This study examines a real-time status update system featuring energy-harvesting sensing devices and intensive packet processing. Diverging from existing research in the same area, our approach employs partial offloading, where packets are split between local server and mobile edge computing server. Based on the available energy, the device determines the offloading ratio in order to minimize both the age of information (AoI) and the energy consumption. Due to the randomness of the environment and the need to take sequential decisions, the problem is formulated using a Markov decision process. The state transition over subsequent decision epochs is modelled using a multi-dimensional Markov chain to facilitate writing the Bellman equation and finding the optimal offloading ratio. The numerical results show that the partial offloading scheme exploits the available energy more effectively to decrease the AoI compared with both the total and binary offloading schemes unless the channel is in a good state with high probability.

This paper considers a computation-intensive status update system with energy harvesting technology. The status update packets can be processed locally or partially offloaded to a mobile edge computing server. Using the stochastic hybrid system approach, we analyze the moment generating function of the age of information (AoI) for three different disciplines (preemption, discarding, and blocking) under zero-wait policy. The numerical results show that the preemption discipline gives the best performance while the discarding discipline is the worst. The results demonstrate the importance of obtaining higher AoI moments and facilitate choosing an offloading ratio that satisfies a given average AoI constraint.

Integrating cognitive radio technology with energy harvesting capabilities to the Internet of Things networks offers a promising avenue for tackling spectrum scarcity and battery limitation issues. This study explores a single-channel cognitive radio network including two secondary users who generate two distinct traffic types: packets with deadlines and status update information. Packets are dropped only when failing to reach their destination within the deadline. Leveraging the drift-plus-penalty approach, we develop a scheduling policy to minimize the average drop rate of these packets subject to a data freshness constraint. Simulation results depict how variations in main system parameters, such as the primary user’s transmission power and energy arrival rate, impact the average drop rate. Additionally, experimental simulations confirm that our proposed policy outperforms the performance of a suggested baseline policy with a notable gap.

In this work, we investigate the security performance of an underlay cognitive radio network (CRN) with ambient backscatter communication (AmBC), where a backscattering device (BD) shares the spectrum and the receivers with the secondary user. Different from the related work, we consider a secondary user transmitter (ST) with multiple antenna in an AmBC-CRN with multiple receivers and multiple passive eavesdroppers. The ST performs joint antenna-user selection to enhance its security performance and overcome the performance degradation caused by BD interference. Considering the Nakagami-m fading model, closed-form expressions are derived for the secrecy outage probability for both the ST and the BD transmissions. Monte Carlo simulations are performed to validate the derived closed-form expressions. Numerical results show that employing joint antenna-user selection enhances the ST security performance by exploiting antenna and user diversity.

Currently, the pervasive adoption of internet of things technology sheds light on the significance of real-time status update systems. The age of information (AoI) metric has been devised to reflect the strict information timeliness of such systems. Bufferless packet management scheme has demonstrated higher effectiveness in the AoI minimization. However, the existing preemptive (PR) and non-preemptive (NP) service disciplines operate independently of the system state, thereby reducing the system’s adaptability to varying system parameter settings, such as service time distribution and traffic loading condition. Therefore, we propose an elapsed-time-based hybrid PR/NP discipline to govern the service preemptions in a single-source M/Er/1/1 queueing scheme. The preemption is declined when the elapsed service time surpasses a predetermined threshold (controlling parameter). The average AoI is analyzed using the stochastic hybrid system approach. The numerical study demonstrated that the proposed discipline, owing to its threshold parameter, fulfills the optimality of the AoI performance, especially under highly dispersed Erlang service time distributions and higher traffic loading conditions.

In this paper, a general spectrum access scheme is introduced for multi-class secondary users operating in a hybrid interweave/underlay cognitive radio network. The hybrid channel access mode combines the benefits of interweave transmission (opportunistic access with high throughput) and that of the underlay transmission (anytime transmission with controlled power). Additionally, classifying the SUs helps to meet their different quality of service (QoS) requirements. The proposed scheme tackles three challenges: resolving the contention of the SUs to access the channel, scheduling an arbitrary number of SU classes, and providing a tunable spectrum resources allocation scheme. Specifically, each class of SUs is assigned a number of time slots for exclusive hybrid channel access according to their QoS requirements. The numerical results show the superiority and flexibility of the proposed scheme compared to other related work in literature.

In this paper, the hybrid interweave/underlay channel access mode is studied for an energy harvesting (EH) cognitive radio network with multi-class secondary users (SUs). The hybrid channel access mode combines the benefits of interweave transmission (opportunistic access with high throughput) and that of the underlay transmission (any time transmission with controlled power). EH upgrades the SUs' devices to be self sustainable. Additionally, classifying the SUs helps to meet their different quality of service (QoS) requirements. The system is modelled as a mixed observable Markov decision process (MOMDP) to handle the uncertainty in the primary user (PU) activity and consider future rewards. The MOMDP model is solved to maximize the SUs' throughput using two algorithms, namely, the point-based value iteration and the heuristic search value iteration (HSVI). Moreover, skipping the schedule of some SUs is proved to increase the channel utilization. The HSVI is proved to be efficient and reduces the time complexity significantly. Compared to related work in literature, the proposed model is proved to be superior in terms of throughput, tunable to meet the different QoS requirements of SU classes, and can accommodate any number of SU classes. Finally, the effect of some system parameters on the proposed system performance is studied and some insights are derived about the structure of the optimal policy and the system parameters values.

In this paper, a cognitive radio system is studied in which the secondary user (SU) leverages the primary user (PU) channel quality indicator feedback (CQI) and the PU automatic repeat request (ARQ). The SU randomly accesses the PU channel with access probabilities based on its spectrum sensing outcome and the PU feedbacks. The SU's access probabilities are selected though an optimization problem with the objective to maximize the SU's throughput while ensuring the stability of the PU's packet queue. This system is modeled using a multidimensional Markov chain. This model enabled us to derive a closed-form expression for the SU's throughput, which is used in the throughput maximization problem. The proposed scheme is shown to improve the SU service rate compared to the system where no PU feedback is exploited by the SU, the system where the SU utilizes only the PU CQI feedback, and the system where the SU utilizes only the PU ARQ feedback.

In cognitive radio networks, delay requirements for secondary users are not homogeneous. In this paper the secondary user traffic is classified into M prioritized classes based on delay requirements in order to support the delay-sensitive secondary user applications. Moreover, after each interruption from the primary user the priority of the secondary user increases within the same class in order to reduce the handoff delay. A mixed preemptive/non-preemptive resume priority M/G/1 queuing model is used to analyze the multiple spectrum handoffs. The analytical results are applied to evaluate the latency performance of the proposed system and compare it with existing spectrum handoff systems for prioritized and unitary secondary user networks. This comparison is performed based on the target channel sequences mentioned in the IEEE 802.22 wireless regional area networks standard. Numerical results show that the proposed system outperforms existing systems in terms of average handoff delay and extended data delivery time under various traffic parameters. Then, to reduce the average extended data delivery time, a classifying technique for the secondary users with the same delay constraint is suggested, which assigns the priority based on the arrival rates. Simulation results are given to validate the analytical results.

Cognitive radio (CR) is a technology that aims to enhance the use of the underutilized spectrum. Spectrum handoff plays this role by allowing the unlicensed secondary users (SUs) to use the licensed frequency bands of the primary users (PUs). Moreover, it helps the SU to vacate the channel at the presence of the PU and find a suitable channel for the interrupted SU to continue his unfinished transmission. In this paper, an analytical model for the general case of non-identical channels in CR networks is introduced for both fixed and probabilistic sequence approaches for target channel selection. Moreover, a new balancing model is suggested to generate the probabilistic sequence. Both the fixed and probabilistic sequence approaches are optimized using the meta-heuristic methods of particle swarm and genetic algorithm in the seek of finding an optimal approach to minimize the extended data delivery time of the SUs. The results are based on the pre-emptive resume priority M/M/1 queueing network model and are shown for different network cases. Based on the optimization results, a fast hybrid approach is proposed and found to achieve near optimal solution.

This paper elaborates on the suboptimal greedy algorithm for target channel sequence selection as presented by Wanget al.in the paper, “Optimal Target Channel Sequence Design for Multiple Spectrum Handoffs in Cognitive Radio Networks,”IEEE Transactions on Communications, vol. 60, no. 9, pp. 2444–2455, September 2012. They claimed that the greedy algorithm requires comparing six target channel sequences. We prove that only five target channel sequences comparisons are sufficient. Hence, we present a modified algorithm and test it with a set of numerical results. It appears that the new algorithm reduces the processing time compared to the original one especially for a network with a large number of candidate channels.