Probability Of Channel Availability Research Articles

Efficient multichannel energy harvesting with dedicated energy transmitters in CR-IoT networks

Radio Frequency (RF) energy harvesting is strongly believed to be a sustainable solution to the power depletion problem in battery powered IoT devices. In addition to harvesting energy from ambient RF signals, the use of dedicated energy transmitters (ETs) that transmit energy to nearby IoT devices via RF signals has recently been proposed. In this paper, we study the problem of designing an energy harvesting policy for a group of cognitive radio-enabled IoT (CR-IoT) devices served by a number of ETs to maximize the minimum of their charging rates. With the help of cognitive radios, a CR-IoT node is capable of changing its frequency channel of operation allowing for multi-channel energy harvesting. Frequency channels are assumed to be opportunistically accessible depending on the activity of wireless users that are licensed to use those channels. The problem entails the design of the ET’s transmission policy (to what CR-IoT device, and over what channel) and the design of an ambient harvesting policy for every CR-IoT device (when it is not served by ETs). The problem is formulated as a mixed integer linear program (MILP). The objective is to maximize a lower bound on the total harvested energy in a given time frame per CR-IoT node. This optimization is subject to scheduling, total energy budget, and maximum transmit power constraints. Given the intractability of MILP formulations, a sub-optimal algorithm is proposed. Extensive experimentation is carried out to assess the effectiveness of the proposed sub-optimal algorithm by comparing it to the MILP’s solution obtained using IBM CPLEX solver with a limit on the execution time. We also combine our sub-optimal algorithm withe the CPLEX solver to produce a new two-stages algorithm that improves the original one by around 47%. Finally, we investigate the effect of multiple parameters including number of ETs, number of channels, and channel availability probability on the minimum charging rate.

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks (CRNs) have become a successful platform in recent years for a diverse range of future systems, in particularly, industrial internet of things (IIoT) applications. In order to provide an efficient connection among IIoT devices, CRNs enhance spectrum utilization by using licensed spectrum. However, the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection. Specifically, the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User (PU) activity and create a robust routing path. This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain. Thus, a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method, namely, Channel Availability Probability. Moreover, a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique. This protocol combines lower layer (Physical layer and Data Link layer) sensing that is derived from the channel estimation model. Also, it periodically updates and stores the routing table for optimal route decision-making. Moreover, in order to achieve higher throughput and lower delay, a new routing metric is presented. To evaluate the performance of the proposed protocol, network simulations have been conducted and also compared to the widely used routing protocols, as a benchmark. The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio (with an improved margin of around 5–20% approximately) under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks (MCRNs). Moreover, the cross-layer routing protocol successfully achieves high routing performance in finding a robust route, selecting the high channel stability, and reducing the probability of PU interference for continued communication.

Machine Learning-Based DRX Mechanism in NR-Unlicensed

Continuous increase in number of wireless devices and data rate demand has motivated the usage of 5G New Radio in Unlicensed band (NR-U). Coexistence between 5G NR-U with other unlicensed technologies like Wi-Fi is challenging owing to the reduced probability of channel availability. Waiting to get access to unlicensed channel results in more battery consumption of User Equipment (UE). Discontinuous Reception (DRX) mechanism in NR-U can reduce UE’s energy consumption. In this letter, we introduce Machine Learning (ML) based DRX mechanisms. The DRX is modeled through three state semi-Markov model. We utilized the ML classifiers to get access to the unlicensed channel. The simulation results point out that the performance of LSTM is superior to the Support Vector Machine (SVM) and Naive Bayes (NB) classifiers, which achieves up to 3% improvement in Power-Saving Factor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PSF</i> ) and 50% reduction of average delay compared to conventional 5G-DRX.

DRX in NR Unlicensed for B5G Wireless: Modeling and Analysis

The proliferation in the demand of high data rate and improved performance has urged wireless vendors to think of extending 5G New Radio (NR) in unlicensed bands. The unlicensed band is mainly used by Wi-Fi and Wi-Gig resulting in the reduced probability of channel availability. The User Equipment (UE) has to remain active and wait for the availability of unlicensed channel. This process escalates the energy expense of battery-constrained mobile devices. Discontinuous Reception (DRX) introduced in 3GPP can also be used in NR-Unlicensed (NR-U) to reduce UEs energy consumption. This article introduces a DRX mechanism over NR-U networks for both Standalone (SA) and Non-Standalone (NSA) deployment modes. We have used two different flavors, Beam-Search and Beam-Aware, to model DRX mechanism. Hence, the proposed DRX mechanisms are: Standalone DRX with Beam-Search (SBS-DRX), Standalone DRX with Beam-Aware (SBA-DRX), Non-Standalone DRX with Beam-Search (NBS-DRX), and Non-Standalone DRX with Beam-Aware (NBA-DRX). Semi-Markov based modeling is used to show the estimation of power-saving, average delay, and resource utilization. The power-saving achieved with SBA-DRX is 21.13% and 24.84% higher than 5G NR DRX for both Trace 1 and Trace 2, respectively. Moreover, delay achieved with NBA-DRX is 19.16% less than NBS-DRX with 18% less resource utilization.

Game Theory based Channel Assignment and Load balancing for Cognitive Radio Ad-hoc Networks

Though various works have been done for handling end-to-end congestion control in traditional wireless adhoc networks, they lead to abnormal delay in Cognitive Radio Networks (CRN) due to the extra delaycaused by PU activities. While assigning channels along the route towards destination, channel availability, channel quality and channel switching delay should be considered. In this paper, we propose a Game theory based Channel Assignment and Load balancing(GTCALB) technique for multicast routing for CRAHN. In this technique, a channel matrix is constructed for each link with probability of channel availability, delay cost and channel quality. Then Game theory model is applied for each link in which a utility function is derived for each channel. Then the link with minimum overload is selected with a channel having maximum utility function. The proposed GTCALB technique is applied for each route, during the multicast route discovery. By NS2 simulation, it is shown that the GTCALB technique reduces the end-to-end delay and increases the throughput and packet delivery ratio for the constructed multicast routes.

Cognitive AODV routing protocol with novel channel-route failure detection

Performance of routing protocol at network layer in Cognitive Radio Adhoc Networks (CRAHNs) is mainly based on the probability of channel availability for application data transmission. To attain, end-to-end channel-route control messages should be disseminated in an efficient mechanism with minimal channel-route re-connection delays. In CRAHNs, end-to-end channel-route failures can be mainly due to spectrum handoff (dynamic primary user intervention), selfish node activity, CR node handoff and bandwidth degradation. In order to enhance the application throughput, it is pivotal to determine the exact channel-route failure and provide alternate end-to-end channel-route path. To achieve, this paper proposes a “channel-route failure based Cognitive-AODV routing protocol” with the modifications in channel-route-error (channel-RERR) to detect the exact channel-route failure and provide the best alternate end-to-end channel-route path in between source and destination. Experimental results reveal that the performance of proposed Cognitive AODV routing protocol with selfish node activity, spectrum handoff and node handover is outperformed when compared with the existing Cognitive AODV routing protocols.

Virtual Carrier Sensing-Based Random Access in Massive MIMO Systems

The 5th generation mobile communication systems aim to support massive access for future wireless applications. Unfortunately, wireless resource scarcity in random access (RA) is a fundamental bottleneck for enabling massive access. To address this problem, we propose a virtual carrier sensing (VCS) based RA scheme in massive MIMO systems. The essence of the proposed scheme lies in exploiting wireless spatial resources of uplink channels occupied by assigned user equipments (UEs) to increase channel resources for RA. With the proposed scheme, RA UEs are able to exploit the spatial resources that are approximately orthogonal to those of assigned UEs, thus sharing the uplink channel resource with assigned UEs without causing significant interference to them. Specifically, to ensure RA UEs avoid serious interference with assigned UEs, base station (BS) sends tailored virtual carriers to RA UEs on behalf of assigned UEs. RA UEs then conduct VCS to determine whether or not the uplink channel resource is available for RA. Closed-form approximations for probability of channel availability and uplink achievable rate with the proposed scheme are derived. Theoretical analysis and simulation results show that the proposed scheme is able to significantly increase channel resources of RA for massive access.

L-CAQ: Joint link-oriented channel-availability and channel-quality based channel selection for mobile cognitive radio networks

Channel availability probability (CAP) and channel quality (CQ) are two key metrics that can be used to efficiently design a channel selection strategy in cognitive radio networks. For static scenarios, i.e., where all the users are immobile, the CAP metric depends only on the primary users' activity whereas the CQ metric remains relatively constant. In contrast, for mobile scenarios, the values of both metrics fluctuate not only with time (time-variant) but also over different links between users (link-variant) due to the dynamic variation of primary- and secondary-users' relative positions. As an attempt to address this dynamic fluctuation, this paper proposes L-CAQ: a link-oriented channel-availability and channel-quality based channel selection strategy that aims to maximize the link throughput. The L-CAQ scheme considers accurate estimation of the aforementioned two channel selection metrics, which are governed by the mobility-induced non-stationary network topology, and endeavors to select a channel that jointly maximizes the CAP and CQ. The benefits of the proposed scheme are demonstrated through numerical simulation for mobile cognitive radio networks.

Fuzzy feedback algorithm for the spectral handoff in cognitive radio networks

This work proposes a fuzzy feedback algorithm based on a Fuzzy Analytical Hierarchical Process Method (FAHP) to improve decision making for the spectral handoff. We named it Feedback FAHP (FFAHP). A comparative analysis of three algorithms for vertical handoff is performed in order to contrast their performance with the proposed FFAHP algorithm. These three algorithms were assessed with the same decision criteria, which are, probability of channel availability, estimated channel time availability, the signal to interference plus noise ratio and bandwidth.The analysis of the three algorithms is validated using real data of spectral occupancy captured in the GSM frequency band from 824 MHz to 849 MHz. Two types of applications were used for the evaluation of the FFAHP Algorithm: best-effort and real time and, for each of them, four evaluation metrics are used. These metrics are, accumulative average of failed handoffs, accumulative average of handoffs performed, accumulative average of transmission bandwidth and, accumulative average of the transmission delay. The results show that the proposed FFAHP Algorithm provides 21% performance improvement compared to a modified Dijkstra´s Algorithm and, 4% better than the FAHP Algorithm without information feedback. The FFAHP Algorithm provides an efficient and effective process for selecting frequency channels. The results also show that the FFAHP Algorithm has a low average rate of failed handoffs and performed handoffs. In addition, the FFAHP Algorithm shows efficient use of the bandwidth and low delay, all these combined with the fact that the feedback implemented, stabilizes the system avoiding loops with consecutive hops to alternate frequencies.

Achievable Throughput Analysis of Opportunistic Spectrum Access in Cognitive Radio Networks With Energy Harvesting

In this paper, we consider a cognitive radio (CR) network that consists of multiple secondary users (SUs), each of which is equipped with an energy harvesting capability. We consider the opportunistic use of the idle spectrum, unused by primary users (PUs), through a mechanism of two-step opportunistic spectrum access for the SUs, consisting of random channel sensing followed by random channel access. Unlike the majority of previous studies, which have considered CR networks with energy harvesting under a single user setting, the goal of this paper is to investigate the joint impact of sensing probability, access probability, and energy queue capacity on the maximum achievable throughput in a multiuser CR network incorporating energy harvesting. For two extreme cases, those where the energy queue capacity is either infinite or extremely small, we show that the maximum achievable throughput is not affected by the channel access probability if the channel sensing probability is chosen appropriately and the optimal sensing probability is derived as a function of network parameters such as the energy arrival rate, channel availability probability, and number of contending SUs.

Historical Spectrum Sensing Data Mining for Cognitive Radio Enabled Vehicular Ad-Hoc Networks

In vehicular ad-hoc network (VANET), the reliability of communication is associated with driving safety. However, research shows that the safety-message transmission in VANET may be congested under some urgent communication cases. More spectrum resource is an effective way to solve transmission congestion. Hence, we introduce cognitive radio (CR) enabled VANET (CR-VANET), where CR device can detect possible idle spectrum for VANET communications and assist to timely broadcast safety-message. Given high-speed mobility of vehicles and dynamically-changing availability of channels, a novel prediction algorithm is proposed to pick out the channel with the greatest probability of availability, which can meet the quality of service (QoS) requirement of urgent communications and effectively avoid conflict with licensed users. Specifically, the spatiotemporal correlations among historical spectrum sensing data are exploited to form prior knowledge of channel availability probability, and Bayesian inference is used to derive posterior probability of channel availability. Comparing with other spectrum detection methods, the proposed algorithm has more than 8 percent detection performance improvement at false alarm probability 0.2, and thus can avoid access conflict with licensed users dramatically. Furthermore, the proposed algorithm always has larger packet reception probability (PRP) and lower transmission delay compared with conventional VANET broadcasting. Hence, the proposed algorithm can improve reliability of safety-message transmission and enhance driving safety significantly.

Multivariable algorithm for dynamic channel selection in cognitive radio networks

The spectral handoff is important in cognitive wireless networks to ensure an adequate quality of service and performance for secondary user communications. This work presents a multivariable algorithm for dynamic channel selection used in cognitive wireless networks. The channel selection is based on the fuzzy analytical hierarchical process (FAHP) method. The selected criteria for choosing the best backup channel are probability of channel availability, estimated channel time availability, signal to noise plus interference ratio, and bandwidth. These criteria are determined by means of a customized Delphi Method and using the FAHP technique; the corresponding weight and significance is calculated for two applications classified as best effort (BE) and real time (RT). The insertion of the fuzzy logic in the AHP algorithm allows better handling of inaccurate information because, as shown the results, consider more options to evaluate in contrast to a conventional AHP. As a difference with related work, the performance of our proposed FAHP method was validated with captured data in experiments realized at the GSM frequency band (824–849 MHz). This is due to the challenge of finding white spaces to communicate in this frequency band. This band represents more disputes in accessing spectral opportunities than other radio frequency (RF) bands because of the high demand for mobile phone communications. The proposed FAHP algorithm has a practical computational complexity and provides an effective frequency-channel selection. This proposed FAHP algorithm presents a new methodology to select and classify the variables based on a modified version of the Delphi method. The results of the proposed method were contrasted numerically with other three methods.

A spectrum-sharing strategy via cognitive radio nodes to improve utilisation of the spectrum

The main objective of the paper is to improve the overall spectral efficiency by sharing the spectrum among the service providers and to avoid the spectrum scarcity. In the proposed work, if one service provider is found to be overloaded, it utilises the spectrum of the other service provider which is found to be underutilised. Thus, CR nodes sense the available channels for sharing and this provides the list of available channels to the overloaded service provider to utilise the underutilised spectrum efficiently. Spectrum sharing between service providers improves the spectral efficiency, and probability efficiency of sensing and reduces the call blockage. As the number of service providers is increased, this may reduce the high traffic patterns of the calls, so that the secondary user can find the probability of channel availability of the primary user and utilise the available channel efficiently and avoid the scarce resource.

Proactive integrated handoff management in cognitive radio mobile ad hoc networks

In cognitive radio networks, the secondary users (SUs) switch the data transmission to another empty spectrum band to give priority to primary users (PUs). In this paper, channel switching in cognitive radio mobile ad hoc networks (CR-MANETs) through an established route is modeled. The probability of channel availability in this route is calculated based on the PU's activity, SU's mobility, and channel heterogeneity. Based on the proposed model, the channel and link availability time are predicted. These predictions are used for channel assignment in the proposed channel allocation scheme. A handoff threshold as well as a life time threshold is used in order to reduce the handoff delay and the number of channel handoffs originating from the short channel usage time. When the channel handoff cannot be done due to the SU's mobility, the local flow handoff is performed to find an appropriate node in the vicinity of a potential link breakage and transfer the current data flow to it. The local flow handoff is performed to avoid possible flow disruption and also to reduce the delay caused by the link breakage. The study reveals that the channel heterogeneity and SU's mobility must be considered as important factors, which affect the performance of the handoff management in the CR-MANETs. The results emphasize on the improvement of the route maintenance probability after using the local flow handoff. It is also stated that the amounts of handoff requirement and handoff delay are decreased after using the predicted channel usage life time and handoff threshold time.

Design and Analysis of Transmission Strategies in Channel-Hopping Cognitive Radio Networks

In recent years, channel-hopping-based medium access control protocols have been proposed to improve the capacity in a decentralized multichannel cognitive radio (CR) network without using extra control channels. Each CR user has to stochastically follow a default channel-hopping sequence in order to locate a channel and conduct its frame transmission. In this paper, theoretical analysis is conducted on the probability of channel availability and the average frame delay for primary users (PUs) by considering the impact caused by imperfect sensing of CR users and imperfect synchronization between the primary and CR networks. According to the proposed analytical model with realistic considerations, an optimal channel-hopping sequence (OCS) approach is designed for the CR users based on a dynamic programming technique. It is designed by exploiting the optimal load balance between channel availability and channel utilization within the delay constraints of PUs. By adopting the OCS approach, maximum aggregate throughput of CR users can be achieved while considering PU's quality-of-service (QoS) requirements. Moreover, in addition to the paired CR networks, the logical partition problem that occurs in generalized CR networks will also be addressed. This problem can severely degrade the aggregate throughput due to the decreased probability of connectivity between CR users, especially in a CR network with heavy traffic. Therefore, both wake-up successive contention (WSC) and wake-up counter-reset successive contention (WCSC) algorithms are proposed to increase the number of negotiations by both exploring the blind spot of imperfect sensing and amending the contention mechanisms between CR users. Compared to conventional channel-hopping sequences, numerical results illustrate that the proposed approaches can effectively maximize aggregate throughput for CR users under the QoS requirements of PUs.

Joint Admission Control and Channel Selection Based on Multi Response Learning Automata (MRLA) in Cognitive Radio Networks

We use multi response learning automata (MRLA) to control how secondary users should access the licensed primary channels in cognitive radio networks. We seek two aims in this paper: (1) estimating the availability probability of each primary channel and (2) admission control of secondary users to decrease the rate of collisions between them. We consider single and multiple secondary user scenarios. In the first scenario, the secondary user deploys learning automata to estimate the primary channel availability probability for efficient exploitation. In the second scenario, each secondary user deploys an algorithm based on MRLA to estimate primary traffic as well as the behavior of other secondary users in order to control the rate of collisions. Then, to have a better control on the rate of secondary collisions, when the number of secondary users is greater than the number of primary channels, we proposed an admission control scheme. In this scheme, some of secondary users are blocked in each time slot and do not have any interaction with the environment. The convergence of the proposed algorithms with and without admission schemes is analyzed. Simulation results are provided to show the improvement in the secondary users' total throughput and switching cost while maintaining the fairness between them.