Improve Channel Utilization Research Articles

A Novel Adaptive Random Access Window (RAW) Slot Adjustment Model for IoT Network Using an Integrated Scheme

The RAW (Restricted Access Window) component of the IoT network is deployed to reduce traffic and channel contention in dense and heterogeneous sensor network environment. It divides sensor nodes into groups and slots, allowing channel access only to one RAW slot at a time. Several algorithms and improved channel utilization optimization models have been proposed to optimize the RAW parameters, to ensure a contention free network or at least, minimally reduce it. These techniques often rely on previous traffic demands schedules, collision analysis and send/receive matrices to accurately predict the future of stations’ interactions in an IoT environment. Thus systematically adjusting its operations to reduce contention among the stations and the Access Point(AP), thereby ensuring a flexible transmission even in a dense network environment. This paper critically investigated, analysed and proposed a novel approach to RAW size adjustment. The new approach will invoke data mining instruments and optimisation algorithms to improve performance of stations in an IoT environment and thereafter simulated (with respect to some specified QoS parameters) to ascertain it’s (output) performance index with the already existing models

Improved artificial bee colony algorithm-based channel allocation scheme in low earth orbit satellite downlinks

In low earth orbit (LEO) constellations, channel utilization is low owing to variations in the traffic distributions between beams. LEO satellites improve channel utilization via the full-band multiplexing technology. However, such satellites produce significant inter-beam interference. Additionally, this inter-beam interference becomes more aggravated owing to the requirements of multiple satellite coverage. To resolve this issue, we propose a LEO channel allocation scheme based on an improved artificial bee colony (IABC) algorithm. This scheme considers the difference in inter-beam communication traffic and the limitation of co-channel interference. A binary integer-programming model and IABC algorithm allocate channels to maximize the system throughput. The channel allocation matrix corresponds to a feasible solution in the IABC algorithm. The simulation results verify the excellent performance of the proposed scheme in terms of the throughput, blocking rate, and propagation delay compared with the performance of traditional schemes.

Channel Regeneration: Improving Channel Utilization for Compact DNNs

Overparameterized deep neural networks have redundant neurons that do not contribute to the network's accuracy. In this paper, we introduce a novel channel regeneration technique that reinvigorates these redundant channels by re-initializing its batch normalization scaling factor gamma. This re-initialization of BN gamma promotes regular weight updates during training. Furthermore, we show that channel regeneration encourages the channels to contribute equally to the learned representation and further boosts the generalization accuracy. We apply our technique at regular intervals of the training cycle to improve channel utilization. The solutions proposed in previous works either raise the total computational cost or increase the model complexity. Integrating the proposed channel regeneration technique into the training methodology of efficient architectures requires minimal effort and comes at no additional cost in size or memory. Extensive experiments on several image classification and semantic segmentation benchmarks demonstrate the effectiveness of applying the channel regeneration technique to compact architectures.

New Multipriority and Variable Duration Triple Time Slot P-CSMA Protocol for Edge Servers Server Deployment

In this paper, we study the joint impact of task priority and transmission time slot variable computing services on a novel p-persistent random multiple access communication protocol (p-persistent CSMA) in an edge server network. The traditional p-persistent CSMA protocol performs better at low loads, and in order to make the protocol perform better even when the edge server network is accessed by a large number of smart terminals, we consider adding a variable success time slot length and a multichannel, multipriority access model. When the success time slot is longer, the nodes will take longer time to send data, which will reduce the number of nodes retransmitting data, thus improving channel utilization and making the throughput rate of the network increase accordingly. Then, different priority levels are assigned according to the user’s needs, and the higher the priority level, the more channels are occupied and the probability of successful message delivery increases. Finally, the superiority of the proposed protocol is verified by simulation.

Opportunistic capacity based resource allocation for 6G wireless systems with network slicing

With the rapid increase of wireless application demand, 6G Wireless Systems need to further support the heterogeneous communication service contents with limited spectrum resources. Network slicing and cognitive radio are two promising technology to solve this problem. However, the network slicing in 6G wireless communication systems with uncertain shared spectrum barely exists in the literatures. This paper proposes an opportunistic capacity based resource allocation method for network slicing contained 6G wireless communication networks with uncertain shared spectrum. In particular, a 4D conflict graph model to describe the conflict relationship in the process of resource allocation for network slicing is constructed under scenarios of 6G wireless systems. Then, a novel Hasse traversal algorithm is proposed to achieve conflict free node set. To improve the spectrum utilization of shared spectrum, we present a novel statistical spectrum sharing model for shared channel to describe the uncertainty and derive the opportunistic capacity of shared channel. Hence, an opportunistic capacity based resource allocation method is formed for network slicing in 6G wireless systems. Simulation results show that the proposed approach can effectively improves channel utilization and make good use of the shared channel.

A New-Dynamic Adaptive Data Rate Algorithm of LoRaWAN in Harsh Environment

The adaptive data rate (ADR) algorithm is used in LoRaWAN, allocating an appropriate transmission rate for terminal equipment to improve channel utilization and reduce power consumption. However, the standard ADR algorithm is only suitable for static terminal equipment. In addition, due to the complexity of the external environment, the selected data rate will not match the new environment and communication packets will be lost. Therefore, this article proposed a novel and more effective ADR algorithm called new-dynamic ADR (ND-ADR). The algorithm mainly solves two problems, i.e., the standard ADR algorithm cannot be applied to mobile terminal devices and the poor communication quality and high packet loss rate in harsh environments. In this study, we also developed a frame for LoRaWAN (FLoRaWAN), a simulation framework for the star network topology LoRaWAN in OPNET. Furthermore, we built an OKUMURA–HATA model and additionally introduced a noise factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta$ </tex-math></inline-formula> to simulate the loss of wireless communication in harsh environments. Finally, extensive simulation results showed that the number of data packets required by different end nodes for rate allocation was different. Compared with the standard ADR algorithm, even for mobile nodes in harsh environments, the ND-ADR algorithm reduced network energy consumption by about 13%, reduced network delay by about 18%, and increased effective throughput by about 15%. Therefore, the improved ND-ADR algorithm is more suitable for wireless communication of removable nodes in harsh environments. Its advantages are better awareness of link environment, faster data rate regulation, improved channel utilization, and further reduction of network energy consumption.

Group mobility assisted network selection framework in 5G vehicular cognitive radio networks

The heterogeneity nature of networks is the most eminent characteristic in 5G vehicular cognitive radio networks across complex radio environments. Since multiple communicating radios may be in motion at the same time in a vehicle. So, group mobility is the most prominent characteristic that requires to be a deep investigation. Therefore, different communication radios that are moving on a train/bus needed to select the networks simultaneously. Without considering the group mobility feature, there is a possibility that the same network may be selected by each moving node and cause congestion in a particular network. To overcome this problem, a novel network selection technique considering the group mobility feature is proposed to improve the throughput of the network. In this work, a 5G vehicular cognitive radio network scenario is also realized using USRP-2954 and LabVIEW communications system design suite testbed. The performance metrics like transmission delay, packet loss rate, reject rate and, channel utilization for vehicular nodes, are gained to analyze the proposed technique in vehicular cognitive radio networks environment. The proposed technique demonstrates a remarkable improvement in channel utilization for vehicular nodes and outperformed conventional schemes.

Design of 18-mode hole-assisted elliptical-core polarization-maintaining few-mode fiber

We present a novel hole-assisted elliptical-core polarization-maintaining few-mode fiber (EC-PM-FMF) composed of three layers of air holes. The proposed fiber breaks degeneracy of modes by introducing geometry asymmetry. Through optimizing the parameters, the designed fiber could support 18 separate polarization-maintaining modes with the minimum effective refractive index difference (Δneff) between adjacent modes larger than 1.21 × 10−4 over the whole C + L band. Moreover, the designed fiber exhibits good mode and propagation properties including large effective mode area, low nonlinear coefficient, small and flat chromatic dispersion, low confinement loss and bending loss. Thus, the fiber can be used in mode division multiplexing (MDM) system without multi-input multi-output (MIMO) digital signal processing (DSP) technology to improve channel utilization.

TDMA scheduling schemes targeting high channel utilization for energy‐harvesting wireless sensor networks

As a contention-free channel access protocol, Time Division Multiple Access (TDMA) is widely applied in Energy Harvesting Wireless Sensor Networks (EH-WSNs) due to ease of implementation. TDMA scheduling in EH-WSNs faces the problem of low channel utilization since time slots assigned to some nodes may not be used by them due to shortage of energy or data. It is important to design TDMA scheduling schemes that enhance channel utilization. In this paper, the Fixed Frame Size Scheme (FFSS) and the Adaptive Frame Size Scheme (AFSS) are proposed to improve channel utilization of EH-WSNs. The FFSS aims at the optimal TDMA slot assignment for the TDMA scheduling with fixed frame size, and the AFSS considers the TDMA with variable frame size and targets both the optimal frame size and the optimal slot assignment. The optimization problems maximizing channel utilization are formulated for the FFSS and the AFSS, respectively, which take the upcoming energy and data into account. The optimization problems are transformed into assignment-like problems and solved by the Hungarian-based algorithm in polynomial time. Simulation results indicate that the proposed FFSS and AFSS can considerably improve channel utilization in the EH-WSNs compared with the existing TDMA scheduling schemes.

MAC for Machine-Type Communications in Industrial IoT—Part I: Protocol Design and Analysis

In this two-part paper, we propose a novel medium access control (MAC) protocol for machine-type communications in the Industrial Internet of Things. The considered use case features a limited geographical area and a massive number of devices with sporadic data traffic and different priority types. We target supporting the devices while satisfying their Quality-of-Service (QoS) requirements with a single access point and a single channel, which necessitates a customized design that can significantly improve the MAC performance. In Part I of this paper, we present the MAC protocol that comprises a new slot structure, corresponding channel access procedure, and mechanisms for supporting high device density and providing differentiated QoS. A key idea behind this protocol is sensing-based distributed coordination for significantly improving channel utilization. To characterize the proposed protocol, we analyze its delay performance based on the packet arrival rates of devices. The analytical results provide insights and lay the groundwork for the fine-grained scheduling with QoS guarantee as presented in Part II.

A reliable nonfeedback transmission mechanism for asymmetric channels based on machine learning

AbstractCross‐technology communication (CTC) enables data communication between heterogeneous wireless devices, which becomes the focus of current research. Due to the difference of communication distance, the transmission mode of the heterogeneous devices is mostly one‐hop transmission and multihop return. The mechanism of ensuring reliable transmission by sending ACK (ACKnowledge character) leads to longer transmission delays in asymmetric channels, especially for the ZigBee networks with low duty cycle. To solve this problem, this article proposes a new nonfeedback mechanism to reduce the communication delay on asymmetry channel. Through the method of fitting error, the random forest algorithm is improved, so that the training model can predict the current packet loss rate by judging channel conditions, receiver position, payload and other factors, so that the transmitter can accurately calculate the number of packets to be sent. The experimental results show that the loss rate predicted by this algorithm makes the transmission reliability be as high as 99.78%. Compared with the existing CTC technologies such as WeBee and NetCTC, the nonfeedback transmission mechanism put forward by us improves the transmission efficiency by 91.39% and 24.15%. The results show that the proposed mechanism greatly reduces transmission delay and improves channel utilization.

Successive Interference Cancellation Based Throughput Optimization for Multi-Hop Wireless Rechargeable Sensor Networks.

Wireless Sensor Networks are constrained by low channel utilization and limited battery capacity, so they are widely regarded as the mainly performance bottlenecks. In this paper, in order to improve channel utilization and prolong network lifetime, we investigate the cooperation of multi-hop Wireless Rechargeable Sensor Networks (WRSNs) with Successive Interference Cancellation (SIC) technology. In WRSNs, since the flow rate of each node is unknown, the power of the nodes is not constant. However, SIC will not work if the signal power levels at receive node cannot be sorted. To solve this issue, we first construct a minimum energy routing and unify the transmit rate to determine the transmit power. We can also obtain the time scheduling scheme after determining the routing and power. Next, we formulate an optimization problem, with the objective of maximizing the mobile charger’s vacation time over the rechargeable cycle. Finally, we provide a near-optimal solution and prove its feasible performance. Simulation results present that SIC can achieve the better upper boundary on throughput (compared to inference avoidance increasing about 180–450%) and no extra transmit and receive energy consumption in the multi-hop WRSNs.

Study on the Solutions to Heterogeneous ONU Propagation Delays for Energy-Efficient and Low-Latency EPONs

The scattered Optical Network Units (ONUs) in an optical access network have different propagation delays. In an Ethernet passive optical network (EPON) adopting the traditional Interleaved Polling with Adaptive Cycle Time (IPACT) scheme, such ONU propagation delay difference will waste network resources and will degrade both network delay performance and network energy efficiency in delivering low-latency services. In this article, to deal with the heterogeneous ONU propagation delays problem, two solutions are proposed and investigated. The first solution, namely the Upstream Postponing with ONU Dozing (UP-OD) scheme, is to properly postpone the upstream transmissions of those ONUs having relatively short propagation delays to improve channel utilization efficiency, and ONU doze mode is incorporated to enhance network energy efficiency. The second solution, namely the Identical Fiber Length with ONU Sleeping (IFL-OS) scheme, is to adopt an identical distribution fiber length for ONUs to enhance channel utilization, and ONU sleep mode is incorporated for energy consumption reduction. Simulation results show that both the UP-OD scheme and the IFL-OS scheme reduce network delay and improve network energy efficiency in delivering low-latency (<; 1 ms) data, and the IFL-OS scheme shows lower energy consumption in transmitting per bit of low-latency (<; 1 ms) data compared with the UP-OD scheme. Further practical value discussion shows that for the case of serving services requiring 1 ms delay, the UP-OD scheme is suitable for applying in the 1G-EPONs, whereas the IFL-OS scheme is considerable for the 10G-EPONs.

Development of event‐triggered‐based minimum variance recursive estimator for the NLNS using multi‐model approach

The minimum variance-based recursive estimator is designed to estimate the unmeasurable states of a non-linear networked system (NLNS). The measurable states are transmitted from sensor to the estimator (S–E) through a communication network. An event-triggered mechanism is proposed to limit data transmission in the S–E network to improve channel utilisation. The control input is transmitted from the controller to actuator (C–A) through another communication network. The networks in the S–E and C–A channels are affected by random packet delay, loss, and uncertain observation. These effects are modelled using five Bernoulli distributed random variables. The NLNS is converted to ‘m’ local linear stochastic augmented models by piecewise linearisation technique. The ‘m’ number of local estimators are designed for every local model using projection theorem. Global estimation is obtained by blending local estimators using T–S fuzzy approach. The proposed algorithm is recursive and has the advantage of scalability and flexibility. Finally, estimator performance is demonstrated using a non-linear tunnel diode and compared with the other popular filters. Monte Carlo simulation is performed and accumulated mean-square error values are calculated. From the simulation results, the proposed filter shows satisfactory performance.

Distributed Stable Routing with Adaptive Power Control for Multi-Flow and Multi-Hop Mobile Cognitive Networks

In most existing routing algorithms for mobile ad hoc cognitive networks (MACNets), nodes are configured with a fixed even maximal transmission power. Links set up by such the algorithms often suffer from excessive co-channel interference, which significantly downgrades performance of MACNets because the link stability highly depends on not only node mobility but also co-channel interference. In this paper, we investigate how to improve route stability through jointly considering routing with adaptive power adjustment and mobility control. We first propose a set of power adjustment policies which dynamically sets up transmission power of cognitive nodes to enable the duration of links potentially interfered as long as possible. We, then, design a routing metric called integrated link stability (ILS) to quantitatively measure the link stability. This novel metric ILS considers both node mobility and channel interference. Finally, we propose a Joint Stable Routing and Adaptive Power Adjustment (J-SRAPA) algorithm for multi-flow and multi-hop MACNets, with the objective of maximizing network throughput. J-SRAPA dynamically adjusts the transmission power in a distributed way to mitigate co-channel interference and to improve channel utilization ratio accordingly, during both route setup and data transmission. NS2-based simulation results demonstrate that our J-SRAPA significantly outperforms related routing algorithms in terms of network throughput, end-to-end transmission delay, and packet loss ratio; and the higher channel interference degree MACNets experience, the more improvement our J-SRAPA will bring to the networks.

Multimodal Learning Optimization for Enhancing Channel Usage in Wireless Communication

Cooperative spectrum processing is a key factor for improving channel utilization in wireless communication. Optimal utilization and allocation control of the available channels improve the rate of communication reliability. This manuscript introduces a multimodal learning optimization (MLO) technique for ensuring optimal channel usage (CU) in wireless communications. MLO adapts cooperative communications to leverage the throughput of wireless users. The overlapping problem that causes deterioration of wireless throughput is resolved through feature-based channel assignment. The proposed technique focuses on the CU limitation problem caused due to multi-hop communication and interference. Interference and multi-hop constraint is addressed as a dynamic optimization problem (DOP) for selecting precise channels to improve throughput and channel access rate. The analysis of varying communication parameters in DOP is modeled as a bipartite graph in MLO to classify the channel selection features. Channel selection by suppressing interference and joint constraints is independently identified for both single- and multi-hop communications based on the extracted features. The performance of MLO-CU is assessed based on the metrics: throughput, outage, channel utilization and peak transmission factor.

PROLEMus: A Proactive Learning-Based MAC Protocol Against PUEA and SSDF Attacks in Energy Constrained Cognitive Radio Networks

Malicious users can exploit vulnerabilities in cognitive radio networks (CRNs) and cause heavy performance degradation by denial of service (DoS) attacks. During operation, cognitive radios (CRs) spend a considerable amount of time to identify idle (free) channels for transmission. In addition, CRs also need additional security mechanisms to prevent malicious attacks. Proactive model predictive control (MPC)-based medium access control (MAC) protocols for CRs can quicken the idle channel identification by predicting future states of channels in advance. This provides enough time for CRs to carry out other calculations like DoS attack detection. However, such external detection techniques use additional power that makes them inappropriate for energy constrained applications. As a solution, this paper proposes a proactive learning-based MAC protocol (PROLEMus) that shows immunity to two prominent CR-based DoS attacks, namely, primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) attack, without any external detection mechanism. PROLEMus shows an average of 6.2%, 8.9%, and 12.4% improvement in channel utilization, backoff rate, and sensing delay, respectively, with low prediction errors (≤1.8%) saving 19.65% energy, when compared to recently proposed MAC protocols like ProMAC aided with additional DoS attack detection mechanism.

Traffic-Aware Sensor Grouping for IEEE 802.11ah Networks: Regression Based Analysis and Design

Traditional IEEE 802.11 network is designed for the use of small scale local wireless networks. However, the emergence of the Internet of Things (IoT) has changed the scene of wireless communications. Thus, recently, the IEEE task group ah (TGah) has been dedicated to the standardization of a new protocol, called IEEE 802.11ah, which is customized for this type of large-scale networks. IEEE 802.11ah adopts a grouping-based MAC protocol to reduce the contention overhead for each group of devices. However, most existing designs simply randomly partition devices into groups, and less attention has been paid to the problem of forming efficient groups. Therefore, in this paper, we argue that the performance of grouping is closely related to heterogeneity in traffic demands of devices, and propose a traffic-aware grouping algorithm to improve channel utilization. Since channel utilization of a group closely depends on the collision probability, we further derive a regression-based analytical model to estimate the contention success probability with consideration of sensors’ heterogeneous traffic demands. The evaluation via NS-3 simulations shows that the proposed regression-based model is quite accurate even when clients have diverse traffic demands, and our traffic-aware grouping outperforms other baseline approaches, especially when the network is nearly saturated.

N-DCF: MAC overhead reduction using narrow channel contention in wireless networks

This paper presents a medium access control protocol for wireless networks that improves system spectral efficiency using multiple channels. Use of multiple narrow channels has already been proposed as a solution to reduce MAC overhead, but merely dividing a wide channel into narrow channels cannot mitigate the negative effect of packet collisions especially when the network is densely populated. The proposed protocol, called N-DCF, adds protocol support to reduce packet collisions and improve channel utilization. The main idea is to let each node contend on one of the multiple channels and give the node privilege to access other channels when it successfully finishes transmission on its contention channel. When a node gains privilege to a channel, it can access the channel without contention. Privilege is gained probabilistically so that other nodes can have chance to win the channel and obtain privilege for other channels as well. N-DCF reduces channel idle time by conducting random backoff only on one of the narrow channels and reduces packet collisions by assigning contention channel randomly. Idle channels are not wasted because nodes can access other channels once the node wins its contention channel. Performance evaluation shows that the proposed scheme achieves higher throughput than 802.11 DCF without harming the fairness, regardless of node density, packet size, and number of radios.

Predictive spectrum occupancy probability‐based spatio‐temporal dynamic channel allocation map for future cognitive wireless networks

AbstractFor a cognitive radio (CR) user to dynamically access the available primary user channels, the spectrum sensing data is required at various locations; however, the data is not generally available at the points present in between the two spectrum sensors. To obtain this data, it is also not feasible to conduct the spectrum measurement surveys at every location. This calls for an interpolation‐based spectrum occupancy data collection in the space. Moreover, the information about the future primary user activity is necessary for highly enhanced dynamic channel allocation with better quality of experience (QoE) of CR users in terms of improved channel utilization and reduced spectrum sensing energy requirement. Most of the existing works are inclined toward either spatial spectrum interpolation or temporal spectrum prediction. In this work, a novel hybridized approach combining the Kriging‐based statistical spatial interpolation and the recurrent neural networks–based temporal prediction of the spectrum has been proposed to obtain the spectrum occupancy probability information using empirical spectrum measurements data in cellular CR networks. Furthermore, the aforementioned framework has been utilized for dynamic channel allocation with the help of a newly investigated CR channel allocation scheme. The corresponding results have been validated through various CR QoE measures and compared with the existing channel allocation strategies, where it is found that the proposed channel allocation scheme significantly outperforms the other schemes with the enhanced QoE of CR. Finally, a spatio‐temporal channel allocation map has been presented for secondary user to perform its best possible CR operation with efficient dynamic spectrum access.