Secondary Throughput 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.

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.

DDPG-Based Joint Time and Energy Management in Ambient Backscatter-Assisted Hybrid Underlay CRNs

Ambient backscatter (AB) communications and radio frequency (RF)-powered cognitive radio networks (CRNs) address the concerns of energy and spectrum scarcities from different perspectives, and the integration of them has potential benefits for throughput. Motivated by this fact, we study the RF-powered AB-assisted hybrid underlay CRN (ABHU-CRN), and optimize the long-term secondary throughput. Based on the channel states, secondary users choose to perform different actions, and two action spaces are accordingly designed. Due to dynamic and unpredictable environment states, we jointly control the time scheduling and energy management of secondary users, and propose two algorithms, i.e., adjusted-deep deterministic policy gradient (A-DDPG) and combination of A-DDPG and convex optimization (C-ADCO), for the long-term secondary throughput. A-DDPG, a deep reinforcement learning algorithm with continuous spaces, is extended from DDPG to adapt to the design of two action spaces. C-ADCO utilizes the convex optimization that can find the optimal solution to assist A-DDPG to accelerate the convergence. In simulations, the ABHU-CRN under A-DDPG and C-ADCO achieves higher throughput than the optimal throughput of AB-assisted overlay CRN and AB-assisted underlay CRN, which indicates the advantage of the hybrid transmission mode in the ABHU-CRN.

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.

Performance analysis of cooperative cognitive radio networks based on hybrid NOMA/OMA and best relay selection

In non-orthogonal multiple access (NOMA) systems, it may not be advisable to send the superimposed signal of all users to individual users with retransmission requirements during the retransmission phase. This paper investigates a novel cognitive radio network based on hybrid NOMA/orthogonal multiple access (CR-H-NOMA/OMA) in underlay spectrum sharing, whereby a hybrid NOMA/OMA cooperative mechanism is proposed for the secondary network by considering that the access modes of secondary users (SUs) and relays can be switched between NOMA and OMA. In order to compensate the loss of throughput caused by successive interference cancellation (SIC) during the cooperative phase, one or two relays with the best channel quality between themselves and the destination users are selected according to the states of the initial signal decoded by SUs, using truncated automatic repeat request (T-ARQ) to retransmit only the desired signal of SU instead of the entire superimposed signal. Exact closed-form expressions for outage probabilities of SUs and system throughput of the secondary network are derived under the interference constraint of the primary network to evaluate the performance of the proposed cooperative scheme. Numerical and simulation results demonstrate the superiority of the proposed hybrid NOMA/OMA cooperative scheme when compared with conventional pure NOMA and pure OMA based on time division multiple access (TDMA) in terms of secondary throughput. Also, the outage and throughput performance depend critically on the choices of key paraments such as power allocation factor and target rate.

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.

Performance of cognitive radio networks using reconfigurable intelligent surfaces with RF energy harvesting for Nakagami channels

In this paper, we derive the performance of cognitive radio networks (CRNs) with energy harvesting using reconfigurable intelligent surfaces (RISs) for Nakagami fading channel with m-fading figure M. We derive the detection probability when the primary source (PS) harvests energy using radio frequency (RF) signals. A RIS is located between PS and secondary source (SS) where spectrum sensing is performed. We also derive the primary and secondary throughput and optimise harvesting duration to maximise the throughput. We observed significant performance enhancement in detection probability and throughput with respect to CRN without RIS.

Jamming in Eavesdropping on Throughput Maximization in Green Cognitive Radio Networks

This work considers a cognitive radio (CR) network consisting of a set of CR transmit-receive node pairs, one fusion center (FC), multiple primary user emulation attack (PUEA) nodes, an eavesdropper ( <inline-formula><tex-math notation="LaTeX">$E_{av}$</tex-math></inline-formula> ) node and a set of friendly jammers used for protection of CR data transmission from eavesdropping. At the initial time slot of the frame, simultaneous energy harvesting (EH) and spectrum sensing are done through power splitting (PS) mode. CR transmit nodes then amplify and forward the sensed samples of both the primary user (PU) and the PUEA to the FC for cooperative spectrum sensing (CSS). Based on the CSS decision, CR transmit nodes either perform EH over the entire duration or make an opportunistic data transmission in time division mode. The closed form expressions of the optimal sensing duration, power allocation factor and transmit power for each secondary user (SU) are found. The sum secondary throughput of the network is maximized under the constraints of meeting the sensing reliability of the PU, individual energy causality for each SU and the best selected jammer, interference at the PU receiver, individual secondary and secrecy outage probability. Simulation results show a performance gain on the maximum value of the sum secondary throughput by <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 17.56 and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 49.69 percent over the existing works.

Outage probability of MIMO cognitive radio networks with energy harvesting and adaptive transmit power

This paper derives the outage probability of cognitive radio networks (CRNs) with energy harvesting (EH). The primary nodes have a single antenna, whereas the secondary nodes have multiple antennas. A secondary source S harvests energy from radiofrequency (RF) signal received from primary transmitter PT using nr, S antennas. S also adapts its power so that the interference at primary receiver PR is less than threshold I. The main contribution of the paper is to derive the throughput of multiple input multiple output (MIMO) CRN with RF energy harvesting and adaptive transmit power. We also optimize the secondary throughput by choosing the optimal harvesting duration.

On Ensemble Learning-Based Secure Fusion Strategy for Robust Cooperative Sensing in Full-Duplex Cognitive Radio Networks

We propose an ensemble machine learning (EML) based robust cooperative spectrum sensing framework in full-duplex cognitive radio networks (FD-CRNs), which is robust with accuracy against malicious attacks and interference. FD communication improves the spectrum awareness capability of secondary users (SUs) by allowing them to sense and transmit simultaneously over the same frequency band. However, it also complicates the sensing environment by introducing self-interference and co-channel interference. Meanwhile, the presence of malicious attacks such as Primary User Emulation and Spectrum Sensing Data Falsification (SSDF) attacks also degrades the sensing performance. To alleviate the influence of interference and attacks, we design an EML framework that provides robust and accurate fusion performance. In such a context, we analyse the spectrum waste probability, collision probability and secondary throughput in both FD Listen-Before-Talk and Listen-And-Talk protocols. Simulation results show that our proposed EML framework can provide lower and more robust false-alarm probability than single-model based fusion methods with the same detection probability constraint for any size of training sets. It also outperforms the conventional majority vote based fusion strategy in terms of spectrum waste probability, collision probability and secondary throughput for any number of SSDF SUs, only at the cost of slightly higher inference time.

Impact of Battery Charging on Spectrum Sensing of CRN With Energy Harvesting

This paper investigates the impact of battery charging on a cognitive radio network (CRN), where a hybrid overlay-underlay strategy enables mobile secondary nodes to access the licensed spectrum, and the secondary transmitter (ST) utilizes the energy harvested from the radio-frequency (RF) signal and the battery charging in the charging area for spectrum sensing and data transmission. We characterize the impact of battery charging in the charging area by designing an optimal spectrum sensing policy. The objective is to maximize the secondary throughput subject to energy and collision constraints. To be specific, a metric defined as a function of the spectrum sensor detection threshold is proposed to divide the CRN into three network states with respect to the relationship among energy consumption, energy harvesting, and battery charging. Accordingly, ST in the charging area decides to charge the battery or to exploit transmission opportunity. In each network state, we optimize the spectrum sensor detection threshold for the objective, which is a challenging problem since the spectrum sensor detection threshold is intertwined with the energy constraint, the collision constraint, and secondary throughput. Then we characterize the impact of battery charging in the charging area on the state classification and optimal spectrum sensing, respectively. Simulations show the effectiveness of the proposed metric, and validate the impact of battery charging.

Cognitive Relaying With Wireless Energy Harvesting and Accumulation

In this article, we propose a cognitive relaying (CR) scheme with both the secondary source (SS) and the secondary relay (SR) capable of harvesting wireless energy. Along with the data transmission of primary system, a power beacon actively transfers wireless power to SS and SR. The interference from the primary data transmission can increase the amount of harvested energy at SS and SR. After harvesting and accumulating energy for a number of successive time blocks, the CR will be performed in two continuous time blocks. In CR-Block-One, SS will transmit the secondary data to SR by exhausting its available energy. In CR-Block-Two, SR will forward the secondary data to SD if it has correctly decoded the data from SS; otherwise, it will stay silent. We properly model the energy accumulation process of SS and SR by defining a group of discrete energy levels. We analyze the approximate throughputs of both systems by considering various transmit powers of SS and SR. The optimal number of time blocks used for the wireless power transfer is determined through maximizing the secondary throughput while slightly degrading the primary performance. Numerical results show that the secondary data transmission can be well accommodated.

Decision-Driven Time-Adaptive Spectrum Sensing in Cognitive Radio Networks

In cognitive radio systems based on periodic sensing and transmission, a secondary user (SU) senses the activity of a primary user (PU) in the sensing phase and then makes use of the detected spectrum opportunity in the transmission phase. Under the constraint that the PU should be sufficiently protected, it is challenging for the SU to accurately sense and utilize spectrum opportunities in low signal to noise ratio (SNR) environments. However, in existing spectrum sensing schemes, the time-frequency resource blocks available for data transmissions are always wasted once a false alarm event happens in the sensing period where the SU is doing nothing but waiting for another sensing opportunity. By reutilizing these initially wasted time-frequency resource blocks for spectrum sensing, the SU can get more accurate information on the activities of the PU, which enables the SU to make more efficient use of the spectrum opportunities left by the PU. In this paper, a decision-driven time-adaptive spectrum sensing scheme is proposed to improve both spectral efficiency and energy efficiency based on improved resource usage. Specifically, if the PU is detected to be absent in the sensing phase of a frame, the SU transmits data in the transmission phase of the frame; otherwise, the SU will sense the spectrum for a duration of one frame period, where the frame period consists of the transmission phase of the current frame and the sensing phase of the next frame. When both signal and noise are Gaussian, the optimal maximum likelihood ratio detector is derived. Furthermore, the performance upper-bounds of spectrum utilization and secondary throughput are obtained. Finally, both simulation and theoretical results show that the developed scheme can improve spectrum utilization, secondary throughput and energy efficiency effectively in extremely low SNR environments.

Simultaneous Transmitting–Receiving–Sensing for OFDM-based Full-Duplex Cognitive Radio

In this paper, a Transmitting–Receiving–Sensing (TRS) mode of OFDM-based Full-Duplex Cognitive Radio (FD-CR) is proposed. The new mode aims at monitoring the Primary User (PU) activities on the operating channel while establishing an in-band full-duplex communication between two communicating Secondary Users (SUs), i.e. SUs transmit and receive simultaneously on the same frequency band. Assuming that the primary activity covers all the operating band when PU is active, Spectrum Sensing is performed on some Sub-Carriers (SCs) which are neutralized by the two communicating SUs. Letting some SCs null by both SUs helps to accurately performing the spectrum sensing on these SCs since no residual self-interference neither secondary transmission are present. When PU is detected on these SCs, then we consider that he becomes active again; Then SUs should vacate the channel. For the proposed mode, false alarm, detection and collision probabilities are derived in addition to the throughput rate. An optimal selection of the sensing SCs per OFDM symbol is derived with respect to a maximal tolerable rate of collision between PU and SU transmission. Numerical results corroborate the effectiveness of our proposed mode in terms of the PU awareness and the secondary throughput rate.

Censor-Based Cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels

The present contribution proposes a spectrally efficient censor-based cooperative spectrum sensing (C-CSS) approach in a sustainable cognitive radio network that consists of multiple antenna nodes and experiences imperfect sensing and reporting channels. In this context, exact analytic expressions are first derived for the corresponding probability of detection, probability of false alarm, and secondary throughput, assuming that each secondary user (SU) sends its detection outcome to a fusion center only when it has detected a primary signal. Capitalizing on the findings of the analysis, the effects of critical measures, such as the detection threshold, the number of SUs, and the number of employed antennas, on the overall system performance are also quantified. In addition, the optimal detection threshold for each antenna based on the Neyman-Pearson criterion is derived and useful insights are developed on how to maximize the system throughput with a reduced number of SUs. It is shown that the C-CSS approach provides two distinct benefits compared with the conventional sensing approach, i.e., without censoring: i) the sensing tail problem, which exists in imperfect sensing environments, can be mitigated; and ii) less SUs are ultimately required to obtain higher secondary throughput, rendering the system more sustainable.

Optimal Collaborative Spectrum Sharing for a Cognitive Multi-Antenna Relay With Full-Duplex Capability and Stability Constraint

Collaborative spectrum sharing (CSS) between primary and secondary users (PU and SU) is an effective way of utilizing limited radio spectrum. In CSS, cognitive SU acts as a relay for PU, which facilitates the PU to send its packet with a lower error probability by the help of the SU, and consequently, the SU has more chances to find a vacant spectrum. When SU is equipped with multiple antennas, it can further efficiently utilize the radio spectrum by adopting a growing self-interference cancellation technique for full-duplex (FD) transmission. In this paper, both an aggressive and a passive secondary usage of the spectrum are proposed, the operational principles of which are defined using spectrum-sharing probability φ in FD CSS environments. For the spectrum-sharing probability φ, SU for each of the methods sends its own signal and the PU's (relayed) signal together by using superposition transmission. For the remaining probability 1 - φ, the two methods work differently: SU sends its own signal only in the aggressive mode while it sends only the relaying signal for PU in the passive mode. We formulate an FD CSS problem with various physical-layer parameters and the proposed operating modes as a function of the spectrum-sharing probability. Our goal is to maximize the secondary stable throughput while keeping a primary traffic constraint. Closed-form optimal solutions on φ are provided in the paper, the value of which heavily depends on the primary traffic volume, the operating modes, the relative locations of the collaborative nodes and the transmit power budget at SU. The analytical results in the paper are verified with numerical investigation, and the performance enhancement by the proposed methods is evaluated in comparison with benchmark systems. The results show that the aggressive mode is promising if SU has a relatively small transmit power and the primary traffic load is low, while the passive mode is suitable when the primary traffic load is high.

Throughput optimisation of multi-antennas CRN-NOMA with energy harvesting and adaptive transmit power

In this paper, we optimise the throughput of cooperative non orthogonal multiple access (NOMA) for cognitive radio networks (CRN). The secondary nodes harvest energy using the received signal on multiple antennas from node A. Secondary nodes adapt their power to generate interference at Primary Destination (PD) less than threshold I. The source transmits a combination of symbols dedicated to near and far users. The signal is decoded by a relay node R that regenerates it and transmits it to near and far secondary users. We optimise both harvesting duration and power allocation to near and far users to maximise the secondary total throughput. We also suggest two techniques for users' ranking using average or instantaneous channel gains.

RF 충전 후방산란 CR 네트워크에서 2차 시스템의 전송 성능 향상 방안

RF 충전 후방산란 CR 네트워크에서 2차 시스템의 전송 성능 향상 방안

Cognitive Non-Orthogonal Multiple Access With Energy Harvesting: An Optimal Resource Allocation Approach

We consider a cognitive radio system, in which a secondary transmitter harvests energy from a primary transmitter's RF signal. The secondary transmitter, which provides decode-and-forward relaying service for the primary system, transmits its own data by using downlink non-orthogonal multiple access (NOMA). A time-switching protocol is used by the secondary transmitter to harvest energy and decode the primary transmitter's information. Our objective is to achieve maximal secondary throughput, by optimally selecting the time portion used for energy harvesting and the secondary transmitter's power allocation in NOMA transmission. In this paper, two optimization problems are formulated, in which the secondary receiver performs or does not perform successive interference cancellation (SIC), respectively. Although the two problems are nonconvex, we devise a method to transform the problems into equivalent problems under different cases. Then, we theoretically prove that the objective functions of the equivalent problems are quasiconcave, based on which we develop two-level bisection search algorithms to solve the equivalent problems. Interestingly, we show that performing SIC at the secondary receiver does not always guarantee a higher secondary throughput than the case without performing SIC. Computer simulation demonstrates that our algorithm performs better than an equal power allocation algorithm and an orthogonal multiple access algorithm.