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Superiorized adaptive projected subgradient method With Application to MIMO Detection

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 1350-1362页

作者： Fink, Jochen Cavalcante, Renato Luis Garrido Stanczak, Slawomir Tech Univ Berlin D-10623 Berlin Germany Fraunhofer Heinrich Hertz Inst D-10587 Berlin Germany

In this paper, we show that the adaptive projected subgradient method (APSM) is bounded perturbation resilient. To illustrate a potential application of this result, we propose a set-theoretic framework for MIMO detection, and we devise algorithms based on a superiorized APSM. Various low-complexity MIMO detection algorithms achieve excellent performance on i.i.d. Gaussian channels, but they typically incur high performance loss if realistic channel models (e.g., correlated channels) are considered. Compared to existing low-complexity iterative detectors such as individually optimal large-MIMO approximate message passing (IO-LAMA), the proposed algorithms can achieve considerably lower symbol error ratios over correlated channels. At the same time, the proposed methods do not require matrix inverses, and their complexity is similar to IO-LAMA.

关键词： MIMO communication Signal processing algorithms Detectors Perturbation methods Approximation algorithms Hilbert space Resilience MIMO detection nonconvex optimization adaptive projected subgradient method superiorization

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A SET-THEORETIC APPROACH TO MIMO DETECTION 47

A SET-THEORETIC APPROACH TO MIMO DETECTION

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Fink, Jochen Cavalcante, Renato L. G. Utkovski, Zoran Stanczak, Slawomir Tech Univ Berlin Berlin Germany Fraunhofer Heinrich Hertz Inst Berlin Germany

ISBN: (纸本)9781665405409

In this paper, we propose a set-theoretic framework for MIMO detection. Various low-complexity MIMO detection algorithms achieve excellent performance on i.i.d. Gaussian channels, but they typically incur high performance loss if realistic channel models are considered. Compared to existing low-complexity iterative detectors such as approximate message passing (AMP), the proposed algorithms do not impose any structure the channel matrix. Simulations with a realistic channel model show that the proposed methods are competitive with detectors based on orthogonal AMP (OAMP), which compute matrix inverses in each iteration. At the same time, the proposed methods do not require matrix inverses, and their complexity is similar to AMP.

关键词： MIMO detection nonconvex optimization adaptive projected subgradient method superiorization

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ONLINE CHANNEL ESTIMATION FOR HYBRID BEAMFORMING ARCHITECTURES

ONLINE CHANNEL ESTIMATION FOR HYBRID BEAMFORMING ARCHITECTUR...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fink, Jochen Cavalcante, Renato L. G. Stanczak, Slawomir TU Berlin Berlin Germany Fraunhofer Heinrich Hertz Inst Berlin Berlin Germany

ISBN: (纸本)9781509066315

Hybrid analog-/digital beamforming architectures are a promising means of reducing power consumption and hardware costs in large multi-antenna transceivers. However, channel estimation becomes more complicated compared with conventional (fully-digital) architectures because multiple measurements (pilots in subsequent time slots) are required to reconstruct the channel matrix. In this paper, we use variants of the adaptive projected subgradient method to devise online estimation algorithms that exploit temporal correlations of channel samples. Simulations show that these methods are competitive with conventional batch methods in terms of estimation error at a significantly reduced computational cost. We further improve the performance of the proposed methods by exploiting their potential to adapt the analog combiner at runtime.

关键词： Hybrid Beamforming Channel Estimation Online Learning adaptive projected subgradient method

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A Flexible Peak-To-Average Power Ration Reduction Scheme for OFDM Systems by the adaptive projected subgradient method

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2009年第4期57卷 1456-1468页

作者： Cavalcante, Renato L. G. Yamada, Isao Univ Edinburgh Joint Res Inst Signal & Image Proc Inst Digital Commun Edinburgh EH9 3JL Midlothian Scotland Tokyo Inst Technol Sakaniwa & Yamada Lab Dept Commun & Integrated Syst Meguro Ku Tokyo 1528552 Japan

One of the main issues of the orthogonal frequency-division multiplexing (OFDM) modulation is the high peak-to-average power ratio (PAPR) of the transmitted signal, which adversely affects the complexity of power amplifiers. In this paper, we consider transmitters that reduce the PAPR by slightly disturbing the symbols in carriers used to transmit information and by sending dummy symbols-i.e., symbols not conveying information-in unused carriers. The optimal choice of the data and dummy symbols is determined by the solution of a convex optimization problem. To reduce the PAPR with low complexity, we apply a modified version of the adaptive projected subgradient method to a sequence of convex cost functions closely related to the original optimization problem. The resulting algorithm achieves near-optimal PAPR in practical scenarios, generalizes existing algorithms based on Polyak's method, and can easily handle multiple constraints.

关键词： adaptive filters adaptive projected subgradient method OFDM optimization methods

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SIGNAL PROCESSING IN DUAL DOMAIN BY adaptive projected subgradient method

SIGNAL PROCESSING IN DUAL DOMAIN BY ADAPTIVE PROJECTED SUBGR...

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16th International Conference on Digital Signal Processing

作者： Yukawa, Masahiro Slavakis, Konstantinos Yamada, Isao RIKEN Brain Sci Inst Amari Res Unit Wako Saitama Japan Univ Peloponnese Dept Telecommun Sci & Technol Tripoli Greece Tokyo Inst Technol Dept Commun & Integrated Syst Tokyo Japan

ISBN: (纸本)9781424432974

The goal of this paper is to establish a novel signal processing paradigm that enables us to find a point meeting time-variable specifications in dual domain (e.g., time and frequency domains) simultaneously. For this purpose, we define a new problem which we call adaptive split feasibility problem (ASFP). In the ASFP formulation, we have (i) a priori knowledge based convex constraints in the Euclidean spaces R-N and R-M and (ii) data-dependent convex sets in R-N and R-M;the latter are obtained in a sequential fashion. Roughly speaking, the problem is to find a common point of all the sets defined on R-N such that its image under a given linear transformation is a common point of all the sets defined on R-M, if such a point exists. We prove that the adaptive projected subgradient method (APSM) deals with the ASFP by employing (i) a projected gradient operator with respect to (w.r.t.) a 'fixed' proximity function reflecting the convex constraints and (ii) a subgradient projection w.r.t. 'time-varying' objective functions reflecting the data-dependent sets. The resulting algorithm requires no unwanted operations such as matrix inversion, therefore it is suitable for real-time implementation. A convergence analysis is presented and verified by numerical examples.

关键词： convex feasibility problem split feasibility problem projected gradient adaptive projected subgradient method

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Filtering approaches to accelerated consensus in diffusion sensor networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2014年第11期27卷 3266-3279页

作者： Abd-Elrady, Emad Mulgrew, Bernard Abu Dhabi Womens Coll Higher Colleges Technol Abu Dhabi U Arab Emirates Univ Edinburgh Inst Digital Commun Sch Engn Edinburgh EH9 3JL Midlothian Scotland

The main objective in distributed sensor networks is to reach agreement or consensus on values acquired by the sensors. A common methodology to approach this problem is using the iterative and weighted linear combination of those values to which each sensor has access. Different methods to compute appropriate weights have been extensively studied, but the resulting iterative algorithm still requires many iterations to provide a fairly good estimate of the consensus value. In this paper, different accelerating consensus approaches based on adaptive and non-adaptive filtering techniques are studied and applied on the problem of acoustic source localization using the adaptive projected subgradient method. A comparative simulation study shows that the non-adaptive polynomial filters based on Newton's interpolating polynomials and semi-definite programming can provide more accelerated consensus and better estimation accuracy than adaptive filters evaluated using constrained affine projection algorithm or stochastic gradient algorithm provided that the network topology is known beforehand. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： adaptive filtering adaptive projected subgradient method consensus diffusion networks distributed algorithms optimization methods semi-definite programming

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adaptive Robust Distributed Learning in Diffusion Sensor Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第10期59卷 4692-4707页

作者： Chouvardas, Symeon Slavakis, Konstantinos Theodoridis, Sergios Univ Athens Dept Informat & Telecommun Athens 15784 Greece Univ Peloponnese Dept Telecommun Sci & Technol Tripolis 22100 Greece

In this paper, the problem of adaptive distributed learning in diffusion networks is considered. The algorithms are developed within the convex set theoretic framework. More specifically, they are based on computationally simple geometric projections onto closed convex sets. The paper suggests a novel combine-project-adapt protocol for cooperation among the nodes of the network;such a protocol fits naturally with the philosophy that underlies the projection-based rationale. Moreover, the possibility that some of the nodes may fail is also considered and it is addressed by employing robust statistics loss functions. Such loss functions can easily be accommodated in the adopted algorithmic framework;all that is required from a loss function is convexity. Under some mild assumptions, the proposed algorithms enjoy monotonicity, asymptotic optimality, asymptotic consensus, strong convergence and linear complexity with respect to the number of unknown parameters. Finally, experiments in the context of the system-identification task verify the validity of the proposed algorithmic schemes, which are compared to other recent algorithms that have been developed for adaptive distributed learning.

关键词： adaptive filtering adaptive projected subgradient method consensus distributed learning diffusion networks

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Distributed Asymptotic Minimization of Sequences of Convex Functions by a Broadcast adaptive subgradient method

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2011年第4期5卷 739-753页

作者： Cavalcante, Renato L. G. Rogers, Alex Jennings, Nicholas R. Yamada, Isao Heinrich Hertz Inst Nachrichtentech Berlin GmbH Fraunhofer Inst Telecommun D-10587 Berlin Germany Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England Tokyo Inst Technol Dept Commun & Integrated Syst Tokyo 1528550 Japan

We propose a non-hierarchical decentralized algorithm for the asymptotic minimization of possibly time-varying convex functions. In our method, each agent in a network has a private, local (possibly time-varying) cost function, and the objective is to minimize asymptotically the sum of these local functions in every agent (this problem appears in many different applications such as, among others, motion planning, acoustic source localization, and environmental modeling). The algorithm consists of two main steps. First, to improve the estimate of a minimizer, agents apply a particular version of the adaptive projected subgradient method to their local functions. Then the agents exchange and mix their estimates using a communication model based on recent results of consensus algorithms. We show formally the convergence of the resulting scheme, which reproduces as particular cases many existing methods such as gossip consensus algorithms and recent decentralized adaptive subgradient methods (which themselves include as particular cases many distributed adaptive filtering algorithms). To illustrate two possible applications, we consider the problems of acoustic source localization and environmental modeling via network gossiping with mobile agents.

关键词： adaptive projected subgradient method decentralized estimation and detection via network gossiping decentralized optimization via network gossiping gossip algorithms for decentralized adaptive filtering

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An adaptive projected subgradient Approach to Learning in Diffusion Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2009年第7期57卷 2762-2774页

作者： Cavalcante, Renato L. G. Yamada, Isao Mulgrew, Bernard Univ Edinburgh Digital Commun Res Inst Joint Res Inst Signal & Image Proc Edinburgh EH9 3JL Midlothian Scotland Tokyo Inst Technol Dept Commun & Integrated Syst Sakaniwa & Yamada Lab Tokyo 1528552 Japan

We present an algorithm that minimizes asymptotically a sequence of nonnegative convex functions over diffusion networks. In the proposed algorithm, at each iteration the nodes in the network have only partial information of the cost function, but they are able to achieve consensus on a possible minimizer asymptotically. To account for possible node failures, position changes, and/or reachability problems (because of moving obstacles, jammers, etc.), the algorithm can cope with changing network topologies and cost functions, a desirable feature in online algorithms where information arrives sequentially. Many projection-based algorithms can be straightforwardly extended to (probabilistic) diffusion networks with the proposed scheme. The system identification problem in distributed networks is given as one example of a possible application.

关键词： adaptive filtering adaptive projected subgradient method consensus convex optimization diffusion networks distributed processing

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Peak-to-average power ratio reduction in ofdm systems by the adaptive projected subgradieent method

Peak-to-average power ratio reduction in ofdm systems by the...

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33rd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Cavalcante, Renato L. G. Yamada, Isao Tokyo Inst Technol Dept Commun & Integrated Syst Tokyo 1528552 Japan

ISBN: (纸本)9781424414833

One of the main drawbacks of the OFDM modulation is the high peak-to-average power ratio (PAPR) of the transmitted signal. In this study, we devise a low-complexity transmitter that mitigates the PAPR problem and satisfies multiple requirements of a given system, such as error vector magnitude (EVM) constraints, minimum transmitted power of the data symbols, among others. To reduce the PAPR, we use the adaptive projected subgradient method to suppress a sequence of convex cost functions over closed convex sets describing desired properties of the transmitted signal. Numerical examples show that the proposed scheme reduces the PAPR to reasonable levels with few iterations.

关键词： OFDM adaptive projected subgradient method optimization methods

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