Hakim Mabed

A Dynamic Traffic Model for Frequency Assignment

We are interested in improving the quality of frequency assignment via a more accurate modeling of the traffic over the network. For this purpose, we propose here an original model for FAP, which takes into account both spatial and temporal variation of the traffic. The proposed model is assessed with a hybrid genetic algorithm and compared against a classical model. Experimental results on both artificial and real data show significant improvements of the quality of frequency plan in terms of traffic capacity and robustness of the network.

Intelligent Neighborhood Exploration in Local Search Heuristics

Standard tabu search methods are based on the complete exploration of current solution neighborhood. However, for some problems with very large neighborhood or time-consuming evaluation, the total exploration of the neighborhood is impractical. In this paper, we present an adaptive exploration of neighborhood using extension and restriction mechanisms represented by a loop detection mechanism and a tabu list structure. This approach is applied to the K-coloring problem and evaluated on standard benchmarks like DIMACS in comparison with more powerful recently published algorithms

Genetic Tabu search for robust fixed channel assignment under dynamic traffic data

The contribution of this work is twofold. Firstly, we introduce a new channel assignment model for GSM radio networks. In this model both spatial and temporal variations of traffic are taken into account in order to improve network capacity and robustness. Secondly, using this model, we develop an original and effective hybrid algorithm to get high quality frequency plans. This algorithm combines a problem specific crossover and a Tabu search procedure. The proposed model and hybrid algorithm are evaluated using both artificial and real data. Computational results allow us to confirm the effectiveness of the proposed approach.

Constraint Propagation with Tabu List for Min-Span Frequency Assignment Problem

In this paper we focus on decision tree management for effectiveness enhancement of constraint programming hybrid algorithms. We propose a deterministic method that dynamically manages the cut of tree-branches and the access to the decision variables. Both principles are based on the discovery and the memorizing of variable/value associations that lead to unfeasible solutions. The work is thus about a learning system which progressively extends the algorithm knowledge on the problem and increases its effectiveness. We have tested the method on GRAPH and CELAR frequency assignment benchmarks. The current results are on the level of the best-known ones on the Min-Span problems and they improve the success percentages on most of these problems.

Geometric Criteria to Improve the Interference Performances of Cellular Network

Networks based on cellular concept suffer of bad performance when calls are done during handoff, i.e. during the transfers from one cell to another one. A part of these bad performances are due to the lack of management of overlapping area between cells. These imperfections find expression in cells discontinuity and distortion, in irregularity on inter-antenna distances, and in cells size variation. In this paper, we present several new criteria for cellular networks optimization based on geometrical computation on handoff and interference zones. The relevance of the model is studied on the basis of numerical and visual appreciation accorded to different networks. The huge impact of cell geometry improvement on radio quality of the network will be also discussed.

Nogood Recording with Tabu Search for CSP (Application to FAP)

In order to increase the effectiveness and the adaptability of methods, the Constraints Programming has gradually evolved to hybrid algorithms which combine exact and approximated methods in order to get the advantages of each of them. In this paper we propose a deterministic hybrid method which combines the backtracking, the constraints propagation and the Tabu Search. The method is based on finding and storing the variable/value associations that lead to infeasible solutions. It allows the algorithm to dynamically manage the cuts of branches and the access to the decision variables. The work is thus about a learning system which progressively extends the algorithm knowledge on the problem and increases its effectiveness. Benchmarks on frequency assignment problem, CELAR and GRAPH, were used for test. The results are at the level of the best ones currently known and they improve the success percentages on most of these problems.

Hypergraph T-coloring for automatic frequency planning problem in wireless LAN

Frequency assignment is one of the main issues in radio networks planning. The multiple interferences are seldom taken into account in literature. There is not a framework with their modeling. A hypergraph modeling of the network gives a more realistic representation of this phenomenon. We generalize the T-coloring problem for graphs to hypergraphs. We apply this new modeling to IEEE 802.11b/g wireless networks and study its interest.

3-D BSS geometric indicator for WLAN planning

Optimal planning of wireless networks is one of the most fundamental problems in WLAN design. This paper discusses the organisation of BSS in WLAN extended service set planning for large-scale deployment. We take advantage of a new geometric criterion to evaluate the coverage quality and apply them to WLAN planning. The obtained results show that using such criteria facilitates WLAN planning and improves the WLAN QoS.

Multi-period Channel Assignment

The well-known fixed channel assignment scheme for cellular networks is not flexible enough to follow the evolution of traffic. This paper introduces a multi-period channel assignment model. In addition to the usual objective of minimizing the interference, the model integrates another requirement to minimize the transition cost from a frequency plan to another one. Several heuristic solution approaches are also proposed. Experimental results on real data are presented to compare the multi-period model and the fixed model, and to assess the effectiveness of the proposed solution algorithms.

Optimization of Radio Resource Allocation for Multimedia Multicast in Mobile Networks

In this paper we present a mathematical modeling of Radio Resource Management (RRM) for multicast service diffusion based on Multimedia Broadcast Multicast Service (MBMS) standard. In this model, a flexible allocation approach named F2R2M is proposed, combining three candidate transport channels with scalable video transmission technology. The allocation procedure is implemented based on simulated annealing algorithm with a two- dimensional optimization objective and lexicographic order evaluation criteria. Experiments prove that, comparing with existing channel allocation approaches, F2R2M obtains allocation solution with equal QoS and lower transmission power consumption. Moreover, it reduces the possibility of achieving saturation of power or channelization codes when simulation scenarios have more users and heavy traffic load.