Search

5G New Radio (NR) operating in the unlicensed spectrum is accelerating the Fourth Industrial Revolution by supporting Internet of Things (IoT) networks or Industrial IoT deployments. Specifically, LTE-Advanced (LTE-A) is looking to achieve spectrum integration through coexistence with multi-radio access technology (RAT) systems in the same unlicensed bands with both licensed-assisted and stand-alone access. The listen-before-talk (LBT) mechanism is mainly considered to enable an LTE operator to protect other incumbent unlicensed systems. In this article, we investigate the behaviors of multiple LTE operators along with the deployment of WiFi networks in the unlicensed spectrum from both short- and long-term points of view. In countries without mandatory LBT requirements, we show that an LTE operator is susceptible to collusion with another LTE operator, thus exploiting scarce spectrum resources by deceiving other wireless networks into thinking that channels are always busy; hence, mandatory usage of LTE with LBT is highly recommended at national level to achieve harmonious coexistence in the unlicensed spectrum. We discuss several possible coexistence scenarios to resolve the operator's dilemmaas well as to improve unlicensed spectrum efficiency among multi-RAT systems, which is viable in the near future. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimal cooperative sensing technique for cognitive radio to maximize sensing performance based on energy detection. In most spectrum sensing research, many cooperation methods have been proposed to overcome the sensitivity of energy detection so that both primary and secondary users are better off in terms of spectral efficiency. However, without assigning a proper sensing threshold to each sensing node, cooperation may not be effective unless the received average primary user signal-to-noise ratio (SNR) is identical. We show that equal threshold energy detection severely degrades sensing performance when cooperative sensing nodes experience diverse average SNRs, and it is not unusual for even single-node sensing to be better than cooperative sensing. To this end, based on the Neyman–Pearson criterion we formulate an optimization problemto maximize sensing performance by using optimized thresholds. Since this is a non-convex optimization problem, we provide a condition that convexifies the problem and thus serves as a sufficient optimality condition. We find that, perhaps surprisingly, in all practical cases one may consider this condition satisfied, and thus optimal sensing performance can be obtained. Through extensive simulations, we demonstrate that the proposed technique achieves a globally optimal solution, i.e., it maximizes the probability of detection under practical operating parameters such as the target probability of false alarm, different SNRs, and the number of cooperative sensing nodes. [ABSTRACT FROM AUTHOR]