road to a systematic design of RIS-assisted communication networks for Smart Cities in the years to come. [less ▲]

Detailed reference viewed: 172 (35 UL)
Full Text
Peer Reviewed
See detailExtended Adjacency and Scale-dependent Graph Fourier Transform via Diffusion Distances
Elias, Vitor R.M.; Alves Martins, Wallace UL; Werner, Stefan

in IEEE Transactions on Signal and Information Processing over Networks (2020)

This paper proposes the augmentation of the adjacency model of networks for graph signal processing. It is assumed that no information about the network is available, apart from the initial adjacency ... [more ▼]

This paper proposes the augmentation of the adjacency model of networks for graph signal processing. It is assumed that no information about the network is available, apart from the initial adjacency matrix. In the proposed model, additional edges are created according to a Markov relation imposed between nodes. This information is incorporated into the extended-adjacency matrix as a function of the diffusion distance between nodes. The diffusion distance measures similarities between nodes at a certain diffusion scale or time, and is a metric adopted from diffusion maps. Similarly, the proposed extended-adjacency matrix depends on the diffusion scale, which enables the definition of a scale-dependent graph Fourier transform. We conduct theoretical analyses of both the extended adjacency and the corresponding graph Fourier transform and show that different diffusion scales lead to different graph-frequency perspectives. At different scales, the transform discriminates shifted ranges of signal variations across the graph, revealing more information on the graph signal when compared to traditional approaches. The scale-dependent graph Fourier transform is applied for anomaly detection and is shown to outperform the conventional graph Fourier transform. [less ▲]

Detailed reference viewed: 167 (7 UL)
Full Text
Peer Reviewed
See detailAdaptive Graph Filters in Reproducing Kernel Hilbert Spaces: Design and Performance Analysis
Elias, Vitor R. M.; Gogineni, Vinay C.; Alves Martins, Wallace UL et al

in IEEE Transactions on Signal and Information Processing over Networks (2020)

This paper develops adaptive graph filters that operate in reproducing kernel Hilbert spaces. We consider both centralized and fully distributed implementations. We first define nonlinear graph filters ... [more ▼]

This paper develops adaptive graph filters that operate in reproducing kernel Hilbert spaces. We consider both centralized and fully distributed implementations. We first define nonlinear graph filters that operate on graph-shifted versions of the input signal. We then propose a centralized graph kernel least mean squares (GKLMS) algorithm to identify nonlinear graph filters' model parameters. To reduce the dictionary size of the centralized GKLMS, we apply the principles of coherence check and random Fourier features (RFF). The resulting algorithms have performance close to that of the GKLMS algorithm. Additionally, we leverage the graph structure to derive the distributed graph diffusion KLMS (GDKLMS) algorithms. We show that, unlike the coherence check-based approach, the GDKLMS based on RFF avoids the use of a pre-trained dictionary through its data independent fixed structure. We conduct a detailed performance study of the proposed RFF-based GDKLMS, and the conditions for its convergence both in mean and mean-squared senses are derived. Extensive numerical simulations show that GKLMS and GDKLMS can successfully identify nonlinear graph filters and adapt to model changes. Furthermore, RFF-based strategies show faster convergence for model identification and exhibit better tracking performance in model-changing scenarios. [less ▲]

Detailed reference viewed: 40 (1 UL)
Full Text
Peer Reviewed
See detailOn the Convergence of Max-Min Fairness Power Allocation in Massive MIMO Systems
Chaves, Rafael S.; Cetin, Ediz; Lima, Markus V.S. et al

in IEEE Communications Letters (2020)

Power allocation techniques, among which the max-min fairness power allocation (MMFPA) is one of the most widely used, are essential to guarantee good data throughput for all users in a cell. Recently, an ... [more ▼]

Power allocation techniques, among which the max-min fairness power allocation (MMFPA) is one of the most widely used, are essential to guarantee good data throughput for all users in a cell. Recently, an efficient MMFPA algorithm for massive multiple-input multiple-output (MIMO) systems has been proposed. However, this algorithm is susceptible to the initial search interval employed by the underlying bisection search. Even if the optimal point belongs to the initial search interval, this algorithm may fail to converge to such a point. In this paper, we use the Perron-Frobenius theory to explain this issue and provide search intervals that guarantee convergence to the optimal point. Furthermore, we propose the bound test procedure as an efficient way of initializing the search interval. Simulation results corroborate our findings. [less ▲]

Detailed reference viewed: 97 (4 UL)