Idle Channel Research Articles

FMCPR: Flexible Multiparameter-Based Channel Prediction and Ranking for CR-Enabled Massive IoT

The cognitive radio-enabled <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$massive$ </tex-math></inline-formula> Internet of Things (CR- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> IoT) is envisioned to shape the future of densely connected IoT devices in the sixth-generation networks to support the hyperconnected society. In conventional CR networks, secondary users (SUs) sense the whole block of spectrum to find idle channels, which is an energy-consuming, delay-inducing, and processing-intensive task. With the large scale of resource-constrained heterogeneous devices in CR- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> IoT, the sensing process becomes a major hurdle for CR- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> IoT devices to achieve efficient utilization of the limited device and network resources. Thus, a novel multiparameter-based flexible scheme is proposed for idle channel prediction and channel ranking, which considers priorities as well as heterogeneity of users. The scheme uses a probabilistic approach and employs multiple parameters simultaneously to evaluate the suitability of a channel before selecting it for transmission. In addition, valid channel obsolescence, a major problem inherent with channel prediction and ranking, is countered by the proposed scheme. The scheme is evaluated under the impact of variable primary and SUs’ arrivals and under multiple channel failures rates and variable sensing and frame time duration. The proposed scheme is also compared with its own modified version that disregards channel failures, and with the random channel selection approach followed by IEEE 802.22. The overall evaluation is conducted under realistic spectrum sensing. Simulation results show that for different parameter values, the proposed scheme improves the collision probability by 11%–55%, reduces sensing time and energy by 60% and 65%, respectively, and enhances throughput by 4%–70%, and spectrum utilization efficiency by 11%–40%.

Interference-Avoid Based Spectrum Sharing Approach for Loosely-Coupled Cognitive Radio Networks

Cognitive radio (CR) network is a promising technology to solve the problem of spectrum scarcity arising from the growth of usage of wireless networks and mobile services. CR networks can find idle channels and access them opportunistically with spectrum sensing technology. In this paper, resource allocation with sensing-based interference recording is considered for several loosely-coupled cognitive radio networks, in which the base station controls the end-users’ (EUs) transmission by making channel allocation strategies based on recorded interference information. We proposed a method to record this information and an allocation strategy with low computational complexity, to increase the capacity of tasks with fixed data size in the system. Numerical results show the effectiveness of our model for recording the statistical behaviour of the cognitive network interference and allocating channels for higher system capacity.

A Multilevel Scheduling MAC Protocol for Underwater Acoustic Sensor Networks(UASN)

Underwater acoustic sensor networks (UASNs) have attracted great attention in recent years and utilizes as a part of oceanic applications. This network has to deal with propagation delay, energy constraints and limited bandwidth which are strenuous for designing a Medium Access Control (MAC) protocol for underwater communication. There also exists an idle channel listening and overhearing problem which sets down the energy into starvation in the contention-based MAC protocols. Alternatively, lengthy time slots and time synchronization equated by schedule-based MAC protocols, outcomes the variable transmission delay and degrades the network performances. To iron out these problems, we propose a cluster-based MAC protocol, tagged as Multilevel Scheduling MAC (MLS-MAC) protocol for UASN in the paper. The cluster head is a decision maker for packet transmission and aids to inflate the lifetime of sensor nodes. To reinforce the channel efficiency, the multilevel scheduling in data phase is initiated with two queues depending on the applications fixed by the cluster head. The simulation result shows that the MLS-MAC has increased the network throughput and has decreased energy consumption.

Genetic algorithm-based hybrid spectrum handoff strategy in cognitive radio-based internet of things

In Cognitive radio-based Internet of Things (CR-IoT) systems, the return of the primary user (PU) causes the secondary user (SU) that is communicating to face the spectrum handoff problem. In the process of spectrum handoff, the user terminal cant get the idle channels in time because of the unknown channel usage state.To solve this problem, a hybrid spectrum handoff algorithm based on genetic algorithm is proposed. The algorithm considers the regularity of PU activities in space and time, defines the idle probability of channels from the perspective of week attributes and time periods, obtains the optimal time period length using genetic algorithm,generates a channel idle probability table, and provides the target channel sequence for SUs in combination with the proposed channel ordering scheme. Simulation results show that when the total number of SUs is within 10 \(\sim \) 20, the proposed algorithm has a spectrum handoff outage probability of less than 7%, an average delivery time of less than 13s, a total packet error rate of less than 5.5%, a channel utilization of consistently above 70%, and an average detection times of less than 7 times.

Price- and rate-aware multi-channel spectrum access for profit enhancement in opportunistic networks with QoS guarantees

Cognitive radio (CR) is a key technology that can enable opportunistic spectrum access, which enables secondary users (SUs) to dynamically exploit the under-utilized channels in the licensed spectrum, owned by primary radio networks (PRNs), referred to as dominant firms . Such sharing is subject to interference, SU QoS and cost constraints, in which SUs should not introduce harmful interference to PR users, achieve QoS rate demand and pay a price for using the licensed PR spectrum. The price of accessing idle PR channels depends on the level of channel utilization and price paid by PRs to access the channels, while the amount of needed spectrum to serve the rate demand of each SU heavily depends on the link-quality of the various channels. In this paper, the spectrum assignment problem in a CR network (CRN), referred to as follower firm , is investigated with the target of serving the largest possible number of SUs with the least possible total price paid to the PRNs (highest CRN profit) while being aware of the time-varying achieved transmission rate and level of utilization of the various PR channels. The problem is mathematically expressed as an optimization problem with the goal of maximizing the number of served SUs and the profit made by the CRN, which has been shown to be a binary linear programming (BLP) problem. Due to the high complexity of solving such optimization, we use the well-known sequential-fixing optimization method to obtain sub-optimal solutions. Simulation results indicate that our channel-assignment optimization significantly increases the CRN profit by reducing the price paid to the PRNs while achieving comparable performance offered by previous price-unaware protocols.

A router architecture with dual input and dual output channels for Networks-on-Chip

Networks-on-Chip (NoCs) are the most viable and scalable solution for connecting thousands of processing cores, and are emerging as the interconnect infrastructure for future Chip-Multiprocessors. Latency and throughput are the primary metrics for on-chip network performance, while chip area and power budgets are increasingly dominated by interconnect networks. Hence, it is critical to achieve higher performance gains at a lower cost. Previous research has proposed a router architecture that forwards packets through idle bidirectional channels to improve network performance, but it introduces significant area and power overheads. In this paper, we propose DIDO: a router architecture with dual input and dual output channels. First, the dual output channels provide extra flexibility for forwarding packets and reduce packet contention. Then, the redesigned input ports and crossbar shorten the pipeline and reduce the crossbar overhead. Finally, the removal of the virtual channel allocator allows DIDO to operate at higher frequencies. Simulation results indicate that DIDO reduces average packet latency by up to 44.8% and improves network throughput by up to 37.8% compared to the baseline router at the same frequency. Synthesis results indicate that the DIDO’s area and power overhead are comparable to the baseline router, while the maximum frequency is boosted by 18%.

Modeling and Performance Analysis of Channel Assembling Based on Ps-rc Strategy with Priority Queues in CRNs

Based on two types of priority queues, this paper proposes a polling scheduling strategy with reserved channel (Ps-rc strategy) for predefined priority services in cognitive radio networks (CRNs). Channel assembling (CA) technology and spectrum adaptation (SA) technology are adopted to dynamically adjust the assembled channels of secondary users (SUs) to improve the performance of the secondary network. Specifically, the SUs in CRNs are divided into two queues with different priorities; based on polling scheduling, a part of the idle channel is reserved for high-priority queue during the polling stage of the low-priority queue. The purpose is to increase the service quality (QoS) of high priority on the basis of providing fair scheduling. Furthermore, the CA-based channel access process of the proposed strategy is presented and modeled by continuous time Markov chain (CTMC). Then, the process of resource flow between users is mapped on CTMC, and the transition conditions and parameter sets of channel assembling covering all user activities of the system are derived. Finally, the system performance of the proposed CA-based Ps-rc scheduling strategy is simulated and evaluated, including network capacity, spectrum utilization, blocking probability, and forced termination probability. Numerical results show that the proposed strategy can improve the QoS of the predefined high-priority service without causing excessive starvation problem of low-priority service.

Dynamic channel allocation strategy for quality of service enhancement in wireless ad hoc networks

AbstractThe quality of service (QoS) is a momentous performance index of wireless ad hoc networks. However, unreasonable channel allocation will degrade the QoS. During the process of channel allocation, if user requests for idle channels are rejected, the QoS will lower accordingly. In this paper, we propose a dynamic channel allocation strategy for QoS enhancement to enhance the QoS and reduce the rejection rate of idle channel requests. First of all, the channel availability model is modeled as a Continuous Time Markov Chain in a bid to calculate the channel availability which is used to compute the channel availability factor (CAF) and the channel availability threshold (CAT). Then, the dynamic channel allocation process is achieved based on the comparison result of CAF and CAT. Ultimately, the QoS is guaranteed by exploiting the earliest deadline first algorithm to forward packets and enhanced by using the packet segmentation algorithm to divide packets into packet flows. The simulation results prove that the proposed channel allocation strategy increases the packet delivery ratio and throughput, decreases the end‐to‐end delay and total overhead and shows a QoS enhancement than existing schemes.

An Enhanced Reliable Access Scheme for Massive IoT Applications in Ubiquitous IoT Systems

As a key infrastructure for intelligent manufacturing, the Industrial Internet of Things (IIoT) can connect people, massive intelligent terminals, automated control equipment, and convergence nodes to achieve a high integration of industrial systems with advanced computing, sensor technology, fifth-generation technology (5G), and the Internet. How to realize the robust and reliable connection of massive IoT applications has become the research focus of industry and academia. However, the massive IoT industrial equipment, diverse business types, complex networking mode, and broadcast characteristics of wireless media have posed serious challenges to providing stable and reliable connections in industrial communication scenarios. In order to overcome this problem, the paper proposes an enhanced reliable access scheme (ERAS) for massive IoT applications in software-defined industrial heterogeneous networks. Based on the idea of software-defined networking (SDN), the ERAS is designed from two perspectives of the control plane and the data plane. Based on the idea of greedy algorithms, ERAS uses priority, interference set, deadline, and other information to find the local optimal solution and then obtain the global optimum. First, low-priority periodic data is accessed to the channel based on the earliest deadline. Then, high-priority data (such as control and interactive services) judges the channel occupancy status according to the access flow table, and if there is an idle channel in the current time slot, it is normally accessed. Otherwise, the subslots reserved with high priority in the control channel are used to preempt the low-priority channel. This preemption mechanism can ensure reliable and real-time connections for massive IIoT. Simulation results show that the ERAS performs better than traditional methods in terms of delay, deadline guarantee rate, and packet loss rate.

Entanglement Assisted Radars With Transmitter Side Optical Phase Conjugation and Classical Coherent Detection

Entanglement is a unique quantum information processing feature. With the help of entanglement we can build quantum sensors whose sensitivity is better than that of classical sensors. In this paper we are concerned with the entanglement assisted (EA) bistatic quantum radar applications. By employing the optical phase conjugation (OPC) on transmitter side and classical coherent detection on receiver side we show that the detection probability of the proposed EA target detection scheme is significantly better than that of corresponding classical and coherent states-based quantum detection schemes. The proposed EA target detection scheme is evaluated by modelling the radar return channel as the lossy and noisy Bosonic channel and assuming imperfect distribution of entanglement over the idler channel.

Energy and Bandwidth Based Link Stability Routing Algorithm for IoT

Internet of Things (IoT) is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet links. Particularly, some of the nodes in IoT are mobile and dynamic in nature. Hence maintaining the link among the nodes, efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data processing. In such networks, many routing-problems arise due to difficulties in energy and bandwidth based quality of service. Due to the mobility and finite nature of the nodes, transmission links between intermediary nodes may fail frequently, thus affecting the routing-performance of the network and the accessibility of the nodes. The existing protocols do not focus on the transmission links and energy, bandwidth and link stability of the nodes, but node links are significant factors for enhancing the quality of the routing. Link stability helps us to define whether the node is within or out of a coverage range. This paper proposed an Optimal Energy and bandwidth based Link Stability Routing (OEBLS) algorithm, to improve the link stable route with minimized error rate and throughput. In this paper, the optimal route from the source to the sink is determined based on the energy and bandwidth, link stability value. Among the existing routes, the sink node will choose the optimal route which is having less link stability value. Highly stable link is determined by evaluating link stability value using distance and velocity. Residual-energy of the node is estimated using the current energy and the consumed energy. Consumed energy is estimated using transmitted power and the received power. Available bandwidth in the link is estimated using the idle time and channel capacity with the consideration of probability of collision.

On the performance of overlaid wireless energy harvesting cognitive industrial sensor networks under jamming attacks

Two or more wireless sensor networks coexist in the same space while low energy consuming devices mobiles in a secondary network harvest ambient RF energy from transmissions by nearby active transmitters in the primary network. The channels are allocated to the primary network, while the overlaid secondary network can access the idle channel allocated to perform data transmission opportunistically and operate properly. In this paper, with the jammer implanted, we propose a novel solution in which we execute a deception strategy to exhaust the energy of the jammers. As a result, the energy constraint jammers will be challenging to achieve jamming attacks when the secondary transmitters (STs) transmit information. We formulate the problem first to tackle the issue; that is, we regard throughput optimization issues for ST under jamming attacks as a Markov decision process (MDP). Then, since the focus is mainly on the throughput of the secondary network, a learning algorithm is adopted to maximize it. Through the learning process, the STs can adapt to the dynamics of the primary network while executing proper actions to benefit the overall throughput online. Simulations validate the efficiency and the convergence of the algorithm we proposed.

Optimal Resource Allocation for GAA Users in Spectrum Access System Using Q-Learning Algorithm

Spectrum access system (SAS) is a three-tier layered spectrum sharing architecture proposed by the Federal Communications Commission (FCC) for Citizen broadband radio service (CBRS) 3.5 GHz band. The available 150 MHz spectrum is dynamically shared among Incumbent Access (IA), Primary Access Licensees (PAL) and General Authorized Access (GAA) users. IA users are the highest priority federal military users, PAL users are the licensed users and the GAA users are the least priority unlicensed users. In this scenario, PAL operators are willing to give access to their idle spectrum to GAA users to generate extra revenue. SAS will ensure to protect IA users and PAL users from interference caused by lower-tier users. It is the responsibility of SAS to allocate resources to GAA users but the method to do so is left open. In this article, a novel auction algorithm based on Q-learning for dynamic spectrum access (SAS-QLA) is proposed. In SAS-QLA, multiple GAA users dynamically and intelligently bid using Q-learning to access PAL reserved idle channels. SAS will decide to allocate the channels to GAA users with maximum bidding offers. GAA users have their own quality of service (QoS) demands i.e., transmission rate, packet loss, bidding efficiency, and maintain the preference of available PAL reserved idle channels based on Q-learning considering the available QoS. The proposed scenario is also modeled as a knapsack NP-hard problem and solved using dynamic programming and distributed relaxation method. Numerical results demonstrate the effectiveness of the SAS-QLA algorithm in improving the bidding efficiency, maximizing the data rate per unit cost and spectrum utilization.

Intelligent Dynamic Real-Time Spectrum Resource Management for Industrial IoT in Edge Computing.

Intelligent dynamic spectrum resource management, which is based on vast amounts of sensing data from industrial IoT in the space–time and frequency domains, uses optimization algorithm-based decisions to minimize levels of interference, such as energy consumption, power control, idle channel allocation, time slot allocation, and spectrum handoff. However, these techniques make it difficult to allocate resources quickly and waste valuable solution information that is optimized according to the evolution of spectrum states in the space–time and frequency domains. Therefore, in this paper, we propose the implementation of intelligent dynamic real-time spectrum resource management through the application of data mining and case-based reasoning, which reduces the complexity of existing intelligent dynamic spectrum resource management and enables efficient real-time resource allocation. In this case, data mining and case-based reasoning analyze the activity patterns of incumbent users using vast amounts of sensing data from industrial IoT and enable rapid resource allocation, making use of case DB classified by case. In this study, we confirmed a number of optimization engine operations and spectrum resource management capabilities (spectrum handoff, handoff latency, energy consumption, and link maintenance) to prove the effectiveness of the proposed intelligent dynamic real-time spectrum resource management. These indicators prove that it is possible to minimize the complexity of existing intelligent dynamic spectrum resource management and maintain efficient real-time resource allocation and reliable communication; also, the above findings confirm that our method can achieve a superior performance to that of existing spectrum resource management techniques.

Discrete Hughes‐Hartogs optimized dual vector handoff for spectrum management in cognitive radio networks

SummaryCognitive radio network (CRN) has occupied rich awareness on amazing potentiality of spectrum utilization for the secondary users. In CRN, effective spectrum management has received more attention by increasing number of communication devices and several techniques have been evolved in recent years. However, efficient spectrum sensing with minimum delay reducing number of spectrum handoffs and maximum throughputs are some of the major challenges to be addressed. In this proposed work, discrete Hughes‐Hartogs optimal and dual vector spectrum management (DHHO‐DVSM) in CRN are used for effective utilization of channel selection. The expected maximization Q channel selection is designed to obtain an efficient idle channels for improved transmission of the secondary user. The optimal channel is attained by the number of idle channels maintained from the Q channel selection list. Based on each SU requests, discrete Hughes‐Hartogs particle swarm optimal channel selection is proposed to find the best channel by computing the fitness function for discrete factor. Finally, dual constraints‐based vector spectrum handoff model is proposed to produce the hyper plane based on the probability conditional factors with the objective of minimizing the number of handoffs for secondary users. The simulation result shows that the proposed method has transparent edges over conventional channel selection models. In addition, results are verified to learn the optimal channel selection with minimal sensing delay, increasing throughput and minimal handoffs. The proposed algorithm DHHO‐DVSM is 22.8% comparatively better than E‐CRNs and 41% improvement than SpecPSO.

Intelligent Dynamic Spectrum Resource Management Based on Sensing Data in Space-Time and Frequency Domain.

Edge computing offers a promising paradigm for implementing the industrial Internet of things (IIoT) by offloading intensive computing tasks from resource constrained machine type devices to powerful edge servers. However, efficient spectrum resource management is required to meet the quality of service requirements of various applications, taking into account the limited spectrum resources, batteries, and the characteristics of available spectrum fluctuations. Therefore, this study proposes intelligent dynamic spectrum resource management consisting of learning engines that select optimal backup channels based on history data, reasoning engines that infer idle channels based on backup channel lists, and transmission parameter optimization engines based genetic algorithm using interference analysis in time, space and frequency domains. The performance of the proposed intelligent dynamic spectrum resource management was evaluated in terms of the spectrum efficiency, number of spectrum handoff, latency, energy consumption, and link maintenance probability according to the backup channel selection technique and the number of IoT devices and the use of transmission parameters optimized for each traffic environment. The results demonstrate that the proposed method is superior to existing spectrum resource management functions.

Power allocation technique with soft performance guarantees in hybrid OFDMA–NOMA cognitive radio systems: Modeling and simulation

The integration between orthogonal frequency division multiple access (OFDMA), non-orthogonal multiple access (NOMA), and cognitive radio (CR) technology has been recently configured as promising candidate to support the massive high-speed connectivity requirements of beyond fifth generation (B5G) systems. This combination is referred to in the literature as a hybrid OFDMA–NOMA CR network. In such a system, each primary user’s (PU) idle channel, i.e., sub-band, is opportunistically utilized to serve multiple secondary users (SUs) by exploiting power-domain NOMA. This study soughs to explore the resource allocation technique for a hybrid OFDMA–NOMA CR system to further elaborate the potential capabilities of this system. Specifically, a power minimization resource allocation framework is developed, in which the objective is to serve the contending CR users, i.e., SUs, with minimum possible total transmission power, while achieving a set of relevant NOMA and CR constraints. These constraints include the dynamic nature of PU activities, rate demand, probability of success and imposed CR power mask constraints. However, the developed power minimization framework is non-convex in nature. Therefore, we employ the sequential convex approximation (SCA) along with a second-order cone approach to deal with the non-convexity issues of the developed optimization problem, and thus evaluating the corresponding optimization parameters. Compared to the conventional orthogonal resource allocation techniques, simulation results reveal that our proposed resource allocation technique for the hybrid OFDMA–NOMA CR system has a considerable performance enhancement in terms of the overall network throughput and total required transmit power.

Caching-Aware Intelligent Handover Strategy for LEO Satellite Networks

Recently, many Low Earth Orbit (LEO) satellite networks are being implemented to provide seamless communication services for global users. Since the high mobility of LEO satellites, handover strategy has become one of the most important topics for LEO satellite systems. However, the limited on-board caching resource of satellites make it difficult to guarantee the handover performance. In this paper, we propose a multiple attributes decision handover strategy jointly considering three factors, which are caching capacity, remaining service time and the remaining idle channels of the satellites. Furthermore, a caching-aware intelligent handover strategy is given based on the deep reinforcement learning (DRL) to maximize the long-term benefits of the system. Compared with the traditional strategies, the proposed strategy reduces the handover failure rate by up to nearly 81% when the system caching occupancy reaches 90%, and it has a lower call blocking rate in high user arrival scenarios. Simulation results show that this strategy can effectively mitigate handover failure rate due to caching resource occupation, as well as flexibly allocate channel resources to reduce call blocking.

Retracted: A hybrid‐supervised learning model for compressive spectrum sensing in fifth generation(5G) cognitive Radio enabled internet of things (IoT) networks

Summary5G Internet of Things (IoT) networks are characterized by wideband radio frequency spectrum utility and are therefore of primary importance for efficient means of sensing the wideband spectrum characterized by high bandwidth. A cognitive radio network (CRN) intelligently does the sensing of authorized users ideal spectrum and allocates the same to the demanded unauthorized user. Conventional energy detection and k‐means schemes associated with CRN perform well for narrowband applications, whereas they are not quite suitable for wideband applications. Hence, a compressive collaborative sensing scheme together with deep neural network learning model (CCS‐DLNN) has been proposed to sense the information from the compressed and reconstructed information signal. Based on the extracted features, decision on presence or absence of primary user (PU) in the received signal has been observed. This paper proposes a deep learning neural network model for learning the dynamic change in the input spectra. Accordingly, this paper also updates the weights associated with the neurons to converge upon the target objective. The performance of the proposed sensing scheme has been evaluated pertaining to probability of detection, sensing error, and accuracy of detection of idle channels. The proposed work will be very useful for the upcoming generation departing to be implemented with 5G IoT networks.

A hybrid access method for broadcasting of safety messages in IEEE 802.11p VANETs

Vehicular Ad-hoc Networks (VANETs) can improve the road safety by transmitting safety-critical messages such as beacons and emergency messages. IEEE 802.11p VANETs have adopted the carrier sense multiple access with collision avoidance (CSMA/CA) mechanism for the multiple access control. The 802.11p media access control (MAC) protocol, however, can not guarantee the reliability of broadcasting data, since the reception of transmitted messages are not acknowledged. Moreover, the backoff scheme of the 802.11p MAC utilizes a fixed-size contention window for safety message broadcasting, which causes high collision probabilities especially in dense environments. In order to improve such drawbacks, we propose a hybrid access method as follows: Nodes are equipped to reserve time slots for the next round of broadcasting, while unoccupied time slots are preserved for those which have emergency needs. In addition, implicit feedbacks are enabled for detecting collisions incurred during random channel accesses in preserved time slots. We devise a mathematical model which optimally controls the parameters of our scheme while minimizes the cost caused by idle channels and collisions. Extensive simulations show that our mechanism can remarkably improve the performance of VANETs in broadcasting of the safety messages.