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.

EP-Based Joint Active User Detection and Channel Estimation for Massive Machine-Type Communications

With the advent of the Internet of things, massive machine-type communications (mMTC) have become one of the most important requirements for next generation communication systems. In the mMTC scenarios, grant-free nonorthogonal multiple access on the transmission side and compressive sensing-based multi-user detection (CS-MUD) on the reception side are a promising solution because many users sporadically transmit small data packets at low rates. In this paper, we propose a novel CS-MUD algorithm for the active user and data detection in the mMTC systems. The proposed scheme consists of the maximum a posteriori probability (MAP) based active user detector (MAP-AUD) and the MAP-based data detector (MAP-DD). By exchanging the extrinsic information between MAP-AUD and MAP-DD, the proposed algorithm improves the active user detection performance and the reliability of the data detection. In addition, we extend the proposed algorithm to exploit group sparsity. By jointly processing the multiple received data with common activity, the proposed algorithm dramatically enhances the active user detection performance. We show by numerical experiments that the proposed algorithm achieves a substantial performance gain over existing algorithms.

MAP-Based Active User and Data Detection for Massive Machine-Type Communications

Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity.

Modeling Dyadic Data with Binary Latent Factors

Dynamic and thermodynamic models of adaptation.

Integer least-squares problems, concerned with solving a system of equations where the components of the unknown vector are integer-valued, arise in a wide range of applications. In many scenarios the unknown vector is sparse, i.e., a large fraction of its entries are zero. Examples include applications in wireless communications, digital fingerprinting, and array-comparative genomic hybridization systems. Sphere decoding, commonly used for solving integer least-squares problems, can utilize the knowledge about sparsity of the unknown vector to perform computationally efficient search for the solution. In this paper, we formulate and analyze the sparsity-aware sphere decoding algorithm that imposes l0-norm constraint on the admissible solution. Analytical expressions for the expected complexity of the algorithm for alphabets typical of sparse channel estimation and source allocation applications are derived and validated through extensive simulations. The results demonstrate superior performance and speed of sparsity-aware sphere decoder compared to the conventional sparsity-unaware sphere decoding algorithm. Moreover, variance of the complexity of the sparsity-aware sphere decoding algorithm for binary alphabets is derived. The search space of the proposed algorithm can be further reduced by imposing lower bounds on the value of the objective function. The algorithm is modified to allow for such a lower bounding technique and simulations illustrating efficacy of the method are presented. Performance of the algorithm is demonstrated in an application to sparse channel estimation, where it is shown that sparsity-aware sphere decoder performs close to theoretical lower limits.

Sparsity-Aware Sphere Decoding: Algorithms and Complexity Analysis

QSdpR: Viral quasispecies reconstruction via correlation clustering.

RNA viruses are characterized by high mutation rates that give rise to populations of closely related viral genomes, the so-called viral quasispecies. The underlying genetic heterogeneity occurring as a result of natural mutation-selection process enables the virus to adapt and proliferate in face of changing conditions over the course of an infection. Determining genetic diversity (i.e., inferring viral haplotypes and their proportions in the population) of an RNA virus is essential for the understanding of its origin and mutation patterns, and the development of effective drug treatments. In this paper we present QSdpR, a novel correlation clustering formulation of the quasispecies reconstruction problem which relies on semidefinite programming to accurately estimate the sub-species and their frequencies in a mixed population. Extensive comparisons with existing methods are presented on both synthetic and real data, demonstrating efficacy and superior performance of QSdpR.

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Viral Quasispecies Reconstruction via Correlation Clustering

We study the problem of approximating a partially observed matrix by a product of two low-rank matrices where the data as well as the factors are constrained to be binary. This computationally challenging task is motivated by the single individual haplotyping problem which attracted considerable attention in computational biology and is of critical importance for personalized medicine applications. We analyze a binary-constrained variant of the alternating minimization algorithm for solving the aforementioned problem in the scenario where the matrices are rank-one, establish its performance and convergence properties, and in doing so provide the first theoretical guarantees for haplotype reconstruction expressed in terms of the minimum error-correction score. Sample complexity required for reconstruction is derived and experiments are performed on both synthetic and real datasets, demonstrating superiority of the proposed framework over competing methods.

Binary matrix completion with performance guarantees for single individual haplotyping

Sparse integer least-squares problems come up in a wide range of applications including wireless communications and genomics. The sphere decoding algorithm can find near-optimal solution to these problems with reduced average complexity if the knowledge of sparsity of the unknown vector is used in decoding. In this paper, we formulate a sphere decoding approach that relies on the l0-norm constraint on the unknown vector to solve sparse integer least-squares problems. The expected complexity of this algorithm is derived analytically for sparse alphabets associated with common applications such as sparse channel estimation and validated via simulations. The results indicate superior performance and speed compared to the classical sphere decoding algorithm.

Somsubhra Barik

Papers

Sparsity-Aware Sphere Decoding: Algorithms and Complexity Analysis

QSdpR: Viral quasispecies reconstruction via correlation clustering.

Viral Quasispecies Reconstruction via Correlation Clustering

Binary matrix completion with performance guarantees for single individual haplotyping

Expected complexity of sphere decoding for sparse integer least-square problems