Primary User Rate Research Articles

Deep Reinforcement Learning-Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications

The combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency and spectral efficiency of the upcoming beyond fifth generation network (B5G), especially for support the wireless sensor communications in Internet of things (IoT) system. However, how to realize intelligent frequency, time, and energy resource allocation to support better performances is an important problem to be solved. In this paper, we study joint spectrum, energy, and time resource management for the EH-CR-NOMA IoT systems. Our goal is to minimize the number of data packets losses for all secondary sensing users (SSU), while satisfying the constraints on the maximum charging battery capacity, maximum transmitting power, maximum buffer capacity, and minimum data rate of primary users (PU) and SSUs. Due to the non-convexity of this optimization problem and the stochastic nature of the wireless environment, we propose a distributed multidimensional resource management algorithm based on deep reinforcement learning (DRL). Considering the continuity of the resources to be managed, the deep deterministic policy gradient (DDPG) algorithm is adopted, based on which each agent (SSU) can manage its own multidimensional resources without collaboration. In addition, a simplified but practical action adjuster (AA) is introduced for improving the training efficiency and battery performance protection. The provided results show that the convergence speed of the proposed algorithm is about 4 times faster than that of DDPG, and the average number of packet losses (ANPL) is about 8 times lower than that of the greedy algorithm.

Energy-efficient cooperative spectrum sensing over imperfect reporting channels with collision constraint

Recently, energy-efficient cooperative spectrum sensing (CSS) has been studied extensively in cognitive radio networks (CRNs). Most studies assume that the occupancy state of the primary user (PU) remains constant during the transmission of secondary users (SUs). However, the PU could change its activity multiple times during the transmission period, resulting in collisions with each other. In this paper, the energy efficiency (EE) maximization problem for CRNs with CSS is examined by explicitly considering the collision probability of the PU state to transition while the SU transmits data. Specifically, two types of interference based on the sensing errors and PU re-occupancy as well as imperfect reporting channels are considered simultaneously. The goal is to achieve the maximum average EE of SUs while ensuring detection accuracy by jointly optimizing the number of collaborative SUs, frame duration and transmission power, subject to the constraints of collision probability, average transmission power, average interference power and data rate. To address the non-convex optimization problem, the optimal frame duration is obtained first based on the properties of collision probability. Then, based on Dinkelbach’s method, an energy-efficient CSS iterative power control algorithm is developed. Numerical results verify the effectiveness of the proposed algorithm. It is shown that the proposed algorithm is better to the existing schemes regarding to EE. The impacts of the PU traffic parameters, reporting errors, transmission power and rate constraints on the collision probability and EE are evaluated.

Cooperative beamforming in cognitive radio systems without feedback of receiver beamforming vectors to transmitter

In this paper, we investigate the problem of transceiver beamforming and power allocation in the downlink of a multiple input multiple output Cognitive radio system. In this system, a base station (BS) services multiple primary users (PU) and multiple secondary users (SU). In order to perform transceiver beamforming and power allocation, three new algorithms are proposed. The first one is an iterative single-objective constrained algorithm, in which the signal to interference plus noise (SINR) of SUs are improved at each iteration subject to constraints on quality of services of PUs (PUs SINR should be above a predefined threshold) and total transmit power of BS. The second and third algorithms, are based on a multi-objective constraint optimization problem, in which the goal is to improve both PUs and SUs SINR at each iteration subject to previous constraints of the first algorithm. The difference between the second and third algorithms is that in the joint maximization of the second algorithm, the cross-influence of each user on the others is ignored, but in the third algorithm, the cross effect of each users SINR on the others is considered. The novelty of these algorithms is the independency of the transmit precoding vectors from the receive beamforming vectors. Therefore, the feedback of receive beamforming to the BS is not needed. The performances of three proposed algorithms are evaluated in simulations vis bit error rate of PUs and SUs, SINR of PUs and SUs and the percent of BS power allocation to SUs.

Performance analysis of centralized dynamic spectrum access via channel reservation mechanism in cognitive radio networks

The two main goals in wireless networks are to provide channel allocation opportunities for incoming services and to assure the accomplishment of ongoing services. In this paper, a channel reservation scheme in cooperative cognitive radio networks (CRNs) based on dynamic spectrum access (DSA) is proposed. In this scheme, a centralized cognitive manager (CCM) provides the dynamic coordination access for primary users (PUs) and secondary users (SUs) through partitioning the spectrum into non-reserved channels (N-CRN) and reserved channels (R-CRN). N-CRN can be exploited by licensed PUs and unlicensed SUs, while R-CRN is dedicated for active SUs until the successful completion of their service. If a SU session in N-CRN is interrupted by the sudden arrival of PU, then a spectrum handover occurs to R-CRN to resume its session. The reserved channel number R is deduced upon the reduction of both forced termination and blocking probabilities for SUs while satisfying PUs’ traffic demands. Also, overall system cost and SU cost functions are investigated. Simulation results show significant reduction in blocking and forced termination probabilities and improvement in utility function and service completion rate of SUs while meeting PU’s quality of service (QoS) aims. For example, when the arrival traffic rates per channel λp=0.5, and for R=1,R=2 and R=3, the values of specific SU forced termination probabilities Pft are 0.1597, 0.116, 0.0799 respectively compared to 0.2106 for the case of no channel reservation. Furthermore, blocking probability and service completion rate of PU are evaluated to demonstrate the effect of channel reservation variation on the system performance.

Joint beamforming and time switching designs for energy-constrained cognitive two-way relay networks

In this paper, we investigate a joint beamforming and time switching (TS) design for an energy-constrained cognitive two-way relay (TWR) network. In the network, the energy-constrained secondary user (SU) relay employs TS protocol to harvest energy from the signals sent by the circuit-powered primary user (PU) transmitter, and then exploits the harvested energy to perform information forwarding. Our aim is to maximize the sum rate of SUs under the constraints of the data rate of PU, the energy harvesting and the transmit power of the SU relay. To determine the beamforming matrix and TS ratio, we decouple the original non-convex problem into two subproblems which can be solved by semidefinite relaxation and successive convex optimization methods. Furthermore, we derive closed form expressions of the optimal solutions for each subproblem, which facilitates the design of a suboptimal iterative algorithm to handle the original sum rate maximization problem. Simulation results are presented to illustrate the effectiveness and superior performance of the proposed joint design against other conventional schemes in the literature.

Wireless Powered Protocol Exploiting Energy Harvesting During Cognitive Communications

In this letter, a novel wireless powered protocol is proposed to maximize the system throughput of an energy harvesting (EH) based cognitive radio network, while satisfying a minimum primary user rate requirement. For EH, we exploit both dedicated wireless power transfer from primary base station as well as ambient ones available due to wireless information transfer among primary and secondary users. Specifically, we prove convexity of the optimization problem and obtain semi-closed-form for globally optimal solution. Numerical results validate the analysis, and show an average performance improvement of 70% over benchmark scheme for various system parameters.

Analysis of Spectral Sensing Using Angle-Time Cyclostationarity.

This work presents a novel spectral sensing method for the detection of signals presenting nonlinear phase variation over time. The introduced method is based on the angle-time cyclostationarity theory, which applies transformations to the signal to be sensed in order to mitigate the effects of nonlinear phase variation. The architecture is employed for sensing binary phase shift keying (BPSK) signals, being also compared with time cyclostationarity. The obtained simulation results clearly demonstrate the efficiency of the proposed approach, while presenting improved performance in terms of the detection rate of primary users increased by about 8 dB.

Analysis of Spectrum Handoff delay using Finite Queuing model in Cognitive Radio Networks

Spectrum handoff is an inevitable phenomenon to exploit dynamic spectrum access in cognitive radio networks (CRNs) for better spectrum utilisation. When a licensed or primary user reclaims its operating channel, the cognitive user has to initiate a suitable handoff procedure for successful completion of its ongoing transmission. This paper analyses the prioritised proactive spectrum handoff decision for a finite capacity cognitive radio network using preemptive resume priority (PRP) M/G/1/K queuing model. Cumulative handoff delay (CHD) and total service time (TST) are taken to investigate the performance of the handoff strategies. We establish an analytical model of cumulative handoff delay and total service time of proactive switching spectrum handoff for the proposed finite length queuing network. The impact of queue length on performance measuring metrics such as CHD and TST in terms of arrival rate of primary users and mobility factor of spectrum holes are presented extensively.

Full-Duplex Amplify-and-Forward Relay Selection in Cooperative Cognitive Radio Networks

This paper investigates relay selection policies in a full-duplex (FD) based underlay cooperative cognitive radio network. We consider a scenario consisting of multiple pairs of primary users (PUs) with access to the spectrum, and the multiple pairs of secondary users (SUs) with no dedicated spectrum access. Each pair (PU or SU) comprises of a unique set of transmitter and receiver. The secondary transmitter (ST) supports the FD transmission with the simultaneous transmission of the information to its own receiver and reception of the designated PU signal for relaying. First, we formulate an optimization problem to maximize the PU rate by optimizing the transmission power at the relay (i.e., ST), keeping the SU rate fixed. Second, we propose two low-complexity relay selection schemes based on the sum rate maximization, namely, centralized successive and distributed prioritized matching schemes. Numerical results show that the proposed schemes perform close to the exhaustive selection schemes.

Strategic Behavior of Cognitive Radio Networks With Different Information

The equilibrium joining probabilities for a single primary user (PU) and secondary users (SUs) in the case of no queue length information, and the equilibrium Nash balance thresholds for PU and SUs to join the system in the cases of partial queue length information and full queue length information observed by PU and SUs are investigated in a cognitive radio system. PU stochastically sends PU requests and each SU is assumed to only carry one SU request. Whenever a PU (or an SU) request is completed, a reward is issued to the PU (or the SU). However, a holding cost is charged also for each SU and PU during their average sojourn time in the system respectively. Both of PU requests and SU requests are selfish in this research and their objectives are to maximize their own benefit. Our major conclusion includes a threshold probability value for PU (or SU) to join the system identified for the case of no queue length information, a Nash balance PU (SU) threshold value is determined for the case of partial queue length information; and the multi-dimensional Nash balance threshold is established for the case of full queue length information. These Nash balance threshold integer values depend on not only the Nash Balance PU-threshold value but also the specific number of SU requests in the system at that arriving time. In addition, we discuss the sensibility of input parameters to the obtained equilibrium joining probabilities and Nash balance thresholds. We mathematically verify that SUs’ equilibrium joining probability does not necessarily increase with the transmission rate of PU in no queue length information case and observe numerically that the Nash balance thresholds adopted by an arriving SU are fickle with the change of PU's transmission rate in both of partial and full queue length information cases. Our results and observations provide a guideline and important managerial insights in making decisions with strategic users in the design of CRNs.

Improved Blind Spectrum Sensing by Covariance Matrix Cholesky Decomposition and RBF-SVM Decision Classification at Low SNRs

An improved blind spectrum sensing scheme is established by the covariance matrix Cholesky decomposition and radial basis function (RBF)-support vector machine (SVM) decision classification at low signal-to-noise ratios (SNRs). Under strong background noises, the proposed scheme improves the recognition rate of primary users (PUs) than that of the current blind spectrum sensing. First, the ratio of the maximum-to-minimum eigenvalue of a covariance matrix obtained by the Cholesky decomposition is used to construct the statistics. Second, the statistics are labeled with “+1” or “−1,” namely, the energy characteristics of the training samples are extracted and marked with “+1” for PUs and “−1” for noises. Finally, an RBF-SVM classification model, with an intelligent RBF as the SVM kernel function, is obtained by training the above-mentioned statistics and the labels. Thus, the received signals are classified as PUs or not be trained in the SVM model. The threshold possesses self-learning ability, and it distinguishes PU signals from noises effectively. The classification among PU signals and noises is implemented by the optimal SVM decision boundary, derived from maximizing the margin of the decision boundary of trained samples for efficient detection. In addition, the complexity of the statistic construction is lower than that of the conventional maximum minimum eigenvalue (MME). The simulation results show that the RBF in our scheme has 77.5% accuracy at −10 dB, and it outperforms linear kernel function significantly by about 27.5% in accuracy at −10 dB. In addition, the average error probability of the proposed scheme is reduced by about 26% when compared with those of original SVM schemes at −20 dB. The proposed scheme also outperforms the current MME detection in detection probability over 10% at −20 dB. Therefore, the proposed blind spectrum sensing scheme can be efficiently used to detect the PUs by the covariance matrix Cholesky decomposition and the RBF-SVM decision classification in the fifth-generation (5G) communications, especially at low SNRs.

Optimization or Alignment: Secure Primary Transmission Assisted by Secondary Networks

Security is a challenging issue for cognitive radio (CR) to be used in future 5G mobile systems. Conventionally, interference will degrade the performance of a primary user (PU) when the spectrum is shared with secondary users (SUs). However, when properly designed, SUs can serve as friendly jammers to guarantee the secure transmission of PU. Thus, in this paper, we propose two schemes to improve the sum rate of SUs while guaranteeing the secrecy rate of PU. In the first scheme, the secondary transceivers are jointly designed to maximize their sum rate while satisfying a threshold on the PU’s secrecy rate. Due to the non-convex nature, it is first converted into a convex one and then, an alternating optimization algorithm based on the second-order cone programming is proposed to solve it. In the second scheme, the principle of interference alignment is employed to eliminate interference from PU and other SUs at each secondary receiver, and the interference from SUs is zero-forced at the primary receiver. Thus, interference-free transmission can be performed by the legitimate CR network, with eavesdropping towards PU disrupted by SUs. The key features and performances of the two proposed schemes are also compared. Finally, simulation results are presented to verify the effectiveness of the two proposed schemes for secure CR networks.

Interference alignment based on adaptive eigenmodes in a multiple-input, multiple-output cognitive radio network

This article aims to optimize the information rate of a cognitive radio network with multiple secondary users. A primary user rate optimization approach based on dichotomy of the degree of freedom is proposed, where the primary users’ eigenmodes are adjusted according to its rate requirement. In order to provide a higher sum rate of secondary users, two interference alignment schemes are presented. The first one is an interference sub-space alignment scheme, which aims to align the sub-spaces spanned by interference from other secondary users with the sub-space spanned by interference from primary user. However, interference sub-space alignment may not be favorable in low signal-to-interference ratio region due to the negligence of the influence of noise. Thus, an iterative interference alignment scheme which maximizes the secondary system sum rate based on Grassmann manifold is developed. To accelerate the convergence speed, the objective function in Grassmann manifold is transformed into two parts without the inversion operation using the extensions of the Minkowski inequality. Simulation results show that interference sub-space alignment is more effective than Grassmann manifold to mitigate interference in the system with more secondary users. We further validate the effectiveness of Grassmann manifold and interference sub-space alignment in comparison with the existing schemes employing a water filling algorithm.

Optimal Service Rate in Cognitive Radio Networks With Different Queue Length Information

Cognitive radio (CR) technology effectively overcomes spectrum inefficiency by providing the capability that unlicensed users can share the radio frequency spectrum with licensed users. In this paper, we consider a CR system with heterogeneous users. A single primary user (PU) randomly generates service requests and a licensed band processes these requests. This PU band also can be flexibly accessed by secondary users (SUs) if available. The CR system is regarded as a preemptive priority queueing system. Under different information levels, we investigate the equilibrium strategic behaviors of PU and SUs. Based on users’ strategies, we study SU’s sojourn time (i.e., the period beginning from the time an SU request enters the system and ending from the time the SU request is completed), and obtain the analytical solutions of SU’s mean sojourn time. By theoretical and numerical analysis, the SU’s mean sojourn time is found not decreasing with the service rate of PU. This phenomenon is counterintuitive, and it implies the increase of the service rate of PU does not necessarily reduce the mean sojourn time of SU. In this sense, we investigate and find optimal service rates of PU in different information levels to meet the PU’s QoS requirement and simultaneously to maximize SU’s throughput from the viewpoint of the service providers.

Strategic Behavior of Cognitive Radio Networks with Different Information

The equilibrium joining probabilities for a single primary-user (PU) and secondary-users (SUs) in the case of no-information, and the equilibrium Nash balance thresholds for PU and SUs to join the system in the cases of partial-information and full-information observed by PU and SUs are investigated in a cognitive radio system with both of a PU buffer and a SU buffer. PU stochastically sends PU requests and each SU is assumed to only carry one SU request. Whenever a PU (or an SU) request is completed, an award is issued to the PU (or the SU). However, a holding cost is charged also for each SU and PU during their average sojourn time in the system respectively. Both of PU requests and SU requests are selfish in this research and their objectives are to maximize their own benefit. Our major conclusion includes that a threshold probability value for PU (or SU) to join the PU buffer is identified for the case of no-information, a Nash balance PU (SU) threshold value of number in PU buffer (SU buffer) is determined for the case of partial-information when an arriving PU (SU) request may join in the PU (SU) buffer; and the multi-dimensional Nash balance threshold integer values of SU number in SU buffer are established for the case of full-information when an arriving SU may join in the SU buffer. These Nash balance threshold integer values depend on not only the Nash Balance PU-threshold value but also the specific number of SU requests in the SU buffer at that arriving time. In addition, we discuss the sensibility of input parameters to the obtained equilibrium joining probabilities and Nash balance thresholds. We observe that SUs' equilibrium joining probability does not necessarily increase with the transmission rate of PU in no-information case and the Nash balance thresholds adopted by an arriving SU are fickle with the change of PU's transmission rate in both of partial-case and full-information case. Our results and observations provide a guideline and important managerial insights in making decisions with strategic users in the design of CRNs.

Antenna selection and adaptive power allocation for IA‐based underlay CR

In underlay spectrum sharing cognitive radio (CR), the secondary users (SUs) share the spectrum with the primary users (PUs) in such a way that the interference caused by the SUs should be less than the interference temperature (IT) threshold. Interference Alignment (IA) is considered as a novel interference management technique for CR networks (CRN) which eliminates the interference at the PUs and also the interference among the SUs. However, most of the iterative solutions for IA aimed at removing the interference between the users, does not take into account the QoS (Quality of Service) of the PUs and SUs. Also the achievable rate of PUs by employing IA is less than that of the maximum achievable rate in a MIMO (Multiple input multiple output) system with no interference. In this paper we first present an Antenna Selection (AS) scheme to significantly improve the QoS of PUs and then we have proposed an adaptive power allocation (PA) scheme which aims at optimum PA in view of the proportional fairness among SUs. Numerical results are derived for comparing the proposed schemes in terms of outage performance of PUs and SUs. Simulation results show the effectiveness of the proposed methods in terms of SNR.

Analytic model for network resource management between ISPs and users

Fixed Communication Provider (FCP) is a consortium of Internet Service Providers (ISPs) which users can switch easily and freely between their ISPs. In order to increase the QoS of the ISPs, we propose a two class service model as the following. ISPs divide their available bandwidth into two parts to provide their end users with optimal services. One dedicated to primary users, and the other for secondary users. Primary users are those who pay more and thus, expect dedicated bandwidth that is always available. Secondary services are provided by ISPs for the other users who cannot afford the dedicated bandwidth. In this study, by defining the utility functions for both user types, we aim at dividing the ISP bandwidth between these two services such that the utility function of the users is maximized. Since the primary users do not always use the maximum bandwidth, an algorithm is proposed for ISPs to estimate the primary users required bandwidth in each time segment based on the previous segments. Based on this estimate, the expected bandwidth is dedicated to the primary users, and the remaining bandwidth is devoted to the secondary users to improve the Quality of Service (QoS). On the other hand, an ISP is penalized if it fails to provide the primary users with the required bandwidth mentioned in their contract. Therefore, there exists a trade-off between the conservative estimation of the primary users rate, QoS of the secondary users and the achieved utility of the ISPs.

Achievable Rates of UAV-Relayed Cooperative Cognitive Radio MIMO Systems

We study the achievable rate of an uplink MIMO cognitive radio system where the primary user (PU) and the secondary user (SU) aim to communicate to the closest primary base station (BS) via a multi-access channel through the same unmanned aerial vehicle (UAV) relay. The SU message is then forwarded from the primary BS to the secondary network with a certain incentive reward as a part of the cooperation protocol between both the networks. A special linear precoding scheme is proposed to enable the SU to exploit the PU free eigenmodes. We analyze two scenarios in which the UAV relay gain matrix is either fixed or optimized. We derive the optimal power allocation that maximizes the achievable rate of the SU respecting power budget, interference, and relay power constraints. Numerical results highlight the cognitive rate gain of our proposed scheme with respect to various problem parameters. We also highlight the effect of UAV altitude on the SU and PU rates. Finally, when the relay matrix is optimized, we show that the PU rate is remarkably enhanced and that the SU rate is only improved at high-power regime.

Destination-Aided Wireless Power Transfer in Energy-Limited Cognitive Relay Systems

This paper considers an energy-limited cognitive relay network where a secondary transmitter (ST) assists to forward the traffic from a primary transmitter (PT) to a primary receiver (PR), in exchange for serving its own secondary receiver (SR) in the same frequency. The multiple-antenna ST is assumed to be energy-constrained and powered by both information flow from source (PT) and dedicated energy streams from destinations (PR and SR), which is called destination-aided wireless power transfer (DWPT) scheme. Then, the relay processing matrix, cognitive beamforming vector and power splitter are jointly de- signed to maximize the rate of secondary users under the energy causality constraint and the constraint that the demanded rate of primary users is satisfied. For the perfect channel information state (CSI) case, by adopting semi-definite relax (SDR) technique and Charnes-Cooper transformation, the global optimal solution is given. To reduce the complexity, matrix decomposition, zero forcing (ZF) scheme, and dual method are jointly employed to derive a suboptimal solution. For the imperfect CSI case, S- procedure is used to transform the worst-case robust problem into a tractable semi-definite program (SDP). Simulation results reveal that our proposed DWPT scheme is greatly preferred for both perfect and imperfect CSI cases when ST is close to PR/SR.

Distributed Energy Efficient Cross-layer Optimization for Multihop MIMO Cognitive Radio Networks with Primary User Rate Protection

Due to the unique physical-layer characteristics associated with MIMO and cognitive radio (CR), the network performance is tightly coupled with mechanisms at the physical, link, network, and transport layers. In this paper, we consider an energy-efficient cross-layer optimization problem in multihop MIMO CR networks. The objective is to balance the weighted network utility and weighted power consumption of SU sessions, with a minimum PU transmission rate constraint and SU power consumption constraints. However, this problem is highly challenging due to the nonconvex PU rate constraint. We propose a solution that features linearization-based alternative optimization method and a heuristic primal recovery method. We further develop a distributed algorithm to jointly optimize covariance matrix at each transmitting SU node, bandwidth allocation at each SU link, rate control at each session source and multihop/multi-path routing. Extensive simulation results demonstrate that the performance of the proposed distributed algorithm is close to that of the centralized algorithm, and the proposed framework provides an efficient way to significantly save power consumption, while achieving the network utility very close to that achieved with full power consumption.