Abstract-Ultra-Reliable and Low Latency Communications (URLLC) is a challenging class of services to be supported by the fifth generation of mobile networks (5G). Among the URLLC services, many use cases, especially those related to factory automation, involve communications with relatively static radio conditions and a periodic generation of control or data packets. The transmission of these packets requires extremely low latency and ultra-reliable communication to enable realtime control of automation processes. In this paper, we discuss a mechanism of deterministic resource allocation to meet the URLLC requirement in terms of reliability and latency, including initial transmissions and controlled retransmissions. A joint resource allocation and modulation and coding schemes selection is performed so that the resource consumption is minimized, subject to latency and reliability constraints. We show that when applying the proposed resource allocation technique it is possible to achieve very low error rates.
In this paper, we derive an efficient iterative algorithm for the recovery of block-sparse signals given the finite data alphabet and the non-zero block probability. The non-zero block number is supposed to be far smaller than the total block number (block-sparse).The key principle is the separation of the unknown signal vector into an unknown support vector s and an unknown data symbol vector a. Both number ( s 0 ) and positions (s i ∈ {0, 1}) of non-zero blocks are unknown. The proposed algorithms use an iterative two-stage LASSO procedure consisting in optimizing the recovery problem alternatively with respect to a and with respect to s. The first algorithm resorts on 1 -norm of the support vector and the second one applies reweighted 1 -norm, which further improves the recovery performance. Performance of proposed algorithms is illustrated in the context of sporadic multiuser communications. Simulations show that the reweighted-1 algorithm performs close to its lower bound (perfect knowledge of the support vector).
This paper addresses finite-alphabet block-sparse signal recovery by considering support detection and data estimation separately. To this aim, we propose a maximum a posteriori (MAP) support detection criterion that takes into account the finite alphabet of the signal as a constraint. We then incorporate the MAP criterion in a compressed sensing detector based on a greedy algorithm for support estimation. We also propose to consider the finite-alphabet property of the signal in the bound-constrained least-squares optimization algorithm for data estimation. The MAP support detection criterion is investigated in two different contexts: independent linear modulation symbols and dependent binary continuous phase modulation (CPM) symbols. The simulations are carried out in the context of sporadic multiuser communications and show the efficiency of proposed algorithms compared to selected state-of-the-art algorithms both in terms of support detection and data estimation.
In this paper, we address M-QAM blind equalization based on information theoretic criteria. We propose two new cost functions that force the probability density functions (pdf) at the equalizer output to match the known constellation pdf. They involve kernel pdf approximation. The kernel bandwidth of a Parzen estimator is updated during iterations to improve the convergence speed and to decrease the residual error of the algorithms. Unlike related existing techniques, the new algorithms measure the distance error between observed and assumed pdfs for the real and imaginary parts of the equalizer output separately. We show performance and complexity gain against the CMA, the most popular blind equalization technique, and classical pdf fitting approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.