Outage Probability Constraint Research Articles

Outage constrained transmission design for NOMA-based integrated sensing and communication systems

This paper investigates a framework for integrated sensing and communication (ISAC) based on non-orthogonal multiple access (NOMA). In this framework, a dual-function base station (BS) utilizes NOMA technology to send superimposed signals to various users, and this superimposed signal also acts on target sensing simultaneously. Considering the channel estimation error, ensuring the communication performance, and sensing performance requirements, a transmit power optimization problem of ISAC system using NOMA is studied. Specifically, in the statistical channel state information (CSI) error model, the total system communicate power is minimized while ensuring all single users' rate outage probability (OP) constraints and the requirements for the beampattern gains of all single radar targets. Unfortunately, the proposed problem is challenging to solve and non-convex. But we have devised a feasible way to deal with this problem. First, we use Bernstein inequality to transform the rate OP constraint, and this challenging non-convex problem is then successfully solved using a method based on semi-definite relaxation (SDR). The numerical outcomes demonstrate that the system's overall transmission power will increase due to the channel estimation error. The numerical findings also show that the ISAC system performs better with NOMA assistance than with OMA when comparing the NOMA and OMA schemes.

Energy-Efficient Coordinated Beamforming in Multi-Pair MISO Networks With CDI and Eavesdroppers

This paper investigates the energy-efficient coordinated beamforming design for multi-pair multiple-input single-output (MISO) networks with passive eavesdroppers. To be practical, it is assumed that only channel distribution information (CDI) of the network is known by the transmitters/sources, and the dynamic energy consumption model (DECM) is employed. In order to achieve a green network design, an energy efficiency (EE) maximization problem is formulated subjecting to the individual available power constraints, the rate outage probability constraints, and the information leakage probability constraints. To solve the formulated non-convex problem, semidefinite relaxation (SDR) and first-order lower bound are applied to transform the problem, and then an efficient algorithm is proposed based on successive convex approximation (SCA) and Dinkelbach's approaches. The proposed algorithm is theoretically proved to converge to a stationary point of the considered problem. Further, a distributed version of the proposed algorithm is designed, with which each transmitter is able to optimize its own beamforming vector with local CDI. Moreover, the computational complexities and the signaling overheads of the two developed algorithms are analyzed and compared. Simulation results show that both algorithms achieve good EE performance, and the EE performance achieved by the distributed algorithm is very similar to that achieved by the centralized one. Additionally, it is shown that similar to the conventional scenarios without eavesdroppers, the achieved system EE also has a saturation point w.r.t. the available power of the transmitters, and by employing our proposed algorithms, the network security is significantly enhanced.

Robust Secure Transmission for Active RIS Enabled Symbiotic Radio Multicast Communications

In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.

A Robust Symbol-Level Precoding Method for Multibeam Satellite Systems

Compared to interference mitigating precoding, interference exploiting symbol-level precoding (SLP) requires less transmit power to guarantee the quality of service in a multibeam satellite system. However, due to the large roundtrip time (RTT), it is impractical to obtain real-time channel state information on the satellite side. The random channel state information (CSI) phase error of outdated CSI could cause serious performance deterioration of SLP. To compensate the CSI phase error, we propose an outdated CSI-based robust SLP (RSLP) method, which optimizes the transmit power under outage probability constraints. The central limit theorem (CLT) and second-order Taylor expansion are used to relax the outage probability constraints into convex ones. In addition, because only outdated CSI-based robust block-level precoding exists, we present two comparative RSLP methods accordingly. Without violating outrage probability constraints, the proposed RSLP method requires much lower transmit power than comparative RSLP and existing robust block-level precoding methods. The complexity of the proposed RSLP method is also lower than that of two comparative RSLP methods.

Robust Transmit Beamforming for Secure Integrated Sensing and Communication

This paper studies a downlink secure integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) transmits confidential messages to a single-antenna communication user (CU) while performing sensing on targets that may act as suspicious eavesdroppers. To ensure the quality of target sensing while preventing their potential eavesdropping, the BS combines the transmit confidential information signals with additional dedicated sensing signals, which play a dual role of artificial noise (AN) for degrading the qualities of eavesdropping channels. Under this setup, we jointly design the transmit information and sensing beamforming, with the objective of minimizing the weighted sum of beampattern matching errors and cross-correlation patterns for sensing subject to secure communication constraints. The robust design takes into account the channel state information (CSI) imperfectness of the eavesdroppers in two practical CSI error scenarios. First, we consider the scenario with bounded CSI errors of eavesdroppers, in which the worst-case secrecy rate constraint is adopted to ensure secure communication performance. In this scenario, we present the optimal solution to the worst-case secrecy rate constrained sensing beampattern optimization problem, by adopting the techniques of S-procedure, semi-definite relaxation (SDR), and a one-dimensional (1D) search, for which the tightness of the SDR is rigorously proved. Next, we consider the scenario with Gaussian CSI errors of eavesdroppers, in which the secrecy outage probability constraint is adopted. In this scenario, we present an efficient algorithm to solve the more challenging secrecy outage-constrained sensing beampattern optimization problem, by exploiting the convex restriction technique based on the Bernstein-type inequality, together with the SDR and 1D search. Finally, numerical results show that the proposed designs can properly adjust the information and sensing beams to balance the tradeoffs among communicating with CU, sensing targets, and confusing eavesdroppers, so as to achieve desirable sensing transmit beampatterns while ensuring the CU’s secrecy requirements for the two scenarios.

Robust Design of IRS-Aided Multi-Group Multicast System With Imperfect CSI

In this paper, the robust design for the intelligent reflective surface (IRS) assisted wireless multi-group multicast system is considered, in which two optimization design problems under two different channel state information (CSI) error models are separately discussed, i.e., the fairness-based problems and the quality-of-service (QoS)-based problems for both the bounded CSI error model and the statistical CSI error model. In order to deal with the non-convex constraints of the considered problems, i.e., bounded CSI error based constraint and statistical CSI error based constraint, S-procedure is adopted to convert the non-convex SINR constraint with bounded CSI error into linear matrix inequalities (LMIs), and the Bernstein-type inequality is utilized to transform the outage probability constraint with statistical CSI error into a second-order cone (SOC) constraint and linear inequalities. Following that, two efficient algorithms based on alternate optimization (AO) are proposed to solve the fairness problems and QoS problems, wherein the semi-definite programming (SDP), penalty convex-concave procedure (CCP) and semi-definite relaxation (SDR) are utilized. Furthermore, we analyze the complexity of the proposed algorithms. Finally, some numerical simulation results are presented to verify the effectiveness of the proposed algorithms, and the impacts of the CSI error and the discrete precision of IRS reflection phase shift on the system performance are analyzed, which provides some insights for the IRS deployment and system robust design.

Waveform Design and Optimization for Integrated Visible Light Positioning and Communication

In this paper, we investigate an energy efficient waveform design for integrated visible light positioning and communication (VLPC) systems by exploiting the relationship between visible light positioning (VLP) and visible light communication (VLC). We propose that the direct current component and the alternating current component of the VLPC signals are utilized for positioning and communication, respectively. With a single LED-lamp, we propose a received-signal-strength based 3D VLP scheme, and further derive the Cramer-Rao lower bound (CRLB). Then, by exploiting the inherent coupling relationship between VLP and VLC, the positioning results are utilized for channel estimation of VLC, which can significantly reduce the channel estimation pilot overhead. Furthermore, we optimize the waveform design by minimizing the CRLB, while satisfying both the outage probability of communication rate and total transmit power constraints. However, this problem turns to be non-convex and intractable. To address this challenging problem, we utilize the Conditional Value-at-Risk to conservatively transform the outage probability constraint into a deterministic form. By exploiting the block coordinate descent algorithm, the waveform design problem can be efficiently solved by alternately optimizing VLP and VLC convex sub-problems and dual problem. Finally, simulation results verify both the effectiveness and robustness of the proposed waveform design.

Physical Layer Security for RIS-Aided Wireless Communications With Uncertain Eavesdropper Distributions

In this article, the physical layer security (PLS) is investigated for reconfigurable intelligent surface (RIS)-aided wireless communication systems, where one RIS is deployed to assist the communications between a pair of transmitter (Alice) and receiver (Bob), under a passive eavesdropper (Eve) attack. For the Eve, different from the bounded channel state information uncertainty model, the distribution of the Eve’s location is introduced into the wiretap link. In the proposed system, considering that the Eve can overhear signals transmitted from Alice or reflected by the RIS, two scenarios are studied for RIS-aided secure communication systems: one is that the Eve distributes close to Alice without the RIS orientation, and the other is that the Eve locates close to Bob and in the presence of the RIS. After investigating the probability distribution functions of the Eve’s location and the wiretap link, the novel cumulative density functions (CDFs) of the received signal-to-noise ratios (SNRs) at the Eves are, respectively, derived for the two considered scenarios, taking into account the effects of RIS reflection coefficients, pathloss, and Eve’s location distribution. The closed-form expressions for the probability of the nonzero secrecy capacity and the ergodic secrecy capacity are obtained, providing insights into the impact of the Eve’s location uncertainty and the RIS design on the secrecy performance. Moreover, based on the derived CDFs for received SNRs at Eves, the secrecy outage probabilities are, respectively, analyzed. Specifically, under the constraint of the secrecy outage probability, the closed forms of the minimum required SNRs at Bob and the number of RIS elements are also obtained. Simulation and analytical results corroborate the derived expressions and reveal the tradeoff between the system’s energy efficiency and the number of RIS elements.

SNR-Outage-Based Robust Artificial Noise-Aided Beamforming for Correlated MISO Wiretap Channels Under Gaussian Channel Uncertainties

This article studies robust artificial noise-aided beamforming for correlated multiple-input-single-output eavesdropping channels under Gaussian channel uncertainties. In the case of high reception correlation, under a secrecy outage probability constraint, the achievable secrecy rate is very small and does not satisfy the requirements of some high-rate applications. In view of this, optimization designs on the premise of quality-of-service (QoS) assurance are considered. By exploiting the correlation and employing a novel QoS criterion of the signal-to-noise ratio (SNR) outage probability, two optimization problems, namely minimization of the target SNR at eavesdroppers and maximization of the loose secrecy rate, are formulated. Despite less rigorous secrecy, a much lower target SNR at eavesdroppers and a higher loose secrecy rate are achieved. Compared with the conventional SNR-QoS, SNR-outage-QoS is a more flexible criterion that can be applied to Gaussian channel uncertainties, although challenging probabilistic constraints are introduced into the optimization problems. To make the optimization problems feasible, two approaches, namely, the Bernstein-type inequality approach and ellipsoid bounding approach, are applied to approximate the probabilistic constraints. Simulation results show that the robust transmit designs contribute to a secrecy performance gain from the perspective of SNR-outage-QoS.

Optimal Channel Switching and Randomization Over Flat-Fading Channels for Outage Capacity Maximization

In this letter, the optimal channel switching and randomization problem is formulated and its solution is characterized for flat-fading Gaussian noise channels with the aim of outage capacity maximization under average power and outage probability constraints. For the single user scenario, it is proved that the optimal solution can always be realized by performing one of the following strategies: (1) Transmission over a single channel with no randomization. (2) Channel switching between two channels with no randomization. (3) Randomization between two parameter sets over a single channel. Hence, the solution can easily be obtained by considering only these three strategies. However, for the multiuser scenario, obtaining the optimal solution can have very high computational complexity. Therefore, an algorithm is proposed to calculate an approximately optimal channel switching and randomization solution (with adjustable approximation accuracy) based on the solution of a linearly constrained linear optimization problem.

Robust precoding design for inter-satellite cooperation-based low-earth orbit satellite Internet of Things

With the widespread application of the Internet of Things (IoT), a large demand for supporting massive access of IoT devices distributed over a large area urges researchers to focus on the low-earth orbit (LEO) satellite IoT (SIoT) network. Due to the high-speed movement of LEO satellites, SIoT is composed of dozens of satellites to thousands of LEO satellites covering the whole world, which inevitably causes inter-satellite interference. Hence, in this paper, we propose an inter-satellite cooperation framework for a SIoT network based on non-orthogonal multiple access(NOMA) and multiple-beam techniques, which can support massive access and reduce interference. However, the satellite cannot catch accurate channel state information(CSI), resulting in residual interference. In this context, we formulate a robust precoding optimization problem based on inter-satellite cooperation to minimize the total power consumption of the LEO SIoT network with outage probability constraints on the rates for the IoT devices. Because the original problem is intractable, we utilize some mathematical tools, e.g., second-order Taylor expression, Bernstein-type inequality, and penalty function, to obtain an approximate equivalent second-order cone program (SOCP) problem. Then, we propose a robust precoding algorithm to solve the SOCP problem. Finally, numerical results show the impacts of key system parameters and confirm the effectiveness and robustness of the proposed algorithm.

Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints

Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance.

Does D2D Communication Always Benefit Physical-Layer Security?

Device-to-device (D2D) communication is able to enhance the spectrum efficiency and coverage of wireless networks but its transmission secrecy is vulnerable since D2D terminals are usually resource/power constrained. Various existing endeavors propose physical-layer security (PLS) means to protect D2D transmissions, under the default prerequisite that D2D transmissions always improve the secrecy performance. In this article, we show that this assumption/conclusion is invalid. We investigate a so-called cellular transmission mode assisted by the base station (cellular mode) and compare its secrecy performance with that of the D2D direct transmission (D2D mode). In both modes, subject to the secrecy outage probability constraints, we optimize the transceiver parameters, such as the signal power, secrecy rates, and transmission thresholds, to maximize the secrecy throughputs. All of the optimal solutions are obtained by closed-form expressions in both modes. Numerical results show that neither of the two transmission modes is always the better but highly depends on the system parameters, such as the locations of nodes, transmit power, and secrecy requirements, and consequently provides the answer that D2D communication is not always beneficial for PLS. This immediately suggests that an adaptive switching transmission mode could achieve an enhanced secrecy performance, which is also validated by simulations.

Resource Allocation and 3D Trajectory Design for Power-Efficient IRS-Assisted UAV-NOMA Communications

In this paper, an intelligent reflecting surface (IRS) is introduced to assist an unmanned aerial vehicle (UAV) communication system based on non-orthogonal multiple access (NOMA) for serving multiple ground users. We aim to minimize the average total system energy consumption by jointly designing the resource allocation strategy, the three dimensional (3D) trajectory of the UAV, as well as the phase control at the IRS. The design is formulated as a non-convex optimization problem taking into account the maximum tolerable outage probability constraint and the individual minimum data rate requirement. To circumvent the intractability of the design problem due to the altitude-dependent Rician fading in UAV-to-user links, we adopt the deep neural network (DNN) approach to accurately approximate the corresponding effective channel gains, which facilitates the development of a low-complexity suboptimal iterative algorithm via dividing the formulated problem into two subproblems and address them alternatingly. Numerical results demonstrate that the proposed algorithm can converge to an effective solution within a small number of iterations and illustrate some interesting insights: (1) IRS enables a highly flexible UAV’s 3D trajectory design via recycling the dissipated radio signal for improving the achievable system data rate and reducing the flight power consumption of the UAV; (2) IRS provides a rich array gain through passive beamforming in the reflection link, which can substantially reduce the required communication power for guaranteeing the required quality-of-service (QoS); (3) Optimizing the altitude of UAV’s trajectory can effectively exploit the outage-guaranteed effective channel gain to save the total required communication power enabling power-efficient UAV communications; (4) NOMA communications offer higher degrees of freedom (DoF) than that of the conventional orthogonal multiple access (OMA) scheme to minimize the average power consumption via optimizing the UAV’s trajectory.

Robust transmission design for artificial noise aided multiuser broadcast system in inaccurate channel state information scenario with users of different importance

In this article, robust beamforming (BF) and artificial noise (AN) design method is proposed for multiuser broadcast secure transmission under secrecy outage probability (SOP) constraints. A special scenario is assumed that the transmitter cannot obtain the accurate channel state information (CSI) of the users' channel, and different users have different priorities and importance. This special scenario makes traditional BF algorithms and AN design methods unable to guarantee the effective and secure transmission of information. To deal with intractable non-convex problems in this new design, a series of optimization methods like Bernstein-type inequality (BTI) and S-Procedure are employed to transform these problems into solvable functions. Moreover, a search algorithm is explored for simplifying the computational complexity of the optimization algorithm. After that, the new robust algorithm is compared with former methods in inaccurate CSI scenarios by numerical simulation, which shows the advantages of our algorithm.

Secure Information Transmission for B5G HetNets: A Robust Game Approach

This article investigates the robust secure transmission problem in two-tier B5G heterogeneous networks with multiple noncollusive eavesdroppers and users, where two types of imperfect channel state information (CSI) scenarios, i.e., instantaneous and statistic CSI scenarios, are considered. Given the two-sidedness of co-channel interference in physical-layer security and the selfishness of femtocell base stations (FBSs), an imperfect-CSI-based noncooperative game framework is proposed to maximize the profits of the macro base station (MBS) and FBSs, while guaranteeing user’s Quality-of-Service (QoS) requirement in terms of outage probability. Specifically, based on the involved two CSI scenarios, the original game where the MBS and FBSs act as players is elaborated as two robust game problems. To address channel uncertainties in the objective function, the worst-case and the mean value of the channel gains are used separately. Besides, the remaining channel uncertainties embodied in the intractable outage probability constraints are treated in a unified way, i.e., the extended Bernstein approximation. The existence and uniqueness of the Nash equilibrium (NE) are analyzed, and the sufficient condition on the uniqueness of the NE is derived. Then, two robust iterative algorithms are given to approach the robust game equilibrium. Finally, numerical results are presented to verify the theoretical analysis and show the robustness and effectiveness of the proposed algorithms.

Optimal Power Allocation for Integrated Visible Light Positioning and Communication System With a Single LED-Lamp

In this paper, we investigate an integrated visible light positioning and communication (VLPC) system with a single LED-lamp. First, by leveraging the fact that the VLC channel model is a function of the receiver’s location, we propose a system model that estimates the channel state information (CSI) based on the positioning information without transmitting pilot sequences. Second, we derive the Cramer-Rao lower bound (CRLB) on the positioning error variance and a lower bound on the achievable rate with on-off keying modulation. Third, based on the derived performance metrics, we optimize the power allocation to minimize the CRLB, while satisfying the rate outage probability constraint. To tackle this non-convex optimization problem, we apply the worst-case distribution of the Conditional Value-at-Risk (CVaR) and the block coordinate descent (BCD) methods to obtain the feasible solutions. Finally, the effects of critical system parameters, such as outage probability, rate threshold, total power threshold, are revealed by numerical results.

Robust IRS-Aided Secrecy Transmission With Location Optimization

In this paper, we propose a robust secrecy transmission scheme for intelligent reflecting surface (IRS) aided communication systems. Different from all the existing works where IRS has already been deployed at a fixed location, we take the location of IRS as a variable to maximize the secrecy rate (SR) under the outage probability constraint by jointly optimizing the location of IRS, transmit beamformer and IRS phase shifts with imperfect channel state information (CSI) of Eve, where we consider two cases: a) the location of Eve is known; b) only a suspicious area of Eve is available. We show a critical observation that CSI models are different before and after IRS deployment, thus the optimization problem could be decomposed and solved via a two-stage framework. For case a), in the first stage, universal upper bounds of outage probabilities only related to the location of IRS are derived which can be optimized via successive convex approximation (SCA) method. In the second stage, we develop an alternative optimization (AO) algorithm to optimize beamformer and phase shifts iteratively. For case b), we propose a Max-Min SR scheme based on two-stage framework, where the location of IRS is optimized based on the worst location of Eve. Simulation results indicate the importance of the location of IRS optimization.

Energy-Efficient NOMA Multicasting System for Beyond 5G Cellular V2X Communications With Imperfect CSI

The integration of non-orthogonal multiple access (NOMA) in vehicle-to-everything (V2X) communications has recently shown great potential to improve traffic efficiency, control, and reliability of beyond 5G transportation systems. In V2X communications, it is vital to inspect imperfect channel state information (CSI) because the high mobility of vehicles leads to more channel estimation uncertainties. This paper proposes an energy-efficient power allocation scheme for the road-side unit (RSU) assisted NOMA multicasting in beyond 5G cellular V2X networks. In particular, the energy efficiency maximization problem is investigated under the outage probability of vehicles under imperfect CSI, quality of services (QoS), and power limit constraints. Since the problem is non-convex and difficult to solve directly, we first convert outage probability constraint to non-probabilistic constraint through approximation and adopt a low complexity gradient assisted binary search (GABS) method to obtain the efficient power allocation at RSUs. Then, a successive convex approximation (SCA) technique is exploited to transform the power allocation problem of vehicles associated with each RSU into a tractable concave-convex fractional programming (CCFP) problem. The optimal solution to the CCFP problem is achieved through Dinkelbach and the dual decomposition method. The global optimal power allocation through the GABS-Exhaustive scheme act as a benchmark, which has considerable computational complexity. Simulation results unveil that the proposed suboptimal scheme (GABS-Dinkelbach) can achieve near-optimal performance with very low complexity.

Completion time minimization for multi-antenna UAV-enabled data collection in uncorrelated Rician fading

In 5G, the unmanned aerial vehicle (UAV) is regarded as an essential way to support energy-saving, reliable, and low-cost data collection in wireless sensor networks. However, UAV-based data collection inevitably suffers from more significant data collection delays due to the UAV movement time in large application scenarios. In this paper, we consider a UAV-enabled wireless sensor network under the uncorrelated Rician fading channel, where a multi-antenna UAV is dispatched to collect data from ground sensor nodes (SNs). The objective is to minimize the mission completion time, including UAV flight and data collection time, by jointly optimizing UAV trajectory, UAV-SN transmission scheduling, and trajectory-based time allocation under the constraints of UAV speed, outage probability, data size, and transmission power. To solve this nonconvex problem, we decompose it into two subproblems: 1) trajectory optimization and 2) UAV-SN transmission scheduling and trajectory-based time allocation optimization. In the trajectory optimization, we first rewrite the outage probability constraint into the communication distance constraint and propose a Spiral-based method to find the shortest trajectory covering all SNs. Then, we propose a scheduling initialization algorithm and employ alternating optimization to solve the UAV-SN transmission scheduling and trajectory-based time allocation optimization. Finally, numerical results show that the proposed algorithm outperforms the benchmark and typical algorithms in terms of the mission completion time through a large number of experiments.