Channel Assignment Problem Research Articles

A Novel Mathematical Model for Radio Mean Square Labeling Problem

A radio mean square labeling of a connected graph is motivated by the channel assignment problem for radio transmitters to avoid interference of signals sent by transmitters. It is an injective map h from the set of vertices of the graph G to the set of positive integers , such that for any two distinct vertices x, y, the inequality d(x, y) + ⌈ (h(x))2 + (h(y))2/2 ⌉ ≥ dim(G) + 1 holds. For a particular radio mean square labeling h, the maximum number of h(v) taken over all vertices of G is called its spam, denoted by rmsn(h), and the minimum value of rmsn(h) taking over all radio mean square labeling h of G is called the radio mean square number of G, denoted by rmsn(G). In this study, we investigate the radio mean square numbers rmsn(Pn) and rmsn(Cn) for path and cycle, respectively. Then, we present an approximate algorithm to determine rmsn(G) for graph G. Finally, a new mathematical model to find the upper bound of rmsn(G) for graph G is introduced. A comparison between the proposed approximate algorithm and the proposed mathematical model is given. We also show that the computational results and their analysis prove that the proposed approximate algorithm overcomes the integer linear programming model (ILPM) according to the radio mean square number. On the other hand, the proposed ILPM outperforms the proposed approximate algorithm according to the running time.

Radio Labelings of Lexicographic Product of Some Graphs

Labeling of graphs has defined many variations in the literature, e.g., graceful, harmonious, and radio labeling. Secrecy of data in data sciences and in information technology is very necessary as well as the accuracy of data transmission and different channel assignments is maintained. It enhances the graph terminologies for the computer programs. In this paper, we will discuss multidistance radio labeling used for channel assignment problems over wireless communication. A radio labeling is a one-to-one mapping ℘ : V G ⟶ ℤ + satisfying the condition | ℘ μ − ℘ μ ′ | ≥ diam G + 1 − d μ , μ ′ : μ , μ ′ ∈ V G for any pair of vertices μ , μ ′ in G . The span of labeling ℘ is the largest number that ℘ assigns to a vertex of a graph. Radio number of G , denoted by r n G , is the minimum span taken over all radio labelings of G . In this article, we will find relations for radio number and radio mean number of a lexicographic product for certain families of graphs.

Interference-graph and fuzzy C-means based resource allocation scheme in LTE-V2V communication networks

In this paper, a framework is proposed to improve the effectiveness of the long-term-evolution vehicle-to-vehicle (LTE-V2V) communication system, where the uplink channel of the cellular user equipment (CUE) is reused by multiple V2V links. Since the orthogonal channels occupied by each CUEs are multiplexed by multiple V2V links, the serious interference is generated in the co-tier layer and cross-tier layer. In order to minimize the co-channel interference between CUE link and V2V links, an optimized resource sharing scheme is proposed. Firstly, Fuzzy C-means (FCM) algorithm is proposed to perform the clustering based on the geographical location characteristics of V2V links. In the FCM algorithm, the adjacent V2V links are grouped into a cluster to facilitate the solution of the channel allocation problem. Assuring the V2V links in the same cluster do not share the same channel, the adjacent V2V links are avoided to reuse the same channel. On this basis, the use of reasonable channel allocation scheme can effectively reduce the co-channel interference. Considering that the channel state information (CSI) of mobile links is slowly faded, the total system capacity is maximized by leveraging the CSI while the reliability can be guaranteed for all V2V links. The outage probability constraint is used to ensure the reliability of all V2V links. The channel assignment problem of V2V links is formulated as a weighted two-dimensional matching problem, and the interference bipartite graph is constructed. Finally, Kuhn Munkras (KM) algorithm is introduced to allocate the uplink orthogonal channel of CUEs for the V2V links in the same class in order to maximize the total system capacity. Numerical simulation results show that the algorithm can improve the total system capacity.

Genetic Algorithms and Particle Swarm Optimization for Interference Minimization in Mobile Network Channel Assignment Problem

Genetic Algorithms and Particle Swarm Optimization for Interference Minimization in Mobile Network Channel Assignment Problem

Optimal Resource Allocation for Full-Duplex IoT Systems Underlaying Cellular Networks With Mutual SIC NOMA

Device to device (D2D) and nonorthogonal multiple access (NOMA) are promising technologies to meet the challenges of the next generations of mobile communications in terms of network density and diversity for Internet of Things (IoT) services. This article tackles the problem of maximizing the D2D sum throughput in an IoT system underlaying a cellular network, through optimal channel and power allocation (PA). NOMA is used to manage the interference between cellular users (CUs) and full-duplex (FD) IoT devices. To this aim, mutual successive interference cancellation (SIC) conditions are identified to allow simultaneously the removal of the D2D devices interference at the level of the base station and the removal of the CUs interference at the level of D2D devices. To optimally solve the joint channel and PA problem, a time-efficient solution of the PA problem in the FD context is elaborated. By means of graphical representation, the complex nonconvex PA problem is efficiently solved in constant time complexity. This enables the global optimal resolution by successively solving the separate PA and channel assignment problems. The performance of the proposed strategy is compared against the classical state-of-the-art FD and half-duplex (HD) scenarios, where SIC is not applied between CUs and IoT devices. The results show that important gains can be achieved by applying mutual SIC NOMA in the IoT-cellular context, in either HD or FD scenarios.

Radio antipodal mean number of quadrilateral Snake families

Objectives: In communication engineering, the assignment of channels or frequencies to different transmitters in a communication network without interference is an important problem. Finding the span for such an assignment is a challenging task. The objective of this study is to find the span of quadrilateral snake families. Method: The solution to the channel assignment problem can be found out by modeling the communication network as a graph, where the transmitters are represented by nodes and connectivity between transmitters are given by edges. The labeling technique in graph theory is very useful to solve this problem. Let G=(V;E) be a graph with vertex set V, edge set E. Let u;v 2V(G). The radio antipodal mean labeling of a graph G is a function f that assigns to each vertex u, a non-negative integer f (u) such that f (u) ̸= f (v) if d(u;v) < diam(G) and d(u;v)+⌈f (u)+ f (v)2⌉ diam(G) , where d(u;v) represents the shortest distance between any pair of vertices u and v of G and diam(G) is the diameter of G. The radio antipodal mean number of f, is the maximum number assigned to any vertex of G and is denoted by ramn( f ). The radio antipodal mean number of G, denoted by ramn(G) is the minimum value of ramn( f ) taken over all antipodal mean labeling f of G. Findings: In this study, we have obtained the bounds of radio antipodal mean number of quadrilateral snake families. Novelty: The radio antipodal mean number of quadrilateral snake families was not studied so far. Hence, the establishment of the bounds for radio mean number of quadrilateral snake families will motivate many researchers to study the radio antipodal mean number of other communication networks. Keywords: Radio antipodal mean labeling; quadrilateral snake; alternate quadrilateral snake; double quadrilateral snake; double alternate quadrilateral snake

Q-Learning and MADMM Optimization Algorithm Based Interference Aware Channel Assignment Strategy for Load Balancing in WMNS

Wireless Mesh Networks (WMNs) have been considered one of the main technologies for configuring wireless machines since they appeared. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts on the network and allow access to the internet through the gateway routers. These wireless routers are normally equipped with the multiple radios in the wireless mesh network that operate on multiple channels with the multiple interference, which is caused to reduce the network performance and end-to-end delay. In this paper, we proposed an efficient optimization algorithm to solve the channel assignment problem which cause due to the multichannel multiradios in WMN’s. The main objective of our paper is to minimize the channel interference among networked devices. So, initially we construct a multicast tree with minimum interference by using Q-Learning algorithm, which is helps to minimize the end-to-end delay of packet delivery. From the constructed multicast tree, we intend to develop a channel assignment strategy with the minimum interference by using Modified version of Alternative Direction Method of Multipliers (MADMM) optimization algorithm, which is helps to increase the network throughput and packet delivery ratio. The proposed strategy was implemented by using NS-2 (Network Simulator-2) and the experimental result show that the performance of the proposed method is very high compared to the other method and the performance was calculated by using the feature metrics such as average throughput, packet delivery ratio, end-toend delay and total cost, which is compared with the other existing channel assignment strategies such as Learning Automata and Genetic Algorithm (LA-GA), GA-based approach, link-channel selection and rate-allocation (LCR) and learning automata based multicast routing (LAMR) channel assignment methods.

Channel Assignment for Hybrid NOMA Systems With Deep Reinforcement Learning

The Hybrid Non-Orthogonal Multiple Access (NOMA) is a promising candidate for multiple access techniques of future wireless communication, which integrates orthogonal multiple access and traditional NOMA. The performance of hybrid NOMA systems depends on resource allocation including power and channel. In this letter, we focus on the channel assignment. Since channel assignment needs to be adapted to a real-time changing environment and accomplished in a restricted time slot, we treat the optimization of the dynamic channel assignment problem as a deep reinforcement learning task, to achieve better environmental adaptability with low time complexity. Simulation results show that the proposed method achieves better performance in terms of sum rate and spectral efficiency, compared to conventional methods.

WITHDRAWN: The study of edge coloring and chromatic graph theory and its applications

The field of graph colorings has developed into one of the most popular areas of graph theory. This research introduces graph theory with a coloring theme. It explores connections between major topics in graph theory and graph colorings, including Ramsey numbers and domination, as well as such emerging topics as list colorings, rainbow colorings, distance colorings related to the Channel Assignment Problem, and vertex/edge distinguishing colorings. Discussions of a historical, applied, and algorithmic nature are included. For Brooks' Theorem, which says that the chromatic number is at most appropriate in Everything except two of the events. In the case of a 2, 1-colored line, we show the 2, 1- The chromatic number is at most true 2 + . For a graph with a fractional hue, we 're showing that the fractional chromatic number is at most normal.

Maximum-Service Channel Assignment in Vehicular Radar-Communication

Spectrum sharing between different technologies has become a necessity as the RF spectrum has become more congested than ever due to the increasing number of connected devices and applications all wishing to access the spectrum simultaneously. Vehicular networks are one of many scenarios of spectrum sharing as vehicles are expected to share the same spectrum for radar sensing and communication purposes. Channel assignment among radar sensors and communication transceivers in automotive systems is crucial for the success of future vehicular networks. This paper proposes an optimization framework for the channel assignment to multiple automotive radars and communication transceivers aiming at maximizing the number of served vehicles under hard quality of service requirements. The channel assignment problem is formulated as an integer linear optimization with binary variables. A heuristic solution that is based on ordered sequential channel assignment (OSCA) is proposed and is shown to achieve at least 92% of the solution obtained from branch-and-cut based techniques with much less computational complexity and run-time; at most 2% of the run-time of branch-and-cut.

Circular Distance-Two Labelling of Book Graphs Related to Code Assignment in Computer Wireless Networks

Let d be a positive real number. An L(1,d)-labeling of a graph G is an assignment of nonnegative real numbers to the vertices of G such that the adjacent vertices are assigned two different numbers (labels) whose difference is at least one, and the difference between numbers (labels) for any two distance-two vertices is at least d. The minimum range of labels over all L(1,d)-labelings of a graph G is called the L(1,d)-labeling number of G, denoted by λ_(1,d)(G). The L(1,d)-labeling with d≥1 of graph arose from the code assignment problem of computer wireless network and the L(1,d)-labeling with 0<d≤1 of graph is motivated by channel assignment problem. Let σ and d be two positive real numbers, a circular σ-L(1,d)-labeling of a graph G is a function f: V(G)→[0,σ) satisfying |f(u)-f(v)|σ≥1 if u v∈E(G), and |f(u)-f(v)|σ≥d if u and v are distance two apart, where |f(u)-f(v)|σ=min{|f(u)-f(v)|, σ-|f(u)-f(v)|}. The circular L(1,d)-labeling number of a graph G, denoted by σ_(1,d) (G), is the minimum σ such that there exists a circular σ-L(1,d)-labeling of G. In this paper, the code assignment of 3-D computer wireless network is abstracted as the circular L(1,d)-labeling of book graph, and the authors determined the circular L(1,d)-labeling numbers of book graph for any positive real number d≥2 basing on the properties and constructions of book graphs.

Harmonious labeling of the union of m-cycle and n-path graph

A graph G is called harmonious graph if there is an injective mapping f from the set of vertices of G to the set Zq so that the mapping induced a bijection mapping f ∗ from the set of edges of G to Zq , which is defined as f*(xy) = f(x)+ f(y) for each edge xy∈ E(G). The mapping f is called harmonious labeling. Harmonious labeling concept has been developed since 1980. At that time, harmonious labeling is inspired by channel assignment problems. Nowadays, harmonious labeling has been known for various classes of graphs. However, there are still many certain classes of graphs that have not been determined yet whether they can be harmonious labeled or not. One of the class of graphs that have not been determined yet whether it can be harminous labeled or not is graph Cm ∪ Pn In this paper, we prove that there is a harmonious labeling of graph Cm ∪ Pn for some values m and n.

An Elite Adaptive Niche Evolutionary Algorithm for Duty Clustering Problem in SoWSN

The recent success of emerging low power wireless sensor networks technology has encouraged researchers to create novel duty cycle design algorithm in this area. Since “sensors are constrained in sensing capabilities”, duty cycle design plays a crucial role in maximizing the point coverage rate, while most researches for duty cycle design are related to a duty cycle design algorithm. But unfortunately, the ideal duty cycle design requires an exhaustive search over all combinations of the allowed combinations. In this letter, we present a new elite adaptive niche evolutionary algorithm (EANEA) for duty cycle design problem in Service-oriented wireless sensor networks (SoWSN). In order to extend the network life cycle, we designed an objective function for SoWSN. We also give an EANEA which, depending on a powerful niche operator, blends the merits of both elite selection and adaptive adjusting for the channel assignment problem. Simulation results show that the shown algorithm can achieve a higher point coverage rate over genetic quantum algorithm (QGA) and Shuffled Frog-Leaping Algorithm (SFLA). Moreover, the optimization employs an elite selection to initialize the parameters and avoid local optima proficiently.

Integer and constraint programming approaches for providing optimality to the bandwidth multicoloring problem

In this paper, constraint and integer programming techniques are applied to solving bandwidth coloring problems, in the sense of proving optimality or finding better feasible solutions for benchmark instances from the literature. The Bandwidth Coloring Problem (BCP) is a generalization of the classic vertex coloring problem (VCP), where, given a graph, its vertices must be colored such that not only adjacent ones do not share the same color, but also their colors must be separated by a minimum given value. BCP is further generalized to the Bandwidth Multicoloring Problem (BMCP), where each vertex can receive more than one different color, also subject to separation constraints. BMCP is used to model the Minimum Span Channel Assignment Problem (MS-CAP), which arises in the planning of telecommunication networks. Research on algorithmic strategies to solve these problems focus mainly on heuristic approaches and the performance of such methods is tested on artificial and real scenarios benchmarks, such as GEOM, Philadelphia and Helsinki sets. We achieve optimal solutions or provide better upper bounds for these well-known instances, We also compare the effects of multicoloring demands on the performance of each exact solution approach, based on empirical analysis.

Radio Number Associated with Zero Divisor Graph

Radio antennas use different frequency bands of Electromagnetic (EM) Spectrum for switching signals in the forms of radio waves. Regulatory authorities issue a unique number (unique identifying call sign) to each radio center, that must be used in all transmissions. Each radio center propagates channels to the two nearer radio centers so they must use distinctive numbers to avoid interruption. The task of effectively apportioning channels to transmitters is known as the Channel Assignment (CA) problem. CA Problem is discussed under the topic of graph coloring by mathematicians. The radio number of a graph can be used in many parts of the field communication. In this paper, we determined the radio number of zero-divisor graphs Γ(Zp2×Zq2) for p,q prime numbers.

Radio $k$-labeling of paths

The Channel Assignment Problem (CAP) is the problem of assigning channels (non-negative integers) to the transmitters in an optimal way such that interference is avoided. The problem, often modeled as a labeling problem on the graph where vertices represent transmitters and edges indicate closeness of the transmitters. A radio $k$-labeling of graphs is a variation of CAP. For a simple connected graph $G=(V(G),\,E(G))$ and a positive integer $k$ with $1\leq k\leq {\rm diam(G)}$, a radio $k$-labeling of $G$is a mapping $f \colon V(G)\rightarrow \{0,\,1,\,2,\ldots\}$ such that $|f(u)-f(v)|\geq k+1-d(u,v)$ for each pair of distinct vertices $u$ and $v$ of $G$, where ${\rm diam(G)}$ is the diameter of $G$ and $d(u,v)$ is the distance between $u$ and $v$ in $G$. The \emph{span } of a radio $k$-labeling $f$ is the largest integer assigned to a vertex of $G$. The \emph{radio $k$-chromatic number} of $G$, denoted by $rc_{k}(G)$, is the minimum of spans of all possible radio $k$-labelings of $G$. This article presents the exact value of $rc_{k}(P_n)$ for even integer $k\in\left\{\left\lceil\frac{2(n-2)}{5}\right\rceil, \ldots,\,n-2\right\}$ and odd integer $k\in\left\{\left\lceil\frac{2n+1}{7}\right\rceil,\ldots,\,n-1\right\}$, i.e., at least $65\%$ cases the radio $k$-chromatic number of the path $P_n$ are obtain for fixed but arbitrary values of $n$. Also an improvement of existing lower bound of $rc_{k}(P_n)$ has been presented for all values of $k$.

WITHDRAWN: Grasshopper algorithm based channel assignment for cognitive radio networks

Abstract Cognitive Radio Networks (CRNs) have turn up as an efficient spectrum resource utilization as solution for fixed channel assignment schemes of existing radio networks. To take care of this issue, CRNs use the empty un-used channels (which are not being utilized by radio network at a moment of spans) and to make efficient spectrum utilization. In this work, Grasshopper Algorithm (GA) is used for solving the Channel Assignment (CA) problem. The performance of the GA is compared with Particle Swarm Optimization (PSO) and Genetic algorithms (in terms of Max-Sum-Reward (MSR), Max- Min-Reward (MMR) and Max-Proportional-Fair (MPF) Reward), standard deviation and execution time. Results indicated that the GA improved MPF fitness value by 18.2% and 35.4%, while the execution time is reduced by 206% and 134% and standard deviation is decreased by 98% and 196% contrasted with PSO and Genetic algorithms respectively. As part of embedded application of cognitive radio, GA and reward functions are implemented in hardware using high level synthesis tool and synthesis results are presented in the paper.

Hierarchical Routing and Resource Assignment in Spatial Channel Networks (SCNs): Oriented Toward the Massive SDM Era

In the past few decades, the architecture of optical networks has undergone significant evolution, from the earliest wavelength-division multiplexing (WDM) optical networks to elastic optical networks (EONs) and later to space-division multiplexing (SDM) EONs, to address the continuous growth of Internet traffic. By 2024, Pbps-level optical networks are expected, far exceeding the capacity limit of single-mode fibers. The massive SDM era is on the horizon. In this context, a newly designed architecture for optical networks called the spatial channel network (SCN) architecture, which achieves high cost efficiency by means of practical hierarchical optical cross-connects, has recently been proposed. However, the evolution of optical network architectures will simultaneously present challenges related to network optimization. For instance, with the evolution from WDM optical networks to EONs, the kernel network optimization problem was transformed from the routing and wavelength assignment (RWA) problem into the routing and spectrum assignment (RSA) problem due to the additionally introduced constraint of spectrum contiguity. Similarly, specially designed algorithms are also expected to be essential for addressing the network optimization problem in SCNs. In this paper, we define this new problem as the routing, spatial channel, and spectrum assignment (RSCSA) problem. We propose an integer linear programming (ILP) model and a heuristic algorithm to solve the RSCSA problem. We examine the performance of the proposed approaches via simulation experiments. The results show that both proposed approaches are effective in finding the optimal solutions or solutions close to the lower bounds. To the best of our knowledge, this is the first work to focus on the network optimization problem in SCNs.

Resource Allocation in Intelligent Reflecting Surface Assisted NOMA Systems

This article investigates the downlink communications of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems. To maximize the system throughput, we formulate a joint optimization problem over the channel assignment, decoding order of NOMA users, power allocation, and reflection coefficients. The formulated problem is proved to be NP-hard. To tackle this problem, a three-step novel resource allocation algorithm is proposed. Firstly, the channel assignment problem is solved by a many-to-one matching algorithm. Secondly, by considering the IRS reflection coefficients design, a low-complexity decoding order optimization algorithm is proposed. Thirdly, given a channel assignment and decoding order, a joint optimization algorithm is proposed for solving the joint power allocation and reflection coefficient design problem. Numerical results illustrate that: i) with the aid of IRS, the proposed IRS-NOMA system outperforms the conventional NOMA system without the IRS in terms of system throughput; ii) the proposed IRS-NOMA system achieves higher system throughput than the IRS assisted orthogonal multiple access (IRS-OMA) systems; iii) simulation results show that the performance gains of the IRS-NOMA and the IRS-OMA systems can be enhanced via carefully choosing the location of the IRS.

Quantifying the Impact of Time-Sharing on Route Capacity in Cognitive Radio Networks With Full-Duplex Capability

Recent advances in full-duplex (FD) communications along with the introduction of the concept of device-to-device communications have challenged the applicability of traditional routing and scheduling schemes in multi-hop cognitive radio networks (CRNs). In this letter, we derived a close-form expression to quantify the impact of time-sharing of a given channel assignment along a given path on network performance. Based on the derived expression, we mathematically model the channel-assignment problem that minimizes the number of time-shared hops along a given path as a binary-quadratic optimization problem (BQP). Then, we transfer the BQP into a binary linear programming problem (BLP), which is sub-optimally solvable using a polynomial-time sequential fixing method. Simulation results indicate that routing with assignment that minimizes the needed number of time-shared transmissions along a path can provide significant improvement in network throughput.