Metaheuristics have yielded very promising results for the frequency assignment problem (FAP). However, the results obtainable using currently published methods are far from ideal in complex, large-scale instances. This paper applies and extends some of the most recent advances in evolutionary algorithms to two common variants of the FAP, and shows how, in traditional techniques, two common issues affect their performance: 1) premature convergence and 2) the way in which neutral networks are handled. A recent replacement-based diversity management strategy is successfully applied to alleviate the premature convergence drawback. Additionally, by properly defining a distance metric, the performance in the presence of neutrality can also be greatly improved. The replacement strategy combines the principle of transforming a single-objective problem into a multiobjective one by considering diversity as an additional objective, with the idea of adapting the balance induced between exploration and exploitation to the requirements of the different optimization stages. Tests with 44 publicly available instances yield very competitive results. New best-known frequency plans were generated for 11 instances, whereas in the remaining ones the best-known solutions were replicated. Comparisons with a large number of strategies designed to delay convergence of the population clearly show the advantages of our novel proposals.

Improving Diversity in Evolutionary Algorithms: New Best Solutions for Frequency Assignment

In this paper, we are interested to the prob- lem of designing wireless local networks (WLANs), which involves deciding where to install the access points (APs), and assigning frequency channels to them with the aim to cover the service area and to guarantee enough capacity to users. Our proposal provides different solutions to the prob- lems related to the WLAN design and can be divided into two main parts. In the first part, we focus on the prob- lem of designing a WLAN by
treating separately the AP positioning and the channel assignment problems. For AP positioning issue, we formulate it as a set covering problem. Since the computation complexity limits the exact solu- tion, we propose two heuristics to offer efficient solution. While, for the channel assignment, we define this issue as a minimum interference frequency assignment problem and propose three heuristics. Two heuristics aim to minimize the interference at AP locations.
Whereas, the objective of the third heuristic is to minimize the interference at the TPs level. In the second part, we treat jointly the two aforementioned issues based on the concept of virtual forces. In this case, we start from an initial solution provided by the separated approach and try to enhance it by adjusting the APs’ positions and reassigning their operating frequen- cies. In each part of this work, we start first by describing the problem, then presenting the algorithms proposed to resolve it efficiently and finally we illustrate the obtained results and analyze them.

WLAN planning: Separate and joint optimization of both access point placement and channel assignment

To meet the rising demand of mobile data, IEEE 802.11ac was recently developed to offer better network performance than previous IEEE 802.11 standards, representing the 5G Wi-Fi technology. One of the new technologies added to IEEE 802.11ac is a dynamic bandwidth channel access scheme that supports channel bonding (CB) with an up to 160 MHz bandwidth. However, the use of CB increases the number of stations sharing the same medium, resulting in an increased collision rate and severe interference caused by overlapping sub-channels. This problem is further aggravated in densely deployed wireless networks where the service ranges of access points (APs) are likely to be highly overlapping. To resolve these issues, we present an analytical throughput model that considers the effect of both collisions and interference. With this analytical model, we formulate the CB problem to optimize the throughput along with the traffic demand and propose a genetic algorithm for solving the problem. Further, a dynamic channel bonding algorithm (named DCB) is proposed to solve the problem with lower complexity. Extensive simulation results demonstrate that our proposed algorithm provides a higher performance than other recent dynamic CB algorithms with respect to the throughput achieved for the traffic demand.

Dynamic Channel Bonding Algorithm for Densely Deployed 802.11ac Networks

Two evolutionary algorithms are proposed in this paper to handle with access point channel assignment in Wireless Local Area Network. The objective considered is to minimize the maximum level of interference experienced by the users. Two deterministic heuristics, commonly employed in the considered problem, are used as benchmark. The paper is focused on the IEEE 802.11ac standard, which operates exclusively in the 5 GHz band. This standard provides a bigger number of non-overlapped channels and higher throughput. Tests in three different scenarios and configurations, using channel width of 20, 40 and 80 MHz, are performed.

Using evolutionary algorithms for channel assignment in 802.11 networks

This work presents the development of a wireless Access Point (AP) placement software that uses microwave signal propagation models. We use the Simulated Annealing metaheuristic, to improve the signal coverage according to the physical features of the architectural floor plan. In the experiments, a Graphics Processing Unit (GPU) with CUDA is used to parallelize the simulations, speeding up the runtime required. It is worth noting that our software makes possible to test the provisions of APs without the operational cost of having to move them physically. It proposes a spatial arrangement of APs that provides a greater coverage with usable signal intensity within the simulated environment. Using a limited amount of APs, the test case results showed that it is possible to obtain a significant improvement in the coverage of the Wi-Fi signal by adjusting the APs positions to the metaheuristic proposed solution. In addition, our software is capable of performing simulations with an increasing number of APs if the current quantity is not enough to achieve better quality and signal coverage.

Wireless Access Point Positioning Optimization

A new approach for wireless local area network (WLAN) installation planning is proposed in this paper. This approach is based on a multiobjective genetic algorithm and on greedy algorithms, and it is composed of two steps: network structure design and channel assignment. In the first step, the quantity, position and load balance of the access points are planned taking into account a minimum coverage level, the AP capacity and the traffic demand in the WLAN. In the second step, the channel of each access point is assigned in such a way that the network presents minimal interference and high throughput. An approximation of the efficient solution set, taking into account two design criteria, is delivered by the proposed algorithm. These solutions can be used to provide cost reduction and quality improvement on WLAN design. Results obtained by the proposed algorithm in two reasonable scenarios are presented in order to evaluate its efficacy.

Multiobjective planning of wireless local area networks (WLAN) using genetic algorithms

This manuscript presents a novel approach based on hybrid optimization techniques for planning Wireless Local Area Networks in two stages: i) network structure design for access point (AP) placement and channel assignment and ii) channel assignment enhancement. We consider two objective functions: network load balance and signal-to-interference-plus-noise ratio; and three hard constraints: maximum AP capacities, client demand attendance, and minimum coverage levels. The proposed algorithm delivers an approximation of the efficient solution set, considering the two functions described above. The results from two scenarios were compared to the following four approaches: two multiobjective evolutionary algorithms, a well-known commercial tool, and a greedy technique. Finally, the solutions were subjected to sensitivity analysis to validate their robustness regarding user mobility and AP failures.

Hybrid multicriteria algorithms applied to structural design of wireless local area networks

Multiobjective planning of indoor Wireless Local Area Networks using subpermutation-based hybrid algorithms

This manuscript presents a comparative study between three Multiobjective Evolutionary Algorithms (NSGA-II, GDE3, and MOEA/D-DE) on Wireless Local Area Networks design. The considered problem consists on defining the positions, quantity, channels, and load balance of access points to be installed. Problem features such as equipment limitations, traffic demand, and minimum coverage level required are modeled as constraints. The used algorithms were tested in two scenarios, considering different network profiles. The results show that the developed approach for WLAN planning can help a network designer to define good Wi-Fi projects, improving the signal level, network balance, and reducing interference.

Marlon Paolo Lima

Papers

Using evolutionary algorithms for channel assignment in 802.11 networks

Multiobjective planning of wireless local area networks (WLAN) using genetic algorithms

Hybrid multicriteria algorithms applied to structural design of wireless local area networks

Multiobjective planning of indoor Wireless Local Area Networks using subpermutation-based hybrid algorithms

A comparative study of Multiobjective Evolutionary Algorithms for Wireless Local Area Network design