This article studies a reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output system and formulate the max-min fairness problem that maximizes the minimum achievable rate among...
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This article studies a reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output system and formulate the max-min fairness problem that maximizes the minimum achievable rate among all the users by jointly optimizing the transmit beamforming at access points and the phase shifts at RISs. To address such a challenging problem, we first study the special single-user scenario and propose an algorithm that can transform the optimization problem into a semidefinite program (SDP) or an integer linear program for the cases of continuous or discrete phase shifts, respectively. Then, in order to solve the optimization problem for the multiuser scenario with continuous phase shifts, we propose an alternating optimization algorithm, which can alternately transform the problem into a second-order-cone program and an SDP. Finally, for the multiuser scenario with discrete phase shifts, we design a zero-forcing-based successive refinement algorithm, which can find the suboptimal transmit beamforming and phase shifts by means of alternating optimization. Numerical results show that compared with the benchmark schemes of random phase shifts and without using the RIS, the proposed algorithms can significantly increase the minimum achievable rate. It is also demonstrated that, compared with the case of programming continuous phase shifts, using 2-bit discrete phase shifts can practically achieve the same performance.
The detection of sinusoidal signals embedded in noise is an important topic in passive sonar signalprocessing. The performance of existing tone detection techniques is limited by the Doppler frequency shift, which is...
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The detection of sinusoidal signals embedded in noise is an important topic in passive sonar signalprocessing. The performance of existing tone detection techniques is limited by the Doppler frequency shift, which is caused by the relative motion between moving targets and the receiver. In this study, an algorithm based on long-time coherent integration is proposed to achieve robust tonal signal detection under the influence of a Doppler frequency shift. First, the received narrowband signals radiated by moving targets are split into multiple segments through a window-length constraint. The results obtained by applying a discrete Fourier transform (DFT) to the data segments are modeled as a polynomial phase signal in the frequency domain. Then, the polynomial Radon-polynomial Fourier transform (PRPFT) is applied to simultaneously compensate the time-variant frequency drift and phase difference. To avoid the gain loss due to the discretization of the phase difference compensation in PRPFT, a phase compensation factor searching algorithm is proposed. The simulation results show that the proposed algorithm can provide higher frequency resolution and higher coherent integration gain even in the case of a severe Doppler shift. Furthermore, the results of sea trials demonstrate that the proposed method can perfectly achieve coherent integration for signals with a duration of several hundred seconds under different experimental conditions.
To perform vector control of single-inverter dual induction motors, drives are standardly equipped with current and position sensors. In medium-power railway applications, a shaft encoder is usually mounted on each mo...
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To perform vector control of single-inverter dual induction motors, drives are standardly equipped with current and position sensors. In medium-power railway applications, a shaft encoder is usually mounted on each motor, and the inverter output current is measured using two current sensors. Yet, this configuration features some disadvantages. First, torque-sharing cannot be computed because stator current of each motor is unknown. Similarly, saliency extraction of each motor is not achievable since individual current drawn by each motor is not measured. An option that achieves torque-sharing calculation is to attach two current sensors to each motor, provided that both rotor positions are known. However, industrial inverter/control platforms considered offer a maximum of three input channels for current sensors. Besides, some industry fields tend towards encoderless speed control. Therefore, this article proposes to use three current sensors in total and no shaft encoders. Thanks to a special current sensor arrangement and a voltage step excitation method, saliency-based encoderless control is achieved. In addition, a method is presented to estimate torque-sharing. Experimental measurements, taken on a dual induction motor test bench, will show the capability of the proposed methodology to identify individual loading as well as excellent encoderless control performance.
In this article, we propose a novel solution for nonconvex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization probl...
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In this article, we propose a novel solution for nonconvex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of subproblems corresponding to each variable and then iteratively optimizes each subproblem using a fixed updating rule. However, due to the intrinsic nonconvexity of the original optimization problem, the optimization can be trapped into a spurious local minimum even when each subproblem can be optimally solved at each iteration. Meanwhile, learning-based approaches, such as deep unfolding algorithms, have gained popularity for nonconvex optimization;however, they are highly limited by the availability of labeled data and insufficient explainability. To tackle these issues, we propose a meta-learning based alternating minimization (MLAM) method that aims to minimize a part of the global losses over iterations instead of carrying minimization on each subproblem, and it tends to learn an adaptive strategy to replace the handcrafted counterpart resulting in advance on superior performance. The proposed MLAM maintains the original algorithmic principle, providing certain interpretability. We evaluate the proposed method on two representative problems, namely, bilinear inverse problem: matrix completion and nonlinear problem: Gaussian mixture models. The experimental results validate the proposed approach outperforms AM-based methods.
In this article, we focus on the scalable distributed data-driven state estimation problem using Gaussian processes (GPs). The framework includes two parts: 1) the data-driven training approach and 2) the state-estima...
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In this article, we focus on the scalable distributed data-driven state estimation problem using Gaussian processes (GPs). The framework includes two parts: 1) the data-driven training approach and 2) the state-estimation architecture. First, the objective is to obtain the transition and measurement functions of the considered state-space model by a data-driven training strategy via distributed GPs. In particular, to improve the training efficiency, we employ an online conditioning algorithm, which reduces the computational burden significantly. Then, all nodes exchange their own trained GPs with their respective neighbors. Furthermore, to achieve consensus on local trained GPs, we propose a Wasserstein weighted average consensus algorithm, which differs from the current Kullback-Leibler average consensus on probability densities. Second, based on the training results, we propose a distributed state estimation algorithm to perform fresh state estimation. After obtaining the state estimation results (mean and covariance) and exchanging them with their neighboring nodes, we then execute the Wasserstein weighted average to achieve consensus on state estimations. Also, we analyze the stability and robustness of the proposed distributed state estimation by using GP. Finally, numerical and real-world examples are provided to validate the effectiveness of the proposed training method and data-driven state estimation algorithms.
Sampling is a fundamental problem in graph signalprocessing that selects a node subset to collect samples, so that data in the remaining nodes can be well recovered. Existing eigen-decomposition-free (ED-free) graph ...
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Sampling is a fundamental problem in graph signalprocessing that selects a node subset to collect samples, so that data in the remaining nodes can be well recovered. Existing eigen-decomposition-free (ED-free) graph sampling schemes are not designed to minimize mean square error (MMSE) of reconstructed bandlimited graph signals, often resulting in sub-par MSE performance. In this paper, we propose a lightweight ED-free algorithm to minimize an approximate MSE objective using per-node impulse responses of an ideal low-pass graph filter. Specifically, we first derive a proxy of the MMSE greedy sampling objective without matrix inverse. We then define node-dependent impulse responses of an ideal low-pass graph filter-analogous to the sinc function in traditional signalprocessing-which can be approximated very fast without ED. We use these response vectors to reformulate our derived sampling objective, then show that the optimal sampling set has supported low-pass impulse responses that are as orthogonal as possible-defaulting to uniform sampling for 1D regular kernels when the graph Fourier basis is DFT. For optimization, we propose a fast sampling algorithm to evaluate candidates via vector-vector multiplications by reusing previous greedy results. For faster sampling, we relax the MMSE-based greedy objective to a bounded approximation, so that candidate nodes can be easily appraised using simple scalar multiplications. Extensive experiments show that our sampling method achieved state-of-the-art sampling speed and had the best MSE performance among deterministic ED-free sampling methods in various scenarios.
In a graph search algorithm, a given environment is represented as a graph comprising a set of feasible system configurations and their neighboring connections. A path is generated by connecting the initial and goal c...
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In a graph search algorithm, a given environment is represented as a graph comprising a set of feasible system configurations and their neighboring connections. A path is generated by connecting the initial and goal configurations through graph exploration, whereby the path is often desired to be optimal or suboptimal. The computational performance of the optimal path generation depends on the avoidance of unnecessary explorations. Accordingly, heuristic functions have been widely adopted to guide the exploration efficiently by providing estimated costs to the goal configurations. The exploration is efficient when the heuristic functions estimate the optimal cost closely, which remains challenging because it requires a comprehensive understanding of the environment. However, this challenge presents the scope to improve the computational efficiency over the existing methods. Herein, we propose reinforcement learning heuristic A* (RLHA*), which adopts an artificial neural network as a learning heuristic function to closely estimate the optimal cost, while achieving a bounded suboptimal path. Instead of being trained by precomputed paths, the learning heuristic function keeps improving by using self-generated paths. Numerous simulations were performed to demonstrate the consistent and robust performance of RLHA* by comparing it with the existing methods.
Covariance matrix recovery is a topic of great significance in the field of one-bit signalprocessing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold i...
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Covariance matrix recovery is a topic of great significance in the field of one-bit signalprocessing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold is incapable of recovering the diagonal elements of the covariance matrix. To address this limitation, recent studies have proposed the use of non-zero clipping thresholds. However, the relationship between the estimation error and the sampling threshold is not yet known. In this article, we undertake an analysis of the mean squared error by computing the Fisher information matrix for a given threshold. Our results reveal that the optimal threshold can vary considerably, depending on the variances and correlation coefficients. As a result, it is inappropriate to adopt a constant threshold to encompass parameters that vary widely. To mitigate this issue, we present a recovery scheme that incorporates time-varying thresholds. Our approach differs from existing methods in that it utilizes the exact values of the threshold, rather than its statistical properties, to increase the estimation accuracy. Simulation results, including those of the direction-of-arrival estimation problem, demonstrate the efficacy of the developed scheme, especially in complex scenarios where the covariance elements are widely separated.
Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing g...
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Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signalprocessing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Low-complexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine article is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers.
The convergence rate of the diffusion normalized least mean squares (NLMS) algorithm can be speeded up by decorrelation of signals. However, the diffusion decorrelation NLMS algorithm confronts the conflicting require...
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The convergence rate of the diffusion normalized least mean squares (NLMS) algorithm can be speeded up by decorrelation of signals. However, the diffusion decorrelation NLMS algorithm confronts the conflicting requirements of fast convergence rate and small steady-state error. To address the issue, this paper proposes a family of diffusion Bayesian decorrelation least mean squares (DBDLMS) algorithms based on decorrelated observation models. Firstly, the weight update equations of the proposed DBDLMS algorithms are obtained by performing Bayesian inference over the decorrelated observation models, with variable step-sizes emerging naturally to help address the conflicting requirement. Secondly, the update equations of the decorrelation coefficient vectors are inferred from the Bayesian perspective, with the variable step-sizes emerging again. Subsequently, the performance analysis of the proposed DBDLMS algorithms is carried out. Moreover, a simple and effective approach is derived to estimate the free parameters for the proposed DBDLMS algorithms. Finally, the learning performance of the proposed algorithms are verified by Monte Carlo simulations.
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