Energy Harvesting Cognitive Radio Networks Research Articles

Joint channel allocation and transmit power control for underlay EH‐CRNs: A clustering‐based multi‐agent DDPG approach

AbstractTo address the concerns of energy supply and spectrum scarcity for wireless devices, energy harvesting cognitive radio networks have been proposed. To improve spectrum utilization, secondary users (SUs) access the licensed spectrum in underlay mode, which may cause severe interference to primary users and SUs. The focus is on the underlay energy harvesting cognitive radio networks with multiple pairs of SUs, and formulate the long‐term secondary throughput maximization problem as a mixed‐integer non‐linear programming problem. As traditional approaches could hardly solve the mixed‐integer non‐linear programming problem well, a centralized deep deterministic policy gradient (C‐DDPG) approach is proposed that achieves satisfactory throughput performance. To reduce the computational complexity of C‐DDPG, we further propose a clustering‐based multi‐agent DDPG (CMA‐DDPG) approach that combines the advantages of the centralized deep reinforcement learning approach and the distributed deep reinforcement learning approach. In the CMA‐DDPG, a novel interference‐based clustering algorithm is proposed, which partitions the SUs that cause severe mutual interference into one cluster, and the sizes of state space and action space are smaller than those in C‐DDPG. Numerical results validate the superiority of the proposed approaches in terms of the throughput and outage probability, and validate the clustering performance of the interference‐based clustering algorithm in terms of the outage probability of the secondary network.

Hybrid game theoretic strategy for optimal relay selection in energy harvesting cognitive radio network

SummaryRelay selection plays a crucial role in enhancing the performance of wireless networks particularly in the context of cognitive radio (CR) systems with energy harvesters. In this paper, we propose a novel approach, namely, CGAPSO Shapley, for the best relay selection while simultaneously optimizing the parameters of signal‐to‐interference‐plus‐noise ratio (SINR), throughput, and outage probability. The CGAPSO Shapley algorithm combines the Shapley value, a cooperative game theory concept, with cellular genetic algorithm particle swarm optimization (CGAPSO) to achieve effective and efficient optimization of relay selection. The CGAPSO framework provides a hybrid structure that integrates cellular genetic algorithm (CGA) and particle swarm optimization (PSO), enabling simultaneous evolution of the population and particles within cells. The incorporation of the Shapley value and the hybrid CGAPSO framework enables effective exploration of the solution space and provides decision‐makers with comprehensive insights for relay selection. By utilizing the Shapley value, we assign weights to the relay nodes based on their contributions to the overall optimization objectives, considering their CR capabilities and energy harvesting capabilities. Some benchmark test functions are used to compare the hybrid algorithm with both the standard CGAPSO, Particle swarm optimization gravitational search algorithm (PSOGSA) and PSO algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard algorithms. The novel CGAPSO Shapley approach achieves an outage probability of 0.323324, marking a significant improvement of 60% over the outage probability achieved with conventional approach.

AoI minimization of ambient backscatter-assisted EH-CRN with cooperative spectrum sensing

To evaluate the timeliness and freshness of information from energy-constrained devices, we minimize the weighted average age of information (AoI) of the ambient backscatter-assisted energy harvesting cognitive radio network (AB-EH-CRN), where secondary transmitters (STs) follow the non-orthogonal multiple access (NOMA) strategy to transmit status updates. The secondary receiver (SR) schedules the selected actions for STs to perform spectrum access, and receives status updates from them. We formulate the AoI minimization problem where the action selection and transmission power of STs are considered as variables. To exploit spectrum resources in the AB-EH-CRN with unknown signal-to-noise ratios (SNRs) at the STs, we propose the deep neural network (DNN)-based cooperative spectrum sensing (CSS), and adopt an energy threshold approach for STs to determine the energy allocation between spectrum sensing (SS) and packet transmission. Moreover, we address the AoI minimization problem by the proposed hybrid action and energy penalty deep deterministic policy gradient (HAEP-DDPG) algorithm, which adapts to the hybrid continuous and discrete action spaces. Simulation results validate the advantage of the proposed HAEP-DDPG algorithm compared with comparison schemes in terms of AoI.

Spectrum utilization improvement for multi‐channel EH‐CRN with spectrum sensing

AbstractDue to the ever‐growing applications and services of the Internet of Things (IoT), designing energy‐efficient and spectral‐efficient transmission schemes to support IoT devices for the 6G space–air–ground integrated networks becomes much more challenging. Fortunately, energy harvesting (EH) and cognitive radio (CR) technologies have been proposed to alleviate these challenges. Inspired by this fact, this paper studies the issue of spectrum reuse in terms of spectrum utilization efficiency (SUE) in the energy harvesting cognitive radio network (EH‐CRN), where multiple primary transceiver pairs, one multi‐antenna secondary transmitter (ST), and one secondary base station (SBS) coexist. To characterize the impact of small‐scale fading and improve the SUE of the EH‐CRN with perfect spectrum sensing (SS), an adaptive scheme concerning SS, channel selection, EH, and data transmission (SCED) scheme are proposed, where the ST selects the channels for SS based on the residual energy, and adjusts the duration of EH and data transmission with respect to the sensing results. Then the Markov decision process problem of SUE is formulated, which is challenging due to the infinite system space and action space. To tackle the Markov decision process problem, the system space and action space are discreted, and divide the ST into the energy‐limited case and energy‐sufficient case according to specific energy condition. Moreover, theoretical results are extended to the EH‐CRN with imperfect SS. Numerical results show that the SUE under the SCED scheme in perfect SS and imperfect SS scenarios is better than that under other schemes.

Outage and throughput performance analysis of smart UAV‐assisted full‐duplex NOMA‐based energy harvesting cognitive radio networks

SummaryIn this paper, the throughput and outage performance for an unmanned aerial vehicle (UAV)‐assisted non‐orthogonal multiple access (NOMA)‐based hybrid cognitive radio (CR) network are examined under m‐Nakagami fading channel. To be more specific, a secondary source transmitter ( ) broadcasts a NOMA‐enabled mixed signal to secondary destinations via UAV relay, which operates in full‐duplex (FD) mode and employs decode‐and‐forward protocol. In our approach, an energy harvesting (EH) circuit is employed at both and UAV to harvest energy from the PU radio frequency (RF) signals. These secondary transmitters are also incorporated with energy detector (ED) circuit as well, which detects the activity of the primary user (PU). The status of PU to be busy or idle is confirmed on the basis of the energy detector's results. In turn, this drives and UAV to switch between underlay and overlay protocols in an adaptive manner to increase spectrum efficiency (SE). The closed form mathematical expressions of the secondary outage probability for NOMA‐based UAV equipped CR networks under imperfect successive interference cancellation (i‐SIC) conditions have been evaluated. The administration of the power allocation policy at the and UAV is assessed while maintaining the PU quality of service (QoS). Finally, MATLAB simulation testbed has been used to verify all the analytical closed form expressions.

A Hybrid Communication Scheme for Throughput Maximization in Backscatter-Aided Energy Harvesting Cognitive Radio Networks

Motivated by the benefits of cognitive radio (CR), energy harvesting (EH), and backscatter communication (BC) technologies to support Internet of Things (IoT) systems, we investigate the backscatter-aided energy harvesting cognitive radio networks (EH-CRNs) in a multi-channel scenario. To achieve high throughput on various channels, we propose a novel hybrid communication scheme that the secondary transmitter (ST) selects one channel for spectrum sensing, and performs multiple actions based on the sensing result. To be specific, if the selected channel is detected as busy, the ST potentially performs underlay mode transmission, ambient backscatter communication (AmBC), or radio frequency (RF) EH. Otherwise, the ST performs interweave mode transmission. Based on the ST’s knowledge of the channel availability and the amount of the available energy, the decisions of channel and specific action selections are made. Furthermore, the sequential decision problem is formulated as a mixed observability Markov decision process (MOMDP), and addressed by the classic value iteration algorithm. The proposed scheme could be flexibly adapted to the changes in energy and channel availabilities. Simulations demonstrate the superiority of this scheme in terms of throughput, and show that even without channel selection, the proposed scheme conducted on the channels with different idle probabilities always achieves high throughput.

Performance Analysis of Full-Duplex Multirelaying Energy Harvesting Scheme in Presence of Multiuser Cognitive Radio Network

This paper investigates the outage performance of secondary receiver with an energy harvesting cognitive radio network employing multiple full-duplex (FD) relaying protocol in presence of multi-user spectrum sharing environment. Under this energy harvesting aided FD relaying network scenario, adaptive transmit power policies for the secondary user (SU) source, secondary FD relays and exact outage probability of the secondary user are derived analytically in presence of multiple primary users and multiple secondary destinations. Impact of self interference at the receive antenna of each FD relay in addition to all the transmitting interference at all receive nodes is being addressed. A comparative study considering all the interference together in an FD relaying protocol with that of conventional half-duplex (HD) relaying protocol is carried out. With the same diversity order, a trade-off between FD multi-relay and multi-destination is shown. Closed form expressions of secondary outage probability for FD energy harvested relay equipped CR network has also been evaluated. Maintaining quality of service (QoS) of multiple primary users (PUs), power allocation strategies at each secondary transmitting node is taken care of. Finally, all the analytical closed form expressions have been validated through Monte-Carlo simulations.

Throughput and harvesting time trade-off in a energy harvesting cognitive radio network

Throughput and harvesting time trade-off in a energy harvesting cognitive radio network

Shapley-Value-Based Hybrid Metaheuristic Multi-Objective Optimization for Energy Efficiency in an Energy-Harvesting Cognitive Radio Network

Energy efficiency and throughput are concerns for energy-harvesting cognitive radio networks. However, attaining the maximum level of both requires optimization of sensing duration, harvested energy, and transmission time. To obtain the optimal values of these multiple parameters and to maximize the average throughput and energy efficiency, a new hybrid technique for multi-objective optimization is proposed. This hybrid optimization algorithm incorporates a Shapley value and a game theoretic concept into metaheuristics. Here, particle swarm optimization grey wolf optimization (PSOGWO) is selected as the source for the advanced hybrid algorithm. The concept of the unbiased nature of wolves is also added to PSOGWO to make it more efficient. Multi-objective optimization is formulated by taking a deep look into combined spectrum sensing and energy harvesting in a cognitive radio network (CSSEH). The Pareto optimal solutions for the multi-objective optimization problem of energy efficiency and throughput can be obtained using PSOGWO by updating the velocity with the weights. In the proposed Shapley hybrid multi-objective optimization algorithm, we used Shapley values to set up the weights that, in turn, updated the velocities of the particles. This updated velocity increased the ability of particles to reach a global optimum rather than becoming trapped in local optima. The solution obtained with this hybrid algorithm is the Shapley–Pareto optimal solution. The proposed algorithm is also compared with state-of-the-art PSOGWO, unbiased PSOGWO, and GWO. The results show a significant level of improvement in terms of energy efficiency by 3.56% while reducing the sensing duration and increasing the average throughput by 21.83% in comparison with standard GWO.

Impacts of Sensing Energy and Data Availability on Throughput of Energy Harvesting Cognitive Radio Networks

This paper aims to investigate the impacts of sensing energy and data availability on the secondary throughput of energy harvesting cognitive radio networks (EH-CRNs), where secondary transmitters (STs) are powered by the energy harvested from primary radio frequency (RF) signals. To reach the aim, we consider two extreme cases by employing two data arrival processes: heavy data arrival and general data arrival. In the heavy data arrival case, we consider STs always have data to transmit, and study the impacts of sensing energy on the secondary throughput. In the general data arrival case, we consider the data arrives with a certain probability, and study the impacts of data availability (i.e., data arrival probability and data buffer capacity) on the secondary throughput. Moreover, to compare the secondary throughput under non-cooperative spectrum sensing (NCSS) and cooperative spectrum sensing (CSS), we further study two CRN scenarios for each data arrival case: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Scenario 1</i> with one pair of secondary users (SUs) conducting NCSS, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Scenario 2</i> with multiple pairs of SUs conducting CSS. As the harvest-then-transmit protocol is employed by STs, it is essential to balance energy harvesting and data transmission to achieve high secondary throughput. We thus utilize an energy threshold approach to determine the actions of each ST. Specifically, under the heavy data arrival, we derive the average secondary throughput for each scenario by taking the sensing energy as an energy unit and modeling the energy states of each ST as a Markov chain. Under the general data arrival, we derive the secondary throughput for each scenario by modeling the energy and data states of each ST as a two-dimensional Markov chain. Simulation results show that: (1) both the secondary throughput and the optimal energy threshold decrease with the sensing energy; (2) the secondary throughput increases with the data availability, while the optimal energy threshold decreases with the data availability; (3) from the normalized secondary throughput perspective, CSS is not always better than NCSS.

Energy harvesting cognitive radio networks with strategic users: A two-class queueing model with retrials

Motivated by the rapid growth in wireless communication systems, we develop a single-server queueing model with service retrials for the cognitive radio network (CRN). A key appeal of CRN is its flexible allocation scheme of limited spectrum for two user groups: (1) the licensed user, hereby referred to as primary user (PU), and (2) unlicensed users, referred to as secondary users (SUs). The PU take priority over SUs: when a PU arrives and observes a busy server currently occupied by an SU, it will take over the server by preempting the SU out of service. SUs are strategic and delay sensitive: they decide immediately upon arrivals on whether to join the CRN or to balk, based on the anticipated expected utility (service reward minus delay cost). In case the server is unavailable, SUs temporarily join an orbit queue where they wait for future accesses to service. Another realistic feature of our model is that, after successfully serving a user (PU or SU), the server becomes unavailable and undergoes an energy harvesting phase of a random time. We characterize the equilibrium system performance in the following three steps: First, we obtain the equilibrium joining behavior for utility-maximizing SUs; second, we derive the optimal joining strategy from the perspective of a social planner; and finally, we enforce that users adopt the socially optimal strategy by imposing an admission fee. We also investigate the impact of the energy harvesting time on users?? equilibrium strategies and report numerical experiments to provide qualitative and quantitative insights of our results.

The maximal secondary throughput in cognitive radio networks with energy harvesting

In terms of spectrum reuse and lifetime prolongation, the energy-harvesting cognitive radio networks (EH-CRNs) have been a hot issue in the wireless networking research community. While satisfying the minimal throughput demand of primary users (PUs), it is aimed to maximize the throughput of secondary users (SUs) in the EH-CRN with multiple SUs. Specifically, the problem of secondary throughput maximization (STM) is first formulated as a non-linear optimization problem, then its convexity is proven, and finally an efficient algorithm is proposed that jointly uses the Fibonacci search method and equal interval search method to obtain the optimal time allocation among primary transmitter (PT)'s energy transfer and each SU's packet transmission, and the optimal transmit power of PT. Furthermore, for the scenarios where the circuit power is negligible, the convex problem is first proven, and a more efficient algorithm is presented for the problem of STM. Simulation results demonstrate that, with the increase of the minimal throughput demand of PUs, the reduction rate of the maximal secondary throughput increases.

Optimal Time Allocation for Energy Harvesting Cognitive Radio Networks with Multichannel Spectrum Sensing

From the perspective of time domain and frequency domain, we investigate the energy harvesting cognitive radio networks (EH-CRNs) with multichannel, where the secondary transmitter (ST) opportunistically accesses the licensed subchannels to transmit packets by consuming the harvested energy. To explore the spectrum holes and improve the lifetime of the EH-CRNs, the ST scavenges energy from the radio-frequency (RF) signal in the wide band during the energy harvesting (EH) phase and exploits the harvested energy for sequential sensing and packet transmission during the rest of the time slot. Under the energy constraint, the secondary throughput is improved by optimizing the time allocation among the EH phase, sensing phase, and transmission phase. We formulate the secondary throughput with respect to the durations of the three phases, prove the existence of the optimal time allocation, and discuss the secondary throughput in three cases of the EH-CRNs. Finally, numerical results validate the theoretical results about the secondary throughput and explore the impacts of key system parameters.

Optimization on information freshness for multi‐access users with energy harvesting cognitive radio networks

AbstractFor the multi‐access channel wireless sensor network scenario with limited channel resources, the transmission of real‐time information needs to consider the channel environment, spectrum utilization and the freshness of information. First, we present a cognitive network model where secondary user (SU) shares the primary user (PU) spectrum through the underlay scheme. The sensor nodes with energy harvesting (EH) capabilities dynamically monitor changes in the surrounding environment and transmit status updates to the common secondary destination node. Considering the stability of the data queue in the SU as well as the working state and stable service interference constraints of PU, an optimization problem of minimizing the average AoI and average peak AoI (PAoI) of the EH node is proposed. Second, two policies are used to solve the above problems, including the decentralized probabilistic‐random‐access (PRA) policy and the drift plus penalty (DPP) policyin. In PRA policy, two SU nodes make independent transmission decisions based on corresponding probability distributions, and in DPP policy, the Lyapunov optimization framework is used to optimize scheduling decisions in the timeslots for optimizing the age of data packets. The simulation results show that the optimization of AoI under the DPP policy is more significant than the PRA policy, which can effectively improve the timeliness and freshness of data packets.

A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network

In this article, a novel technique is proposed, namely rank-based multi-objective antlion optimization (RMOALO), and applied to optimize the performance of the energy harvesting cognitive radio network (EHCRN). The original selection method in multi-objective antlion optimizer (MOALO) is suitably changed to improve the algorithm, thus reaching the optimal solution for the problem. The proposed technique shows considerable performance improvement over the method used in the multi-objective antlion optimizer (MOALO). The performance of the proposed RMOALO is demonstrated on five benchmark mathematical functions and compared to multi-objective particle swarm optimization (MOPSO), multi-objective moth flame optimization (MOMFO), MOALO-Tournament, and MOALO-Roulette. The simulation results show an improved convergence of RMOALO and find the optimal solution to the throughput maximization problem. We show that RMOALO provides 16.33 % improved average throughput with the optimal value of sensing duration for the varying amount of harvested energy compared to MOPSO, MOMFO, MOALO-Roulette, and MOALO-Tournament.

AI-Based Secure NOMA and Cognitive Radio-Enabled Green Communications: Channel State Information and Battery Value Uncertainties

In this paper, the security-aware robust resource allocation in energy harvesting cognitive radio networks is considered with cooperation between two transmitters while there are uncertainties in channel gains and battery energy value. To be specific, the primary access point harvests energy from the green resource and uses time switching protocol to send the energy and data towards the secondary access point (SAP). Using power-domain non-orthogonal multiple access technique, the SAP helps the primary network to improve the security of data transmission by using the frequency band of the primary network. In this regard, we introduce the problem of maximizing the proportional-fair energy efficiency (PFEE) considering uncertainty in the channel gains and battery energy value subject to the practical constraints. Moreover, the channel gain of the eavesdropper is assumed to be unknown. Employing the decentralized partially observable Markov decision process, we investigate the solution of the corresponding resource allocation problem. We exploit multi-agent with single reward deep deterministic policy gradient (MASRDDPG) and recurrent deterministic policy gradient (RDPG) methods. These methods are compared with the state-of-the-art ones like multi-agent and single-agent DDPG. Simulation results show that both MASRDDPG and RDPG methods, outperform the state-of-the-art methods by providing more PFEE to the network

Age of Information Minimization for Radio Frequency Energy-Harvesting Cognitive Radio Networks.

The Age of Information (AoI) measures the freshness of information and is a critic performance metric for time-sensitive applications. In this paper, we consider a radio frequency energy-harvesting cognitive radio network, where the secondary user harvests energy from the primary users’ transmissions and opportunistically accesses the primary users’ licensed spectrum to deliver the status-update data pack. We aim to minimize the AoI subject to the energy causality and spectrum constraints by optimizing the sensing and update decisions. We formulate the AoI minimization problem as a partially observable Markov decision process and solve it via dynamic programming. Simulation results verify that our proposed policy is significantly superior to the myopic policy under different parameter settings.

Outage Energy Efficiency Maximization for UAV-Assisted Energy Harvesting Cognitive Radio Networks

The UAV-assisted energy harvesting cognitive wireless network has the feature of fast layout, remarkable spectrum efficiency (SE) and energy efficiency (EE), thereby being envisioned as one significant cutting-edge communication technology in the 5G/6G era. In this study, we consider an UAV-assisted energy harvesting cognitive radio network (UAV-EH-CRN), where an UAV is employed as one cognitive user (CU), hovering in the air to perform spectrum sensing and communicating with the dedicated receiver (DR) on the ground. Based on the sensing results of the primary user (PU), the UAV can adaptively adjust power to transmit with the DR. Moreover, by harvesting renewable energy, the UAV can replenish energy actively for transmission. By introducing outage rate (OR) for guaranteeing the UAVs SE requirements, our goal is to maximize the outage energy efficiency (OEE) for the UAV-EH-CRN, subject to constraints of energy, transmission power, and interference power. To make the non-convex OEE maximization problem solvable, one resource allocation policy portrayed as outage related convex approximation strategy (OR-CAS) is presented. Specifically, the OEE problem is firstly converted into a tractable counterpart by the proposed OR-CAS. Then, making use of the Lagrange duality method and one-dimensional linear programming, the optimal resource allocation solution for the OEE maximization is obtained. Simulation results show that our scheme enable the UAV-EH-CRN to adapt to various on-demand outage SE scenarios by setting different OR thresholds. Moreover, our algorithm can obtain significant EE gains and less time expenditure in comparison with the traditional exhaustive search approach.

Game theory based spectrum sensing and transmission in energy harvesting hybrid cognitive radio networks (EH‐HCRN)

SummaryIn an energy harvesting cognitive radio network (EH‐CRN), the correspondence among the primary transmitter (PT) and primary receiver (PR) for spectrum sensing and transmission becomes ineffective as a result of insufficient SNR, finite sensing duration and interferences. We recommend designing a suitable spectrum sensing structure based on game theory for energy harvesting hybrid CRN to resolve these complications. This comprises three phases: (i) co‐operative relay approach for primary user (PU) transmission; (ii) optimal sensing interval estimation; and (iii) co‐operative energy efficient spectrum sensing and transmission. Initially, a batch of suitable secondary transmitters (STs) are identified as relay nodes for the PTs in the first phase. Next, in the second phase, a sensing interval that maximizes a utility function devised concerning average throughput, collision probability regarding secondary user (SU) and energy detector threshold is determined by applying the game theory model. Finally, co‐operative transmission is utilized in the third phase if the PR could not decode the signal from PT via STs. The presented framework is simulated using NS2, and its efficiency is estimated in terms of the parameters like transmission latency, packet loss, residual energy and possibilities of successful data delivery.

Throughput maximisation for multi‐channel energy harvesting cognitive radio networks with hybrid overlay/underlay transmission

This paper focuses on the issue of joint time and power allocation in multi-channel energy harvesting CR networks (EH-CRNs), where the multi-antenna secondary transmitter (ST) opportunistically accesses the licensed subchannels by a hybrid overlay/underlay transmission approach. To improve spectrum efficiency and energy efficiency of the EH-CRNs, the ST scavenges energy from the radio-frequency signal radiated by the primary transmitter, and exploits the harvested energy for data transmission through subchannels of different states in overlay/underlay mode simultaneously. Moreover, under the interference power constraint, energy constraint, and maximum power constraint, the secondary throughput is improved by optimising the allocation of subchannels, the time scheduling between energy harvesting and data transmission, and the power allocation of the ST among different subchannels. A subchannel allocation scheme with low time complexity is proposed, and the secondary throughput optimisation problem is formulated with respect to the time scheduling and power allocation of the ST. Then it is proved the problem is convex, and the problem is solved by a proposed joint time and power allocation algorithm. Numerical results show that the proposed scheme has an advantage of secondary throughput over the other schemes. Finally, the impacts of key relevant factors on the secondary throughput are explored.