Post-deployment tuning of UMTS cellular networks through dual-homing of RNCs

  • Authors:
  • Samir K. Sadhukhan;Swarup Mandai;Saroj R. Biswas;Partha Bhaumik;Debashis Saha

  • Affiliations:
  • Indian Institute of Management, Calcutta, India;Wipro Technologies, Kolkata, India;IBM Global, Kolkata, India;CSE Dept, Jadavpur Univ, Kolkata, India;Indian Institute of Management, Calcutta, India

  • Venue:
  • COMSNETS'09 Proceedings of the First international conference on COMmunication Systems And NETworks
  • Year:
  • 2009

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Abstract

In conventional UMTS cellular networks, during deployment usually a set of NodeBs is assigned to one Radio Network Controller (RNC), and a set of RNCs to one Serving GPRS Support Node (SGSN) for data services, as weD as to one Mobile Switching Centre (MSC) for voice services. Operators thus far have considered single-homing of RNCs to MSCs/SGSNs (i.e., many-to-one mapping) with an objective to reduce the total cost over a fixed period of time. However, a single-homing network does not remain cost-effective any more when subscribers later on begin to show specific inter-MSC/SGSN mobility patterns (say, diurnality of office goers) over time. This necessitates post-deployment topological extension of the network in terms of dual-homing of RNCs, in which some specific RNCs are connected to two MSCs/SGSNs via direct links resulting in a more complex many-to-two mapping structure in parts of the network. The partial dual-homing attempts to increase link cost minimally and reduce handoff cost maximaDy, thereby significantly reducing the total cost in a post-deployment optimal extension. In this paper, we formulate the scenario as a combinatorial optimization problem and solve the NP-Complete problem using two meta-heuristic techniques, namely Simulated Annealing (SA) and Tabu search (TS). We then compare these techniques with a novel optimal heuristic search method that we propose typically to solve the problem. The comparative results reveal that, though aD of them perform equally well for small networks, for larger networks, the search-based method is more efficient than meta-heuristic techniques in finding optimal solutions quickly.