Minimum Span Frequency Assignment Research Articles

The F/DR-D-10 Algorithm: A Novel Heuristic Strategy to Solve the Minimum Span Frequency Assignment Problem Embedded in Mobile Applications

Wireless communication supports various real-world applications, such as aeronautical navigation, satellite and TV broadcasting, wireless LANs, and mobile communications. The inherent characteristics of the electromagnetic spectrum impose constraints on telecommunication channels and their frequency bandwidths within mobile networks. A persistent challenge in these applications is providing high-demand services to mobile users, where frequency assignment problems, also known as channel assignment problems, assume significance. Researchers have developed several modeling approaches to address different facets of this problem, including the management of interfering radio signals, the assessment of available frequencies, and optimization criteria. In this paper, we present improved algorithms for solving the Minimum Span Frequency Assignment Problem in mobile communication systems using the greedy optimization approach known as F/DR. We solved and evaluated twenty well-known benchmark cases to assess the efficacy of our algorithms. Our findings consistently demonstrate that the modified algorithms outperform the F/DR approach with comparable computational complexity. The proposed algorithm notably achieves the following benchmarks: The modified algorithms consistently produce at least one local optimum better than the traditional algorithm in all benchmark tests. In 95% of the benchmarks evaluated, the probability of discovering a local optimum value (calculated by the modified algorithm) that is better than or equal to the one found by the conventional algorithm exceeds 50%.

MinFAST: Minimum Span Frequency Assignment Technique for Integrated Access and Backhaul Networks

We propose novel centralized minimum span frequency assignment (MS-FA) techniques allowing partially overlapped channels (POCs) to improve cost and spectral efficiency for integrated access and backhaul (IAB) networks. We first revisit the conventional MS-FA problem (MS-FAP) that minimizes the required bandwidth for given quality-of-service (QoS) requirements, and then design a novel integer linear programming (ILP)-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimal</i> MS-FA technique with POCs. However, the optimal technique may not be feasible for large-scale IAB networks due to its NP-hardness. Hence, we propose a novel low-complexity MS-FA algorithm that achieves a near-optimal performance while significantly reducing the computational complexity. To the best of our knowledge, no MS-FA technique exists to consider both POCs and the physical interference model in the literature. Simulation results show that the spectral efficiency becomes significantly improved by reducing the required bandwidth in practical IAB networks.

Minimum Span Frequency Assignment Based on a Multiagent Evolutionary Algorithm

In frequency assignment problems (FAPs), separation of the frequencies assigned to the transmitters is necessary to avoid the interference. However, unnecessary separation causes an excess requirement of spectrum, the cost of which may be very high. Since FAPs are closely related to T-coloring problems (TCP), multiagent systems and evolutionary algorithms are combined to form a new algorithm for minimum span FAPs on the basis of the model of TCP, which is named as Multiagent Evolutionary Algorithm for Minimum Span FAPs (MAEA-MSFAPs). The objective of MAEA-MSFAPs is to minimize the frequency spectrum required for a given level of reception quality over the network. In MAEA-MSFAPs, all agents live in a latticelike environment. Making use of the designed behaviors, MAEA-MSFAPs realizes the ability of agents to sense and act on the environment in which they live. During the process of interacting with the environment and other agents, each agent increases the energy as much as possible so that MAEA-MSFAPs can find the optima. Experimental results on TCP with different sizes and Philadelphia benchmark for FAPs show that MAEA-MSFAPs have a good performance and outperform the compared methods.

Models and methods for frequency assignment with cumulative interference constraints

AbstractIn this paper, a realistic modeling of interferences for frequency assignment in hertzian telecommunication networks is presented. In contrast with traditional interference models based only on binary interference constraints involving two frequencies, this new approach considers the case of cumulative disruptions that are modeled through a unique non‐binary constraint. To deal with these complex constraints, we propose extensions of classical integer linear programming formulations. On a set of realistic instances, we propose hybrid constraint programming and large neighborhood search solution methods to solve minimum interference and minimum span frequency assignment problems. We compare their performances with those of existing heuristics. Finally, we show how the end‐user benefits from using the cumulative model instead of the traditional one.

An Ant Algorithm for Minimum Span Frequency Assignment Problem in Cellular Networks

An Ant Algorithm for Minimum Span Frequency Assignment Problem in Cellular Networks

A memetic algorithm for channel assignment in wireless FDMA systems

A new problem encoding is devised for the minimum span frequency assignment problem in wireless communications networks which is compact and general. Using the new encoding, which reduces search space dramatically over previous problem encodings, an optimization algorithm is developed which combines a genetic algorithm global search with a computationally efficient local search method from the literature. This memetic algorithm is shown to be more effective than six previous approaches in the literature on a suite of established test problems. Further, it shown that the integration of the global search with the local search is important; neither component by itself is nearly as effective.

Generation of lower bounds for minimum span frequency assignment

Minimum span frequency assignment problems require lower bounds for the span in order to assess the quality of individual assignments, and to effectively compare different meta-heuristic algorithms. The generation of good lower bounds requires the identification of critical subproblems. These subproblems are subgraphs of a constraint graph. Sometimes clique subgraphs lead to good lower bounds. However for some problems the clique must be modified in order to obtain the best possible bound. This can be time consuming, requires manual intervention and is dependent on the initial clique obtained and the specific problem. For practical use, a simple bounding technique is needed that can be universally applied to all problems without modification. Two algorithms based on meta-heuristics are presented that aim to find a subproblem that gives the best possible lower bound. This avoids the need for clique detection routines and manual intervention, and gives a robust and automatic method for generating lower bounds for all minimum span problems. These algorithms use mathematical programming formulations of the frequency assignment problem. Extensive testing on a wide range of problems shows that bounds from the new algorithms match those using previous techniques, and in some cases are significantly better.

Minimizing the span of d -walks to compute optimum frequency assignments

In this paper we deal with the Minimum Span Frequency Assignment Problem (MSFAP), that is the problem of assigning a limited set of radio frequencies to the base stations of a radio network so as the bandwidth occupancy is minimized and the overall interference is not too large. We present a new integer linear formulation for MSFAP in the multidemand case, which is the basis of an exact algorithm to compute both lower and upper bounds for MSFAP. Frequency assignments are represented as walks (sequence of nodes) in a graph. We look for a walk of minimum span, where the span of a walk is defined as the cost of a maximum cost subwalk (subsequence). The new approach is able to find optimum solutions over a large set of classical benchmarks.

A Study of Permutation Operators for Minimum Span Frequency Assignment Using an Order Based Representation

The genetic algorithm (GA) described in this paper breeds permutations of transmitters for minimum span frequency assignment. The approach hybridizes a GA with a greedy algorithm, and employs a tec...