Channel Allocation Technique Research Articles

An effective channel allocation designed using hybrid memory dragonfly with imperialist competitive algorithm in distributed mobile adhoc network

SummaryThe channel availability problem reaches a higher degree in mobile ad hoc networks (MANETs) and garners a lot of attention in communication networks. Because increased mobile usage might result in a lack of channel allocation, an improved channel allocation technique is presented to tackle the availability problem. The distributed dynamic channel allocation (DDCA) model is built in this paper using the hybrid memory dragonfly with imperialist competitive (HMDIC) method. Based on optimization logic, this strategy assigns the channel to mobile hosts. The MANET provides a dispersed network within the coverage region in the absence of base station infrastructure. The HMDIC optimizer approach in this circumstance randomly begins every respective node to update and store their pbest value utilizing RAM dragonfly employing satellite images. The constraint values are then used to construct the cost function, which results in a strong kind of global optimum solution. The channels are therefore distributed in an effective manner. The HMDIC algorithm is used in this research to build a novel channel allocation system. It makes advantage of the exploration capabilities to successfully explore the individual node using MDA (Modified Dragonfly Algorithm) and locate the global best solution using imperialist competitive algorithm (ICA). Both of these combined tactics are more effective in accelerating the convergence of the allocation model. To validate the performance, the HMDIC‐based DDCA system provides promising results in terms of assigning available channels, thereby enhancing channel reuse efficiency and fractional interference.

MUSCAT: Distributed multi-agent Q-learning-based minimum span channel allocation technique for UAV-enabled wireless networks

We consider a minimum span channel allocation problem (MS-CAP) to overcome spectrum scarcity and facilitate the efficiency of unmanned aerial vehicle (UAV)-enabled wireless networks. Basically, the MS-CAP minimizes the difference between the maximum and minimum used frequency, i.e., the required total bandwidth, while guaranteeing the quality-of-service (QoS) requirements for each wireless link in the network. The conventional optimal minimum span channel allocation (MS-CA) scheme is based on a centralized approach, assuming that global network information is available at the central controller. In practice, however, this may not be feasible for dynamic environments like UAV-enabled wireless networks since the real-time exchange of network information and channel allocation results with dynamically moving UAVs is formidable. Hence, we propose a novel practical MS-CA algorithm based on distributed multi-agent reinforcement learning (MARL), where each agent independently learns its best strategy with its local observations. To the best of our knowledge, the proposed technique is the first work of designing a distributed MARL for the MS-CAP for multi-UAV-enabled wireless networks in the literature. Numerical results reveal that the proposed distributed MS-CA technique can efficiently save the required total bandwidth while ensuring the QoS requirements of each link, represented by the signal-to-interference plus noise ratio (SINR) threshold, even in dynamic wireless networks. It validates the applicability of the proposed distributed MS-CA framework to dynamic networks.

WiMAX Network Optimization Using Frame Period with Channel Allocation Techniques

Internet connectivity in WiMAX networks, along with various applications, is increasing rapidly, so the connectivity of internet and data transfer speed are always challenge for effective data transmission in wireless networks. Several factors affect the performance of networks. One important factor is to choose a suitable frame period for effective data transmissions. The performance of different frame period with round-robin and strict priority are evaluated in this work. A frame period in round robin performs better than a strict priority in terms of throughput, but a strict priority performs better in terms of drop rates is observed. This paper also demonstrates that an effective frame period, when combined with a proper bandwidth allocation algorithm, yields better results. This work gives the analysis that round robin performs 83.8847% better while strict priority performs 86.0020% better than the earliest deadline first algorithms for 10 subscriber stations in terms of throughput. This work is helpful to researchers and industrialists for actual implementations in WiMAX networks.

Implementation of survivability aware protocols in WSN for IoT applications using Contiki-OS and hardware testbed evaluation

The Internet of Things is a network of devices capable of operating and communicating individually and working for a specific goal collectively. Technologically, many networking and computing mechanisms have to work together with a common objective for the IoT applications to function, and many sensing and actuating devices have to get connected to the Internet backbone. The networks of resource-constrained sensor devices constitute an integral part of IoT application networks. Network survivability is a critical aspect to consider in the case of a network of low-power, resource-constrained devices. Algorithms at different layers of the protocol stack have to work collectively to enhance the survivability of the application network. In this article, the survivability-aware protocols for wireless sensor networks for IoT applications are implemented in real network scenarios. The routing strategy, Survivable Path Routing protocol, and the channel allocation technique, Survivability Aware Channel Allocation, are implemented in Contiki-OS, the open-source operating system for IoT. Furthermore, the implementation scenarios are tested with the FIT IoT Lab hardware testbed. Simulated results are compared with the results obtained from the testbed evaluation.

A Novel Particle Swarm Optimization for Channel Allocation in OFDM Based Cognitive Radio Networks

It has become increasingly apparent that bandwidth scarcity is an issue as wireless communications advance. Alternatively, spectrum sensing techniques are used to detect licensed users. A spectrum sensor can detect energy, matched filters, and cyclostationary features. There are, however, some drawbacks to these methods. Energy detector performance is affected by noise power uncertainty. Every primary user needs a dedicated receiver for matched filter spectrum sensing. Computational effort and observation time are required for cyclo-stationary feature detection. Spectrum use is determined using particle swarm optimization (PSO), an algorithm for determining the best frequency allocation and highest accuracy. Using PSO operations, this paper proposes an improved energy detection method compared to conventional energy detection methods. Detecting energy and using the PSO channel allocation technique to detect fading channels is also mathematically described.

Frequency allocation algorithm

Wireless communication technology's quick advancement has facilitated the ongoing formation of numerous new communication business scenarios and the thorough connectivity of man-machine-object-space. The conflict between few channel resources and high demand is becoming more and more obvious, which adds to the stress on the entire network. In order to enhance communication quality with constrained frequency resources, more optimal channel allocation techniques are urgently required. This paper discusses the increasing demand for frequency resources due to the exponential growth of data traffic, which has necessitated more efficient channel allocation methods to optimize communication quality with limited frequency resources. The evolution of mobile wireless cellular communication systems from the first generation to the current fifth generation, and the imminent emergence of the sixth generation, are also discussed. The paper highlights the importance of ultra-dense networks and device-to-device communication in meeting the capacity and coverage requirements of 5G networks. The advantages and disadvantages of different frequency reuse methods, specifically Fractional Frequency Reuse (FFR) and water injection algorithm, are analyzed. The paper summarizes these methods' principles and application effects in specific situations, emphasizing the need for appropriate frequency reuse methods to achieve optimal channel allocation and communication quality.

Improving the Quality of Service (QoS) and Resource Allocation in Vehicular Platoon Using Meta-Heuristic Optimization Algorithm

The concept of vehicular platooning is emerging due to its self-driving capability, whereas a set of cars are arranged closely and that can drive safely even in high speed. Moreover the communication can also takes place between two platoons and hence the platoon leader can control the speed and direction. Traffic difficulties, like as collisions and delay at road junctions, need the use of platoon mechanisms. The most efficient vehicle traffic management in platoons is acknowledged in Intelligent Transportation System (ITS) for improving energy efficiency, road capacity, and road safety. Essentially, it is critical to analyse the connection characteristic of any two platoons in order to improve connectivity. As a result, under bi-directional platoon architecture, the possibility of connectivity between platoon members and Road Side Units (RSUs) is generated. Providing Quality of Service (QoS) to automotive network is difficult issue because elements such as asynchronous transmission or high mobility, crowded and fragmented channels, hardware defects, and various vehicular gadgets. As a result, it is extremely desired to utilize and distribute resources efficiently. Despite the existence of other bio-inspired meta-heuristic optimization methods, the Bacterial Foraging Algorithm has the benefit of simplicity and efficiency, and so may be used to a wide range of engineering applications. However, in the event of sophisticated analysis, the constraint is a lower convergence rate. To improve resource allocation, this study employs the Improved Bacterial Foraging Optimization based Channel Allocation (IBFOCA) technique. Set of channels allocated to platoon members are signified as bacteria’s position. By using latency, Signal-to-Interference Ratio (SINR) and Quality of Service (QoS), available channel is initially determined. This approach is used to find a channel that is acceptable for platoon members. Simulation results are used to examine the presentation of multi-channel distributions in platoons utilizing suggested optimization strategy. Signal-to-interference ratio is lowered by 29 decibel, throughput is raised by 69 kilobits per second, collision probability is reduced by 21.4 percent, and average batch size is 57 percent with a delay of 49 milliseconds.

Opportunistic Channel Allocation Model in Collocated Primary Cognitive Network

The growing demand of radio spectrum to facilitate the primary/secondary users in a cellular network is a challenging task. Many channel allocation models, applying cognition, have been proposed to increase the radio spectrum utilization. The proposed model peruses three types of users: primary users (PUs), opportunistic primary users (OPUs), and secondary users (SUs) that use the radio resources in collocated primary base stations. Out of these users, the opportunistic primary users and secondary users may request for handover as per their requirements. The objective of the model is to enhance the radio spectrum utilization by the opportunistic utilization of radio resources by OPUs and by enabling cognitive radio base stations to collect free channel information dynamically. The cognitive radio base station maintains the centralized free channel at collocated primary base stations to facilitate the SUs opportunistically. The proposed channel allocation technique maintains the Quality of Experience (QoE) of the users as well. The performance analysis of the model is done by simulation which diversifies the importance of the proposed model in the view of minimum blocked services.

QoS aware distributed dynamic channel allocation for V2V communication in TVWS spectrum

Vehicles use Dedicated Short Range Communication (DSRC) standard for communicating among themselves. But, with the increased number of on-road vehicles, DSRC channels get congested. So, an alternative to DSRC needs to be introduced, to meet the diverse demand for various vehicular applications. Federal Communication Commission released TV White Space (TVWS) for dynamic spectrum access. These TVWS channels has the potential to be used for Vehicle-to-Vehicle (V2V) communication. However, the vehicles are highly mobile and communicate in an adhoc manner. Thus, employing TVWS in V2V communication requires efficient optimized channel allocation technique. In this work, a distributed joint channel and power allocation technique is devised, that considers both spatio-temporal change of TVWS availability and vehicular mobility. The channels are allocated based on user's transmission requirement with an adjusted power level such that significant interference is not caused to neighbouring primary users and other secondary users, transmitting in the same channel. Multiple vehicles are assigned a single channel using the proposed optimization technique to increase the system throughput. Vehicles are enabled with cognitive facility that allows them to sense the spectrum availability instead of relying on the geo-location TVWS database. The optimized channel and power allocation problem is a Mixed Integer Non-Linear Programming model which is solved using Lagrange's dual method. Simulation analysis shows that proposed optimization technique achieves high throughput while maintaining the network's QoS.

Fair Channel Allocation Scheme for Base Station Cooperative Communication in LTE Mobile Network

Unjust provision of channels by base station greatly affects Mobile subscribers. A standard cluster, would be able to serve Many User Equipments (UEs). A typical cluster would contain 3, 7 19, or 21 cells. An issue of ambiguity exist in the demarcation boundary of adjacent cells, thus, radio transmission in a cell from a particular eNode Base Station (eNBs) may leak into neighbouring cells, which causes interferences. Furthermore, the eNBs geographical location is sensitive to obstructions such as towering buildings. The absence of line of sight has a major impact on radio signals, as this would amplify the loss of propagated radio signals, leading to weaken signal strength at the transmitter, that ultimately affecting current the Fixed Channel Allocation (FCA) technique. Despite the effort for fair channel through the current mechanism, users on the edge of the cell experience unfairness. The primary cause for this can be traced to poor received signal power. There are two considered UEs clustered in this simulation scenarios, which is proposed in this paper: (i) random fixed UEs and (ii) 5 step move UEs. A combination of static and dynamic clustering is proposed, thus leading to enhanced channel allocation. From the results, there is a drastic reduction in inter-cell interference, which would increase the performance of the cellular network to become ideal.

A modified energy detection based dynamic spectrum sharing technique and its real time implementation on wireless platform for cognitive radio networks

Cognitive radio offers a flexible and efficient utilization of radio frequency resources by dynamic spectrum sharing as required in next gen (5G) architecture of wireless communication. The channel allocation time, probability of false alarm detection and spectral efficiency are the major performance parameters to characterize a spectrum sharing technique. This paper presents modified energy detection based dynamic channel allocation technique based on sensing the power spectral density of idle spectrum bands i.e. spectrum hole. Receiver operating characteristics (ROC) curves have been used to analyze the detector performance of sensing with respect to probability false alarm at different values of SNR. Allocation of unoccupied bands to the SUs has been done by coalition based cooperative game, which provides SUs with an incentive to cooperate. Based on their worth, SUs get payoffs which have been computed using Shapely values as a one-point solution. Vickrey–Clarke–Groves (VCG) auction mechanism has been used to allocate the spectrum resources fairly to each user. On the basis of allocation time, the present model for dynamic spectrum access appears to be more efficient as compared to the conventional opportunistic spectrum access model.

VoIP-HDK – A novel channel allocation technique for QoS aware VoIP communication over heterogeneous networks

Abstract Channel allocation for VoIP (Voice over IP) is a critical problem in modern-day heterogeneous networks having diverse application requirements. Conventional stochastic and machine-learning based models that predict channel suitability for VoIP underperform under dynamic conditions. In contrast, this paper deploys VoIP-HDK2 – a novel technique that uses an efficient combination of K-Nearest Neighbors algorithm, 2-order Hidden Markov Model, Damerau-Levenshtein distance based model and K-Means Clustering technique to accurately predict and allocate channels with sufficient QoS (Quality of Service) guarantees to VoIP calls. The primary contribution of this paper lays both in the formulation of the proposed methodology involving tailor-made algorithms as well as complete test-bed based dataset creation, execution of VoIP-HDK2 and validation of its performance in terms of maintaining high quality VoIP calls. Comparative performance evaluation records significant reduction in call drops and packet-loss and enhancement in call quality and throughput for VoIP-HDK2 with respect to existing works in literature.

Extended Distributed Suboptimal Channel Assignment Algorithm Based Channel Assignment for Wireless Local Area Networks in a Multi Agent Environment

Due to the universal use of IEEE 802.11 – based networks, the systematic positioned of Wireless Local Area Networks (WLANs) come to be a Challenge. With the intense admiration and stationed of WLANs, well organized administration of wireless bandwidth is elegantly a major progressive. The advanced techniques upgrade the utilization of wireless bandwidth in the factors of wireless local area networks (WLANs) employing advanced channel allocation techniques between Interfering Access Points (Aps). This work formalizes the channel assignment as a multiagent hampering optimization problem in a multi agent environment and intend a latest collective medium assignment procedure called Extended Distributed Suboptimal Channel Assignment (EDSCA), which utilizes Distributed Pseudo tree optimization (DPOP).

Survivability Aware Channel Allocation in WSN for IoT applications

The Proliferation of the Internet of Things applications like healthcare monitoring services and others has accelerated the demand for wireless networks of tiny devices. Network congestion escalates as the number of field devices with wireless connectivity increases. An efficient medium access control strategy that provides a reliable channel by reducing the packet collision is paramount in these congested network topologies. MAC protocols should evaluate properties of the links before allowing nodes to access the shared medium. Network survivability is an important attribute to be considered in IoT scenarios and has multiple stratifications; path, link, and node survivability. Protocols at various layers of protocol stack should operate jointly to prolong the network survivability. This work develops a Survivability Aware Channel Allocation (SACA) technique which is a fuzzy-based mechanism that enables the network components to adapt the preferences to get the channel access. It works along with upper layer protocols with a cross-layer design to prioritize medium access strategies. Simulation results suggest that the proposal works better in terms of network throughput and packet delivery rate and improves the reliability of the network. The new method operates at every node and tries to control performance degradation as the network density expands.

A heuristic channel allocation model with multi lending in mobile computing network

Radio channel allocation is broadly studied in the framework of cellular networks. Channels are scarce resources and to be utilised judiciously by the cells of the network. During the course of channel allocation, channels are normally lent/borrowed to the neighbour cells. Most of the available channel allocation techniques apply only single lending/borrowing. With the emergence of cognitive radio, it has now become possible to utilise the channels opportunistically. Availing the channels are the services which are normally categorised into real-time and non-real-time services. Of these, real-time services are given more priority to serve over the non-real-time services. Further, among the new call and handoff services, the latter are given priority to serve. This work proposes a novel heuristic approach for better channel utilisation for these services, blending the idea of cognitive radio and multi-channel lending/borrowing. The performance study, of the proposed model, is done by simulation which reveals the effective channel utilisation in terms of blocked and dropped services.

A heuristic channel allocation model with multi lending in mobile computing network

Radio channel allocation is broadly studied in the framework of cellular networks. Channels are scarce resources and to be utilised judiciously by the cells of the network. During the course of channel allocation, channels are normally lent/borrowed to the neighbour cells. Most of the available channel allocation techniques apply only single lending/borrowing. With the emergence of cognitive radio, it has now become possible to utilise the channels opportunistically. Availing the channels are the services which are normally categorised into real-time and non-real-time services. Of these, real-time services are given more priority to serve over the non-real-time services. Further, among the new call and handoff services, the latter are given priority to serve. This work proposes a novel heuristic approach for better channel utilisation for these services, blending the idea of cognitive radio and multi-channel lending/borrowing. The performance study, of the proposed model, is done by simulation which reveals the effective channel utilisation in terms of blocked and dropped services.

Performance Evaluation of an Adaptive Channel Allocation Technique for Cognitive Wireless Sensor Networks

This paper deals with a cognitive overlay IEEE 802.15.4e wireless sensor network relying on a low-complexity spectrum sensing technique. In particular, the paper critically compares two spectrum sensing schemes with the aim of identifying the most appropriate solution in terms of performance and implementation complexity. Furthermore, the paper provides an analytical framework to investigate the cognitive overlay IEEE 802.15.4e wireless sensor network behavior in terms of throughput, packet dropping probability, and energy efficiency. The obtained results clearly highlight the good behavior of the proposed approach without burdensome cost or complexity and its advantages for several emerging applications such as those supported by fog computing architecture.

Survey on Channel Allocation Techniques for Wireless Mesh Network to Reduce Contention with Energy Requirement

Objective: In Wireless Mesh Networks the spectrum should be utilized effectively with better Quality of Service (QoS), synchronized time management and minimum delay. Method: The existing channel allocation methods needs improvement in QoS parameters such as end-to-end delay, ripple factor and time factor in accessing the channel. To improve the QoS further, the admission control drop and block probability, efficient channel reservation approach is the better solution which leads to better QoS even for delay sensitive applications. A performance analysis is made among the existing methods for contention reduced channel allocation methods, energy conservation channel allocation methods and as last method Admission Control Drop_Block probability methods. The networks are analyzed by considering the metrics such as Packet loss rate, End-to-end delay and Throughput. The channel count is virtually increased to support for dense networks by utilizing already used channels and by reserving few channels for dynamic requirement for normal and multimedia traffic data. The channel allocation methods are simulated using NS-2.

Low-Complexity and Low-Feedback-Rate Channel Allocation in CA MIMO Systems with Heterogeneous Channel Feedback

In this paper, we propose a low complexity and low-feedback-rate channel allocation for carrier aggregation (CA) MIMO systems as an application to heterogeneous networks (HetNets) with multiple component carriers (CCs) and users. The proposed channel allocation technique relies on the heterogeneous channel feedback concept due to the heterogeneous channel statistics of the different CCs. We apply the heterogeneous channel feedback, where the channel feedback granularity is provided using best- $M$ selected feedback strategy. The precoding matrix indicator (PMI) and rank indicator (RI) are designed with a low complexity so that the PMI/RI reports are calculated more efficiently per CC and user. This approach reduces the complexity significantly in a multiuser and multichannel scenario. Next, we deal with the low-feedback rate and thus devise a channel allocation problem. The problem on one hand provides the minimization of the feedback overhead and, on the other hand, it guarantees the quality-of-service request of each user for a specific target throughput. The problem is considered as a channel assignment problem; thus, the Hungarian method is first used. Next, a low complexity solution is devised using the stable matching algorithm. A complexity analysis and evaluation among the original and the proposed approach for the low-complexity and the low-feedback-rate channel allocation is carried out. This comparison reveals the benefits of the proposed solution for CA MIMO system in HetNets. The proposed approach can be considered as a proposal to the future massive CA, i.e., up to 32 CCs.

Noncontiguous Channel Allocation for Multimedia Communication in Cognitive Radio Networks

In conventional wireless systems, unless a contiguous frequency band with width at least equal to the required bandwidth is obtained, multimedia communication cannot be effected with the desired quality of service. We propose here a novel channel allocation technique to overcome this limitation in a cognitive radio network, which is based on utilizing several noncontiguous channels, each of width smaller than the required bandwidth, but whose sum equals at least the required bandwidth. We present algorithms for channel sensing, channel reservation, and channel deallocation along with transmission and reception protocols with two different implementations based on FDM-FDMA and OFDM-FDMA techniques. Simulation results for both these implementations show that the proposed technique outperforms the existing first-fit and best-fit allocation techniques in terms of the average number of attempts needed for acquiring the necessary number of channels for all traffic situations ranging from light to extremely heavy traffic. Furthermore, the proposed technique can allocate the required numbers of channels in less than 1 s with FDM-FDMA even (4.5 s with OFDM-FDMA ) for 96% traffic load, while the first-fit and best-fit techniques fail to allocate any channel in such situations.