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An important observation in the ultra-reliable and low latency communications is that the size of transmit information is tiny. To support the effective short packet transmission, a sparse vector coding (SVC) scheme where an information is encoded into the positions of the sparse vector was proposed. In this paper, we propose a novel SVC technique further improving the reliability of the short packet transmission. Key idea of the proposed technique is to encode information both in the position as well as symbols. From the performance analysis and numerical evaluations on realistic channel models, we demonstrate that the proposed scheme outperforms the conventional SVC scheme in terms of the block error rate (BLER) and transmission latency. [ABSTRACT FROM AUTHOR]

Major goal of ultra reliable and low latency communication (URLLC) is to reduce the latency down to a millisecond (ms) level while ensuring reliability of the transmission. Since the current uplink transmission scheme requires a complicated handshaking procedure to initiate the transmission, to meet this stringent latency requirement is a challenge in wireless system design. In particular, in the time division duplexing (TDD) systems, supporting the URLLC is difficult since the mobile device has to wait until the transmit direction is switched to the uplink. In this paper, we propose a new approach to support a low latency access in TDD systems, called channel aware sparse transmission (CAST). Key idea of the proposed scheme is to encode a grant signal in a form of sparse vector. This together with the fact that the sensing mechanism preserves the energy of the sparse vector allows us to use the compressed sensing (CS) technique in CAST decoding. From the performance analysis and numerical evaluations, we demonstrate that the proposed CAST scheme achieves a significant reduction in access latency over the 4G LTE-TDD and 5G NR-TDD systems. [ABSTRACT FROM AUTHOR]

Massive machine-type communication (mMTC) is a newly introduced service category in 5G wireless communication systems to support a variety of Internet-of-Things (IoT) applications. In recovering sparsely represented multi-user vectors, compressed sensing-based multi-user detection (CS-MUD) can be used. CS-MUD is a feasible solution to the grant-free uplink non-orthogonal multiple access (NOMA) environments. In CS-MUD, active user detection (AUD) and channel estimation (CE) should be performed before data detection. In this paper, we propose the expectation propagation-based joint AUD and CE (EP-AUD/CE) technique for mMTC networks. The EP algorithm is a Bayesian framework that approximates a computationally intractable probability distribution to an easily tractable distribution. The proposed technique finds a close approximation of the posterior distribution of the sparse channel vector. Using the approximate distribution, AUD and CE are jointly performed. We show by numerical simulations that the proposed technique substantially enhances AUD and CE performances over competing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new detection algorithm for large-scale wireless systems, referred to as post sparse error detection (PSED) algorithm, that employs a sparse error recovery algorithm to refine the estimate of a symbol vector obtained by the conventional linear detector. The PSED algorithm operates in two steps: First, sparse transformation converting the original nonsparse system into the sparse system whose input is an error vector caused by the symbol slicing; and second, the estimation of the error vector using the sparse recovery algorithm. From the asymptotic mean square error analysis and empirical simulations performed on large-scale wireless systems, we show that the PSED algorithm brings significant performance gain over classical linear detectors while imposing relatively small computational overhead. [ABSTRACT FROM PUBLISHER]

Recent theory of compressed sensing (CS) tells us that sparse signals can be reconstructed from a small number of random samples. In reconstruction of sparse signals, greedy algorithms, such as the orthogonal matching pursuit (OMP), have been shown to be computationally efficient. In this paper, the performance of OMP is shown to be dependent on how well information of the underlying signals is preserved in the residual vector. Further, to improve the information preservation, we present a modification of OMP, called oblique projection matching pursuit (ObMP), which updates the residual in a oblique projection manor. Using the restricted isometric property (RIP), we build a solid yet very intuitive grasp of the more accurate phenomenon of ObMP. We also show from empirical experiments that the ObMP achieves improved reconstruction performance over the conventional OMP algorithm in terms of support detection ratio and mean squared error (MSE). [ABSTRACT FROM AUTHOR]

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]

Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme to reduce the required pilot overhead, whereby the spatio-temporal common sparsity of delay-domain MIMO channels is leveraged. Particularly, we first propose the nonorthogonal pilots at the BS under the framework of CS theory to reduce the required pilot overhead. Then, an adaptive structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly estimate channels associated with multiple OFDM symbols from the limited number of pilots, whereby the spatio-temporal common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Moreover, by exploiting the temporal channel correlation, we propose a space-time adaptive pilot scheme to further reduce the pilot overhead. Additionally, we discuss the proposed channel estimation scheme in multicell scenario. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal oracle least squares estimator. [ABSTRACT FROM AUTHOR]

This letter addresses a problem of downlink pilot allocation for massive multiple-input multiple-output (MIMO) systems. When a massive MIMO is employed in frequency division duplex (FDD) systems, significant amount of radio resources are dedicated to the transmission of downlink pilots. Such huge pilot overhead leads to a substantial loss in the maximum data throughput, which motivates us to reduce the number of pilots. In this letter, we propose a pilot reduction strategy based on compressed sensing techniques for orthogonal frequency division multiplexing systems. The pilots are randomly located in a low density manner over the time and frequency domain. To estimate the channels with such low density pilots, we propose a novel sparse channel estimation technique that exploits the common support of the consecutive channel impulse responses over the certain time duration. The evaluation shows that for a massive MIMO with 128 antennas, the proposed scheme achieves significant reduction of pilot overhead, while maintaining good channel estimation performance. [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]