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Unmanned aerial vehicles, sometimes known as drones, are becoming increasingly common in communications networks, drawing interest from both business and academia. Their adaptable capacities and characteristics, which may enable various communication scenarios, are mostly to blame. Drones can improve accessibility, throughput, and service quality in numerous ways. Modern communication technologies have been merged with drones to serve the enormous communication needs beyond fifth-generation (B5G) and 6G networks. Based on these foundations, this work aimed to examine the possible abilities of multi-antenna drones in an indoor cognitive radio (CR) environment for uplink transmission. Using such an integrated solution, a group of multiple-antenna drones can connect with a CR BS by taking advantage of open channels without interfering with the core user's operations. This study presents an energy-efficient resource allocation method for multi-carrier NOMA CR-based systems to increase the system's overall energy efficiency while meeting several CR and NOMA limitations. Additionally, it uses the adaptive power channel assignment (APCA) protocol, which tries to reduce the amount of transmit power used by each drone while still adhering to all necessary quality of service and CR requirements. The proposed NOMA–APCA protocol can choose the minimally powered channel. Comparing the proposed NOMA–APCA protocol's performance against the traditional equal-power allocation allows us to understand better how well it performs. The suggested NOMA–APCA protocol dramatically enhances system performance, as shown by the simulation results regarding total transmit power. [ABSTRACT FROM AUTHOR]

An L (d , 1) -labeling of a graph G = (V , E) is a function f from the vertex set V (G) to the set of nonnegative integers such that the labels on adjacent vertices differ by at least d and the labels on vertices at distance two differ by at least one, where d ≥ 1 . The span of f is the difference between the largest and the smallest numbers in f (V) . The λ 1 d -number of G, denoted by λ 1 d (G) , is the minimum span over all L (d , 1) -labelings of G. We prove that λ 1 d (X) ≤ 2 d + 2 , with equality if 1 ≤ d ≤ 4 , for direct graph bundle X = C m × σ ℓ C n and Cartesian graph bundle X = C m □ σ ℓ C n , if certain conditions are imposed on the lengths of the cycles and on the cyclic ℓ-shift σ ℓ . [ABSTRACT FROM AUTHOR]

Given a graph G, an [Formula: see text]-labeling of G is an assignment f of non-negative integers (labels) to the vertices of G such that [Formula: see text] if [Formula: see text] (i = 1, 2, 3). For a non-negative integer k, a k-[Formula: see text]-labeling is an [Formula: see text]-labeling such that no label is greater than k. The [Formula: see text]-labeling number of G, denoted by [Formula: see text], is the smallest number k such that G has a k-[Formula: see text]-labeling. Chia proved that the [Formula: see text]-labeling number of a tree T with maximum degree Δ can have one of three values: [Formula: see text] and [Formula: see text]. This paper gives some sufficient conditions for [Formula: see text] and [Formula: see text], respectively. As a result, the [Formula: see text]-labeling numbers of complete m-ary trees, spiders and banana trees are completely determined.

The users of 5G and others greatly benefit from Device-to-device (D2D) communication technologies. It improves spectral efficiency, extends coverage, reduces offload traffic and lowers the latency rate in a cellular network. The resource-sharing method utilizing D2D communication must be appropriately designed to minimize interference while cellular user (CU) resources are reused. This paper presents a game theory algorithm to maximize the overall network throughput capacity. At the same time the quality of services for both CU and D2D users (DU) shall be guaranteed. The proposed new method enables the DU to reuse available uplink or downlink resources of the CU. In addition, an optimization problem is framed based on the Chaos Game Theory Algorithm. To reduce the complexity of the objective function, the optimization problem can be divided into two-stage subproblems, i.e., the subchannel assignment (SC) stage, followed by the power allocation (PA) stage as they are NP-hard in Nature. It converges as an optimal solution in convex polynomial time. The proposed approach considered network throughput performance related to the number of iterations, D2D link distance, number of active CUs and maximum transmission power of CU, D2D users, and BS as the parameter metrics. Then they are compared with the existing resource allocation schemes. The numerical results infer that the proposed game theory approach improves the network throughput more than the Hungarian joint resource allocation scheme. [ABSTRACT FROM AUTHOR]

Summary: Cognitive radio networks (CRNs) have emerged as a promising solution to address the spectrum scarcity problem by allowing unlicensed secondary users (SUs) to opportunistically access the underutilized licensed spectrum bands. However, efficient channel assignment in CRNs, especially with user mobility and power control considerations, remains a challenging problem. Traditional channel assignment algorithms often struggle to adapt to changing network conditions, resulting in suboptimal performance. To address these challenges, this research presents a novel Taylor Tuna Optimizer‐based hybrid deep Q‐network (TayTO‐based HDQNet) for channel assignment in CRNs with user mobility and power control considerations. The proposed TayTO‐based HDQNet combines the Elman neural network architecture with the reinforcement learning framework of deep Q‐networks, tuned using the Taylor Tuna Optimizer. This hybrid approach enables intelligent channel assignment decisions while considering power consumption constraints, leading to efficient communication in CRNs. To train the HDQNet agent, a comprehensive dataset is generated through simulations of various CRN scenarios, incorporating channel availability, interference levels, and power consumption for different channel assignment decisions. The HDQNet agent undergoes iterative training using this dataset to develop an optimal channel assignment policy. Experimental results demonstrate the effectiveness of the proposed TayTO‐based HDQNet approach done by MATLAB, achieving a high success rate of 98.47% and surpassing the reliability scores of previous studies. This highlights the improved performance and reliability of the proposed approach for channel assignment in CRNs. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is a new technology in cellular networks which enables direct communications between nearby devices. It could reuse the uplink spectrum of the cellular users (CUs) thus will introduce interference. Efficient resource allocation methods can coordinate interference and further improve network capacity. The difficulty of obtaining perfect channel state information (CSI) makes the resource allocation more complex. By using the statistical information of the unknown channel fading factors, we propose an optimal power control algorithm for any CU and any D2D pair sharing the same channel. Based on that, we formulate the resource allocation problem for multiple CUs and multiple D2D pairs with incomplete CSI as a maximum weight matching problem. We then solve the problem by combining the optimal power control algorithm and the maximum weight matching algorithm. Simulation results demonstrate the efficiency of our algorithms in achieving high D2D rates while guaranteeing the minimum rates required by CUs. [ABSTRACT FROM AUTHOR]

One of the main challenges already faced by communication networks is the efficient management of increasing complexity. The recently proposed concept of cognitive networks has appeared as a candidate for addressing this issue. Cognitive networks are put forward in the framework of the evolution of network architectures as novel paradigms to provide autonomous systems in network reasoning to support end-to-end goals. This paper proposes a new method for modifying the spectral efficiency of topology control using the new vertex coloring algorithm in Thomas cognitive networks. This paper proposed a learning automatabased iterative algorithm for approximating the near-optimal solution to the vertex coloring problem in the channel control of the cognitive network. Vertex coloring is a well-known NPhard optimization problem in graph theory in which each vertex is assigned a color so that no two adjacent vertices have the same color. As the proposed algorithm proceeds, the required number of colors tends to the chromatic number of the graph since the number of channels assigned to radios will be optimal. To show the performance of the proposed algorithm, we compare it with several existing vertex coloring algorithms in terms of the number of iterations and colors required for coloring the graphs and then map this solution to the channel assignment problem. Then, we compare the results of our algorithm with other channel assignment topology control methods. The results show that the proposed algorithm is superior to the others. [ABSTRACT FROM AUTHOR]

We studied radio labelings of graphs in response to the Channel Assignment Problem (CAP). In a graph G, the radio labeling is a mapping ϖ: V(G) → {0, 1, 2,..., }, such as |ϖ(μ') - ϖ(μ")| ≥ diam(G)+1-d(μ', μ"). The label of μ for under ϖ is defined by the integer ϖ(μ), and the span under is defined by span(ϖ) = max{|ϖ(μ') - ϖ(μ")|: μ', μ&" ∈ V(G)}. rn(G) = minϖspan(ϖ) is defined as the radio number of G when the minimum over all radio labeling ϖ of G is taken. G is said to be optimal if its radio labeling is span(ϖ) = rn(G). A graph H is said to be an m super subdivision if G is replaced by the complete bipartite graph Km,m with m = 2 in such a way that the end vertices of the edge are merged with any two vertices of the same partite set X or Y of Km,m after removal of the edge of G. Up to this point, many lower and upper bounds of rn(G) have been found for several kinds of graph families. This work presents a comprehensive analysis of the radio number rn(G) for a graph G, with particular emphasis on the m super subdivision of a path Pn with n(n ≥ 3) vertices, along with a complete bipartite graph Km,m consisting of m v/ertices, where m = 2. [ABSTRACT FROM AUTHOR]

In Beyond the Fifth Generation (B5G) heterogeneous edge networks, numerous users are multiplexed on a channel or served on the same frequency resource block, in which case the transmitter applies coding and the receiver uses interference cancellation. Unfortunately, uncoordinated radio resource allocation can reduce system throughput and lead to user inequity, for this reason, in this paper, channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate. Since the construction model is non-convex and the response variables are high-dimensional, a distributed Deep Reinforcement Learning (DRL) framework called distributed Proximal Policy Optimization (PPO) is proposed to allocate or assign resources. Specifically, several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation. Moreover, agents in the collection stage slow down, which hinders the learning of other agents. Therefore, a preemption strategy is further proposed in this paper to optimize the distributed PPO, form DP-PPO and successfully mitigate the straggler problem. The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

We studied radio labelings of graphs in response to the Channel Assignment Problem (CAP). In a graph $ G, $ the radio labeling is a mapping $ \varpi:V(G) \rightarrow \{0, 1, 2, ..., \}, $ such as $ |\varpi(\mu')-\varpi(\mu'')|\geq diam(G)+1-d(\mu', \mu''). $ The label of $ \mu $ for under $ \varpi $ is defined by the integer $ \varpi(\mu), $ and the span under is defined by $ span(\varpi) = max \{|\varpi(\mu')-\varpi(\mu'')|: \mu', \mu'' \in V(G)\}. $ $ rn(G) = min_{\varpi} span(\varpi) $ is defined as the radio number of $ G $ when the minimum over all radio labeling $ \varpi $ of $ G $ is taken. $ G $ is said to be optimal if its radio labeling is $ span(\varpi) = rn(G). $ A graph H is said to be an $ m $ super subdivision if $ G $ is replaced by the complete bipartite graph $ K_{m, m} $ with $ m = 2 $ in such a way that the end vertices of the edge are merged with any two vertices of the same partite set $ X $ or $ Y $ of $ K_{m, m} $ after removal of the edge of $ G $. Up to this point, many lower and upper bounds of $ rn(G) $ have been found for several kinds of graph families. This work presents a comprehensive analysis of the radio number $ rn(G) $ for a graph $ G $, with particular emphasis on the $ m $ super subdivision of a path $ P_{n} $ with $ n (n \geq 3) $ vertices, along with a complete bipartite graph $ K_{m, m} $ consisting of $ m $ v/ertices, where $ m = 2 $.

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This study discusses various bandwidth allocation methods for network slicing and identifies their limitations. The heuristic-based methods were found to be insufficient in handling the randomness of varying channel quality and bandwidth requirements of each slice, while the AI-based methods were not sufficiently optimized. To address these limitations, this study proposes the MCTS-RA algorithm, which is based on Monte Carlo Tree Search methodology, to handle the randomness of each slice's channel quality and requested bandwidth and optimize the throughput. Additionally, MCTS-RA provides a generic performance index, i.e., MCTS-Level, to evaluate the performance gap of previous works to the optimal solution. The simulation results showed that MCTS-RA improved the overall throughput achieved by previous algorithms by around 10%. Furthermore, the results revealed that the MCTS-Level of previous algorithms was around 100–200, while the optimal solution's MCTS-Level was around 2000. [ABSTRACT FROM AUTHOR]

Abstract The Internet of Things (IoT), which is built on software-defined networking (SDN), employs a paradigm known as channel reassignment. This paradigm has great potential for enhancing the communication capabilities of the network. The traffic loads may be scheduled more effectively with the help of an SDN controller, which allows for the transaction of matching channels via a single connection. The present techniques of channel reassignment, on the other hand, are plagued by problems with optimisation and cooperative multi-channel reassignment, which affect both traffic and routers. In this paper, we provide a framework for SDN–IoT in the cloud that permits multi-channel reassignment and traffic management simultaneously. The multi-channel reassignment based on traffic management is optimised via the use of a deep reinforcement learning technique, which was developed in this paper. We do an analysis of the performance metrics in order to optimise the throughput while simultaneously reducing the rate of packet loss and the amount of delay in the process. This is achieved by distributing the required traffic loads over the linked channels that make up a single connection.

The Internet of Things (IoT), which is built on software-defined networking (SDN), employs a paradigm known as channel reassignment. This paradigm has great potential for enhancing the communication capabilities of the network. The traffic loads may be scheduled more effectively with the help of an SDN controller, which allows for the transaction of matching channels via a single connection. The present techniques of channel reassignment, on the other hand, are plagued by problems with optimisation and cooperative multi-channel reassignment, which affect both traffic and routers. In this paper, we provide a framework for SDN–IoT in the cloud that permits multi-channel reassignment and traffic management simultaneously. The multi-channel reassignment based on traffic management is optimised via the use of a deep reinforcement learning technique, which was developed in this paper. We do an analysis of the performance metrics in order to optimise the throughput while simultaneously reducing the rate of packet loss and the amount of delay in the process. This is achieved by distributing the required traffic loads over the linked channels that make up a single connection. [ABSTRACT FROM AUTHOR]

We provide upper bounds on the L(p, q)-labeling number of graphs which have interval (or circular-arc) representations via simple greedy algorithms. We prove that there exists an L(p, q)-labeling with a span at most max{2(p+ q-1)Δ-4q+2, (2p-1)μ+(2q-1)Δ-2q+1} for interval k-graphs, max{p, q}Δ for interval graphs, 3max{p, q}Δ+p for circular-arc graphs, 2(p+q-1)Δ-2q +1 for permutation graphs and (2p-1)Δ+(2q -1)(μ-1) for cointerval graphs. In particular, these improve existing bounds on L(p, q)-labeling of interval graphs and L(2, 1)-labeling of permutation graphs. Furthermore, we provide upper bounds on the coloring of the squares of aforementioned classes. [ABSTRACT FROM AUTHOR]

The Industrial, Scientific, and Medical (ISM) frequency spectrum is used by the Wireless Local Area Network (WLAN) for its operation. Due to a limited number of available channels for WLAN operation, frequency channel assignment becomes very difficult. In WLAN, the access points (APs) are widely deployed, and because they share the same frequency, they start interfering with each other and causing more energy consumption. So WLAN frequency channels should be managed and assigned carefully to increase the energy efficiency of the wireless network. This paper proposes an energy-efficient channel assignment algorithm (EECAA) to improve the performance of the network. The suggested approach improves the network's energy efficiency in terms of global delay, packet data loss, and the count of data retransmissions. The extensive simulation study demonstrates that, when compared to the channel handoff scheme and D2MD channel allocation algorithm for various traffic and network conditions, the proposed EECAA method greatly increases network throughput. [ABSTRACT FROM AUTHOR]

One of the big challenges in decentralized Wi-Fi networks is how to select channels for the different access points (APs) and their associated stations (STAs) in order to minimize interference and hence maximize throughput. Interestingly enough, de facto standards in terms of uncoordinated channel selection are quite simple, and in many cases result in fairly suboptimal channel allocations. Here, we explore how graph coloring can be used to evaluate and inform decisions on Wi-Fi channel selection in uncoordinated settings. Graph coloring, in its most basic form, is a classic mathematical problem where colors have to be assigned to nodes in a graph while avoiding assigning the same color to adjacent nodes. In this paper, we modeled Wi-Fi uncoordinated channel selection as a graph coloring problem and evaluated the performance of different uncoordinated channel selection techniques in a set of representative scenarios of residential buildings. The results confirm some of the widely accepted consensus regarding uncoordinated channel selection but also provide some new insights. For instance, in some settings, it would be better to delegate the decision on which channel to use to transmit the STAs, rather than having the AP make the decision on its own, which is the usual way. [ABSTRACT FROM AUTHOR]

Motivated by the channel assignment problem, we study the radio labeling of graphs. The radio labeling problem is an important topic in discrete mathematics due to its diverse applications, e.g., frequency assignment in mobile communication systems, signal processing, circuit and sensor network design, etc. A graph labeling problem is an assignment of labels to the vertices or edges (or both) of a graph G that satisfy a mathematical constraint. Radio labeling, a vertex labeling of graphs with non-negative integers, finds an important application in the study of radio channel assignment problems. The maximum label used in a radio labeling is called its span, and the smallest possible span of a radio labeling is called the radio number of a graph. In this area, Chartrand et al. (2005) provided important results by computing the exact values of $rn(G)$ for paths and cycles when $k$ is equal to the diameter for certain cases. In this paper, we determine the radio number $rn(G)$ of $G$ where $G$ is the supersub−division of a path $P_{n}$ with $n \geq 3$ vertices and a complete bipartite graph $K_{2, \alpha }$ with $\alpha \in \mathbb {N}$ .

Wireless mesh networks (WMN) have been attracting the interest of many researchers in these recent years. Several researchers worked on the optimisation of network performances for multi-radio multi-channel WMN with constrained channel resources. Others demonstrated that partially overlapping channels could expand these limited resources. Where there are insufficient resources, interfered links may significantly impact the capabilities of the channel. The problems of channel assignment have been considered NPhard problems. A PRIority-based Minimum Interference Channel Assignment (PRIMICA) algorithm has been developed to minimise the effect of interference on WMN efficiency by assigning the radio to the least-interfering available channel. Channels are assigned based on the priority weight of the interfering node with the lowest value. The proposed algorithm outperformed network throughput, packet loss ratio, and end-to-end delay, as demonstrated by performance simulation. [ABSTRACT FROM AUTHOR]

Wireless Mesh Network (WMN) is surely one of the prominent networks in the modern era which is widely used in numerous evolving applications, viz. broadband home networking (BHN), community and neighbourhood networks (CNN), coordinated network management (CNM), and intelligent transportation systems (ITS), etc. It is a wireless network (WN) with multi-hop formed by many fixed wireless mesh routers (WMR) that are connected wirelessly with a mesh-alike backbone arrangement. In the IEEE 802.11 s network, the node selection, scalability, stability, density of the nodes, mobility of the nodes, transmission power, and routing are major issues that WMN suffers. In this paper, a critical review of MAC protocols and their Quality of Service (QoS) parameters for WMN is presented to attain a better understanding of MAC protocols. Furthermore, the critical comparative analysis and recommendation of MAC procedures for WMN using Multi-objective optimization and statistical testing framework are performed. This framework is used for the analysis and recommendation of different protocols available for QoS parameters. [ABSTRACT FROM AUTHOR]

Multi-radio Multi-channel Wireless Mesh Networks (MRMC WMNs) implemented on IEEE 802.11 standards are widely used owing to their adaptability in practical network scenarios. The channel assignment problem in MRMC WMNs has been extensively studied. However, in most of the existing works, only orthogonal wireless channels are considered owing to the complexity of the channel assignment problem, which results in a low level of spatial utilization. This paper introduces partially overlapping channels into the CSMA-aware interference and shared link capacity models to fully exploit the spectrum resource and improve network capacity, utilizing both orthogonal channels and partially overlapping channels in IEEE 802.11 2.4 GHz bands. We formulate this problem as a mixed-integer linear program (MILP). We propose a traffic-demand-aware collision-free partially overlapping channel assignment (TAC-POCA), which uses all the available channels and achieves collision-free end-to-end flow transmissions to attain load balance. Simulation results show that TAC-POCA leads to better utilization of the spectrum resource and throughput improvement in both grid and random topology networks than in the case of using only orthogonal channels.

The proliferation of unmanned aerial vehicles (UAV), i.e., drones, in communication systems has recently attracted both industry and academia. This is mainly due to their flexible capabilities and features, which can support a wide range of communication applications. More specifically, drones can offer better coverage, better capacity, and a many-fold quality-of-service enhancement. Accordingly, recent communication technologies have been integrated with drones to support the unprecedented communication requirements of beyond fifth-generation (B5G) and 6G networks. Upon these bases, this paper sought to investigate the potential capabilities of multi-antenna drones in an uplink transmission cognitive radio (CR) indoor environment. With such an integrated system, a set of multiple-antenna drones communicates with a CR BS through the opportunistic utilization of the available channels without affecting the primary user’s activities. In particular, this paper proposes an adaptive power channel assignment (APCA) protocol that aims to minimize the per-drone transmit power under a set of relevant CR-related and quality-of-service constraints. The constraints include the minimum rate requirements, the probability of success, per-antenna power, the minimum SNR, and relevant CR-related constraints. Furthermore, this paper attempts to mathematically prove that the formulated optimization problem is convex, thus, the optimal solution can be attained. Accordingly, the conventional convex algorithms are adopted to solve the problem and obtain the solution. The proposed APCA protocol is capable of selecting the channel that requires the minimum power. To investigate the performance of the proposed APCA protocol, we compare its performance against that of the conventional equal-power allocation. Simulation results reveal that the proposed APCA protocol significantly improves system performance in terms of the overall transmit power.

In this work, we evaluate the application of four different metaheuristic optimisation algorithms for solving the channel assignment problem in a multi-radio multi-channel Wireless Mesh Network (WMN) using Dynamic Spectrum Access (DSA). The work advances a near optimal channel assignment (CA) in a WMN that uses DSA by applying soft computing methods. While CA in a WMN is well-studied, and CA for secondary user cognitive radio networks has also been studied in the literature, CA for our specific scenario of an infrastructure DSA-WMN is novel. This scenario poses new challenges because nodes are spread out geographically and so may have different allowed channels and experience different levels of external interference in different channels. A solution must meet two conflicting requirements simultaneously: 1) to avoid interference within the network and with external interference sources, and 2) maintain connectivity within the network; all while staying within the radio interface constraint, i.e., only assigning as many channels to a node as it has radios. We present a novel algorithm, used alongside the metaheuristic optimisation algorithms, which ensures the feasibility of solutions in the search space. Average Signal to Interference and Noise Ratio ( $SINR$ ) over the network is used as the performance measure, with the goal of optimisation being to find the highest average $SINR$ . This is a more realistic performance measure than the binary on/off conflict-based measures most common in the literature. Our energy-based method also has the unique advantage that it is protocol-agnostic, being able to avoid interference from external sources that use different protocols and standards. The algorithms that are compared in this work are Simulated Annealing (SA), the Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Differential Evolution (DE). These algorithms were evaluated through the use of simulation in Network Simulator 3. Various parameters were experimented with for each of the employed algorithms. The resultant best set of parameters was used for the comparison of the four metaheuristic algorithms. It was found that the population-based algorithms (PSO, GA, and DE) all perform satisfactorily for this problem, although DE is superior to the others. SA can give acceptable solutions, but performs poorly in comparison to the population-based algorithms. The paper also considers the computational complexity of the methods. It is found that SA and DE perform well in this regard.

This paper investigates a wireless system with multi-Unmanned Aerial Vehicles (UAVs) for improving the overall throughput. In contrast to previous studies that optimize the locations of UAVs and channel assignment separately, this paper considers the two issues jointly by exploiting Partially Overlapped Channels (POCs). The optimization problem of maximizing network throughput is formulated as a non-convex and non-linear problem. In order to find a practical solution, the problem is decomposed into two subproblems, which are iteratively optimized. First, the optimal locations of UAVs are determined under a fixed channel assignment scheme by solving the mixed-integer second-order cone problem. Second, an efficient POC allocation scheme is determined via the proposed channel assignment algorithm. Simulation results show that the proposed approach not only significantly improves system throughput and service reliability compared with the cases in which only orthogonal channels and stationary UAVs are considered, but also achieves similar performance using the exhaustive search algorithm with lower time complexity.

One of the big challenges in decentralized Wi-Fi networks is how to select channels for the different access points (APs) and their associated stations (STAs) in order to minimize interference and hence maximize throughput. Interestingly enough, de facto standards in terms of uncoordinated channel selection are quite simple, and in many cases result in fairly suboptimal channel allocations. Here, we explore how graph coloring can be used to evaluate and inform decisions on Wi-Fi channel selection in uncoordinated settings. Graph coloring, in its most basic form, is a classic mathematical problem where colors have to be assigned to nodes in a graph while avoiding assigning the same color to adjacent nodes. In this paper, we modeled Wi-Fi uncoordinated channel selection as a graph coloring problem and evaluated the performance of different uncoordinated channel selection techniques in a set of representative scenarios of residential buildings. The results confirm some of the widely accepted consensus regarding uncoordinated channel selection but also provide some new insights. For instance, in some settings, it would be better to delegate the decision on which channel to use to transmit the STAs, rather than having the AP make the decision on its own, which is the usual way.

Wireless network has emancipated people from the bondage of wired network and enhanced the quality of human life. However, there remain a few areas pertaining to wireless network that require attention, such as network congestion, low communication reliability and security. With the increasing scale and extended applications of Internet of Things (IoT), there is a growing demand for reliability, stability and security within the network. Wireless network monitoring is an effective system put in place which involves distributed sniffers that capture the transmitted data of wireless users, facilitating status analysis, fault diagnosis and resource management of the network system. Due to the limited number of sniffers, optimization of hardware configuration and channel assignment of sniffers is paramount, which can significantly improve the amount of captured data and the monitoring quality of the wireless network. Primarily, the concept, classification and characteristics of wireless network monitoring are introduced. Secondarily, the application of optimization algorithms in wireless network monitoring, particularly the channel selection algorithms during the data collection process and the channels and time-slot scheduling algorithms during the data aggregation process are summarized. Finally, the challenges faced when building a wireless network monitoring are discussed and to conclude prospects aimed towards the development of this research field are put forward. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication in the unlicensed spectrum (D2D-U) has been evolved as a promising approach to alleviate the limitation of licensed band spectrum crisis of the fifth generation (5G) cellular network. However, increased Wi-Fi deployment also calls for a need of appropriate resource management to avoid the interference conflicts when D2D users (DUs) operate with the existing Wi-Fi users (WUs) in the same unlicensed spectrum. In this paper, we have proposed an access point (AP) controlled D2D communications in the unlicensed spectrum to accommodate a suitable number of DUs that can opportunistically use the unlicensed frequency spectrum of the existing WUs. We have considered different interfering scenarios in the network and developed a Wi-Fi assisted D2D communication (WADC) model to maintain an amicable coexistence between the admitting DUs and the existing WUs. This is done through an efficient resource allocation with the joint approach of two existing methods known as listen before talk (LBT) and duty-cycle approach. A mathematical formulation of the problem has been presented and a greedy solution has also been developed. The proposed approach admits maximum number of DUs to use the unlicensed spectrum resource of the existing WUs without affecting their performance. Simulation results on different networking parameters show that the proposed approach can offer maximum D2D coverage with appropriate quality of service (QoS). Furthermore, it has also been established that the proposed approach is fairly comparable to the other existing approaches. Finally, a sensitivity analysis of the proposed WADC has been presented to show its robustness for varying input conditions. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has been considered as a promising technology for future wireless communications. In most of the existing NOMA schemes, the ideal information rate based on Shannon capacity is used as the performance metric, assuming perfect successive interference cancellation (SIC) and Gaussian transmit signals without considering practical modulations. The implicit assumptions and the resulting schemes may lead to suboptimal performance in practical NOMA systems. In this paper, we consider multi-user multi-channel NOMA systems using practical quadrature amplitude modulation (QAM) with imperfect SIC. We aim to maximize a more practical performance metric, namely the effective throughput, which takes into account the data rate and error performance. To achieve this goal, we derive both the exact and approximate expressions of the effective throughput. We also formulate a joint resource optimization problem of the power allocation, channel assignment, and modulation selection to maximize the effective throughput. We develop an efficient power allocation solution by proposing a closed-form power allocation within channels and a waterfilling-form power budget allocation among channels. We also develop efficient channel assignment and modulation selection methods with the aid of matching theory and machine learning, respectively. Consequently, we provide an efficient joint resource allocation algorithm via iterative optimization to maximize the effective throughput. Numerical results are presented to verify the superiority of the proposed NOMA scheme over orthogonal multiple access (OMA) and other NOMA schemes. [ABSTRACT FROM AUTHOR]

An efficient channel assignment plays an important role in mitigating co-channel interference in ultra-dense wireless networks. A simple solution is to separate interfering network nodes into orthogonal channels to reduce the interference among them. However, determining the optimal channel assignment is considered to be a non-linear problem, which may also be associated with practical implementation issues such as high computational complexity and control signaling issues. In an effort to cope with these challenging issues, we propose a distributed channel assignment algorithm that efficiently finds the optimal channel configuration by utilizing the concept of belief propagation. Based on a message-passing framework, the proposed distributed channel assignment algorithm maximizes the overall sum rate of the ultra-dense network with a low computational load for each network node. In addition, we design a network protocol and frame format to implement the proposed message-passing framework to real-world wireless networks. The main advantage of the proposed approach is that network nodes autonomously determine the optimal channel assignment and rapidly adapt to dynamic changes of the network. Simulation results confirm that the proposed distributed channel assignment algorithm outperforms conventional algorithms in terms of various network performance aspects, such as the sum rate, scalability, latency, and user mobility.

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.

With the evolution of web computing, Cloud computing and 6G, the data transmission speed has become one of the most imperative subject to be addressed especially for smart cities. The OBS (optical burst switching) networks are emerging as a fast and robust infrastructure for seamless connectivity and data transmission over internet in smart cities. The network traffic is generally heterogeneous in nature and OBS networks are responsible for transmission of network traffic to the intended nodes with fast speed. An appropriate channel allocation to the network traffic is a major challenge for OBS networks due to enormous traffic on optical medium. In order to minimize the consumption of bandwidth, to minimize the latency and to enhance the network throughput, we are proposing PROMETHEE-II based multi criteria channel selection approach in this paper. We have devised client server based approach where OBS central controller controls the core optical switches and channelize the network traffic on suitable channels. The criteria for selecting the appropriate channel is based on network bandwidth, hop count, channel waiting time, channel response time, traffic load on channel, expected traffic, wavelength, and link capacity. We have used AHP method to attain relative importance of each criterion, then applied PROMETHEE method to make pairwise comparison of all the alternative channels that are capable to transmit the data and finally, the channels are ranked to determine the most suitable channel for forwarding the network traffic. The results obtained from the proposed research work helps to enhance the network throughput, to reduce the latency and to enhance the traffic transmission rate. [ABSTRACT FROM AUTHOR]

Most recent works in device-to-device (D2D) underlay communications focus on the optimization of either power or channel allocation to improve the spectral efficiency, and typically consider uplink and downlink separately. Further, several of them also assume perfect knowledge of channel-state-information (CSI). In this paper, we formulate a joint uplink and downlink resource allocation scheme, which assigns both power and channel resources to D2D pairs and cellular users in an underlay network scenario. The objective is to maximize the overall network rate while maintaining fairness among the D2D pairs. In addition, we also consider imperfect CSI, where we guarantee a certain outage probability to maintain the desired quality-of-service (QoS). The resulting problem is a mixed integer non-convex optimization problem and we propose both centralized and decentralized algorithms to solve it, using convex relaxation, fractional programming, and alternating optimization. In the decentralized setting, the computational load is distributed among the D2D pairs and the base station, keeping also a low communication overhead. Moreover, we also provide a theoretical convergence analysis, including also the rate of convergence to stationary points. The proposed algorithms have been experimentally tested in a simulation environment, showing their favorable performance, as compared with the state-of-the-art alternatives. [ABSTRACT FROM AUTHOR]

Innovative cloud computing services based on Heterogeneous Cellular Mesh Networks (HCMN) offered adaptive data storage functions and a flexible environment. Channel Assignment is one of the significant tasks in these networks by using Partially Overlapped Channels (POCs) in Multi-Radio Multi-Channel HCMNs. POCs can help to maximize the network throughput, but the availability of a fewer number of Non-Overlapping Channels makes these processes challenging. Mostly 802.11 Industrial, Scientific, and Medical radio bands are used in commercial and private deployments. The use of the same frequency band in neighboring networks is the leading source of external interference. As interference is a major cause of performance degradation in networks and needs to minimize it, but interference from co-located networks is overlooked in existing POC assignments. In this paper, a centralized Interference Aware Partially Overlapped Channel Assignment is proposed that takes both external and internal interference into account along with the degree of overlap among adjacent channels. Initially, it binds interfaces of each node to its one-hop neighbors and identifies all possible links by constructing a neighborhood graph. Average external interference is measured in terms of channel utilization and the number of interfering radios at each channel. The proposed scheme selects and assigns a channel with minimum total network interference for a link. Simulation results indicated a considerable improvement in terms of throughput, packet loss ratio, and end-to-end delay as compares to the existing scheme. [ABSTRACT FROM AUTHOR]

Channel assignment has become a critical configuration task in Wi-Fi networks due to the increasing number and density of devices which use the same frequency band in the radioelectric spectrum. There have been a number of research efforts that propose how to assign channels to the access points of Wi-Fi networks. However, most of them ignore the effect of clients (also called stations or STAs) in channel assignment, instead focusing only on access points (APs). In this paper, we claim that considering STAs in the channel assignment procedure yields better solutions in comparison with those obtained when STAs are ignored. To evaluate this hypothesis we have proposed a heuristic technique that includes the effect of interferences produced by STAs. Results show that taking STAs into account clearly improves the performance of the solutions both in terms of the achieved utility and in terms of the variability of results. We believe that these results will be useful to the design of future channel assignment techniques which consider the effect of STAs.

Many variations of graph labeling has been defined in the literature. e.g., graceful, harmonious and radio labeling etc. In information technology and in data sciences, we need secrecy of data, different channel assignment and accuracy of transmission of the data. This make the use of graph terminologies indispensable for the computer programs. In this paper, we will discuss multi-distance radio labeling used for channel assignment problem over a wireless communication. A radio (multi-distance) labeling of a graph G is a function h from V(G) to the set of non-negative integers such that |h(u) - h(v)| ≥ diam(G) + 1 - dG(u, v), Where diam(G) and dG(u, v) are diameter and distance between u and v in graph G respectively. The span of a radio labeling h is the maximum integer assigned by h and radio number of a graph G is the minimum span taken over all radio labeling of G. In this article, we will find relations for radio number of a strong product K3 ⊗ Pn, n ≥ 3.

In multi-channel multi-radio wireless mesh networks (MCMR WMNs), assigning each radio with an appropriate channel to maximize the performance is a challenging problem. In this optimization, the primal concern lays on how to mitigate effects of interference to avoid performance degradation. However, collision-freedom for a given traffic demand under the limitation of precious channel resources has not been achieved yet. In this paper, we present a collision-free joint channel assignment and routing scheme called TACCA (Traffic-demand-Aware Collision-free Channel Assignment) for MCMR WMNs. To reduce the required number of channels, TACCA incorporates a property of CSMA (Carrier Sense Multiple Access), i.e., it adopts CSMA-aware interference model and a CSMA-aware shared link capacity model. We formulate a mixed integer linear programming (MILP) to optimize the network utility and enhance the practical usefulness under the given traffic demands. The evaluation results with a MILP solver show that TACCA achieves collision-freedom in both grid and random topology networks with 3-5 orthogonal channels, and exhibits good network utilization performance. In addition, the network simulation results show that TACCA achieves mostly collision-free communications under up-to-date PHY and MAC models, and presents excellent communication performance.