## 1 Introduction  
Deployment of the high-speed and low-latency wireless networks is under way to revolutionize the mission of Information and Communications Technology (ICT) in general. This is much more than just an upwards evolution of the fourth-generation (4G) Long-Term Evolution (LTE) broadband standard into the 5G New Radio (NR), but also a key enabler of Internet of Things (IoT) through the LTE's downscaled derivations LTE-M and narrowband (NB) IoT, which regulate efficient interconnecting of many energy-sufficient low-cost devices, such as smart meters, various sensors, devices in vehicles, etc. [1; 2; 3]. Unfortunately, these are often computationally limited, specifically for assessing the physical-layer transmission performance, aimed to be used by higher-layer protocols to determine the extent of their involvement in the potential error recovery escalation upwards the stack [4]. As energy-efficient error control algorithms require reliable and accurate, but still simple and thus computationally efficient, estimation of transmission performance over various (sometimes mutually diverse) channels, it is quite a specific task to investigate [5; 6]. Generally, digital data transmission is subject to symbol errors due to transmission media imperfections, such as time dispersion (mostly caused by multipath propagation) and additive noise, as well as other non-additive and non-linear imperfections on the part of equipment. With this regard, the long lasting trend of replacing the bit error rate (BER) as a former sole qualifying transmission performance indicator, by the in-service measurable (and thus network operator friendly) block error rate (BLER), has finally ended by adopting the latter in LTE and 5G NR physical layer test standards [7, 8]. Nevertheless, as the only parameter closing in the probability of a bit error, the BER still counts in the analysis, enabling estimation of BLER (by adopting a particular time distribution of erroneous bits within data blocks [7]), whose in-service direct measurement relies on identifying and counting erroneous blocks, which may be unreliable, especially when retransmission requests are rare, so that each failed block becomes more significant [9]. Moreover, it is the overall accuracy of BLER that effectively determines the so-called residual channel to the higher-layer protocols, and thus the cross-layer protocol stack design [7, 8]. Specifically, if the connectionless User Datagram Protocol (UDP) is used at the transport layer for reduced overhead and increased throughput (i.e., \"goodput\"), inaccurate estimation of the physical layer transmission performance might either unnecessarily engage the application layer for this task (with even more overhead, the most expensive at this layer), or have the errors propagated through the stack up to the application itself. With this regard, it is often needed to most efficiently estimate the orthogonal frequency-division multiplexing (OFDM) BER floor mainly affected by the OFDM-inherent transmission impairments-carrier frequency offset (CFO) and large peak-to-average power ratio (PAPR). However, for small cells and IoT end-points with limited computing power, we may consider PAPR as either insignificant (e.g., when the high-power amplifier (HPA) operates in its linear regime) or reduced by clipping or any other PAPR reduction method [10, 11]. Moreover, in indoor or small-cell propagation environment, we may also assume the signal-to-noise ratio (SNR) to be quite large, as well as the OFDM symbol cyclic prefix (CP) guard time to be much longer than expected channel delay spread, thus preventing inter-symbol interference (ISI) due to channel time dispersion. This implies that, practically, CFO remains a very dominant influencer of BER in the environment of interest here. Accordingly, in contrast to earlier proposed computationally demanding estimation of BER being determined mainly by residual CFO, in Section 2, the OFDM BER floor model is developed for small cells and IoT end-points with limited computing power.