Rrh-Sector Selection and Load Balancing Based on Mdp and Dynamic Rrh-Sector-Bbu Mapping in C-Ran

Mouawad, Mostafa; Mah, Firmin; Dziong, Zbigniew

doi:10.2139/ssrn.4058018

Cited by 1 publication

(3 citation statements)

References 27 publications

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“…In this scenario, radio frequency signals are transmitted using fronthaul links between remote radio heads (RRHs), located near antennas, and a central site, at which a pool of baseband units (BBUs) is available for the radio signal processing. In C-RANs, a basic optimization problem concerns the placement and allocation / mapping of BBUs to particular RRHs, e.g., see [13] and [14]. Research works have been also focused on optimization of optical fronthaul networks, including such issues as provisioning of connectivity and minimization of transmission resource usage [15], [16], assuring network survivability [17], [18], and achieving energy efficiency [19], [20].…”

Section: Related Workmentioning

confidence: 99%

“…Namely, the latency is produced if both flows are routed over the outgoing link and, in case the flows come from the same switch input link (denoted by g), the burst transmission latency of flow f at the incoming link is smaller than the burst transmission latency of flow f at the outgoing link (what is specified by parameter γ ). Constraint (12) calculates the overall DU processing load at a PP node, and Constraint (13) assures that the processing load does not exceed the node processing capacity. Constraint (14) assures that the overall traffic load at a network link does not exceed the link capacity.…”

Section: B Mip Modelmentioning

confidence: 99%

“…We save both number of hops and static latency from those paths (lines 8-10). Then, we fill the previously prepared lists to sum: 1) number of RUs that can reach v PP ; 2) number of hops in the shortest paths between those RUs and v PP ; 3) static latency of those paths (lines [11][12][13].…”

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confidence: 99%

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Cost-Aware DU Placement and Flow Routing in 5G Packet xHaul Networks

2023

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Packet-switched xHaul networks based on Ethernet technology are considered a promising solution for assuring convergent, cost-effective transport of diverse radio data traffic flows in dense 5G radio access networks (RANs). A challenging optimization problem in such networks is the placement of distributed processing units (DUs), which realize a subset of virtualized baseband processing functions on general-purpose processors at selected processing pool (PP) facilities. The DU placement involves the problem of routing of related fronthaul and midhaul data flows between network nodes. In this work, we focus on developing optimization methods for joint placement of DUs and routing of flows with the goal to minimize the overall cost of PPs activation and processing in the network, which we refer to as the PPC-DUP-FR problem. We account for limited processing and transmission resources as well as for stringent latency requirements of data flows in 5G RAN. The latency constraint makes the problem particularly difficult in a packet-switched xHaul network since it involves the non-linear and dynamic estimation of the latencies caused by buffering of packets in the switches. The latency model that we apply in this work is based on worst-case calculations with improved latency estimations that skip from processing the co-routed, but non-affecting flows. We use a mixed-integer programming (MIP) approach to formulate and solve the PPC-DUP-FR optimization problem. Moreover, we develop a heuristic method that provides optimized solutions to larger PPC-DUP-FR problem instances, which are too complex for the MIP method. Numerical experiments performed in different network scenarios indicate on the effectiveness of the heuristic in solving the PPC-DUP-FR problem. In particular, the heuristic achieves up to 63% better results than MIP (at the MIP optimality gap equal to 76%) in a medium-size mesh network, in which the MIP problem is unsolvable for higher traffic demands within reasonable runtime limits. In larger networks, MIP is able to provide some results only for the PPC-DUP-FR problem instances with very low traffic demands, whereas the solutions generated by the heuristic are at least 83% better than the ones achieved with MIP. Also, the analysis performed shows a significant impact of the PP cost factors considered and of the level of cost differentiation of PP nodes on the overall PP cost in the network. Finally, simulation results of a case-study packet xHaul network confirm the correctness of the latency model used.

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