For orthogonal space-time block coded orthogonal frequency division multiplexing (OSTBC-OFDM) systems, many of the existing blind detection and channel estimation methods rely on the assumption that the channel is static for many OSTBC-OFDM blocks. This paper considers the blind (semiblind) maximum-likelihood (ML) detection problem of OSTBC-OFDM with a single OSTBC-OFDM block only. The merit of such an investigation is the ability to accommodate channels with shorter coherence time. We examine both the implementation and identifiability issues, with a focus on BPSK or QPSK constellations. In the implementation, we propose reduced-complexity detection schemes using subchannel grouping. In the identifiability analysis, we show that the proposed schemes can ensure a probability one identifiability condition using very few number of pilots. For example, the proposed semiblind detection scheme only requires a single pilot code for unique data identification; while the conventional pilot-based channel estimation method requires L pilots where L denotes the channel length. Our simulation results demonstrate that the proposed schemes can provide performance close to that of their nonblind counterparts.
Federated learning (FL) has been recognized as a rapidly growing research area, where the model is trained over massively distributed clients under the orchestration of a parameter server (PS) without sharing clients' data. This paper delves into a class of federated problems characterized by non-convex and non-smooth loss functions, that are prevalent in FL applications but challenging to handle due to their intricate non-convexity and non-smoothness nature and the conflicting requirements on communication efficiency and privacy protection. In this paper, we propose a novel federated primal-dual algorithm with bidirectional model sparsification tailored for non-convex and non-smooth FL problems, and differential privacy is applied for privacy guarantee. Its unique insightful properties and some privacy and convergence analyses are also presented as the FL algorithm design guidelines. Extensive experiments on real-world data are conducted to demonstrate the effectiveness of the proposed algorithm and much superior performance than some state-of-the-art FL algorithms, together with the validation of all the analytical results and properties.
In real-world applications such as those for speech and audio, there are signals that are nonstationary but can be modeled as being stationary within local time frames. Such signals are generally called quasi-stationary or locally stationary signals. This paper considers the problem of direction-of-arrival (DOA) estimation of quasi-stationary signals. Specifically, in our problem formulation we assume: i) sensor array of uniform linear structure; ii) mutually uncorrelated wide-sense quasi-stationary source signals; and iii) wide-sense stationary noise process with unknown, possibly nonwhite, spatial covariance. Under the assumptions above and by judiciously examining the structures of local second-order statistics (SOSs), we develop a Khatri-Rao (KR) subspace approach that has two notable advantages. First, through an identifiability analysis, it is proven that this KR subspace approach can operate even when the number of sensors is about half of the number of sources. The idea behind is to make use of a ¿virtual¿ array structure provided inherently in the local SOS model, of which the degree of freedom is about twice of that of the physical array. Second, the KR formulation naturally provides a simple yet effective way of eliminating the unknown spatial noise covariance from the signal SOSs. Extensive simulation results are provided to demonstrate the effectiveness of the KR subspace approach under various situations.
Accurate estimation of number of endmembers in a given hyper-spectral data plays a vital role in effective unmixing and identification of the materials present over the scene of interest. The estimation of number of endmembers, however, is quite challenging due to the inevitable combined presence of noise and outliers. Recently, we have proposed a convex geometry based algorithm, namely geometry based estimation of number of endmembers - affine hull (GENE-AH) [1] to reliably estimate the number of endmembers in the presence of only noise. In this paper, we will demonstrate that the GENE-AH algorithm can be suitably used for reliable estimation of number of endmembers even for data corrupted by both outliers and noise, without any prior knowledge about the outliers present in the data. Initially, the GENE-AH algorithm (alongside with its inherent endmember extraction algorithm: p-norm-based pure pixel identification (TRI-P) algorithm) is used to identify the set of candidate pixels (possibly including the outlier pixels) that contribute to the affine dimension of the hyperspectral data. Inspired by the fact that the affine hull of the hyperspectral data remains intact for any data set associated with the same endmembers (that may not be in the data set), using GENE-AH again on the corrupted data with the identified candidate pixels removed, will yield a reliable estimate of the true affine dimension (number of endmembers) of that given data. Computer simulations under various scenarios are shown to demonstrate the efficacy of the proposed methodology.
This paper considers the energy-efficient precoding matrix design for relay-aided multiuser downlink multiple-input single-output wireless systems. The precoders of the base station (BS) and the relay station (RS) are designed to maximize the transmit energy efficiency, defined as the ratio between the system sum rate and the total power consumption, under the quality-of-service constraints of the users and the transmit power constraints on the BS and the RS. In view of the fact that this precoder design problem is a nonconvex fractional programming, a successive Dinkelbach and convex approximation (SDCA) algorithm is proposed to handle this problem. Simulation results are provided to demonstrate the effectiveness of the proposed SDCA algorithm, and significant EE improvement as the number of antennas at the BS and the RS increases.
In this paper, we consider two-way orthogonal frequency division multiplexing (OFDM) relay channels, where the direct link between the two terminal nodes is too weak to be used for data transmission. The widely known per-subcarrier decode-and-forward (DF) relay strategy, treats each subcarrier as a separate channel, and performs independent channel coding over each subcarrier. We show that this per-subcarrier DF relay strategy is only a suboptimal DF relay strategy, and present a multi-subcarrier DF relay strategy which utilizes cross-subcarrier channel coding to achieve a larger rate region. We then propose an optimal resource allocation algorithm to characterize the achievable rate region of the multi-subcarrier DF relay strategy. The computational complexity of this algorithm is much smaller than that of standard Lagrangian duality optimization algorithms. We further analyze the asymptotic performance of two-way relay strategies including the above two DF relay strategies and an amplify-and-forward (AF) relay strategy. The analysis shows that the multi-subcarrier DF relay strategy tends to achieve the capacity region of the two-way OFDM relay channels in the low signal-to-noise ratio (SNR) regime, while the AF relay strategy tends to achieve the multiplexing gain region of the two-way OFDM relay channels in the high SNR regime. Numerical results are provided to justify all the analytical results and the efficacy of the proposed optimal resource allocation algorithm.
To improve wireless heterogeneous network service via macrocell and femtocells that share certain spectral resources, this paper studies the transmit beamforming design for femtocell base station (FBS), equipped with multiple antennas, under an outage-based quality-of-service (QoS) constraint at the single-antenna femtocell user equipment characterized by its signal-to-interference-plus-noise ratio. Specifically, we focus on the practical case of imperfect downlink multiple-input single-output (MISO) channel state information (CSI) at the FBS due to limited CSI feedback or CSI estimation errors. By characterizing the CSI uncertainty probabilistically, we formulate an outage-based robust beamforming design. This nonconvex optimization problem can be relaxed into a convex semidefinite programming problem, which reduces to a power control problem when all CSI vectors are independent and identically distributed. We also investigate the performance gap between the optimal transmission strategy (that allows maximum transmission degrees of freedom (DoF) equal to the number of transmit antennas) and the proposed optimal beamforming design (with the DoF equal to one) and provide some feasibility conditions, followed by their performance evaluation and trade-off through simulation results.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful imaging modality to study the pharmacokinetics in a suspected cancer/tumor tissue. The pharmacokinetic (PK) analysis of prostate cancer includes the estimation of time activity curves (TACs), and thereby, the corresponding kinetic parameters (KPs), and plays a pivotal role in diagnosis and prognosis of prostate cancer. In this paper, we endeavor to develop a blind source separation algorithm, namely convex-optimization-based KPs estimation (COKE) algorithm for PK analysis based on compartmental modeling of DCE-MRI data, for effective prostate tumor detection and its quantification. The COKE algorithm first identifies the best three representative pixels in the DCE-MRI data, corresponding to the plasma, fast-flow, and slow-flow TACs, respectively. The estimation accuracy of the flux rate constants (FRCs) of the fast-flow and slow-flow TACs directly affects the estimation accuracy of the KPs that provide the cancer and normal tissue distribution maps in the prostate region. The COKE algorithm wisely exploits the matrix structure (Toeplitz, lower triangular, and exponential decay) of the original nonconvex FRCs estimation problem, and reformulates it into two convex optimization problems that can reliably estimate the FRCs. After estimation of the FRCs, the KPs can be effectively estimated by solving a pixel-wise constrained curve-fitting (convex) problem. Simulation results demonstrate the efficacy of the proposed COKE algorithm. The COKE algorithm is also evaluated with DCE-MRI data of four different patients with prostate cancer and the obtained results are consistent with clinical observations.
Feng and Chi (1998) reported a two-step lattice super-exponential algorithm (2S-LSEA) for blind equalization of single-input single-output (SISO) channels that is superior to Shalvi and Weinstein's (1993) FIR filter based super-exponential algorithm (SEA) in faster convergence speed, lower computational complexity, and more reliable performance to a variety of channels, besides modularity and low sensitivity to parameter quantization effects of lattice structure. In this paper, a 2S-LSEA for multi-input multi-output (MIMO) channels is proposed that is also superior to Yeung and Yau's (1998) SEA for MIMO channels in the same preceding advantages of the 2S-LSEA for SISO channels. Some simulation results are presented to support the efficacy of the proposed 2S-LSEA for MIMO channels.
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