# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 6 Discussion
### Pareto Optimality  
Considering only latency as the optimization metric is often insufficient in practice. A hastily delivered erroneous MPDU might require retransmission, which will further increase the time delay. Hence, applications will in practice impose combined latency and data integrity requirements. Approaching Pareto optimality is achieved by setting the highest allowed BER value and selecting the MCS that generates the least latency. In our case study, the BER limit is set to \(10^{-5}\). Decision threshold demapping is used and up to 10 decoding iterations are allowed. The \(10^{-5}\) threshold value is based on the short packet transmission requirements in 5G NR URLLC. While shorter packets with a \(10^{-5}\) BER are likely error-free, 262 kB long payloads will on average include 21-bit errors. As reasoned in Section 2.2, the constraint value is kept as a reference since XR video streaming applications are error-tolerant to some extent. Any data points surpassing the BER constraint are discarded; among those in accordance with it, the minimal achieved MPDU latency is extracted. Repeating the process across all \(\frac{F_{k}}{N_{0}}\) values yields a set of minimal latency points, illustrated in Figure 16a. It demonstrates that communication below 3 dB \(\frac{F_{k}}{N_{0}}\) is not feasible when applying the \(10^{-5}\) BER constraint, marking the point at which a session transfer to a more robust RAT must take place. The remaining points show that single MPDU latency may range from 0.28 to 1.37 ms between 3- and 15 dB. Figure 16b contains Pareto optimal curves when the constraint and optimization variable are inverted. The curves apply to four distinct maximal PHY latency constraints, and the data points on them represent the proposed MCS for provoking the smallest BER at different noise levels. All BER results converge towards zero beyond a certain \(\frac{F_{k}}{N_{0}}\) value. The curves corresponding to stricter latency constraints are offset towards the right, where noise levels are lower. This is due to the transmission taking place at higher MCS indexes, making it more prone to errors. The two curves corresponding to latency constraints 0.5- and 0.75 ms only exist beyond 5.75- and 6.75 dB. As per to Figure 10c in Section 5.2, the decoder could become a bottleneck at lower \(\frac{F_{k}}{N_{0}}\) values and compromise the latency constraint. Figure 15: Trade-off between latency and BER at two distinct MCS and \(\frac{F_{k}}{N_{0}}\) combinations. Marker size represents the average number of incurred iterations, ranging from 1 to 4.75. The grey curve represents a reference \(\frac{1}{x}\) function fitted to the data points. Both Y-axes are rounded to two decimal points. The 0.13 μs MCS 2 latency difference from top to bottom is contained within the roundoff. The lowest achievable latency is further evaluated in Figure 17, where the number of allowed decoding iterations is swept from 1 to 100. The data points show considerable similarity to the non-optimized version in Figure 13, except for the results obtained using only a few decoding iterations. Due to the high bit error count at high MCS indexes, these are substituted with their more robust counterparts. Two communicating devices may also agree on the ideal transmission and reception parameters based on momentary channel conditions. The collective approach assumes the RX' MAC layer has control over the number of PHY LDPC decoding iterations and a parallel communication channel between the two devices exists, avoiding additional time delays. The result is a set of optimal MCS and allowed iteration count combinations, yielding minimal PHY latency. The newly-defined set of points, also outlined in Figure 17, show that switching to a higher indexed MCS at the cost of additional decoding iterations is often beneficial. Most of these switches occur either below 8 dB \(\frac{E_{b}}{N_{0}}\) or at less than 20 decoding iterations.