# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 7 Conclusions  
The present work studies PHY latency in mmWave Wi-Fi networks. It considers both an ideal scenario and a simulated latency-inducing IEEE 802.11 ad PHY based on performance figures reported in state-of-the-art literature. Moreover, the simulations include BER results for transmission over an AWGN channel and give insight into individual PHY component data, such as the number of incurred LDPC decoding iterations. The ideal case results show a dependency of the MPDU latency on the employed MCS that grows stronger with the increase in MPDU length. Consequently, the remaining parts of the study focus on the largest allowed payload lengths (262 kB), also emphasizing data-hungry real-time XR applications. Aggregating PPDUs shows a high increase in latency and is discarded during the ideal case study. Evaluating PHY simulation results reveal that latency induced by the RX DBB is highly dependent on the number of incurred decoding iterations, further governed by the amount of noise in the wireless channel. The resulting latency is evaluated for 1-100 decoding iterations, with a closer look at both BER and latency values at 10 and 100 iterations. Three different demapping algorithms--exact, approximative, and decision threshold--are also studied, with the latter yielding the most timely data reception. Figure 17: Selection of points with minimal latency that satisfy the \(10^{-5}\) BER constraint. Colours represent the MCS at which the Pareto optimal points are achieved, the expected latency region is highlighted by the white polygon on the back plane, and iteration-independent optimal points are circled in grey. Figure 18: Left to right: comparison of expected latency regions (**a**); BER comparison for the two PHY simulation cases (**b**), with the left and right parts of the distributions corresponding to study steps 2 and 3, respectively. In regard to BER, the largest benefits in allowed decoding iteration tuning are identified at 3-20 iterations. The exact tuning region further depends on the available time and the MCS in use. Although the smallest achievable latency values and the MCSs for reaching them are discussed during the evaluation of decoding iterations on latency, the results are revisited in search of Pareto optimality. A set of optimal points is identified in accordance with the \(10^{-5}\) BER constraint, that generate between 0.28- and 1.37 ms of latency, depending on the selected MCS in Table 2. The lower limit, 0.28 ms, is also the lowest achievable latency during the transmission of 262 kB long payloads. Reducing latency below it would require shortening the payload if the application allows it. In error-intolerant applications, the \(10^{-5}\) BER constraint may be insufficient and cause retransmission; hence, they would need stricter constraints to avoid generating more latency. In addition to latency-oriented optimization, a group of Pareto curves for generating the least amount of bit errors and complying with four discrete latency constraints is determined. These show the attainable BER regions per latency constraint and can be used in applications emphasizing low-latency operation before reliable data delivery. A final comparison of the studied values is conducted. While latency reduction below that of the ideal case is not possible, parallel MCS and decoder iteration tuning can reduce data loss. These dynamic latency management mechanisms can also work towards lowering the BER by leveraging redundant time when available. The presented results are all based on 262 kB long PPDU payloads, except for the ideal scenario analysis. The transmission of long sequences inherently takes up a large amount of time, often surpassing the latency generated in the RX DBB. The 2.71 ms latency incurred at MCS index 2--offering the highest reliability--is far from the 1 ms latency constraint imposed by some latency-sensitive applications. Such applications often also feature much shorter payloads.