Channel Assignment Problem Research Articles

Radio fuzzy graphs and assignment of frequency in radio stations

Based on the rules of the Federal Communications Commission (FCC) of the United States, there is a problem when FM radio stations are situated nearby and transmit on channels when they are satisfactorily far separated. The difficulty of taking an optimal assignment of channels for a particular set of radio stations according to some given limitations on the distances among the stations as well as other factors are referred to as the Channel Assignment Problem. That problem has been designed mathematically in different ways. In this problem, it is managed by the introduction of radio k-colourings of graphs for assignment of frequency in radio stations. Depending on radio distances, radio fuzzy graphs have been defined. Complete radio fuzzy graphs, cyclic radio fuzzy graphs, and star radio fuzzy graphs are introduced, and several properties have been proved. Based on these theories, a survey on Kolkata (India) radio stations has been verified.

Distributed energy‐efficient channel assignment in cognitive mesh network for IoT systems

AbstractInternet of Things (IoT) has attracted a lot of attention to solve the real‐world problem to improve quality of life. Cognitive radio mesh networks (CRMNs) can provide connectivity to a large number of IoT nodes and have the potential to self‐organize and reconfigure to make efficient use of spectrum resources. The growing concern regarding energy consumption in communication infrastructure and low power IoT nodes has led researchers to regard minimizing of energy consumption. In this paper, we propose a distributed energy‐efficient channel assignment in CRMNs for the IoT systems. We formulate an optimization problem for channel assignment and sharing in CRMNs for IoT systems with an objective to maximize energy‐efficiency of the network. We consider constraints on both intra and inter‐link interference. We then propose two distributed approaches to solve the channel assignment problem: (1) greedy approach and (2) game theoretic approach while considering the scalability and self‐organization requirements of the CRMNs for the IoT systems. We then present extensive simulation results to demonstrate the performance of proposed schemes.

Spectrum-Aware Routing in Full-Duplex Cognitive Radio Networks: An Optimization Framework

Routing and channel assignment schemes for cognitive radio networks (CRNs) are often designed assuming the half-duplex (HD) transmission capability per user. However, recent advances in full-duplex (FD) communications and self-interference suppression techniques challenge the traditional HD transmission capability, in which FD communication can significantly improve spectrum utilization. In this work, we investigate the routing and channel assignment problem in FD-based CRNs. Two types of FD communications are considered. The first type only allows for simultaneous transmission and reception over different channels, while the second type allows for simultaneous transmission and reception over the same channel. Specifically, for a given cognitive radio (CR) source–destination pair, we first formulate the channel assignment problem for each path between the communicating pair as an optimization problem with the main objective of minimizing the number of distinct assigned channels for that path such that the number of simultaneous active hops across the path is maximized. We show that the optimization problem is a binary linear programming problem, which is, in general, nondeterministic polynomial time-hard. Thus, we present a near-optimal solution based on a sequential fixing procedure, where the binary variables are iteratively determined by solving a sequence of relaxed programs. Accordingly, we develop a novel routing scheme that selects the best path along with the channel assignment such that the highest capacity is achieved. Simulation results are provided, which show that a careful routing and channel assignment scheme for FD CRNs can significantly improve the network performance.

MUcast: Linear Uncoded Multiuser Video Streaming With Channel Assignment and Power Allocation Optimization

Multiuser video transmission, where the server transmits videos to multiple users that require different contents at the same time, becomes more and more popular with the development of wireless communication technology. One key problem in multiuser video transmission is how to optimally allocate system resources such as transmission power and channels to multiple users to achieve the best system performance. To resolve the problem, in this paper, we propose an uncoded multiuser video streaming system, which exploits diversities of video contents and channel conditions of multiple users. We first solve the channel assignment problem with known power allocation by taking into account the intra-block energy diffusion and inter-block energy aliasing. Then, with the obtained channel assignment, we derive a closed-form solution to the multiuser power allocation optimization problem. Finally, we conduct simulations to evaluate the proposed uncoded multiuser video streaming system by comparing with three other approaches, and the simulation results show that the proposed method can achieve the best system performance.

A new graph radio k-coloring algorithm

Due to the rapid growth in the use of wireless communication services and the corresponding scarcity and the high cost of radio spectrum bandwidth, Channel assignment problem (CAP) is becoming highly important. Radio [Formula: see text]-coloring of graphs is a variation of CAP. For a positive integer [Formula: see text], a radio [Formula: see text]-coloring of a simple connected graph [Formula: see text] is a mapping [Formula: see text] from the vertex set [Formula: see text] to the set [Formula: see text] of non-negative integers such that [Formula: see text] for each pair of distinct vertices [Formula: see text] and [Formula: see text] of [Formula: see text], where [Formula: see text] is the distance between [Formula: see text] and [Formula: see text] in [Formula: see text]. The span of a radio [Formula: see text]-coloring [Formula: see text], denoted by [Formula: see text], is defined as [Formula: see text] and the radio[Formula: see text]-chromatic number of [Formula: see text], denoted by [Formula: see text], is [Formula: see text] where the minimum is taken over all radio [Formula: see text]-coloring of [Formula: see text]. In this paper, we present two radio [Formula: see text]-coloring algorithms for general graphs which will produce radio [Formula: see text]-colorings with their spans. For an [Formula: see text]-vertex simple connected graph the time complexity of the both algorithm is of [Formula: see text]. Implementing these algorithms we get the exact value of [Formula: see text] for several graphs (for example, [Formula: see text], [Formula: see text], [Formula: see text], some circulant graph etc.) and many values of [Formula: see text], especially for [Formula: see text].

Reinforcement learning in learning automata and cellular learning automata via multiple reinforcement signals

Many scientific and engineering problems are decentralized in nature. Various distributed approaches have been developed for solving these problems, and among them, cellular learning automaton has demonstrated to be an effective model for systems consisting of a large number of interacting components. In the cellular learning automata approach, each such component is modeled by a learning automaton. The learning automaton associated with a component aims to learn the action which best suites with its neighboring components. This objective becomes more challenging when the automaton is required to find the optimal subset of its available actions. The common learning automata algorithms can deal with this problem by considering all combinations of their allowable actions as new action sets. However, this approach is only applicable for small action spaces. The current work extends some common learning automata algorithms so that they can efficiently learn the optimal subset of their actions through parallel reinforcements. These parallel reinforcements represent the favorability of each action in the performed subset of actions; consequently, the learning automaton would be able to learn the effectiveness of each action individually. By integrating the new LA models in a cellular learning automaton, each component of the system is able to interact with its neighbors simultaneously via multiple actions. In order to investigate the effectiveness of the proposed models, their applicability on a channel assignment problem is investigated experimentally. The achieved results demonstrate the efficiency of the proposed multi-reinforcement learning schemes.

Tuning Pi Controller Bases on Chemical Reaction Optimization Algorithm

This paper we present Chemical Reaction Optimization (CRO) algorithm for determining optimal parameters of PI controller. The model of doubly fed induction generator (DFIG) is used as a plant in this paper. Tuning PI controller using traditional method such as Ziegler-Nichols (ZN) method usually produces large overshoot and Integral time absolute error, integral absolute error and integral square error performance indices. Therefore, recently researchers have applied random search approach such as genetic algorithm (GA) and particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO) to find optimal parameters for PI controller. Among modern heuristics algorithm, CRO was introduced in 2010, it combines features of both GA and Simulated Annealing (SA) to find global minimum in search space. CRO has been applied to solve successfully many optimization problems such as: Minimum transportation cost, resource-constrained project scheduling problem, channel assignment problem in wireless mesh networks, standard continuous benchmark functions, and so on. In this paper we present to apply CRO algorithm to search optimal parameters for PI controller. The comparison between tuning PI controller by CRO and traditional Ziegler-Nichols method is presented. The simulation results show the advantages of PI tuning using CRO compared to traditional method in terms of performance index and setting time.

Spectrum Assignment in Hardware-Constrained Cognitive Radio IoT Networks Under Varying Channel-Quality Conditions

ABSTRACT The integration of cognitive radio (CR) technology with future Internet-of-Things (IoT) architectures is expected to allow effective massive IoT deployment by providing huge spectrum opportunities to IoT devices. Several communication protocols have been proposed for CR networks while ignoring the adjacent channel interference (ACI) problem by assuming sharp filters at the transmit and receive chains of each CR device. However, in practice, such an assumption is not feasible for low-cost hardware-constrained CR-capable IoT (CR-IoT) devices. Specifically, when large number of CR-IoT devices are operating in the same vicinity, guardband channels (GBs) are needed to mitigate the ACI problem. Introducing GB constraint spectrum efficiency and protocol design. In this paper, we develop a channel assignment mechanism for hardware-constrained CR-IoT networks under time-varying channel conditions with GB-awareness. The objective of our assignment is to serve the largest possible number of CR-IoT devices by assigning the least number of idle channels to each device subject to rate demand and interference constraints. The proposed channel assignment in this paper is conducted on a per-block basis for the contending CR-IoT devices while considering the time-varying channel conditions for each CRIoT transmission over each idle channel such that spectrum efficiency is improved. Specifically, our channel assignment problem is formulated as a binary linear programming (BLP) problem, which is NP hard. Thus, we propose a polynomial-time solution using a sequential fixing algorithm that achieves a suboptimal solution. Simulation results demonstrate that our proposed assignment provides significant increase in the number of served IoT devices over existing assignment mechanisms.

Traffic aware channel assignment with node stability in wireless mesh networks

In a wireless network the most challenging issue is Channel assignment. The channel assignment problem is codependent with the routing problem. We need to compute again the channel assignment as and when the traffic pattern changes. Anyways, previously followed channel assignment algorithms will assign channels from scratch. It will end up with an entirely dissimilar configuration of nodes; in turn it will disturb the action of the particular network. It takes little time to create links and to establish new channels. This time is significant in assigning links for wireless networks. This leads to channel reassignment. This algorithm considers the existing channel assignment and tries to go along with the new stream of traffic flow design in the finest possible way by changing the channel on a restricted number of radios. In order to provide node stability, we used entropy function. In this paper, we demonstrate a channel reallocation algorithm with node permanency and appraise its performance by using NS2. Experimental outcomes show that the node stability can progress the performance of network when compared with the earlier methods.

Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment

At the present time, Wi-Fi networks are everywhere. They operate in unlicensed radio-frequency spectrum bands (divided in channels), which are highly congested. The purpose of this paper is to tackle the problem of channel assignment in Wi-Fi networks. To this end, we have modeled the networks as multilayer graphs, in a way that frequency channel assignment becomes a graph coloring problem. For a high number and variety of scenarios, we have solved the problem with two different automated negotiation techniques: a hill-climber and a simulated annealer. As an upper bound reference for the performance of these two techniques, we have also solved the problem using a particle swarm optimizer. Results show that the annealer negotiator behaves as the best choice because it is able to obtain even better results than the particle swarm optimizer in the most complex scenarios under study, with running times one order of magnitude below. Finally, we study how different properties of the network layout affect to the performance gain that the annealer is able to obtain with respect to the particle swarm optimizer.

Distributed Resource Allocation for IBFD-Enabled NOMA Systems

A joint power control and channel assignment problem for an in-band full duplex (IBFD)-enabled non-orthogonal multiple access (NOMA) system is investigated. To strike a balance between performance and complexity, a matching-based distributed channel assignment mechanism is proposed based on the optimal design of power control, and it achieves a stable Pareto-optimal solution to the resulting combinatorial optimization problem in polynomial time. Simulations are conducted to show the efficiency of the proposed mechanism and the benefits when IBFD and NOMA are integrated.

A comparison of integer programming models for the partial directed weighted improper coloring problem

Given a complete directed graph G with weights on the vertices and on the arcs, a θ-improper k-coloring of G is an assignment of at most k different colors to the vertices of G such that the weight of every vertex v is greater, by a given factor 1θ, than the sum of the weights on the arcs (u,v) entering v, with the tail u of the same color as v. For a given real number θ and an integer k, the Partial Directed Weighted Improper Coloring Problem ((θ,k)-PDWICP) is to determine the order of the largest induced subgraph G′ of G such that G′ admits a θ-improper k-coloring. The (θ,k)-PDWICP appears as a natural model when solving channel assignment problems that aim to maximize the number of simultaneously communicating mobiles in a wireless network. In this paper, we compare integer programming approaches to solve this NP-hard problem to optimality. A polynomial-time computable upper bound inspired by the Lovász ϑ(G) function, and valid inequalities based on the knapsack and set-packing problems are embedded in a branch-and-bound procedure. Comparisons with a branch-and-price scheme clearly show that the latter is much more effective than branch-and-cut-based methods.

RTMCH: real-time multichannel MAC for wireless video sensor networks

Exclusive benefits gained by using the image and video sensors in Wireless Sensor Networks (WSNs), make the Wireless Video Sensor Networks (WVSNs), design and development of their structures, a trending topic. The main challenges of WSNs which are more critical in WVSNs are throughput and end-to-end delay. The radio transceivers recently used in WSN motes can work on different channels with very low switching time. Therefore, many multichannel protocols are proposed to improve the efficiency and throughput and decrease the packet drop rate of the network. Proposed algorithms rarely provide a particular solution to the end-to-end problem. Such delays are necessary parameters in real-time applications. In this paper a real-time multichannel protocol called RTMCH is suggested to address the end-to-end delay of a stream in WVSN. A flow-based channel assignment strategy is used for this purpose. Orthogonal channels are assigned to each flows based on many-to-one data-flow to minimize the contention and collision between different flows. Transmission power is a parameter that can be controlled to achieve desired link quality and to control link delay. Channel assignment problem aligns to a constrained optimization problem to make the specified end-to-end delay of each flow. A channel assignment and real-time packet forwarding scheme are then presented. Simulation results based on realistic channel and radio model shows that the RTMCH can efficiently use multiple channels and transmission power to meet specified end-to-end delay. The results also show better performance for RTMCH over a recent real-time protocol and basic multichannel schemes.

Spectrum Assignment in Cognitive Radio Networks for Internet-of-Things Delay-Sensitive Applications Under Jamming Attacks

The expected widespread usage of Internet-of-Things (IoT) devices and the associated diversity of applications will place extra pressure on network resources including bandwidth availability. In networking within IoT, spectrum scarcity requires the consideration of the adaptive cognitive radio (CR) technology to achieve interference-free and on-demand IoT solutions for a number of applications. However, CR networks share the same security weaknesses of legacy wireless networks. Jamming is considered a common attack, where jammers can attack networks in a proactive or reactive approach. In this paper, we study the channel assignment problem under both proactive and reactive jamming attacks. Specifically, we propose a novel probabilistic-based channel assignment mechanism that aims at minimizing the invalidity ratio of CR packet transmissions subject to delay constraints. The proposed scheme exploits the statistical information of licensed primary users activities, fading conditions and jamming attacks over idle channels to provide communicating CR IoT devices with the most secured channels of lowest invalidity ratios. Simulation results show that our proposed security, availability, and quality-aware channel assignment algorithm can significantly improve network performance.

Spectrum Graph Coloring and Applications to Wi-Fi Channel Assignment

We introduce and explore a family of vertex-coloring problems, which, surprisingly enough, have not been considered before despite stemming from the problem of Wi-Fi channel assignment. Given a spectrum of colors, endowed with a matrix of interferences between each pair of colors, the Threshold Spectrum Coloring problem fixes the number of colors available and aims to minimize the interference threshold, i.e., the maximum of the interferences at the vertices. Conversely, the Chromatic Spectrum Coloring problem fixes a threshold and aims to minimize the number of colors for which respecting that threshold is possible. As the main theoretical results, we prove tight upper bounds for the solutions to each problem. Since both problems turn out to be NP-hard, we complete the scene with experimental results. We propose a DSATUR-based heuristic and study its performance to minimize the maximum vertex interference in Wi-Fi channel assignment, both for randomly-generated graphs and for a real-world scenario. Further, for all these graphs, we experimentally check the goodness of the theoretical bounds.

Channel assignment problem and n-fold t-separated $$L(j_1,j_2,\ldots ,j_m)$$ L ( j 1 , j 2 , … , j m ) -labeling of graphs

This paper considers the channel assignment problem in mobile communications systems. Suppose there are many base stations in an area, each of which demands a number of channels to transmit signals. The channels assigned to the same base station must be separated in some extension, and two channels assigned to two different stations that are within a distance must be separated in some other extension according to the distance between the two stations. The aim is to assign channels to stations so that the interference is controlled within an acceptable level and the spectrum of channels used is minimized. This channel assignment problem can be modeled as the multiple t-separated $$L(j_1,j_2,\ldots ,j_m)$$ -labeling of the interference graph. In this paper, we consider the case when all base stations demand the same number of channels. This case is referred as n-fold t-separated $$L(j_1,j_2,\ldots ,j_m)$$ -labeling of a graph. This paper first investigates the basic properties of n-fold t-separated $$L(j_1,j_2,\ldots ,j_m)$$ -labelings of graphs. And then it focuses on the special case when $$m=1$$ . The optimal n-fold t-separated L(j)-labelings of all complete graphs and almost all cycles are constructed. As a consequence, the optimal n-fold t-separated $$L(j_1,j_2,\ldots ,j_m)$$ -labelings of the triangular lattice and the square lattice are obtained for the case $$j_1=j_2=\cdots =j_m$$ . This provides an optimal solution to the corresponding channel assignment problems with interference graphs being the triangular lattice and the square lattice, in which each base station demands a set of n channels that are t-separated and channels from two different stations at distance at most m must be $$j_1$$ -separated. We also study a variation of n-fold t-separated $$L(j_1,j_2,\ldots ,j_m)$$ -labeling, namely, n-fold t-separated consecutive $$L(j_1,j_2,\ldots ,j_m)$$ -labeling. And present the optimal n-fold t-separated consecutive L(j)-labelings of all complete graphs and cycles.

Resource Allocation for D2D Communications Underlaying a NOMA-Based Cellular Network

This letter investigates the power control and channel assignment problem in device-to-device (D2D) communications underlaying a non-orthogonal multiple access (NOMA) cellular network. With the successive interference cancellation decoding order constraints, our target is to maximize the sum rate of D2D pairs while guaranteeing the minimum rate requirements of NOMA-based cellular users. Specifically, the optimal conditions for power control of cellular users on each subchannel are derived first. Then, based on these results, we propose a dual-based iterative algorithm to solve the resource allocation problem. Simulation results validate the superiority of proposed resource allocation algorithm over the existing orthogonal multiple access scheme.

Joint routing and channel assignment using online learning in cognitive radio networks

Cognitive radio networks (CRNs) are the solution for the problem of underutilizing the licensed spectrum for which there are more requests in the last couple of decades. In CRNs, Secondary users (SUs) are permitted to access opportunistically the licensed spectrum owned by primary users (PUs). In this paper, we address the problem of joint routing and channel assignment for several flows generated by source SUs to a given destination. We consider a more realistic model based on Markov modulated Poisson process for modeling the PUs traffic at each channel and the SUs try to exploit short lived spectrum holes between the PUs packets at the selected channel. The SUs want to cooperatively minimize the end-to-end delay of source SUs flows meanwhile the quality of service requirements of the PUs would be met. To consider partial observation of SUs about PUs activity at all channels and quick adaptation of SUs decisions to environment changes and cooperative interaction of SUs, we use decentralized partially observable markov decision process for modeling the problem. Then, an online learning based scheme is proposed for solving the problem. Simulation results show that the performance of the proposed method and the optimal method is close to each other. Also, simulation results show that the proposed method greatly outperforms related works at control of interference to the PUs while maintains the end-to-end delay of SU flows in a low level.

A Fair Multi-Channel Assignment Algorithm With Practical Implementation in Distributed Cognitive Radio Networks

Designing an efficient spectrum assignment (SA) mechanism is a key issue for realizing dynamic spectrum access in cognitive radio network. In multi-channel selection based SA schemes, secondary users (SUs) are able to utilize multiple channels simultaneously to enhance the network throughput. However, a fairness problem may happen if few SUs utilize too many idle data channels that other SUs are left with no idle channels, thus increasing the blocking probability and reducing the fairness. Aiming at improving the network throughput with multi-channel selection capability while maintaining fairness among the SUs, in this paper, we propose a fair multi-channel assignment scheme (FMCA) for distributed cognitive radio networks. For the FMCA scheme, we design a new MAC framework for sensing and access contention resolution, which is integrated into the FMCA scheme. Channel-aggregation (CA) technique is used in each SU to enable the multi-channel selection ability. Considering both of the idle data channel utilization efficiency and the transmit power budget constrained CA ability of each SU, we analytically formulate a channel assignment problem according to the well-known Jain’s fairness criterion. Our objective is to find a channel assignment with maximal fairness index for all SUs. The optimization problem is turned out to be a quadratic integer programming (QIP). According to the definition of Jain’s fairness criterion, we design an algorithm to get the optimal solution of the QIP. With the optimal channel assignment solution, the FMCA scheme is realized in the channel assignment phase of the proposed MAC protocol. Extensive simulation results show that the proposed FMCA scheme gets a good tradeoff between throughput and fairness compared with the existing SA schemes.

Channel Assignment Mechanism for Multiple APs Cochannel Deployment in High Density WLANs

In Wireless Local Area Networks (WLANs), cochannel deployment can bound channel access delay and improve network capacity due to mitigating the collision and interference among different Access Points (APs). In this paper, we present a network model and an interference model for multiple APs cochannel deployment and propose a channel assignment mechanism which formulates the channel assignment problem into a time slot allocation problem. Meanwhile, we assign the channel based on the vertex coloring algorithm and make extra polls by utilizing the time slot reservation strategy to improve the channel assignment. Furthermore, we optimize the polling list of APs through classifying the clients to improve the channel utilization. The simulation results show that our proposed algorithm can improve the performance in terms of network throughput, transmission delay, and packet loss rate compared with the DCF (Distributed Coordination Function) and TMCA algorithms.