Designing Cellular Mobile Networks Using Non{Deterministic Iterative Heuristics

Abstract Network planning in the highly competitive, demand-adaptive and rapidly growing cellular telecommunications industry is a fairly complex and crucial issue. It comprises collective optimization of the supporting, switching, signaling and interconnection networks to minimize costs while observing imposed infrastructure constraints. This work focuses on the problem of assigning cells to switches, which comprise the Base Station Controller and Mobile Switching Center, in a cellular mobile network. As a classic instance of the NP-hard Quadratic Assignment Problem (QAP), deterministic algorithms are incapable of nding optimal solutions in the vast complex search space in polynomial time. Hence, a randomized, heuristic algorithm, such as Simulated Evolution is used in this work to optimize the transmission costs in cellular networks. The results achieved are compared with existing methods available in literature. Key words: Network planning, Cellular Mobile Network, Assignment, Quadratic Assignment Problem, Heuristics, Evolutionary Heuristics, Soft Computing

Designing Cellular Mobile Networks Using Non{Deterministic Iterative Heuristics

Abstract Network planning in the highly competitive, demand-adaptive and rapidly growing cellular telecommunications industry is a fairly complex and crucial issue. It comprises collective optimization of the supporting, switching, signaling and interconnection networks to minimize costs while observing imposed infrastructure constraints. This work focuses on the problem of assigning cells to switches, which comprise the Base Station Controller and Mobile Switching Center, in a cellular mobile network. As a classic instance of the NP-hard Quadratic Assignment Problem (QAP), deterministic algorithms are incapable of nding optimal solutions in the vast complex search space in polynomial time. Hence, a randomized, heuristic algorithm, such as Simulated Evolution is used in this work to optimize the transmission costs in cellular networks. The results achieved are compared with existing methods available in literature. Key words: Network planning, Cellular Mobile Network, Assignment, Quadratic Assignment Problem, Heuristics, Evolutionary Heuristics, Soft Computing

How Good Is Neural Combinatorial Optimization?

Traditional solvers for tackling combinatorial optimization (CO) problems are usually designed by human experts. Recently, there has been a surge of interest in utilizing Deep Learning, especially Deep Reinforcement Learning, to automatically learn effective solvers for CO. The resultant new paradigm is termed Neural Combinatorial Optimization (NCO). However, the advantages and disadvantages of NCO over other approaches have not been well studied empirically or theoretically. In this work, we present a comprehensive comparative study of NCO solvers and alternative solvers. Specifically, taking the Traveling Salesman Problem as the testbed problem, we assess the performance of the solvers in terms of five aspects, i.e., effectiveness, efficiency, stability, scalability and generalization ability. Our results show that in general the solvers learned by NCO approaches still fall short of traditional solvers in nearly all these aspects. A potential benefit of the former would be their superior time and energy efficiency on small-size problem instances when sufficient training instances are available. We hope this work would help better understand the strengths and weakness of NCO, and provide a comprehensive evaluation protocol for further benchmarking NCO approaches against other approaches

Spectrum and power optimization for wireless multiple access networks.

Emerging high-density wireless networks in urban area and enterprises offer great potential to accommodate the anticipated explosion of demand for wireless data services. To make it successful, it is critical to ensure the efficient utilisation of limited radio resources while satisfying predefined quality of service. The objective of this dissertation is to investigate the spectrum and power optimisation problem for densely deployed access points (APs) and demonstrate the potential to improve network performance in terms of throughput and interference. Searching the optimal channel assignment with minimum interference is known as an AfV-haxd problem. The increased density of APs in contrary to the limited usable frequencies has aggravated the difficulty of the problem. We adopt heuristic based algorithms to tackle both centralised and distributed dynamic channel allo cation (DCA) problem. Based on a comparison between Genetic Algorithm and Simulated Annealing, a hybrid form that combines the two algorithms achieves good trade-off between fast convergence speed and near optimality in centralised scenario. For distributed DCA, a Simulated Annealing based algorithm demon strates its superiority in terms of good scalability and close approximation to the exact optimal solution with low algorithm complexity. The high complexity of interactions between transmit power control (TPC) and DCA renders analytical solutions to the joint optimisation problems intractable. A detailed convergence analysis revealed that optimal channel assignment can strengthen the stability condition of TPC. Three distributed algorithms are pro posed to interactively perform the DCA and TPC in a real time and open ended manner, with the ability to appropriately adjust power and channel configurations according to the network dynamics. A real network with practical measurements is employed to quantify and verify the theoretical throughput gain of their inte gration. It shows that the integrated design leads to a substantial throughput improvement and power saving compared with conventional fixed-power random channel allocation system

Proceedings of AUTOMATA 2010: 16th International workshop on cellular automata and discrete complex systems

International audienceThese local proceedings hold the papers of two catgeories: (a) Short, non-reviewed papers (b) Full paper

Network partition for switched industrial ethernet using combined search heuristics

Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2011Title from PDF of title page, viewed on June 6, 2011Includes bibliographical references (p. 44-46)Thesis advisor: Xiaojun ShenVitaA large industrial company needs a cost efficient telecommunication network to support heavy telecommunication needs among its different departments which are distributed in various locations. Because of the huge amount of daily communications, the network designer must partition the communicating devices into subnets each of which is supported by a high speed Ethernet. Then, the subnets are connected by a second level switch device called controller which handles inter-subnet communications. An optimization problem is how to partition n communicating devices into k groups such that the amount of intra-network traffic is balanced among the k groups and at the same time the inter-network traffic is minimized for a given traffic demand. This problem is known as the Network Partition Problem (NPP). The NPP problem has been studied by some researchers, but because of its NPhardness, only limited progress has been reported by two recent papers. The later one slightly improved on the results obtained by the previous one, and both papers used genetic algorithms. This thesis investigated the NPP problem and concluded by extensive tests that it is very difficult to improve further if we purely follow the method of genetic algorithms. Motivated by searching for new approaches, this thesis tried another evolutionary algorithm, i.e., the simulated annealing (SA) to see any hope to get a breakthrough. Encouraging results were obtained for some cases but not show overall superiority. Finally, this thesis investigated the approach that combines these two methods in searching for a better result. Extensive simulations demonstrated that this method work efficiently. By the combination of these two methods, we obtained obvious improvements on previous published results. This approach studied in this thesis can be applicable to practically solving other NP-hard problems also.Introduction -- Model description and problem definition -- Our approach -- Results -- Conclusion and future work -- Appendi