# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 1 Introduction  
The Internet of Things (IoT) is advancing towards 26 billion connected units by 2020 according to estimations [1]. For this to become a reality, it is necessary to provide connectivity for a large number of energy-constrained smart devices in an energy efficient manner, while maximizing throughput andrange. As endpoints in IoT are typically embedded devices, often battery powered and deployed in outdoor environments, they require scalable low-power wireless technologies. Existing low-power wireless technologies used in IoT can be divided in two classes: (1) Wireless Personal Area Networks (WPAN) and (2) Low-Power Wide Area Networks (LPWAN). WPAN technologies such as Radio-Frequency Identification (RFID), ZigBee, and Bluetooth, although applicable to low-power device communication, have a number of constraints that limit their capabilities, mostly regarding the maximum number of connected devices, throughput and transmission range. On the other hand, LPWAN technologies such as LoRa and SigFox provide long range communication (up to 15 and 50 km, respectively), but have a very low throughput (up to 50 and 0.1 kbps, respectively). A new wireless standard, IEEE 802.11 ah [2] (also known as Wi-Fi HaLow), introduces a trade-off between throughput and range and fills the gap between WPAN and LPWAN. IEEE 802.11 ah operates in unlicensed sub-GHz frequency bands (863-868 MHz in Europe, 755-787 MHz in China and 902-928 MHz in North-America) and can provide connectivity between at most 8192 low-power devices at rates from 150 kbps to 78 Mbps over a range up to 1 km. Hence, IEEE 802.11 ah supports much higher throughput than existing LPWAN technologies and both higher throughput and range than WPAN technologies. Moreover, LoRa and SigFox have severe restrictions concerning downlink (DL) traffic. LoRaWAN class A devices can only receive a downlink packet in response to an uplink (UL) packet at the same (for \(RX1 DROffset=0\)) or lower (for other values of \(RX1 DROffset\) and \(RX2\)) data rate than the uplink [3]. SigFox allows only 140 uplink messages (up to 12 bytes) and four downlink messages (up to 8 bytes) per day, which makes reliability infeasible as only a few uplink packets can even be confirmed. Those restrictions make high-throughput combined uplink and downlink (UL/DL) communication infeasible for both technologies. Ergo, IEEE 802.11 ah fills the gap when it comes to reliable and high-throughput applications over a somewhat smaller area. It offers the same data rate for downlink and uplink traffic and, hence, enables applications with frequent actuation (i.e., control-loops), high-throughput applications (e.g., video streaming, firmware updates) and reliable monitoring (i.e., acknowledging every transmitted packet). So far, a number of IEEE 802.11 ah performance improving algorithms have been proposed, but only for typical Wireless Sensor Network (WSN) monitoring traffic. Monitoring in WSNs corresponds to periodically transmitting the measured values and sleeping for the remainder of the interval. Almost 80% of available research on Wi-Fi HaLow data-link layer only addresses use cases with uplink traffic. Only some research addresses specific use cases with either downlink or bidirectional traffic (energy efficiency, latency, hidden node mitigation). Next to this, IEEE 802.11 ah introduced the Restricted Access Window (RAW) feature for Media Access Control (MAC). However, it does not define how to configure Traffic Indication Map (TIM) and RAW mechanism. This freedom can lead to significant improvements in performance, but also to degradation of the network, as the performance of these mechanisms is greatly dependent on traffic patterns. To make the most of both TIM and RAW and exploit their benefits, it is necessary to configure them properly. Today, there is a lack of practical knowledge on both (1) its operation with Resource Allocation (RA), which can be used for downlink scheduling, and (2) its performance in parallel with TIM segmentation.