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In this paper, we propose an efficient beam training technique for millimeter-wave (mmWave) communications. Beam training should be performed frequently when some mobile users are under high mobility to ensure the accurate acquisition of the channel state information. To reduce the resource overhead caused by frequent beam training, we introduce a dedicated beam training strategy which sends the training beams separately to a specific high mobility user (called a target user) without changing the periodicity of the conventional beam training. The dedicated beam training requires a small amount of resources because the training beams can be optimized for the target user. To satisfy the performance requirement with a low training overhead, we propose the optimal training beam selection strategy which finds the best beamforming vectors yielding the lowest channel estimation error based on the target user’s probabilistic channel information. This dedicated beam training is combined with the greedy channel estimation algorithm that accounts for sparse characteristics and temporal dynamics of the target user’s channel. Our numerical evaluation demonstrates that the proposed scheme can maintain good channel estimation performance with significantly less training overhead compared to the conventional beam training protocols. [ABSTRACT FROM AUTHOR]

In this paper, we put forth a new joint sparse recovery algorithm called signal space matching pursuit (SSMP). The key idea of the proposed SSMP algorithm is to sequentially investigate the support of jointly sparse vectors to minimize the subspace distance to the residual space. Our performance guarantee analysis indicates that SSMP accurately reconstructs any row K-sparse matrix of rank r in the full row rank scenario if the sampling matrix $\mathbf {A}$ satisfies $\mathop{\mathrm{krank}}\limits (\mathbf {A}) \ge {K}+1$ , which meets the fundamental minimum requirement on $\mathbf {A}$ to ensure exact recovery. We also show that SSMP guarantees exact reconstruction in at most ${K}-{r}+\lceil \frac {{r}}{{L}} \rceil $ iterations, provided that $\mathbf {A}$ satisfies the restricted isometry property (RIP) of order ${L}({K}-{r})+{r}+1$ with $\delta _{{L}({K}-{r})+{r}+1} < \max \left \{{ \frac {\sqrt {{r}}}{\sqrt {{K}+\frac {{r}}{4}}+\sqrt {\frac {{r}}{4}}}, \frac {\sqrt {{L}}}{\sqrt {K}+1.15 \sqrt {{L}}} }\right \}$ , where L is the number of indices chosen in each iteration. This implies that the requirement on the RIP constant becomes less restrictive when r increases. Such behavior seems to be natural but has not been reported for most of conventional methods. We also show that if ${r}=1$ , then by running more than K iterations, the performance guarantee of SSMP can be improved to $\delta _{\lfloor 7.8{K} \rfloor } \le 0.155$. Furthermore, we show that under a suitable RIP condition, the reconstruction error of SSMP is upper bounded by a constant multiple of the noise power, which demonstrates the robustness of SSMP to measurement noise. Finally, from extensive numerical experiments, we show that SSMP outperforms conventional joint sparse recovery algorithms both in noiseless and noisy scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a novel transmission scheme to support massive machine-type communications (MTC) devices sending very short packets for Internet-of-Things (IoT) applications. The proposed scheme, termed as pilot-less one-shot (PLOS) transmission, does not require the pilot signaling. The key idea behind PLOS is to encode information into the inter-block nonzero positions and intra-block nonzero positions of a sparse vector. In the receiver, we propose a deep neural network-based scheme, referred to as deep learning-based PLOS (DL-PLOS) to recover the nonzero positions of the sparse vector. From the simulations results, we demonstrate that PLOS is effective in the short packet transmission and DL-PLOS outperforms the conventional greedy algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the performance guarantee of the rank aware order recursive matching pursuit (RA-ORMP) algorithm in recovering a group of jointly sparse vectors. Specifically, we show that RA-ORMP accurately reconstructs any group of $r$ linearly independent jointly $K$ -sparse vectors, provided that the sampling matrix has unit $\ell _{2}$ -norm columns and satisfies the restricted isometry property (RIP) of order $K+1$ with $\delta _{K+1} < {\sqrt{r}} /\left({\sqrt{K+\frac{r}{4}}+\sqrt{\frac{r}{4}}}\right)$. We show the near-optimality of the proposed guarantee by providing a group of $r$ jointly $K$ -sparse vectors that cannot be recovered by RA-ORMP under $\delta _{K+1} \geq \sqrt{\frac{r}{K}}$. We also present a condition of RA-ORMP in the more realistic scenarios where a sampling matrix might not have unit $\ell _{2}$ -norm columns and the measurements are contaminated with noise. [ABSTRACT FROM AUTHOR]

Orthogonal matching pursuit (OMP) is a greedy algorithm widely used for the recovery of sparse signals from compressed measurements. In this paper, we analyze the number of iterations required for the OMP algorithm to perform exact recovery of sparse signals. Our analysis shows that OMP can accurately recover all $K$-sparse signals within $\lceil 2.8 \; K \rceil$ iterations when the measurement matrix satisfies a restricted isometry property (RIP). Our result improves upon the recent result of Zhang and also bridges the gap between Zhang's result and the fundamental limit of OMP at which exact recovery of $K$-sparse signals cannot be uniformly guaranteed. [ABSTRACT FROM PUBLISHER]

As an extension of orthogonal matching pursuit (OMP) for improving the recovery performance of sparse signals, generalized OMP (gOMP) has recently been studied in the literature. In this paper, we present a new analysis of the gOMP algorithm using the restricted isometry property (RIP). We show that if a measurement matrix \mmb\Phi\in\cal R^m\times n satisfies the RIP with isometry constant \delta\max\{9,S+1\K}\leq{1\over 8}, then gOMP performs stable reconstruction of all K-sparse signals \bf x\in\cal R^n from the noisy measurements \bf y=\mmb\Phi\bf x+\bf v, within \max\left\K,\left\lfloor8K\over S\right\rfloor\right\ iterations, where \bf v is the noise vector and S is the number of indices chosen in each iteration of the gOMP algorithm. For Gaussian random measurements, our result indicates that the number of required measurements is essentially m=\cal O\left(K\logn\over K\right), which is a significant improvement over the existing result m=\cal O\left(K^2\logn\over K\right), especially for large K. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm referred to as multipath matching pursuit (MMP) that investigates multiple promising candidates to recover sparse signals from compressed measurements. Our method is inspired by the fact that the problem to find the candidate that minimizes the residual is readily modeled as a combinatoric tree search problem and the greedy search strategy is a good fit for solving this problem. In the empirical results as well as the restricted isometry property-based performance guarantee, we show that the proposed MMP algorithm is effective in reconstructing original sparse signals for both noiseless and noisy scenarios. [ABSTRACT FROM AUTHOR]

As a greedy algorithm to recover sparse signals from compressed measurements, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In this paper, we introduce an extension of the OMP for pursuing efficiency in reconstructing sparse signals. Our approach, henceforth referred to as generalized OMP (gOMP), is literally a generalization of the OMP in the sense that multiple N indices are identified per iteration. Owing to the selection of multiple “correct” indices, the gOMP algorithm is finished with much smaller number of iterations when compared to the OMP. We show that the gOMP can perfectly reconstruct any K-sparse signals (K>1), provided that the sensing matrix satisfies the RIP with \deltaNK<{{\sqrt{N}}\over{\sqrt{K}+3\sqrt{N}}}. We also demonstrate by empirical simulations that the gOMP has excellent recovery performance comparable to \ell1-minimization technique with fast processing speed and competitive computational complexity. [ABSTRACT FROM PUBLISHER]

Orthogonal matching pursuit (OMP) is a greedy search algorithm popularly being used for the recovery of compressive sensed sparse signals. In this correspondence, we show that if the isometry constant \deltaK+1 of the sensing matrix \mmb\Phi satisfies \deltaK+1<{1\over\sqrt{K}+1} then the OMP algorithm can perfectly recover K-sparse signals from the compressed measurements \bf y=\mmb\Phi\bf x. Our bound offers a substantial improvement over the recent result of Davenport and Wakin and also closes gap between the recovery bound and fundamental limit over which the perfect recovery of the OMP cannot be guaranteed. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a multiuser detection technique when the signal sparsity is changing over time. The key ingredient of our method is a clever switching between the CS reconstruction algorithm and classical detection depending on the sparsity level of the signals being detected. Since none of these approaches is uniformly better in a situation where the sparsity level is varying, proposed switching algorithm can effectively combine the merits of both. We show that the proposed switching algorithm provides substantial performance gain over individual algorithms in the multiuser detection of CDMA downlink. [ABSTRACT FROM PUBLISHER]