Channel Utilization Rate Research Articles

Dynamic adaptation of contention window boundaries using deep Q networks in UAV swarms

In flying ad hoc networks (FANET), medium access layer (MAC) protocols play an essential role in ensuring better network performance. The effective utilization of network resources and providing access fairness are key research issues in this area, and the contention window size directly influences these factors. This paper mainly focuses on addressing the upper and lower boundaries of the contention window in Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) with a binary exponential back-off algorithm. Existing solutions include static and dynamic window adjustment techniques only to update the upper boundary of the contention window size. However, the static window adjustment technique is unsuitable for changing network conditions, while the dynamic window adjustment technique may need to be more efficient in dense network environments. The proposed solution uses a Deep Q-learning algorithm(DQN) framework to adjust the contention window adaptively based on the reward function, resulting in efficient and dynamic adaptation to changing network conditions among unmanned aerial vehicles (UAV). The proposed methodology is evaluated through simulations, and performance is measured using channel utilization, efficiency, delay, and collision rate. The simulation findings show that, in comparison to generic MAC protocols, the proposed algorithm is 5% more efficient in throughput and incurs a four-fold reduction in delay.

Optimal Broadcast Scheduling Algorithm for a Multi-AUV Acoustic Communication Network

In systems of multiple autonomous underwater vehicles (AUVs), to achieve cooperative operation and cluster intelligence, information is often disseminated via broadcasting. However, due to the long propagation delay and slow transmission rate of underwater acoustic communication, traditional broadcast scheduling algorithms require a long broadcast period to avoid signal collision. To improve the channel utilization rate as much as possible and improve the update rate for broadcast information, we propose an optimal broadcast scheduling algorithm. This algorithm uses the location information of AUVs to adjust the broadcast sequence and broadcast schedule, to achieve the shortest possible collision-free broadcast period in the broadcast network for the current node distribution. Simulation experiments show that this algorithm can achieve a broadcast period much shorter than that of traditional TDMA and higher channel utilization without signal collision. In addition, the simulations prove the feasibility of applying the algorithm in an actual MAC protocol.

An adaptive context-aware vertical handover decision algorithm for heterogeneous networks

In a heterogeneous network, vertical handovers occur frequently between different access technologies to meet the requirements of mobile users. The selection of an access technology for a mobile user for handover largely depends on the contextual information of the user and network at that point of time. In this work, we have proposed an adaptive, context-aware vertical handover decision algorithm for heterogeneous networks. Both static and dynamic contextual information about the user and the network are given as inputs to our proposed algorithm. They are provided proactively by the networks to the mobile nodes on a timely basis. An optimal network scanning procedure is introduced as part of the handover process to minimize the energy consumption of the nodes. We also propose a utility function-based cell load balancing mechanism to improve overall resource utilization. In addition, a delay-sensitive handover traffic-based hybrid channel allocation mechanism is proposed. The simulation results clearly suggest that the proposed work significantly enhanced the overall performance of the system as compared to some existing work in terms of call blocking probability, channel utilization rate, consumed energy, and handover delays.

NLOS node localization for improving warning message distribution in VANETS

SummaryVehicular Ad‐hoc Network (VANET) is a vehicle‐to‐vehicle communication technology, which is highly promising due to its continuous demand, applicability, and suitability for public safety. The vehicular nodes establish communication with each other for exchanging traffic data and road maps, especially when warning messages need to be broadcasted on the network during emergency situations. Therefore, accurate vehicle information is necessary in such circumstances to improve the accuracy of Non‐Line‐of‐Sight (NLOS) node localization. In this paper, a Driving Training‐Based Optimization for NLOS Node Localization (DTO‐NL) Scheme is proposed for facilitating better warning message delivery during emergency situations in vehicular networks through a dynamic increase and decrease in the search based on the position of unknown NLOS nodes from the considered anchor nodes employed for localization. The performance of the suggested technique has been analyzed based on parameters like warning message delivery rate, latency, localization error, channel utilization rate, energy consumption, and packet delivery ratio. The proposed DTO‐NL scheme enhances the warning message delivery rate by 9.81%, 11.28%, 14.38%, and 17.64% compared to WIDVBA , ROA‐APS , ILL‐WOA‐NLOS , and WDHPBDS techniques .

Balanced Off-Chain Payment Channel Network Routing Strategy Based On Weight Calculation

AbstractOff-chain payment channel network is an effective solution to deal with low throughput and high load on the blockchain. However, the quality of the routing scheme will directly affect the performance of the off-chain network system. Existing routing schemes cannot select an appropriate transaction path according to the actual needs of user nodes. And they may also lead to network congestion, channel imbalance and node centralization. This paper proposes a new balanced routing selection scheme based on weight calculation called BRBW, which builds a weight model by Analytic Hierarchy Process. BRBW comprehensively considers the channel capacity, handling fee and path length to improve transaction success rate, reduce channel congestion and maintain the long-term sustainability of the payment channel. It uses a modified maximum flow algorithm to find paths with sufficient capacity and selects the transaction path for a large payment through linear programming. Finally, we do some experiments and compare with state-of-the-art approaches in the test environment. Simulation results show that under the same network environment, the payment success rate of the BRBW is above 75%, and the channel utilization rate is about 10% higher than other schemes.

An OFDM-Based Frequency Domain Equalization Algorithm for Underwater Acoustic Communication with a High Channel Utilization Rate

The underwater acoustic communication technique for high-speed and highly reliable information transmission in the ocean has been one of the popular research focuses facing the fast-growing information technology sector and the accelerating development of ocean resources. In order to achieve a high information transmission rate with limited underwater acoustic channel bandwidth, researchers have paid much attention to the underwater acoustic communication technique based on orthogonal frequency division multiplexing (OFDM). A traditional OFDM-based frequency domain equalization algorithm relies on cyclic prefixes for the effective resistance to the multipath effect of an underwater acoustic channel. However, a redundant cyclic prefix may lead to a severe waste of energy and bandwidth in the underwater acoustic system if it is too long. The high utilization rate of OFDM signal channel will not be practically achieved in this case. Based on the limitations of the existing frequency domain equalization algorithm, this paper studied the influence of the multipath effect on the OFDM signal transmission. Subsequently, the principles of the OFDM-based frequency domain equalization were further explored for an improved structural model design of the communication system. On this basis, a novel frequency domain adaptive equalization algorithm was put forward. In addition, the proposed algorithm was optimized to address the problem of increased computation. The simulation results proved that the novel frequency domain equalization algorithm delivers a better symbol error ratio than the existing algorithm, and the compensation for the multipath effect through frequency selective fading. The proposed algorithm can realize the information transmission at a low symbol error ratio when fewer cyclic prefixes are used, so that it takes up a lower number of channels with cyclic prefixes in the OFDM communication system.

Graph coloring-based multichannel MAC protocol in distributed underwater acoustic sensor networks

In this paper, the multichannel medium access control (MAC) problem in distributed underwater acoustic sensor networks (UASNs) were investigated. Compared with single-channel MAC protocols in terrestrial radio networks, there exist multichannel hidden terminal problem and long-delay hidden terminal problem in multichannel MAC protocol due to long propagation delay in UASNs. In addition, energy constraint makes channel allocation a challenging problem in distributed UASNs. To solve these aforementioned problems, a new multichannel MAC protocol, called graph coloring-based multichannel MAC protocol (GCMAC) is present. The protocol GCMAC is a synchronized MAC protocol which splits the time into three phases, namely, channel negotiation phase, channel selecting phase and data transmission phase. Specially, the rule for selecting channel is carefully designed based on graph coloring theory to avoid collision and maximize the utilization rate of channels in channel selecting phase. Simulation results show that GCMAC can greatly improve the system throughput and energy efficiency by effectively solving the hidden terminal problems and channel allocation problem.

Hybrid invasive weed optimization and squirrel search algorithm‐based NLOS nodes localization mechanism for improving reliable data dissemination in VANETs

SummaryIn emergency situations, the localization of Non Line of Sight (NLOS) nodes is essential for enhancing the reaction time of the vehicle drivers in response to the stimulus generated in VANETs. However, the existence of NLOS nodes intentionally or intentionally introduces broadcasting storm and congestion in the network. In this paper, a Hybrid Invasive Weed Optimization and Squirrel Search Algorithm‐Localization Mechanism (HIWO‐SSA‐LM) is proposed for improving the localization of NLOS nodes to enhance the reliable data dissemination under emergency situations in VANETs. This proposed HIWO‐SSA‐LM integrates the reproductive capability of Invasive Weeds into the reproductive potential of a Flying Squirrel Algorithm for sustaining the degree of balance under exploitation and exploration involved during NLOS node localization. The predominance of the proposed HIWO‐SSA‐LM is explored based on experiments conducted using EstiNet 8.1 simulator using emergency message delivery rate, channel utilization rate, and neighborhood awareness rate by varying the number of vehicles and NLOS nodes in the network.

An improved communication resource allocation strategy for wireless networks based on deep reinforcement learning

With the advent of 5G networks, user demand for high-speed, low-latency, and high-reliability services continues to grow. When traditional communication technologies cannot meet the needs, wireless network LoRa technology has emerged. Although LoRa has low power consumption, Long-distance, and other advantages, terminal nodes still face frequent data collection and energy consumption issues how to more efficiently combine the deep reinforcement learning method for LoRa wireless network communication and allocate resources reasonably and effectively. This paper proposes a communication channel resource allocation strategy based on deep reinforcement learning, the CL-LoRa strategy. It uses extended preamble and low-power interception technologies to achieve on-demand synchronization and low-power communication. The basic idea of this strategy is to detect channel quality based on CAD, coordinate node scheduling, and wireless channel allocation. The node will choose different ways to acquire the channel according to the current network load, namely CSMA-CA competition and dynamic duty cycle communication. In this way, the channel utilization rate is improved, and the energy consumption problem of the long-distance communication data volume is perfectly solved. The duty cycle access method is based on the imbalance of energy consumption in the Internet of Things. It uses the remaining energy of the remote central node to dynamically adjust the duty cycle of the node, wake up the working time of the node, and send more beacons to the sleeping node. Reduce the sleep delay of the node. Through theoretical analysis of CL-LoRa protocol performance, compared with DDC-LoRa protocol and ADC-LoRa protocol, CL-LoRa protocol can increase channel utilization by 9%, reduce terminal energy consumption by 1.6%, and increase throughput by 1.5%.

Concurrent Control Algorithm for Large-scale Remote Multi-user Information Management System

When the current concurrency control algorithm is used to control of the multi-user information management system, the system’s channel transmission capability is low, and the time it takes is long. In this paper, a concurrency control algorithm for large-scale remote multi-user information management system is proposed. According to the average use rate of the large-scale remote multi-user information management system, the concurrency control structure and state of the system are analyzed and judged; Through the analysis of the results, the delay of data link layer in multi-user information management system is carried out modeling; Combined with the queuing delay and accessing delay, the large-scale remote multi-user information management system control can be realized. Experimental results show that the channel utilization rate of the proposed algorithm is over 98.3%, which can transmit large amounts of information in a relatively short time and concurrency control of information management system. Therefore, the proposed algorithm has high channel utilization and efficiency of information transmission

Transmission State Prediction from MAC in Cognitive Radio via Optimized Deep Learning Architecture

Cognitive Radio (CR) is the hottest network paradigm, which permits the Secondary Users (SUs) like wireless devices/users for intelligent accessing of unallocated radio spectrum. Such accessing happens by enabling interference-free transmission of Primary Users (PUs), who are allotted with some deserved radio spectrum portions. This radio communication paradigm has effective usage in vehicular networks, where communication should be established from vehicles to static stations (vehicle-to-infrastructure) or within vehicles (vehicle-to-vehicle), without allotting dedicated frequencies. Nevertheless, the major issue in designing CR is that it must be built to aid in efficient transmitting and sensing of data through the available radio spectrum channels. This paper proposes a Model Predictive Control (MPC)-based prediction model, via a Deep Learning approach. Here, a Deep Belief Network (DBN) allows in predicting the PU transmission state as idle or busy. Moreover, this paper comes out with a new optimization concept that achieves more accurate and precise prediction. The weight of DBN is optimally selected to pave way for effective performance. Further, a new hybrid algorithm named as Cuckoo Search-Grasshopper Optimization Algorithm (CS-GOA) is proposed. The performance of the proposed model is compared over the other conventional models, in terms of channel utilization and back off, and proved for supremacy. The throughput of the proposed model, even at 50 SUs is better, when compared to other methods. CS-GOA achieved better channel utilization and backoff rate, as compared to ProMAC and NN, when the numbers of SUs and PUs in the architecture are varied with time.

An improved rank criterion-based NLOS node detection mechanism in VANETs

PurposeAn Improved Rank Criterion-based NLOS node Detection Mechanism (IRC-NLOS-DM) is proposed based on the benefits of a reputation model for effective localization of NLOS nodes during the dynamic exchange of emergency messages in critical situations.Design/methodology/approachThis proposed IRC-NLOS-DM scheme derives the benefits of a reputation model that influentially localizes the NLOS nodes under dynamic exchange of emergency messages. This proposed IRC-NLOS-DM scheme is an attempt to resolve the issues with the routing protocols that aids in warning message delivery of vehicles that are facing NLOS situations with the influence of channel contention and broadcast storm. It is developed for increasing the warning packet delivery rate with minimized overhead, delay and channel utilization.FindingsThe simulation results of the proposed IRC-NLOS-DM scheme confirmed the excellence of the proposed IRC-NLOS-DM over the existing works investigated based on the channel utilization rate, neighborhood prediction rate and emergency message forwarding rate.Practical implicationsIt is proposed for reliable warning message delivery in Vehicular Ad hoc Networks (VANETs) which is referred as the specialized category of mobile ad hoc network application that influences Intelligent Transportation Systems (ITS) and wireless communications. It is proposed for implementing vehicle safety applications for constructing a least cluttered and a secure environment on the road.Originality/valueIt is contributed as a significant mechanism for facilitating reliable dissemination of emergency messages between the vehicular nodes, which is essential in the critical environment to facilitate a risk-free environment. It also aids in creating a reliable environment for accurate localization of Non-Line of Sight (NLOS) nodes that intentionally introduces the issues of broadcasting storm and channel congestion during the process of emergency message exchanges.

Hybrid seagull and thermal exchange optimization algorithm‐based NLOS nodes detection technique for enhancing reliability under data dissemination in VANETs

SummaryThe reliability of data dissemination in vehicular ad hoc network (VANET) necessitates maximized cooperation between the vehicular nodes and the least degree of congestion. However, non‐line of sight (NLOS) nodes prevent the establishment and sustenance of connectivity between the vehicular nodes. In this paper, a hybrid seagull and thermal exchange optimization (TEO) algorithm‐based NLOS node detection technique is proposed for enhancing cooperative data dissemination in VANETs. It inherits three different versions of the proposed hybridized algorithm; three different approaches for localization of NLOS nodes depending upon its distance from the reference nodes are incorporated. It is considered as a reliable attempt in effective NLOS node localization as it is predominant in maintaining the balancing the degree of exploration and exploitation in the search process. In the first variant, the method of the roulette wheel is utilized for selecting one among the two optimization algorithm. In the second adoption, this hybridization algorithm combines TEO algorithm only after the iteration of SEOA algorithm. In the final adoption, the predominance of the seagull attack mode is enhanced by including the heat exchange formula of TEO algorithms for improving exploitation capability. The simulation experiments of the proposed HS‐TEO‐NLOS‐ND scheme conducted using EstiNet 8.1 exhibited its reliability in improving the emergency message delivery rate by 14.86%, a neighborhood awareness rate by 13%, and the channel utilization rate by 11.24%, compared to the benchmarked techniques under the evaluation done with different number of vehicular nodes and NLOS nodes in the network.

Efficiency increase in the 802.11 Ac standard performance due to the influence of Airtime Fairness technology

The article considers one of the critical problems of wireless network performance, namely, monopolization of airtime by slow clients.The advantages of wireless Wi-Fi technology are considered. The main versions of Wi-Fi standards are analyzed. It is shown that a wide variety of Wi-Fi standards is aimed at improving network performance indicators, which is expressed in increasing the transmission speed, optimizing the use of frequency bands, increasing the efficiency of Wi-Fi in segments with high subscriber density, as well as in the possibility of multiple streaming of high definition video.The IEEE 802.11ac standard is analyzed. The choice of 802.11 ac technology is based on its availability and flexibility. It is indicated that the main problem point of this standard is the inconvenience of its performance under strong differentiation of client devices, which is reflected in a decrease in network bandwidth as a whole.The network performance using Airtime Fairness technology is analyzed. To solve the problem of monopolization of airtime by slow clients, the cyclic method of dividing airtime into given intervals has been used. The necessary equipment, hardware and initial conditions for the study are indicated. It is shown that the network efficiency is determined by the following productivity indicators: average packet productivity, average bandwidth and wireless channel utilization.It has been explained that with changes in system settings due to activation of Airtime Fairness technology algorithms, there is an increase in packet productivity and average bandwidth, as well as a decrease in the utilization rate of the wireless channel, which proves the presence of positive changes in the wireless network.

The Application of Deep Reinforcement Learning to Distributed Spectrum Access in Dynamic Heterogeneous Environments With Partial Observations

This papera <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> investigates deep reinforcement learning (DRL) based on a Recurrent Neural Network (RNN) for Dynamic Spectrum Access (DSA) under partial observations, referred to as a Deep Recurrent Q-Network (DRQN). Specifically, we consider a scenario with multiple independent channels and multiple heterogeneous Primary Users (PUs). Two key challenges in our problem formulation are that we assume our DRQN node does not have any prior knowledge of the other nodes' behavior patterns and attempts to predict the future channel state based on previous observations. The goal of the DRQN is to learn a channel access strategy with a low collision rate but a high channel utilization rate. With proper definitions of the state, action and rewards, our extensive simulation results show that a DRQN-based approach can handle a variety of communication environments including dynamic environments. Further, our results show that the DRQN node is also able to cope with multi-rate and multi-agent scenarios. Importantly, we show the following benefits of using recurrent neural networks in DSA: (i) the ability to learn the optimal strategy in different environments under partial observations; (ii) robustness to imperfect observations and (iii) the ability to utilize multiple channels, and (iv) robustness in the presence of multiple agents. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> A parton of this work was presented at MILCOM 2018 in [1].

SDN-Based Centralized Downlink Scheduling with Multiple APs Cooperation in WLANs

Conventional DCF and RTS/CTS mechanisms perform the channel contention by a distributed and independent manner, which can lead to severe cochannel interference and low channel utilization in multiple APs dense deployment scenario. In this paper, we propose a channel scheduling cooperation algorithm called CCT-SDN (centralized concurrent transmission based on SDN) that enables multiple APs (Access Points) to perform cooperatively a centralized downlink transmission control, thus achieving higher system throughput and channel utilization by avoiding cochannel interference and implementing concurrent transmission. This design inherits the merit of the conventional distributed random channel access and adopts standardized OpenFlow protocol and Software Defined Network (SDN) architecture to make a centralized concurrent downlink traffic transmission decision among APs. Meanwhile, we also present a novel neighborhood relation storage scheme called SPRIM to enhance the retrieving efficiency of SDN controller, which enables CCT-SDN to perform a real-time control. Moreover, we also develop a theoretical model to prove the improvement of CCT-SDN. Furthermore, our solution does not require any modifications to existing ubiquitous 802.11 terminal devices and thus is likely to be widely deployed. Finally, extensive simulation results on Mininet-WiFi verify that CCT-SDN can achieve significant performance in terms of aggregate throughput, channel utilization, and packet loss rate in different deployment scenarios.

The multi-objective capacity optimization of wind-photovoltaic-thermal energy storage hybrid power system with electric heater

This paper proposes a wind-photovoltaic-thermal energy storage hybrid power system with an electric heater, which adopts the idea of concentrated solar power plant but omits the expensive solar field, and utilizes the reformed power block from conventional small-scale thermal power plant. From the perspective of thermal power plant reconstruction, the hybrid system aims to minimize the levelized cost of energy and maximize the utilization rate of transmission channels. The capacity optimization is solved by multi-objective particle swarm optimization algorithm. Final optimal capacity is obtained by the Pareto front and decision-making method. Moreover, the probability of exceeding is introduced to correct the theoretical power output calculated by mathematical models and improve the practicality of simulation. Finally, by comparing wind-photovoltaic-thermal energy storage system, wind-photovoltaic-battery system and wind-photovoltaic system, it can be concluded that the proposed system effectively increases the utilization rate of transmission channels and presents a better reliability and economy performance.

The Capacity Optimization of Wind-Photovoltaic-Thermal Energy Storage Hybrid Power System

This paper proposes a Wind-Photovoltaic-Thermal Energy Storage hybrid power system with an electric heater. The proposed system consists of wind subsystem, photovoltaic subsystem, electric heater, thermal energy storage and steam turbine unit. The electric heater is used to convert the redundant electricity from wind or photovoltaic subsystem into heat, which is stored in thermal energy storage. When the system output is less than the load demand, thermal energy storage system releases heat to generate electricity. In this paper, the optimal objective is to minimize the levelized cost of energy and maximize the utilization rates of renewable energy and transmission channel. The fitness function is compiled according to the scheduling strategy, and the capacity optimization problem is solved by particle swarm optimization algorithm in MATLAB. The case analysis show that the proposed system can effectively increase the utilization rate of renewable energy and transmission channel.

Performance Analysis of Dynamic Threshold Estimation Techniques Based on the One-Tier Cognitive Radio Network

Cognitive Radios (CRs) use dynamic threshold estimation (DTE) techniques to better detect primary user signals under noise uncertainty regimes. However, DTE techniques have rarely been compared before, particularly under the one tier CR network (CRN) model, making it difficult to assess their comparative performance characteristics under this regime. Thus, in this paper, we have investigated the performance of some notable DTE methods under the one-tier CRN model. We used the auction game model in our investigation to compare fairly the spectrum efficiency performance of each technique. Our findings show that DTEs generally perform better than the fixed threshold method particularly under unpredictable noise uncertainty regimes. Our results show further that the channel utilization (CU) rate of the fixed threshold method, popularly used by researchers, plummets by 50.26% for a 1 dB increase in the noise uncertainty level, while the CU rate of the DTE techniques interestingly increased by an average of 4%. Our investigation will enable CR Engineers to better understand the performance characteristics of DTE techniques under the one-tier CRN model.

Weighted Distance Hyperbolic Prediction-Based Detection Scheme for Non Line Of Sight nodes in VANETs

Vehicular Ad hoc Network (VANET) is considered as the potential network for implementing risk-free environment that facilitates least cluttered and safer interaction between the vehicular nodes. The issue of handling channel contention and broadcasting storm during the exchange of warning messages between the emergency vehicles and the Non Line Of Sight (NLOS) nodes is an area of major concern in congestive scenarios for vehicular network. NLOS nodes under emergency situation need to be effectively predicted such that the emergency vehicle is efficient enough in delivering the required information to the NLOS nodes in the stipulated time period with reduced overhead and time delay under a high degree of channel utilization. Weighted Distance Hyperbolic Prediction-Based Detection Scheme (WDHPBDS) is proposed as an effective mechanism that forecasts the NLOS nodes under dynamic moments of vehicular nodes using the merits of range free algorithm that integrates dynamic properties of hyperbolic properties. The simulation of WDHPBDS is investigated using EstiNet 8.1 network simulator by combining the data and management planes of the vehicular network. The results of WDHPBDS analyzed using warning message delivery rate, neighborhood awareness rate and channel utilization rate is confirmed to be 24%, 21% and 27% better to the compared NLOS detection schemes.