Hiep Vu-Van

Cooperative spectrum sensing with collaborative users using individual sensing credibility for cognitive radio network

Cognitive radio (CR) has been considered as one of the promising next-generation communication systems owning to its ability of sensing and making use of vacant channels that are currently unused by licensed users. Reliable spectrum sensing of licensed users is required for CR network to minimize interference to licensed users. However, an individual CR user may find it difficult to reliably detect the activity status of licensed users due to fading and shadowing effects of the channel. In this paper, we propose a cooperative spectrum sensing scheme for collaborative users in which the sensing credibility of each individual user is utilized to improve local and global sensing performance, and reduce the number of reports transmitted through the control channel between CR users and the fusion center (FC). Using this method, few CR users who satisfy the credibility thresholds will transmit their local decisions to the FC one by one according to the states order of their credibility. That is, the CR user with higher reliability will transmit local decision sooner and others with less reliability can use this sensing information to update their local decisions. Finally, all local decisions received at the FC are combined into a global decision using the Chair - Varshney data fusion rule.

A sequential cooperative spectrum sensing scheme based on cognitive user reputation

The spectrum sensing plays an essential role in Cognitive Radio (CR), which enables opportunistic access to underutilized licensed spectrums. Cooperative spectrum sensing can help improve sensing performance. However, when the number of cognitive users is large, the latency and network traffic for reporting sensing results to the Fusion Center (FC) become extremely large, which may result in an extended sensing time and collision in the control channel between Cognitive Users (CUs) and the FC. In this paper, we propose an extended Sequential Cooperative Spectrum Sensing (SCSS) scheme in which the reputation of individual CU is used to ensure that the FC efficiently collects local sensing data from CUs. The proposed scheme reduces the number of sensing reports required while improving sensing performance in comparison with the conventional SCSS scheme even when few malicious users exist in the network.

Energy-Efficient Channel Handoff for Sensor Network-Assisted Cognitive Radio Network

The visiting and less-privileged status of the secondary users (SUs) in a cognitive radio network obligates them to release the occupied channel instantly when it is reclaimed by the primary user. The SU has a choice to make: either wait for the channel to become free, thus conserving energy at the expense of delayed transmission and delivery, or find and switch to a vacant channel, thereby avoiding delay in transmission at the expense of increased energy consumption. An energy-efficient decision that considers the tradeoff between energy consumption and continuous transmission needs to be taken as to whether to switch the channels. In this work, we consider a sensor network-assisted cognitive radio network and propose a backup channel, which is sensed by the SU in parallel with the operating channel that is being sensed by the sensor nodes. Imperfect channel sensing and residual energy of the SU are considered in order to develop an energy-efficient handoff strategy using the partially observable Markov decision process (POMDP), which considers beliefs about the operating and backup channels and the remaining energy of the SU in order to take an optimal channel handoff decision on the question “Should we switch the channel?” The objective is to dynamically decide in each time slot whether the SU should switch the channel or not in order to maximize throughput by utilizing energy efficiently. Extensive simulations were performed to show the effectiveness of the proposed channel handoff strategy, which was demonstrated in the form of throughput with respect to various parameters, i.e., detection probability, the channel idle probabilities of the operating and backup channels, and the maximum energy of the SU.

Optimal Throughput for Cognitive Radio with Energy Harvesting in Fading Wireless Channel

Energy resource management is a crucial problem of a device with a finite capacity battery. In this paper, cognitive radio is considered to be a device with an energy harvester that can harvest energy from a non-RF energy resource while performing other actions of cognitive radio. Harvested energy will be stored in a finite capacity battery. At the start of the time slot of cognitive radio, the radio needs to determine if it should remain silent or carry out spectrum sensing based on the idle probability of the primary user and the remaining energy in order to maximize the throughput of the cognitive radio system. In addition, optimal sensing energy and adaptive transmission power control are also investigated in this paper to effectively utilize the limited energy of cognitive radio. Finding an optimal approach is formulated as a partially observable Markov decision process. The simulation results show that the proposed optimal decision scheme outperforms the myopic scheme in which current throughput is only considered when making a decision.

Cooperative Spectrum Sensing with Double Adaptive Energy Thresholds and Relaying Users in Cognitive Radio

Cognitive radio (CR) technology has been proposed to improve spectrum utilization by allowing cognitive radio users to opportunistically access under-utilized frequency band. The requirement of cognitive radio system is reliable detecting signals from licensed users to avoid harmful interference. However, due to the effects of the channel fading and shadowing, individual cognitive radio may not able to reliably detect the presence of a licensed user. In this paper, we propose a cooperative spectrum sensing scheme with double adaptive threshold and relaying users in order to improve sensing performance of both local sensing performance and global sensing performance in a CR network. In the proposed scheme, the detection threshold is changeable to adapt with the fluctuation of the received signal power in each local detector of cognitive user. In addition, the CR users with higher reliability act as a relaying user for the object of helping their neighbors to improve sensing performance. Finally, all the local observations will be sent to the fusion center to determine the global decision by using the Likelihood rule without any requirement on priori information of the licensed user.

A Novel Spectrum Sensing Scheme Based on Wavelet Denoising with Fuzzy for Cognitive Radio Sensor Networks

Ability of cognitive radio (CR) is the most promising solution to solve problems of a common wireless sensor network (WSN), which is assumed to assign a fixed frequency band. Cognitive radio sensor network (CRSN) is the combination of CR’s ability into WSN to improve spectrum utilization of each wireless sensor nodes. In order to avoid interference to other users in CRSN, reliable detection of the licensed user signal in the interested spectrum band is a pre-requirement. If we do not know any information about licensed user signal, energy detection will be optimal detection method. However, the energy detector is strongly affected by noise and shadowing of sensing environment. In this paper, we propose a novel spectrum sensing based on wavelet denoising with fuzzy to make cognitive radio users to be high reliable sensor in noisy sensing environment. The simulation results demonstrate the effectiveness of the proposed scheme.

Secure Multi-hop Data Transmission in Cognitive Radio Networks Under Attack in the Physical Layer

Cognitive radio networks (CRNs) have a shortcoming in that attackers can increase their ability to disturb secondary users (SUs). This paper focuses on jamming attacks in the physical layer, in which several attackers try to interrupt SUs by injecting the interference into their communications. Once a jammer transmits interfering signals on the channel during the defined time, all ongoing transmissions on this channel will be corrupted. It is quite difficult for SUs to protect a single-hop data transmission from jammers. So, obtaining a solution for secure multi-hop data transmission in the presence of jammers becomes a more challenging task in CRNs. This paper investigates a strategy to find the optimal route and channels for transmission between cognitive transmitters and receivers in the presence of jammers in CRNs. In this scenario, the jammers are located randomly and their jamming behavior is assumed to follow a Gaussian distribution. We provide an optimal link–channel pair allocation scheme in which the secondary transmitter (the source) selects the best relay and a suitable channel for each hop in the source-to-destination route to protect the information intended to the secondary receiver (the destination) from the jammers. Simulation results prove the efficiency of the proposed scheme in a CR network.

Joint Full-Duplex/Half-Duplex Transmission-Switching Scheduling and Transmission-Energy Allocation in Cognitive Radio Networks with Energy Harvesting

The full-duplex transmission protocol has been widely investigated in the literature in order to improve radio spectrum usage efficiency. Unfortunately, due to the effect of imperfect self-interference suppression, the change in transmission power and path loss of non-line-of-sight fading channels will strongly affect performance of full-duplex transmission mode. This entails that the full-duplex transmission protocol is not always a better selection compared to the traditional half-duplex transmission protocol. Considering solar energy-harvesting-powered cognitive radio networks (CRNs), we investigate a joint full-duplex/half-duplex transmission switching scheduling and transmission power allocation in which we utilize the advantages of both half-duplex and full-duplex transmission modes for maximizing the long-term throughput of cognitive radio networks. First, we formulate the transmission rate of half-duplex and full-duplex links for fading channels between cognitive user and base station in which the channel gain is assumed to follow an exponential distribution. Afterward, by considering the availability probability of the primary channel, the limitation of the energy-harvesting capacity of the cognitive user, and the transmission capacity of half-duplex and full-duplex links, we describe the problem in terms of long-term expected throughput. The problem is then solved by adopting the partially observable Markov decision process framework to find the optimal transmission policy for the transmission pair between cognitive user and base station in order to maximize the long-term expected throughput. The optimal policy consists of either the half-duplex or the full-duplex transmission protocols as well as the corresponding amount of transmission energy in each time slot. In addition, to reduce the complexity in formulation and calculation, we also apply the actor–critic-based learning method to solve the considered problem. Finally, the performance of the proposed scheme was evaluated by comparing it with a conventional scheme in which the context of energy harvesting and long-term throughput is not considered.

Efficient Channel Selection and Routing Algorithm for Multihop, Multichannel Cognitive Radio Networks with Energy Harvesting under Jamming Attacks

We study jamming attacks in the physical layer of multihop cognitive radio networks (MHCRNs) where energy-constrained relays forward information from the source to the destination. Meanwhile, a jammer can transmit interfering signals on a channel such that all ongoing transmissions on this channel will be corrupted. In this paper, all jammers can attack only one of the predefined channels in each time slot. Moreover, they can randomly switch channels to start jamming another channel at the beginning of every time slot. The switching behavior is assumed to follow a Gaussian distribution. Due to limited battery capacity in the relays, energy harvesting is utilized to solve the energy-constrained problem in the cognitive radio network. Subsequently, relays are able to harvest energy from non-radio frequency (non-RF) signals such as solar, wind, or temperature. In this paper, we determine the throughput/delay ratio as a key metric to evaluate the performance in MHCRNs. Owing to the limited battery capacity in the relays and the jamming problem, the source needs to select proper relays and channels for each data transmission frame to optimize overall network performance in terms of end-to-end delay, throughput, and energy efficiency. Therefore, we provide two novel multihop allocation schemes to maximize achievable end-to-end throughput while minimizing delay in the presence of jammers. Through simulation results, we validate the effectiveness of the proposed schemes under multiple jamming attacks in MHCRNs.

POMDP-Based Throughput Maximization for Cooperative Communications Networks with Energy-Constrained Relay under Attack in the Physical Layer

In this paper, we investigate jamming attacks in the physical layer against cooperative communications networks, where a jammer tries to block the data communication between the source and destination. An energy-constrained relay is able to assist the source to forward the data to the destination even when the jammer tries to block the direct link. Due to a limited-capacity battery of the relay, a non-radio frequency energy harvester equipped in the relay helps to prolong its operation. We propose a scheme based on a partially observable Markov decision process (POMDP) to find the optimal action for the source such that we can maximize the achievable throughput of cooperative communications networks. Under this scheme, the source dynamically selects the appropriate action mode for its transmission in order to obtain maximum throughput under the jamming attack. Simulation results verify that the proposed scheme is superior to the Myopic scheme where only current throughput is taken into account for making decisions.