In the coexisting new radio-based access to unlicensed spectrum (NR-U) and WiGig networks (CNWNs), directional-sensing-based listen-before-talk (LBT) mechanisms, i.e., directional LBT (dirLBT) and paired LBT (pairLBT), have been proposed to address the exposed node problem caused by traditional omnidirectional LBT (omniLBT) mechanism. In this paper, we are the first to leverage the stochastic geometry to analyze the large-scale CNWN performance when NR-U base stations (NBSs) adopt the directional-sensing-based LBT mechanisms. The analytical expressions for the downlink successful transmission probabilities (STPs) of CNWNs are derived and validated by Monte Carlo simulations. Based on these STPs, the area spectral efficiency (ASE) of CNWNs is derived. Equipped with these results, the effect of NBS sensing threshold, density and sensing beamwidth on the STP and ASE performance are analyzed numerically. Moreover, the STP and ASE performance are compared when NBSs adopt dirLBT, pairLBT and omniLBT mechanisms. Furthermore, the asymptotic ASE of CNWNs when NBS density approaches infinity is derived and validated. The results show that directional-sensing-based LBT mechanisms outperform the omniLBT mechanism in terms of ASE in the CNWNs, especially in ultra-densely deployed scenarios. Under our simulation environment, the dirLBT mechanism can improve the ASE by up to 82.5% as compared with the omniLBT mechanism. Additionally, the NBS sensing threshold for directional-sensing-based LBT should be higher than −73 dBm to achieve a better STP and ASE as compared with that without adopting LBT in NBSs. Besides, there exists an optimal NBS density to maximize the STP and ASE of CNWNs, and when NBS density becomes larger than$200,000$NBSs per$\text {km}^{2}$, deploying more NBS has limited enhancement on the ASE. These results indicate that directional-sensing-based LBT mechanisms should be employed in the ultra-densely deployed CNWNs, and the NBS sensing threshold and sensing beamwidth should be carefully chosen to ensure the superiority of directional-sensing-based LBT mechanisms.

The 5G New Radio-based in unlicensed spectrum (NR-U) has been proposed to harmoniously coexist with the Wireless Gigabyte (WiGig) network in the 60 GHz unlicensed spectrum. It employs the directional listen-before-talk (dirLBT) mechanism to improve the throughput of the coexisting NR-U and WiGig networks (CNWNs). In this paper, we are the first to leverage the stochastic geometry to analyze the performance of the large-scale CNWNs. The medium access probabilities (MAPs) of NR-U base station (NBS) and WiGig access point (WAP) are both derived in closed-form. Based on these MAPs, the downlink successful transmission probabilities (STPs) of NR-U and WiGig networks, which is determined by the retaining probability of the serving NBS/WAP and the downlink coverage probability of NR-U/WiGig network, are given in analytical expressions. All these MAPs and STPs are validated by Monte Carlo simulations to verify the correctness of our proposed model. Moreover, the effect of NBS and WAP density on the mean STP of the large-scale CNWNs are analyzed numerically. The results show that the dirLBT adopted by NR-U outperforms omnidirectional LBT in terms of the mean STP, especially in ultra-densely deployed CNWNs scenario. Furthermore, there exists an optimal NBS density to maximize the mean STP. The results indicate that the dirLBT mechanism should be adopted in the densely deployed CNWNs with proper chosen of NBS density.

The Age of Information (AoI), which evaluates the information freshness, and the energy efficiency (EE) play key roles in cellular IoT networks. This is due to that outdated data can hardly provide any useful information for delay-sensitive applications and the IoT devices usually have limited battery life. In particular, the AoI can be significantly affected by the transmission latency, which becomes the bottleneck for the AoI performance in ultradensely deployed cellular IoT networks. Moreover, it is desirable for cellular IoT networks to achieve low AoI with high EE. The data compression (DC) can decrease the AoI and improve the EE by reducing the transmission latency. Therefore, in this work, the AoI and EE performance in a large-scale densely deployed uplink cellular IoT network are jointly analyzed with the DC technology. Specifically, the closed-form results of AoI are derived and validated by Monte Carlo simulations. Based on these results, the AoI–EE ratio is defined to evaluate the tradeoff between the AoI and the EE. Equipped with these results, the effects of compression ratio (CR) and status update packet generation rate (SUPGR) on both the AoI and the AoI–EE ratio are analyzed numerically. The results show that by jointly optimizing the CR and SUPGR, the AoI can be decreased by up to 82% and the AoI–EE ratio can be reduced by up to 83% as compared with the case that only adjusts the SUPGR without the DC. It indicates that the DC should be widely adopted in IoT devices, which can improve the information freshness with low-energy consumption, especially in an ultradensely deployed scenario.