This letter focuses on joint user activity and data detection in the uplink grant-free non-orthogonal multiple access systems based on approximate message passing (AMP) and expectation maximization (EM) algorithms. The proposed Joint-EM-AMP detection algorithm consists of three steps in each iteration. First, AMP decouples the superimposed received signal into uncoupled scalar problems. Then, at the denoising step, AMP computes the posterior means and variances of the transmitted symbols with the extended modulation constellation. The third step is to estimate user activity parameters using EM based on the frame-wise joint sparsity of user activity. In contrast to existing state-of-the-art algorithms, the proposed Joint-EM-AMP algorithm demonstrates significant performance gain in terms of bit error rate, which will be verified in simulation results.

Non-line-of-sight (NLOS) error mitigation for the time-of-arrival (TOA) localization has been extensively studied, but these methods cannot be directly applied for the time-difference-of-arrival (TDOA) systems. Recent work has applied convex optimization for NLOS error mitigation in TDOA systems. Issues remain unsolved with this technique include the convex hull problem, reference-anchor selection problem, and difficulties dealing with a wide range of NLOS-caused ranging errors. This letter proposes a new technique to transform a TDOA model into a TOA model and develops a semidefinite programming method with new constraints for effective NLOS error mitigation in TDOA systems. Major advantages of this method include: 1) it resolves the issues such as convex hull and reference-anchor selection issues that existing schemes are facing; 2) it does not require any a priori information about NLOS links or NLOS error statistics; and 3) it achieves a better performance than existing convex optimization schemes, which is verified in both simulation and real experiments.

This paper investigates the problem of received signal strength (RSS) based target localization with sensor position uncertainties and unknown model parameters. Transmit power and path-loss exponent (PLE) are two key parameters of the path-loss model that is critical for RSS schemes to work well. In practice, however, these parameters change dynamically and are difficult to estimate in real time. Four combinations of the two parameters are considered: both parameters are known; only one of the two is known; and both are unknown. We develop new semidefinite programming (SDP) methods to relax the nonconvex maximum likelihood estimator (MLE) problem for each case, and these methods are further optimized when sensor position uncertainties are taken into consideration. We present a comprehensive set of simulation results to compare the performance of the proposed scheme with existing methods.

This letter investigates simultaneous wireless information and power transfer in two-way decode-and-forward relay networks, where the relay is an energy-constrained node but is capable of harvesting energy from the radio frequency signal transmitted by the source nodes for forwarding its information. The achievable sum-rate of such network with the power splitting relay protocol is analyzed first. Then, the optimal design of the power allocated for energy harvesting and for information decoding in the sense of maximum achievable sum-rate is determined. Simulation results are presented to assess the influence of various system parameters. The performance of the optimal design is compared with that of amplify-and-forward relay protocols.

Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002–2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author’s country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.