Interference Temperature Constraints Research Articles

Resource Allocation for NOMA-Enabled Cognitive Satellite–UAV–Terrestrial Networks With Imperfect CSI

Recently, non-orthogonal multiple access (NOMA)-enabled cognitive satellite-unmanned aerial vehicle (UAV)-terrestrial networks have attracted extensive attention for the advantages of enhancing spectrum efficiency and coping with the exponential growth of access users. In this paper, we propose a joint subchannel assignment and power allocation algorithm for NOMA-enabled cognitive satellite-UAV-terrestrial networks to further enhance the transmission performance, where imperfect channel state information is taken into consideration. Specifically, we formulate a mixed integer non-linear programming resource allocation problem to optimize the sum rate of the secondary network, in which the demands of the interference temperature constraint for primary users, the minimum transmission rate of each secondary user, the maximal transmitter power of the UAV, and the maximal number of secondary users that each subchannel can serve are satisfied. To tackle this tough non-convex problem, we first decouple it into two subproblems, namely, subchannel assignment and power allocation. Next, a heuristic subchannel assignment algorithm and a Taylor series and successive convex approximation-based power allocation algorithm are designed to solve the above subproblems, respectively. By alternately optimizing the sum rate of the secondary network, we finally solve the mixed integer non-linear programming optimization problem. Numerical results reveal the impacts of key parameters on system performance and indicate that our proposed scheme outperforms benchmarks in large-scale networks.

Physical Layer Security Performance Analysis of IRS-Aided Cognitive Radio Networks

Cognitive radio (CR) acts as a significant player in enhancing the spectral efficiency (SE) of wireless telecommunications; simultaneously, the intelligent reflecting surface (IRS) technique is a valid technique for increasing the confidentiality properties of wireless telecommunications systems through the modulation of the amplitude and phase shift of the channel. Therefore, we take into consideration an IRS-assisted multiple-input single-output (MISO) CR system to raise the confidentiality rate, which is composed of a primary network with a primary receiver (PR) and an eavesdropping link, as well as a secondary network with a secondary receiver (SR) and SR transmitter (SR-TX). In particular, we minimize the SR’s transmit power under the interference temperature (IT) and confidentiality capacity constraints via the joint optimization of the beamforming vector and artificial noise (AN) constraint matrix at SR-TX together with the phase shift matrix of IRS. Numerical outcomes indicate that various transmit antenna values and the IRS element numbers at SR-TX can greatly reduce transmit power while assuring secure communication.

Intelligent Reflecting Surfaces for Sum-Rate Maximization in Cognitive Radio Enabled Wireless Powered Communication Network

In this paper, we study a cognitive radio (CR)-enabled wireless powered communication network (WPCN) with an intelligent reflecting surface (IRS). A cognitive wireless powered communication network (CWPCN) consisting of one secondary wireless powered communication network, in which a hybrid access point broadcasts wireless energy to multiple users on downlink and receives information signals on uplink, shares the same spectrum with the existing primary wireless communication network. In this paper, we consider an underlay CWPCN in which the secondary network is regulated by a given interference temperature constraint (ITC) such that interference with the primary network is kept no greater than a predefined threshold. To enhance the performance of the CWPCN, we consider an IRS-assisted network in which multiple passive reflection elements are configured to reflect the signals in any desired phase/direction. Our main goal is to maximize the sum throughput of secondary users while managing the interference constraint. For this, we jointly optimize the uplink and downlink phase shift matrices of the IRS elements with optimal time slots for wireless energy transfer (WET) on downlink and wireless information transfer on uplink. In finding the optimal solution, the formulated optimization problem is non-convex, complex, and intractable. In this paper, we propose an alternating optimization (AO)-based solution with a successive convex approximation (SCA) technique. We show through simulation results that the proposed IRS-assisted network provides a significant enhancement in performance over the conventional CWPCN.

Spectrum Sharing in Cognitive-Radio-Inspired NOMA Systems Under Imperfect SIC and Cochannel Interference

In this article, we investigate the performance of spectrum sharing inunderlay cognitive radio (CR)-inspired nonorthogonal multiple access (NOMA) systems, at which a primary transmitter with a weak channel shares its spectrum with a secondary transmitter (ST) with a strong channel to promote the network spectrum efficiency. In this article, we derive closed-form expressions of the outage probabilities (OPs), in terms of elementary functions, and the ergodic capacities (ECs) of the two transmissions, in terms of exponential integral function, assuming imperfect successive interference cancellation (SIC) and cochannel interference. Moreover, the feasible range for the power-allocation factor to achieve a NOMA instantaneous data-rate gain for both nodes is investigated. The results reveal the degradation effects of imperfect SIC on the OP and EC of the ST. Additionally, the simulation shows a better sum EC of the proposed NOMA scheme compared with two reference schemes, namely the orthogonal multiple access and the underlay CR with interference temperature constraint scheme. Finally, the accuracy of the analytical results is verified with representative numerical simulations.

Development of a Mathematical Model for Multi-user Coded-Cooperation Based Cognitive Radio System and Its Outage Probability Analysis

In this paper, mathematical system model for a single and multi-user coded-cooperation-based cognitive radio system is developed. In this model, both source and relay are communicating to a single destination with the help of each other. Further, all possible multi-user scenarios are developed and their end-to-end outage probability (Pout) is calculated for the underlay mode of cognitive radio. The performance of the system is analyzed in the form of channel gain and interference temperature constraint for the Rayleigh fading channel. The proposed system concludes that the coded cooperation with cognitive radio outperforms the available techniques in the form of bandwidth, diversity, spectrum utilization efficiency and thus improving the quality of communication. Furthermore, the theoretical analysis of the outage probability for both system models is validated by asymptotic analysis. The proposed system can be set as a standard for all those cognitive radio applications which require better spectrum efficiency even if there is a scarcity of multiple physical antennas.

Joint 3D trajectory and resource optimization for a UAV relay-assisted cognitive radio network

In this paper, we consider a new spectrum sharing scenario for a cognitive relay network, where a secondary unmanned aerial vehicle (UAV) relay receives information from the ground secondary base station (SBS) and transmits information to the ground secondary user (SU), coexisting with the primary users (PUs) at the same wireless frequency band. We investigate the optimization of the UAV relay's three-dimensional (3D) trajectory to improve the communication throughput performance of the secondary network subject to the interference constraints of the PUs. The information throughput maximization problem is studied by jointly optimizing the UAV relay's 3D trajectory and the transmit power of the SBS and the UAV, subject to the constraints on the velocity and elevation of the UAV relay, the maximum and average transmit power, and the information causality, as well as a set of interference temperature (IT) constraints. An efficient algorithm is proposed to solve the admittedly challenging non-convex problem by using the path discretization technique, the successive convex approximation technique and the alternating optimization method. Finally, simulation results are provided to show that our proposed design outperforms other benchmark schemes in terms of the throughput.

Performance of NOMA-Enabled Cognitive Satellite-Terrestrial Networks With Non-Ideal System Limitations

Satellite-terrestrial networks (STNs) have received significant attention from research and industry due to their capability of providing a stable connection to rural and distant areas, where the allocation of terrestrial infrastructures is uneconomical or difficult. Moreover, the STNs are considered as a promising enabler of fifth-generation communication networks. However, expected massive connectivity in future communication networks will face issues associated with spectrum scarcity. In this regard, the integration of cognitive radio and non-orthogonal multiple access (NOMA) techniques into STNs is considered as a promising remedy. Thereafter, in this article, we investigate NOMA-assisted cognitive STN under practical system conditions, such as transceiver hardware impairments, channel state information mismatch, imperfect successive interference cancellation, and interference noises. Generalized coverage probability formulas for NOMA users in both primary and secondary networks are derived considering the impact of interference temperature constraint and its correctness is verified through Monte Carlo simulation. Furthermore, to achieve performance fairness among the users, power allocation factors based on coverage fairness for primary and secondary NOMA users are provided. Moreover, the numerical results demonstrate superior performance compared to the ones obtained from an orthogonal multiple access scheme and examine the imperfection's impact on the system performance in terms of coverage and throughput.

Cooperative Interference Management for Over-the-Air Computation Networks

Recently, over-the-air computation (AirComp) has emerged as an efficient solution for access points (APs) to aggregate distributed data from many edge devices (e.g., sensors) by exploiting the waveform superposition property of multiple access (uplink) channels. While prior work focuses on the single-cell setting where inter-cell interference is absent, this article considers a multi-cell AirComp network limited by such interference and investigates the optimal policies for controlling devices' transmit power to minimize the mean squared errors (MSEs) in aggregated signals received at different APs. First, we consider the scenario of centralized multi-cell power control. To quantify the fundamental AirComp performance tradeoff among different cells, we characterize the Pareto boundary of the multi-cell MSE region by minimizing the sum MSE subject to a set of constraints on individual MSEs. Though the sum-MSE minimization problem is non-convex and its direct solution intractable, we show that this problem can be optimally solved via equivalently solving a sequence of convex second-order cone program (SOCP) feasibility problems together with a bisection search. This results in an efficient algorithm for computing the optimal centralized multi-cell power control, which optimally balances the interference-and-noise-induced errors and the signal misalignment errors unique for AirComp. Next, we consider the other scenario of distributed power control, e.g., when there lacks a centralized controller. In this scenario, we introduce a set of interference temperature (IT) constraints, each of which constrains the maximum total inter-cell interference power between a specific pair of cells. Accordingly, each AP only needs to individually control the power of its associated devices for single-cell MSE minimization, but subject to a set of IT constraints on their interference to neighboring cells. By optimizing the IT levels, the distributed power control is shown to provide an alternative method for characterizing the same multi-cell MSE Pareto boundary as the centralized counterpart. Building on this result, we further propose an efficient algorithm for different APs to cooperate in iteratively updating the IT levels to achieve a Pareto-optimal MSE tuple, by pairwise information exchange. Last, simulation results demonstrate that cooperative power control using the proposed algorithms can substantially reduce the sum MSE of AirComp networks compared with the conventional single-cell approaches.

Hybrid Analog-Digital Precoder Design for Securing Cognitive Millimeter Wave Networks

Millimeter wave (mmWave) communications and cognitive radio technologies constitute key technologies of improving the spectral efficiency of communications. Hence, we conceive a hybrid secure precoder for enhancing the physical layer security of a cognitive mmWave wiretap channel, where a secondary transmitter broadcasts confidential information signals to multiple secondary users under the interference temperature constraint of the primary user (PU). The optimization problem is formulated as jointly optimizing the analog and digital precoder for maximizing the minimum secrecy rate of all the secondary users under practical constraints. In particular, our design satisfies the constraint on the maximum interference power received by multiple PUs, as well as the secondary users’ minimum quality-of-service (Qos), and the unit-modulus constraint on the analog precoder. Due to the non-convexity of the resultant objective function and owing to the coupling between the analog and digital precoder, the optimization problem formulated is nonconvex and nonlinear, hence it is very challenging to solve directly. Hence, we first transform it into a tractable form, and develop a penalty dual decomposition (PDD) based iterative algorithm to locate its Karush-Kuhn-Tucker (KKT) solution. Finally, we generalize the proposed PDD algorithm to a secure hybrid precoder design relying on practical finite-resolution phase shifters and show that the proposed PDD algorithm can be straightforwardly adapted to handle the scenario, where each PU is equipped with multiple antennas and the CSI of multiple eavesdroppers (Eves) is imperfectly known. Our simulation results validate the efficiency of the proposed iterative algorithm.

Cell-Free Satellite-UAV Networks for 6G Wide-Area Internet of Things

In fifth generation (5G) and beyond Internet of Things (IoT), it becomes increasingly important to serve a massive number of IoT devices outside the coverage of terrestrial cellular networks. Due to their own limitations, unmanned aerial vehicles (UAVs) and satellites need to coordinate with each other in the coverage holes of 5G, leading to a cognitive satellite-UAV network (CSUN). In this paper, we investigate multi-domain resource allocation for CSUNs consisting of a satellite and a swarm of UAVs, so as to improve the efficiency of massive access in wide areas. Particularly, the cell-free on-demand coverage is established to overcome the cost-ineffectiveness of conventional cellular architecture. Opportunistic spectrum sharing is also implemented to cope with the spectrum scarcity problem. To this end, a process-oriented optimization framework is proposed for jointly allocating subchannels, transmit power and hovering times, which considers the whole flight process of UAVs and uses only the slowly-varying large-scale channel state information (CSI). Under the on-board energy constraints of UAVs and interference temperature constraints from UAV swarm to satellite users, we present iterative multi-domain resource allocation algorithms to improve network efficiency with guaranteed user fairness. Simulation results demonstrate the superiority of the proposed algorithms. Moreover, the adaptive cell-free coverage pattern is observed, which implies a promising way to efficiently serve wide-area IoT devices in the upcoming sixth generation (6G) era.

Intelligent Reflecting Surface-Assisted Cognitive Radio System

Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs). Meanwhile, intelligent reflecting surface (IRS), also known as reconfigurable intelligent surface (RIS), has been recently proposed as a promising approach to enhance energy efficiency (EE) of wireless communication systems through intelligently reconfiguring the channel environment. To improve both SE and EE, in this paper, we introduce multiple IRSs to a downlink multiple-input single-output (MISO) CR system, in which a single SU coexists with a primary network with multiple PU receivers (PU-RXs). Our design objective is to maximize the achievable rate of SU subject to a total transmit power constraint on the SU transmitter (SU-TX) and interference temperature constraints on the PU-RXs, by jointly optimizing the beamforming at SU-TX and the reflecting coefficients at each IRS. Both perfect and imperfect channel state information (CSI) cases are considered in the optimization. Numerical results demonstrate that IRS can significantly improve the achievable rate of SU under both perfect and imperfect CSI cases.

Intelligent Reflecting Surface Aided MIMO Cognitive Radio Systems

In cognitive radio (CR) systems, the spectrum efficiency (SE) of the secondary users (SUs) is always limited by the interference temperature constraint imposed on the primary users (PUs). Intelligent reflecting surface (IRS) has been recently proposed as a revolutionary technique which can help to enhance the SE of wireless communications. In this paper, we propose to employ an IRS to assist the SUs’ data transmission in the multiple-input multiple-output (MIMO) CR system. By jointly optimizing the transmit precoding (TPC) of the SU transmitter (ST) and the phase shifts of the IRS, we aim to maximize the achievable weighted sum rate (WSR) of SUs subject to the ST's total power, the PU's interference temperature and unit modulus constraints. To solve this complicated optimization problem in which the variables are coupled, the block coordinate descent (BCD) algorithm is introduced to alternately solve the subproblems. For each subproblem, the Lagrange dual or inner approximation method is adopted with a low complexity. Simulation results confirm the benefits of employing IRS in a MIMO CR system. The performance comparisons of the proposed algorithm with several other benchmarks are carried out by evaluating the impacts of various parameters on the WSR.

Robust Energy-Efficient Maximization for Cognitive NOMA Networks Under Channel Uncertainties

Resource allocation (RA) is a key technique to guarantee the quality of service of users and maximize the system capacity in cognitive nonorthogonal multiple access (NOMA) networks. However, traditional RA approaches have been proposed under perfect channel state information (CSI) which is too ideal in practical systems due to the impact of link delays, quantization errors, etc. In this article, with imperfect CSI, a downlink robust RA algorithm is proposed for robust transmission to maximize the sum energy efficiency (EE) of secondary users (SUs) under channel uncertainties. Meanwhile, it protects the minimum data rates of SUs, satisfying the maximum transmission power constraints of base stations and the maximum interference temperature constraint of each primary user (PU). The formulated problem is nonconvex, thus challenging to solve. For delay-tolerant services, to deal with the intractability of the problem caused by outage probability constraints, the closed-form expressions of outage probabilities of SUs and PUs are derived under Gaussian CSI error models. For delay-sensitive services, the robust constraints with bounded uncertainty sets are transformed into convex ones. Based on successive convex approximation and the parameter transformation approach, the original problem is converted into a closed-form geometric programming problem solved by dual decomposition methods and subgradient methods. Additionally, users’ outage probabilities and the minimum required transmit power under the two modeling approaches are analyzed. Computational complexity and sensitivity analysis are provided. The simulation results show the proposed algorithm serves good robustness and EE.

Throughput Maximization With Energy Harvesting in UAV-Assisted Cognitive Mobile Relay Networks

In order to extend the communication coverage and improve system performance, the applications of unmanned aerial vehicles (UAVs) in wireless communications have attracted a lot of attention in the industry. In this paper, we propose a power control algorithm in energy harvesting (EH)-based cognitive mobile relay networks where an UAV is equipped with a decode-and-forward (DF) relay to cooperate the communication of secondary user (SU). Assuming that the only power source for SU transmitter with EH is a battery with infinite capacity, we solve a throughput maximization problem to optimize the transmit powers of SU and the mobile relay, subject to the causality constraint of energy usage at SU transmitter, the maximum transmit power constraint of the mobile relay, and the interference temperature (IT) constraint to protect the communication of primary user (PU). When formulating this throughput maximization problem, we adopt an offline scheme with deterministic settings. For simplicity, the original multi-variable optimization problem is transformed into a single variable optimization problem via the optimal throughput principle of the DF relaying communication system. Furthermore, we solve this new optimization problem via the Lagrange dual method, and we derive the closed-form expressions of the optimal solutions. The simulation results illustrate the optimized system performance that the optimal throughput of the secondary system can be achieved by the proposed dynamic power control algorithm.

Wireless legitimate surveillance via jamming in MISO cognitive radio networks

In this paper, we study proactive eavesdropping over multiple-input signal-output (MISO) cognitive radio networks, where a legitimate monitor tries to eavesdrop on the suspicious secondary link via jamming. Different from the existing work on jamming-aided proactive eavesdropping, our jamming signals here can change the suspicious secondary transmitter’s beamforming strategy in favor of the legitimate monitor’s eavesdropping. To be specific, we are interested in maximizing the achievable eavesdropping rate by optimizing the jamming beamforming vector, subject to the interference temperature constraint at the primary receiver and the maximum transmit power constraint at the monitor. The original jamming beamforming problem is non-convex and thus difficult to solve optimally. To gain more useful insights, we first consider the case where the suspicious secondary transmitter has a single-antenna, and the corresponding optimal jamming beamforming solution is derived in closed-form. We then extend our study to the general case where the secondary transmitter has multiple antennas. For this case, we propose an efficient algorithm to deal with the corresponding jamming beamforming design problem. The proposed solution reveals that the optimal beamforming vector balances the interference to the suspicious secondary receiver and the primary receiver. Finally, numerical results show that the proposed optimal scheme significantly improves the eavesdropping rate as compared to the benchmark schemes.

An Analysis on Secure Millimeter Wave NOMA Communications in Cognitive Radio Networks

In this paper, an easy analysis framework is developed to evaluate the reliability and security of the secondary receivers (SU-Rxs) in millimeter wave (mmWave) non-orthogonal multiple access (NOMA) aided cognitive radio (CR) networks, where secondary transmitters (SU-Txs) transmit confidential messages to SU-Rxs belonging to different regions under the interference temperature constraint of the primary receiver (PU-Rx). Considering that randomly located eavesdroppers and primary user may be in the signal beam emitted by the SU-Txs, information leakage and interference to the primary network will be inevitable issues. Motivated by these challenges, combining the key features of multi-cell mmWave networks and NOMA communications, the closed-form expressions of three key performance metrics of connection outage probability (COP), secrecy outage probability (SOP), and secrecy throughput (ST) are derived by using the sector eavesdropper-exclusion zone, and the reliability and secrecy performance of the secondary networks are measured by different system parameters. The obtained performance evaluation results have demonstrated that the interference temperature constraint of the PU-Rx can be used to balance the security and reliability of the secondary networks. The reliability and security of paired NOMA users can be improved by using reasonable power allocation factor and sector eavesdropper-exclusion zone. Meanwhile, the connection performance of paired NOMA users is better than orthogonal multiple access (OMA). Furthermore, various system parameters can be reasonably set in conjunction with blockage and inter-cell interference to achieve optimal network’s performance.

Hardware- and Interference-Limited Cognitive IoT Relaying NOMA Networks With Imperfect SIC Over Generalized Non-Homogeneous Fading Channels

Internet-of-Things (IoT) technology has received much attention due to its great potential to interconnect billions of devices in a broad range of applications. IoT networks can provide high-quality services for a large number of users and smart objects. On the other hand, massive connectivity in IoT networks brings problems associated with spectral congestion. This issue can be solved by applying cognitive radio (CR) and non-orthogonal multiple access (NOMA) techniques. In this respect, this paper studies the performance of cooperative CR-NOMA enabled IoT networks over a generalized α - μ fading channel model. Closed-form analytical expressions of the end-to-end outage probability (OP) for the secondary NOMA users are derived using the Meijer's G-function with a consideration of the impacts of the interference temperature constraint, primary interference, residual hardware impairments and imperfect successive interference cancellation. Moreover, to acquire some useful insights on the system performance, asymptotic closed-form OP expressions are provided. Additionally, the impact of α and μ fading parameters on the outage performance is examined and, as a result, it is concluded that the system performance sufficiently improves as α and/or α increase. Furthermore, the outage performance of the proposed system model is shown to outperform that of an identical IoT network operating on orthogonal multiple access. Finally, the provided closed-form OP expressions are validated with Monte Carlo simulations.

Maximally Improper Signaling in Underlay MIMO Cognitive Radio Networks

Improper Gaussian signaling is a well-known technique that has been shown to improve performance in different multi-user scenarios. In this paper, we analyze the benefit of improper signaling in underlay cognitive radio when users are equipped with multiple antennas. Specifically, we assume that the primary user is protected by the so-called interference temperature constraint, which guarantees a prescribed rate requirement. In this setting, we study how the maximum tolerable interference power changes when the interference is additionally constrained to be maximally improper (strictly noncircular, or rectilinear). We observe that the correlation structure of a maximally improper interference is an additional degree of freedom that can be exploited to improve the SU performance. Because of that, we propose two different protection strategies for the PU where this structure is either constrained or unconstrained, and derive the interference temperature threshold for both cases. We then focus on the secondary user and provide designs of the transmission parameters under the proposed protection strategies.

Robust proactive eavesdropping in UAV-enabled wireless communication networking

In this paper, we considered a robust beamforming design problem for a proactive eavesdropping via jamming, in which a full-duplex legitimate monitor tries to eavesdrop on a suspicious communication link between the secondary pairs in an unmanned aerial vehicle (UAV)-enabled cognitive radio network. Due to the channel estimation errors, we assume that the channel state information is not perfectly known. To ensure the successful eavesdropping, jamming signals are designed to disrupt the suspicious receivers and the primary receiver at the same time, which should have the good tradeoff between those two effects. We aimed to maximize the achievable eavesdropping rate under the transmitting power constraint at the legitimate monitor and the interference temperature constraint at the primary receiver, which was formulated as a non-convex problem with infinite constraints. We firstly transform the original problem into a simplified one with finite constraints. Then, an analytical solution in significant low-complexity is proposed by decomposing the simplified problem. Numerical results are finally presented to evaluate the performance of our proposed schemes in UAV-enabled wireless communication networking.

Cognitive UAV Communication via Joint Maneuver and Power Control

This paper investigates a new scenario of spectrum sharing between unmanned aerial vehicle (UAV) and terrestrial wireless communication, in which a cognitive/secondary UAV transmitter communicates with a ground secondary receiver (SR), in the presence of a number of primary terrestrial communication links that operate over the same frequency band. We exploit the UAV's mobility in three-dimensional (3D) space to improve its cognitive communication performance while controlling the co-channel interference at the primary receivers (PRs), such that the received interference power at each PR is below a prescribed threshold termed as interference temperature (IT). First, we consider the quasi-stationary UAV scenario, where the UAV is placed at a static location during each communication period of interest. In this case, we jointly optimize the UAV's 3D placement and power control to maximize the SR's achievable rate, subject to the UAV's altitude and transmit power constraints, as well as a set of IT constraints at the PRs to protect their communications. Next, we consider the mobile UAV scenario, in which the UAV is dispatched to fly from an initial location to a final location within a given task period. We propose an efficient algorithm to maximize the SR's average achievable rate over this period by jointly optimizing the UAV's 3D trajectory and power control, subject to the additional constraints on UAV's maximum flying speed and initial/final locations. Finally, numerical results are provided to evaluate the performance of the proposed designs for different scenarios, as compared to various benchmark schemes. It is shown that in the quasi-stationary scenario the UAV should be placed at its minimum altitude while in the mobile scenario the UAV should adjust its altitude along with horizontal trajectory, so as to maximize the SR's achievable rate in both scenarios.