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GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES
, 2002
"... GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phas ..."
Abstract

Cited by 593 (81 self)
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GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phase. The best overall solution is kept as the result. In this chapter, we first describe the basic components of GRASP. Successful implementation techniques and parameter tuning strategies are discussed and illustrated by numerical results obtained for different applications. Enhanced or alternative solution construction mechanisms and techniques to speed up the search are also described: Reactive GRASP, cost perturbations, bias functions, memory and learning, local search on partially constructed solutions, hashing, and filtering. We also discuss in detail implementation strategies of memorybased intensification and postoptimization techniques using pathrelinking. Hybridizations with other metaheuristics, parallelization strategies, and applications are also reviewed.
Hierarchical cellular network design with channel allocation
 European Journal of Operational Research
"... The design of a cellular network is a complex process that encompasses the selection and configuration of cell sites and the supporting network infrastructure. This investigation presents a net revenue maximizing model that can assist network designers in the design and configuration of a cellular ..."
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Cited by 6 (0 self)
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The design of a cellular network is a complex process that encompasses the selection and configuration of cell sites and the supporting network infrastructure. This investigation presents a net revenue maximizing model that can assist network designers in the design and configuration of a cellular system. The integer programming model takes as given a set of candidate cell locations with corresponding costs, the amount of available bandwidth, the maximum demand for service in each geographical area, and the revenue potential in each customer area. Based on these data, the model determines the size and location of cells, and the specific channels to be allocated to each cell. To solve problem instances, a maximal clique cut procedure is developed in order to efficiently generate tight upper bounds. A lower bound is constructed by solving the discrete optimization model with some of the discrete variables fixed. Computational experiments on seventytwo problem instances demonstrate the computational viability of our new procedure. 1
Dynamic Channel Assignment with Cumulative CoChannel Interference
"... This paper studies the problem of centralized dynamic channel assignment (DCA) in wireless cellular systems under space and timevarying channel demand. The objective is to minimize the number of channels required to satisfy demand while also satisfying cochannel interference constraints. Cumulative ..."
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This paper studies the problem of centralized dynamic channel assignment (DCA) in wireless cellular systems under space and timevarying channel demand. The objective is to minimize the number of channels required to satisfy demand while also satisfying cochannel interference constraints. Cumulative cochannel interference constraints govern channel reuse, via a threshold decision criterion based on the carriertointerference ratio. The paper makes two contributions. First, it provides an empirical bound on the difference between the minimal number of channels required based only on geographic reuse distance versus the cumulative interference case in the context of linearly increasing demand. The bound is characterized using only the reuse distance. It is obtained with an Integer Programming (IP) based strategy that uses channel assignments for one demand state to assign channels for the next state. Geographic locality constraints are applied to limit reassignments. The impact of cumulative interference constraints is observed to be small for small geographic localities. Second, the paper presents a new, fast DCA heuristic that is based on the characteristic channel reuse patterns used by the IPbased strategy. The heuristic and IPbased method yield similar results for the zero blocking condition. The DCA heuristic is applied to the problem of estimating the blocking probabilities of call arrivals modeled by a two state discretetime Markov chain and uniformly distributed holding times. The blocking performance for an ensemble of spatial load imbalance distributions is uniquely characterized using the heuristic and IP solutions. I.
Bandwidth Allocation in Cellular Networks with Multiple Interferencesâˆ—
"... We study the problem of bandwidth allocation with multiple interferences. In this problem the input consists of a set of users and a set of base stations. Each user has a list of requests, each consisting of a base station, a frequency demand, and a profit that may be gained by scheduling this reque ..."
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We study the problem of bandwidth allocation with multiple interferences. In this problem the input consists of a set of users and a set of base stations. Each user has a list of requests, each consisting of a base station, a frequency demand, and a profit that may be gained by scheduling this request. The goal is to find a maximum profit set of user requests S that satisfies the following conditions: (i) S contains at most one request per user, (ii) the frequency sets allotted to requests in S that correspond to the same base station are pairwise nonintersecting, and (iii) the QoS received by any user at any frequency is reasonable according to an interference model. In this paper we consider two variants of bandwidth allocation with multiple interferences. In the first each request specifies a demand that can be satisfied by any subset of frequencies that is large enough. In the second each request specifies a specific frequency interval. Furthermore, we consider two interference models, multiplicative and additive. We show that these problems are extremely hard to approximate if the interferences depend on both the interfered and the interfering base stations. On the other hand, we provide constant factor approximation algorithms for both variants of bandwidth allocation with multiple interferences for the case where the interferences depend only on the interfering base stations. We also consider a restrictive special case that is closely related to the Knapsack problem. We show that this special case is NPhard and that it admits an FPTAS.
CoChannel Interference
"... This paper studies the problem of centralized dynamic channel assignment (DCA) in wireless cellular systems under space and timevarying channel demand. The objective is to minimize the number of channels required to satisfy demand while also satisfying cochannel interference constraints. Cumulative ..."
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This paper studies the problem of centralized dynamic channel assignment (DCA) in wireless cellular systems under space and timevarying channel demand. The objective is to minimize the number of channels required to satisfy demand while also satisfying cochannel interference constraints. Cumulative cochannel interference constraints govern channel reuse, via a threshold decision criterion based on the carriertointerference ratio. The paper makes two contributions. First, it provides an empirical bound on the difference between the minimal number of channels required based only on geographic reuse distance versus the cumulative interference case in the context of linearly increasing demand. The bound is characterized using only the reuse distance. It is obtained with an Integer Programming (IP) based strategy that uses channel assignments for one demand state to assign channels for the next state. Geographic locality constraints are applied to limit reassignments. The impact of cumulative interference constraints is observed to be small for small geographic localities. Second, the paper presents a new, fast DCA heuristic that is based on the characteristic channel reuse patterns used by the IPbased strategy. The heuristic and IPbased method yield similar results for the zero blocking condition. The DCA heuristic is applied to the problem of estimating the blocking probabilities of call arrivals modeled by a two state discretetime Markov chain and uniformly distributed holding times. The blocking performance for an ensemble of spatial load imbalance distributions is uniquely characterized using the heuristic and IP solutions. I.
REACTIVE GRASP WITH PATH RELINKING FOR BROADCAST SCHEDULING Clayton W. Commander 96 Communications Group,
"... The Broadcast Scheduling Problem (BSP) is a well known NPcomplete problem that arises in the study of wireless networks. In the BSP, a finite set of stations are to be scheduled in a time division multiple access (TDMA) frame. The objective is a collision free transmission schedule with the minimum ..."
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The Broadcast Scheduling Problem (BSP) is a well known NPcomplete problem that arises in the study of wireless networks. In the BSP, a finite set of stations are to be scheduled in a time division multiple access (TDMA) frame. The objective is a collision free transmission schedule with the minimum number of TDMA slots and maximal slot utilization. Such a schedule will minimize the total system delay. We present variations of a Greedy Randomized Adaptive Search Procedure (GRASP) for the BSP. Pathrelinking, a postoptimization strategy is applied. Also, a reactivity method is used to balance GRASP parameters. Numerical results of our research are reported and compared with other heuristics from the literature.