## 3 Network Scenario
### Flow Latencies  
One of the fundamental assumptions in this work is that the packet Xhaul transport network is capable of ensuring acceptable latencies of individual packet flows, which is an essential requirement for reliable RAN operation, as discussed in [7; 54]. This assumption also supports the realization of soft network slicing, where the data flows belonging to different slices use common resources of the packet transport network and their slice-specific latency requirements are satisfied. Therefore, the maximum one-way latencies of packet flows must not exceed specific levels related to these flows, and these guarantees should concern all bursts belonging to a flow. Hence, the considered network-planning problem imposes relevant latency constraints on individual network flows. The study uses the same reliable flow latency-estimation model as in [20; 21]. For consistency, the model is presented here again.  
The latency of a flow consists of a static and dynamic component. The static component includes the propagation delays in network links (assuming signal speed \(2\times 10^{5}\) km/s), the store-and-forward delays in switches (can be bounded), and the burst transmission times in links. The dynamic component represents the burst buffering delays at switch output ports. The static latencies are deterministic, but the buffering delays depend on the buffers' state and occupation. Therefore, in this work, the buffering latencies are estimated using the worst case latency model proposed in [8] and derived using the network calculus theory, as discussed in [55].  
The mentioned model assumes that the latencies are produced by (a) higher or equal-priority bursts that might be selected for transmission before the burst of the given flow and (b) the largest lower priority burst currently in transmission. Let \(\mathcal{H}(f,e)\) and \(\mathcal{L}(f,e)\) be the sets of, respectively, higher/same-priority flows and lower priority flows interfering with flow \(f\) in link \(e\) and \(L(q,e)\) denote the latency produced by interfering flow \(q\) in link \(e\). The worst case buffering latency of flow \(f\) routed through path \(p\) can be expressed as:  
Figure 2: FH traffic prioritization: (a) SP–FH and (b) DP–FH.  
\[L^{\text{buf}}(f)=\sum_{e\in P}\left(\sum_{q\in H(f,e)}L(q,e)+\max_{q\in\mathcal{L} (f,e)}L(q,e)\right) \tag{1}\]  
where the first component is the sum of latencies produced by the interfering flows of the higher and same priority and the second component is the maximum latency produced by a lower priority flow.  
This study mainly focuses on the packet-switched Xhaul transport network's latency guarantees, whereas the service level's latency constraints, imposed by the 5G services, are not explicitly included. Introducing relevant constraints to the optimization model can ensure the 5G service level latency guarantees. To this end, a proper latency model reflecting the specific implementation of the PP hardware and software realizing the DU and CU functions is required. The development of such a model is out of the scope of this study and is left for future work. The lack of a service-level latency model is somehow mitigated by the assumption that URLLC services are realized within the access network (i.e., in the proximity of RUs) using a MEC server colocated with the PP performing the CU functions. Thus, the transport latencies are minimized for these latency-sensitive services.