Our goal is to find the optimal multi-channel sensing order over the next multiple time-slot in order to maximize expected throughput of CRNs. We consider a practical scenario in which switching delay between channels and errors in spectrum sensing are inevitable. In addition, the correlation in spectrum occupancy across time slots and frequency channels is also considered. Cognitive users (CUs) are assumed that constraints in radio devices do not allow them to sense and transmit more than one channel at the same time. For the given scenario and fading channels, based on the theory of optimal stopping and POMDP framework, we are interested in finding the optimal sensing order of channels over the next multiple time slots and the optimal transmission threshold corresponding to each channel in the order. Our target is to maximize average throughput for the whole operation time of CRNs.

In time-slotted multi-channel environment, we considered CRNs in which CUs are powered by energy harvesters. The CUs are under the consideration that hardware constraints on radio devices only allow them to sense and transmit on one channel at a time. For the given scenario, when the arrival of harvested energy and the battery capacity are finite, we are interested in optimizing (i) the channel-sensing schedule (consisting of finding the optimal action (silent or active) and sensing order of channels) and (ii) the optimal transmission energy set corresponding to channels in the sensing order for the operation of CU in order to maximize expected throughput of CRN over multiple time slots.

Frequency-switching delay, energy-switching cost, correlation in spectrum occupancy across time and frequency and errors in spectrum sensing are also considered in this work. The target of this work is to maximize average throughput for the whole operation time of the CRNs.

SCSS technique has been well studied until now. However, works in literature have focused on finding optimal number of CUs reporting sensing results to fusion center (FC) in order to maximize throughput of CRNs. Therefore, this work is armed to further improve throughput by further shortening the reporting overhead and reducing the global probability of false alarm in comparison to other schemes in literature.

To do this, we firstly use a two-state discrete time Markov chain process to model activities of primary users (PU) on primary channel. For sensing scheme, energy detection technique is used at CUs and soft combination is considered at the FC. Then, based on the theory of optimal stopping, the FC will sequentially ask each CU to report its sensing result until the stopping condition that provides the maximum expected throughput for the CRN is satisfied.

In this case, secrecy throughput constraints should be considered. CU only transmits if the secrecy throughput is detected higher than a specific threshold. For a scenario where CUs are powered by energy harvesters, the arrival of harvested energy packets and battery capacity are finite, we propose a scheme to optimize the channel-sensing schedule (consisting of finding the optimal action (silent or active) and the optimal transmission energy in order to maximize the expected secrecy throughput of the CRN over multiple time slots. Activities on primary channel is modeled using a two-state discrete time Markov chain process and fading channels are also considered in this work.

Figure 2. (a) Main Activity (b) Target building list (c) Choice of starting point (d) Path displayed between given points.

Underwater acoustic sensor networks consist of devices with sensing, processing, and communication capabilities that are deployed to support aquatic applications ranging from environmental monitoring to intrusion detection. Underwater acoustic networking has been a topic of research for decades. Like terrestrial communication, the spectrum resource in underwater networks is heavily shared by various acoustic systems, but it is still underutilized temporally and spatially. To make underwater applications viable and promote the environment-friendly communication, there is a need to design an efficient cognitive communication protocol that addresses several challenges unique to underwater communications. Maritime communications play an essential role in providing variety of services to users aboard. The demand for safe and stable maritime communications has also been increased due to the increase in number of marine users. Existing maritime communication systems are based on high frequency (HF), very high frequency (VHF), and ultrahigh frequency (UHF) radios which are currently insufficient for growing maritime requirements. Cognitive radio seems to be a technology to alleviate this issue in ship-to-ship communication. Moreover, in maritime networks, ship-to-ship communication is continuously perturbed by sea waves that cause fluctuations in the signal strength resulting in fragile communication links. Therefore, we proposed a technique to provide stable link for cognitive maritime communications by ensuring cooperation among marine users.

As part of our interest in cognitive radio vehicular ad hoc networks (CR-VANETs), our group has been studying spectrum-aware vehicle-to-vehicle (V2V) communication. IEEE 802.11p/1609 is the standard for Dedicated Short Range Communication (DSRC), which is an amendment of IEEE 802.11. DSRC channels are reserved for automobile communications only, but these have been found insufficient due to the increasing demands of vehicular applications. Therefore, the performance of vehicular networks may degrade when these channels are overloaded. For that reason, cognitive radio seems to be a technology to resolve this issue in VANETs.

We proposed a spectrum-aware geographic routing protocol for V2V communication. The vehicles are allowed to use any of the licensed TV channels when primary user (PU) activity is not affected. Spectrum sensing is performed with the cooperation of the vehicles, so that each vehicle has the list of spectrum holes, which is updated among neighboring vehicles as an additional entry in a beacon message. Communication between two vehicles occurs only when both the vehicles are on the same channel. In this way, we achieved an improvement in packet delivery and reduction in packet delay by predicting the future positions of all moving vehicles in the network.

Cooperative cognitive radio networks (CCRNs) use cooperative relays to forward signal from the source to the destination. Cooperative communication schemes having all relays participating in transmission may cause unnecessary wastes of most valuable spectrum resources. So it is mandatory to effectively select a transmission mode for CCRNs. In order to show the performance of relay selection, most of the literatures compared bit error rate (BER) or symbol error rate (SER) according to the signal-to-noise ratio and provide closed-form solution with higher complexity. In actual wireless communication networks, not only a single performance metric but a multilateral metrics (MMs) of quality of service i.e., time delay, energy efficiency, actual interference should be considered. The context of the transmission mode in cooperative communication networks becomes more complex CCRNs due to the spectrum access of the primary users. Therefore, we proposed an efficient transmission mode selection based on reinforcement learning for CCRNs that selects an optimal action on the networks environment to maximize the total reward of the MMs. We demonstrate that proposed scheme can efficiently determine the transmission mode and outperforms conventional relay selection approaches for a single metric in CCRNs.

In cooperative cognitive radio networks, the transmission power of each relay is limited by the interference constraint of the primary user (PU) receiver. Optimizing multi relay selection (MRS) and power allocation to the relays require an exhaustive search (ES) for all possible relay combinations, since these approach use a large amount valuable resources and entails high computational complexity (CC). To mitigate the CC problem and efficient utilization of resources to support the applicability of cognitive radio for future internet of things (IoT), a low complexity approach is essentially needed for MRS and PA. We propose a low complexity scheme to maximize the SUs throughput using a timer based MRS which determines the relays order via handshaking mechanism. Consequently, the instantaneous channel state information can be learned by the SU source and the SU source assigns transmission power to the relays order sequentially. We demonstrate that proposed scheme with much lower complexity can achieve near optimal throughput performance to the optimal MRS and low energy consumption in transmission which can be applicable for future IoT.

Acoustic signal propagation over underwater wireless sensor networks (UWSNs) is significantly affected by narrow bandwidth, poor-quality channels, high propagation delay, and high attenuation in transmission from source to destination. Specifically, the high propagation delay can cause a high collision rate in the acoustic link in UWSNs, which degrades both energy efficiency and throughput. Therefore, we are working on propagation delay aware medium access control protocol, energy efficient cooperative scheme, and delay aware relay selection for cooperative UWSNs (CoUWSNs).

Conventional CSMA/CA MAC protocol sets the NAV timer by considering the maximum propagation based on the maximum transmission range of the source which waste the transmission duration as a result the throughput is decreased and the end-to-end delay is increased as the packet generation rate and number of nodes increases. For medium access control (MAC), we currently working on optimal network allocation vector (NAV) based carrier sense multiple access/collision avoidance (CSMA/CA) MAC protocol for underwater acoustic wireless sensor networks that estimates the propagation delay between source to the destination based on the receive signal strength (RSS) of the receiver. We demonstrate that proposed optimal NAV timer for CSMA/CA MAC protocol can minimize the sleep time of the neighbor’s nodes and provides better throughput and lower latency than Conventional CSMA/CA MAC protocol.

Although CR technology with multi-antenna deployment can partially enhance the spectrum efficiency, the energy scarcity is still a major bottleneck for the communication services and the long lifetime of users. Recently, simultaneous wireless information and power transfer (SWIPT) where transmitters can simultaneously provide both data and energy to receivers has become an interesting research area. Especially, when we consider SWIPT in multi-input multi-output (MIMO) cognitive radio networks as shown in the figure (a), secondary system can utilize the licensed spectrum of the primary networks while assure energy for secondary users. Time switching (TS) and power splitting (PS) are two practical designs for SWIPT receivers. We focus on the PS structure as shown in the figure (b) since it is more general and gives better rate-energy tradeoffs than the TS structure.

Our aim is to exploit the advantages of MIMO and SWIPT techniques to build a secondary network which obtains different criteria such as transmit power minimization, weighted sum harvested energy maximization, etc., under the constraints of primary network and quality of services of users.

In military network, the user often operates in high congested and contested environments. Conventional users are statically configured to work within a pre-allocated spectrum. Subsequently, the performance of the network is so limited due to the attack (i.e., jamming) from the adversary network. In this research, we equip the users with cognitive ability (i.e., sensing, learning, self-configuring) to improve the performance of the interested network in military environment. A scheme based on game theory will be proposed to select the best channels for communication users and for jamming users. The proposed scheme can improve the success rate of communication users of the interested network and increase corrupt communication rate of the adversary network.