Massive machine-type communications (mMTC) is one of the key application scenarios of fifth generation (5G) and beyond cellular networks. Bringing the unique technical challenge of supporting a huge number of MTC devices (MTCD) in cellular networks, how to efficiently estimate the channel, detect the active users and data in this scenario is an open research topic. In this regard, this paper aims to present an overview of different techniques to address the problem of channel estimation, activity and data detection specifically for the mMTC scenario. In order to highlight potential solutions and to propose new research directions, we discuss the performance of the state-of-the-art techniques in the literature using a unified evaluation framework. INDEX TERMS 5G, channel estimation, detection, massive access, mMTC, random access.
This article presents a chipless radio frequency identification (RFID) tag which operates in the frequency span from 0.86 to 2.5 GHz. This new miniature chipless tag is developed to work for Brazilian RFID regulation. It does not require a ground plane and has a compact size of 85.50 × 53.98 mm2, comparable to a credit card. The tag has been measured in a bi and mono‐static arrangement with two double‐ridge horn antennas, with a significantly response at frequency range measurements.
A double‐sided substrate‐integrated waveguide (SIW) slot antenna with stripline feed, as 4‐by‐4 SIW slot array antenna, operating in millimeter‐waves bands is proposed. The whole antenna and feeding system are fabricated on a single substrate with 2 dielectric layers and 3 copper layers, which takes some advantage, such as, small size, low profile, low cost, and 2 radiation direction propagations. The design process and experimental results are presented. The prototypes can be used in dual band frequency ranges, 25 and 28 GHz, with 8 dBi gain and 63° for half power beamwidth in both sides, front and back antenna sides, and 2 GHz bandwidth. Thus, this component can be applied for 5G indoor environments.
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.