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Physical (PHY) layer abstraction is an effective method to reduce the runtimes compared with link simulations but still accurately characterize the link performance. As a result, PHY layer abstraction for IEEE 802.11 WLAN and 3GPP LTE/5G has been widely configured in the network simulators such as ns-3, which achieve faster system-level simulations quantifying the network performance. Since the first publicly accessible 5G NR Sidelink (SL) link simulator has been recently developed, it provides a possibility of implementing the first PHY layer abstraction on 5G NR SL. This work deploys an efficient PHY layer abstraction method (i.e., EESM-log-SGN) for 5G NR SL based on the offline NR SL link simulation. The obtained layer abstraction which is further stored in ns-3 for use aims at the common 5G NR SL scenario of OFDM unicast single layer mapping in the context of Independent and Identically Distributed (i.i.d.) frequency-selective channels. We provide details about implementation, performance, and validation. 

The performance of Wi-Fi networks depends on the ability of devices to adapt their transmissions to dynamic channel/network conditions. Hence, “Rate Adaptation Algorithms (RAAs)” have been devised to allow nodes to select appropriate modulation and coding schemes (and other parameters) in response to varying channel/network conditions. These algorithms are neither standardized nor typically divulged by vendors, and devising a ‘performance-optimal’ RAA for specific scenario remains an active topic that necessitates a complex, multi-parameter cross-layer (PHY/MAC) approach. The ns-3 network simulator offers detailed models of the Wi-Fi medium access control (MAC) layer, including three reference RAA implementations; however testing and validation of these RAA models has been very limited to date. This paper reports on initial test and validation for ns-3 RAA models via 802.11n/ac/ax simulations. After describing the RAA scope and implementations, we explore and summarize insights from test results as to a) whether the ns-3 RAAs are able to achieve the correct rates as configuration is varied and b) how they respond to step changes in the received signal-to-noise ratio (SNR) as a means for exploring their convergence properties.