Transmission Outage Probability Research Articles

Free-Space Optical (FSO) Satellite Networks Performance Analysis: Transmission Power, Latency, and Outage Probability

Free-Space Optical (FSO) Satellite Networks Performance Analysis: Transmission Power, Latency, and Outage Probability

Covert Rate Study for Full-Duplex D2D Communications Underlaid Cellular Networks

Device-to-device (D2D) communications underlaid cellular networks have emerged as a promising network architecture to provide extended coverage and high data rate for various Internet of Things (IoT) applications. However, because of the inherent openness and broadcasting nature of wireless communications, such networks face severe risks of data privacy disclosure. This paper investigates the covert communications in such networks for providing enhanced privacy protection. Specifically, this paper explores the critical covert rate performance in a full-duplex D2D communication underlaid cellular network consisting of a base station, a cellular user, a D2D pair with a transmitter and a full-duplex receiver, and a warden, where the D2D receiver can operate over either the full-duplex (FD) mode or the half-duplex (HD) mode. We first derive transmission outage probabilities of cellular and D2D links under the FD and HD modes, respectively. Based on these probabilities, we further provide theoretical modelling for the covert rate under each mode and explore the corresponding covert rate maximization by jointly optimizing the transmit powers of the D2D pair and the cellular user. To improve the covert rate performance, we propose a general mode in which the D2D receiver can flexibly switch between these two modes. Under the general mode, we also investigate the theoretical modelling and maximization problems of covert rate. Finally, we present extensive numerical results to illustrate the covert rate performances under the FD, HD, and general modes.

Energy-efficient power control strategy of the delay tolerable service based on the reinforcement learning

In recent years, the rapid development of Internet technology and its applications has led to an exponential growth in the number of Internet users and wireless terminal devices, resulting in a corresponding increase in energy consumption. This has necessitated the need to reduce energy consumption while maintaining the quality of communication services. To this end, we investigate the possibility of improving energy efficiency (EE) of delay tolerable (DT) services by allocating resources based on the time-domain water-filling algorithm. We first transform the non-convex problem of maximizing EE into a convex problem of minimizing transmission power to obtain the optimal solution, and then use the greedy algorithm to obtain an upper bound. Furthermore, to capture a more realistic scenario, an Approximate Statistical Dynamic Programming (ASDP) algorithm is introduced, but its effect on enhancing EE is limited. To overcome this limitation, three Deep Reinforcement Learning (DRL) algorithms are implemented. The simulations results show that the settings of maximum transmit power and SNR during agent training have an impact on the performance of the agent. Finally, by comparing the mean values of transmission power, outage probability, equilibrium power and performance improvement percentage of several algorithms, we conclude that the Deep Deterministic Policy Gradient (DDPG) algorithm produces the best agent performance in the environment with a fixed SNR of 2 (dB).

Joint selection of FD/HD and AF/DF for covert communication in two-hop relay systems

This paper focuses on the covert communication in a two-hop relay system consisting of a transmitter, a relay, a receiver and a warden, where the relay can flexibly select between the transmission modes of full-duplex (FD)/half-duplex (HD) as well as between the forwarding modes of amplify-and-forward (AF)/decode-and-forward (DF). For a given combination of transmission mode and forwarding mode, we first provide theoretical modeling for the covert outage probability, transmission outage probability and covert rate. Under such a combination, we then explore the optimal designs of the transmitter transmission rate and the relay transmit power for covert rate maximization, subjecting to the constraints of covert outage probability and upper bound on relay transmit power. Finally, we provide extensive numerical results to illustrate the performance comparison among different combinations of transmission mode and forwarding mode, and how to jointly select a proper combination of transmission mode and forwarding mode to achieve the maximum covert rate in a relay system with given settings of transmitter transmission rate, self-interference coefficient of FD, constraints of covert outage probability, and upper bound on relay transmit power.

Energy-Efficient Covert Wireless Communication Through Probabilistic Jamming

In this work, we propose a novel energy-efficient covert wireless communication scheme through probabilistic jamming, where a jammer probabilistically transmits artificial noise (AN) with variable power against a warden’s detection. For the proposed scheme, the closed-form expressions for the warden’s detection error probability, the transmission outage probability, and the system energy efficiency are derived to reveal the impacts of the prior AN transmission probability on communication covertness, reliability, and system energy efficiency. To further improve the system energy efficiency, an optimization problem is formulated to determine the optimal prior AN transmission probability and the optimal transmit power of private information subject to a covertness constraint, which shows that continuous jamming is generally not optimal to maximize the energy efficiency in covert communication systems. Simulation results verify our analysis and show the superiority of proposed scheme comparing to a widely adopted scheme where the jammer always transmits AN with random transmit power.

Infinite and Finite Block-Length FD Transmissions With Spatially-Correlated RIS Channels

The reconfigurable intelligent surface (RIS)-assisted wireless communication system is studied in this paper, where two full-duplex (FD) users simultaneously communicate with each other. The reception at each user is facilitated through a dedicated RIS. Owing to the geometry and placements of meta-atoms, the analysis incorporates spatial-correlation among the channels related to RIS. Using moment-matching approach, a new analytical framework is presented for spatially-correlated channels considering the optimal phase-shifts from RIS meta-atoms. The effects of residual self-interference (RSI), self-reflections and interference from the RIS of transmitting user are considered in the analysis. The expressions are derived for the outage probability and ergodic capacity for infinite block-length transmission, and for finite block-length transmission, the expressions are obtained for block-error rate (BLER), ergodic capacity and system goodput. To gain further insights, the expression for the asymptotic outage probability is also derived. The finding of this paper confirms the ability of RIS-assisted communication to counter the detrimental effects of self-interference in FD scenario, even in presence of spatial-correlation. The derived expressions are validated using the Monte-Carlo simulations.

Partial Relay Selection for Covert Communication in Two-Hop Relay Systems

This paper investigates the impact of relay selection on covert communication performance in a two-hop wireless relay system, where a transmitter intends to transmit messages to a receiver via a selected relay, subjecting to detection from a warden. To this end, we first propose a partial relay selection scheme based on the signal-to-interference-plus-noise-ratio (SINR) at the relay. We then provide theoretical models for the detection error probability, transmission outage probability, and covert rate. We further explore the optimal designs of the transmit power and target transmission rate for the covert rate, subjecting to the constraints of covertness and the upper bound of transmit power. Finally, we provide extensive numerical results to illustrate the covert rate performance via the partial relay selection scheme in a two-hop relay system.

Age-of-Information Minimization in Healthcare IoT Using Distributionally Robust Optimization

In this article, we consider a cellular-based healthcare Internet of Things (IoT) system with imperfect channel state information (CSI), where a healthcare IoT device first receives radio frequency (RF) energy from the small cell base station (SBS) and then transmits physiological status updates to the corresponding SBS as timely as possible. A newly proposed metric, named Age of Information (AoI), will be introduced to characterize the data freshness, which is determined by the status updates generation probability and the information transmission outage probability. To minimize the average AoI, we formulate a distributionally robust optimization problem under an energy harvesting probability (chance) constraint and an information transmission probability constraint. Since the distributionally robust probability constraints are nonconvex, we use the conditional value-at-risk (CVaR)-based method to express constraint specifications related to distributional ambiguity. To tackle the NP-hard problem efficiently, we decompose the AoI minimization problem into two subproblems and propose a low-complexity iterative algorithm to obtain a suboptimal solution. Simulation results show that there exists an AoI-energy tradeoff in the considered healthcare IoT, and the CVaR-based method can achieve a better performance than the nonrobust method.

Performance Analysis of Product Channel for Relaying-Assisted Edge Computing in IoT Networks

In this paper, we analyze the typical product channel which is often encountered in wireless relaying channels, for relaying-assisted edge computing in Internet of Things (IoT) networks. Such analysis is of vital importance, as it is often encountered in wireless transmission. Specifically, we firstly derive a closed-form expression of the transmission outage probability in product channels, through solving involved complicated multivariate integral. We then simplify the expression through some approximation to the involved Bessel function, which can help obtain some meaningful findings to the system design. We finally provide some numerical results to verify that the presented analysis on the production channels is effective.

UAV-Enabled Data Collection for Wireless Sensor Networks With Distributed Beamforming

This paper studies an unmanned aerial vehicle (UAV)-enabled wireless sensor network, in which one UAV flies in the sky to collect the data transmitted from a set of ground nodes (GNs) via distributed beamforming. We consider two scenarios with delay-tolerant and delay-sensitive applications, in which the GNs send the common/shared messages to the UAV via adaptive- and fixed-rate transmissions, respectively. For the two scenarios, we aim to maximize the average data-rate throughput and minimize the transmission outage probability, respectively, by jointly optimizing the UAV’s trajectory design and the GNs’ transmit power allocation over time, subject to the UAV’s flight speed constraints and the GNs’ individual average power constraints. However, the two formulated problems are both non-convex and thus generally difficult to be optimally solved. To tackle this issue, we first consider the relaxed problems in the ideal case with the UAV’s flight speed constraints ignored, for which the well-structured optimal solutions are obtained to reveal the fundamental performance upper bounds. It is shown that for the two approximate problems, the optimal trajectory solutions have the same multi-location-hovering structure, but with different optimal power allocation strategies. Next, for the general problems with the UAV’s flight speed constraints considered, we propose efficient algorithms to obtain high-quality solutions by using the techniques from convex optimization and approximation. Finally, numerical results show that our proposed designs significantly outperform other benchmark schemes, in terms of the achieved data-rate throughput and outage probability under the two scenarios. It is also observed that when the mission period becomes sufficiently long, our proposed designs approach the performance upper bounds when the UAV’s flight speed constraints are ignored.

Covert Communication for Wireless Networks with Full-Duplex Multiantenna Relay

In this work, we investigated a covert communication method in wireless networks, which is realized by multiantenna full-duplex single relay. In the first stage, the source node sends covert messages to the relay, and the relay uses a single antenna to send interference signals to the adversary node to protect the covert information being transmitted. In the second stage, the relay decodes and forwards the covert information received in the first stage; at the same time, the relay uses zero-forcing beamforming to send interference signals to the warden to ensure covert transmission. The detection error rate, transmission outage probability, maximum effective covert rate, and other performance indicators are derived in two stages, and the total performance of the system is derived and analyzed. Then, the performance indicators are verified and analyzed by simulation. Our analysis shows that the maximum effective covert rate of using the characteristics of multiantenna to interfere with Willie in the second stage is taken as the total covert performance of the system, and the transmission interruption probability is significantly less than that of the first stage, so the corresponding maximum effective concealment efficiency will be greater.

NOMA-Based Cooperative Relaying Transmission for the Industrial Internet of Things

With the continuous maturity of the fifth generation (5G) communications, industrial Internet of Things (IIoT) technology has been widely applied in fields such as smart factories. In smart factories, 5G-based production line monitoring can improve production efficiency and reduce costs, but there are problems with limited monitoring coverage and insufficient wireless spectrum resources, which restricts the application of IIoT in the construction of smart factories. In response to these problems, we propose a hybrid spectrum access mechanism based on Non-Orthogonal Multiple Access (NOMA) cooperative relaying transmission to improve the monitoring coverage and spectrum efficiency. As there are a large number of production lines that need to be monitored in smart factories, it is difficult to realize real-time monitoring of all production lines due to insufficient wireless resources. Therefore, we divide the production lines into high priority and low priority, and introduce cognitive radio technology to increase the number of monitoring production lines. In order to better describe the wireless fading channel environment in the factory, the two-wave with diffuse power (TWDP) channel is discussed to simulate the real factory environment and the outage probability of the secondary production line data transmission is derived in the proposed mechanism. Compared with the traditional mechanism, the proposed transmission mechanism can ensure the continuity of the secondary transmission, greatly reduce the outage probability of the secondary transmission, and improve the efficiency of the monitoring of the production lines.

Quantized Federated Learning Under Transmission Delay and Outage Constraints

Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy. Although various communication schemes have been proposed to expedite the FL process, most of them have assumed ideal wireless channels which provide reliable and lossless communication links between the server and mobile clients. Unfortunately, in practical systems with limited radio resources such as constraint on the training latency and constraints on the transmission power and bandwidth, transmission of a large number of model parameters inevitably suffers from quantization errors (QE) and transmission outage (TO). In this paper, we consider such non-ideal wireless channels, and carry out the first analysis showing that the FL convergence can be severely jeopardized by TO and QE, but intriguingly can be alleviated if the clients have uniform outage probabilities. These insightful results motivate us to propose a robust FL scheme, named <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedTOE</monospace> , which performs joint allocation of wireless resources and quantization bits across the clients to minimize the QE while making the clients have the same TO probability. Extensive experimental results are presented to show the superior performance of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedTOE</monospace> for deep learning-based classification tasks with transmission latency constraints.

On the Physical Layer Security of Untrusted Millimeter Wave Relaying Networks: A Stochastic Geometry Approach

The physical layer security (PLS) of millimeter wave (mmWave) communication systems is investigated, where the secure source-to-destination communication is assisted by an untrusted relay selected from a group of them and there are also several passive eavesdroppers (Eves) in the network. In the considered system model, while the distributions of the untrusted relays and Eves follow a homogeneous Poisson Point Process (PPP). To maximize the instantaneous secrecy rate, a novel joint relay selection and power allocation (JRP) method is developed where the destination and source aim for jamming the reception of both the untrusted relays and passive Eves. New expressions of the optimal power allocation (OPA) are derived for both non-colluding Eves (NCE) and colluding Eves (CE). Subsequently, by considering the impact of potential blockages, new closed-form equations are derived for analyzing the system’s ergodic secrecy rate (ESR) and secrecy outage probability (SOP) for transmission over fading mmWave channels. Finally, numerical examples are provided for demonstrating the superiority of our proposed JRP method over the relevant benchmarks found in the literature. Interestingly, the ESR increases with the density of untrusted relays for both the NCE and CE scenarios, which is a benefit of the improved probability of selecting a relay with a stronger second-hop channel. Furthermore, in the low transmit power regime, employing relatively low mmWave frequencies achieves better ESR, while in the high transmit power regime, high mmWave frequencies provide higher ESR.

UAV Anti-Jamming Video Transmissions With QoE Guarantee: A Reinforcement Learning-Based Approach

Unmanned aerial vehicles (UAVs) that are widely utilized for video capturing, processing and transmission have to address jamming attacks with dynamic topology and limited energy. In this paper, we propose a reinforcement learning (RL)-based UAV anti-jamming video transmission scheme to choose the video compression quantization parameter, the channel coding rate, the modulation and power control strategies against jamming attacks. More specifically, this scheme applies RL to choose the UAV video compression and transmission policy based on the observed video task priority, the UAV-controller channel state and the received jamming power. This scheme enables the UAV to guarantee the video quality-of-experience (QoE) and reduce the energy consumption without relying on the jamming model or the video service model. A safe RL-based approach is further proposed, which uses deep learning to accelerate the UAV learning process and reduce the video transmission outage probability. The computational complexity is provided and the optimal utility of the UAV is derived and verified via simulations. Simulation results show that the proposed schemes significantly improve the video quality and reduce the transmission latency and energy consumption of the UAV compared with existing schemes.

Analysis of SWIPT-Enabled Relay Networks with Full-Duplex Destination-Aided Jamming

Simultaneous wireless information and power transfer (SWIPT) is a major breakthrough in the field of low-power wireless information transmissions. In this paper, the secrecy performance of the SWIPT-enabled relay network with full-duplex destination-aided jamming is assessed, where both the power-splitting (PS) and time-switching (TS) schemes at the relay are considered with the linear and nonlinear energy harvesting models. The relay harvests energy from the confidential signal and artificial noise sent by the source and destination, respectively, and forwards the amplified signal to the destination, in the presence of an eavesdropper. The analytical closed-form expressions of the connection outage probability (COP), secrecy outage probability (SOP), and transmission outage probability (TOP) for PS- and TS-based schemes are derived, and the closed-form expression of the lower bound of ergodic secrecy capacity (ESC) is calculated. The asymptotic-form expressions of the COP, SOP, TOP, and ESC are further analyzed to capture the valuable information in the high SNR regime. Numerical results verify the correctness of analytical results, reveal the effects of the PS/TS ratio, and transmit the signal-to-noise ratio on secrecy performance.

Performance analysis of cooperative communications at road intersections using stochastic geometry tools

Vehicular safety communications (VSCs) provide relevant solutions to avoid congestion and road accidents, more particularly at road intersections since these areas are more prone to accidents. In this context, one of the main impairments that can affect the performance of VSCs is interference. In this paper, we study the performance of cooperative transmissions at intersections in presence of interference for VSCs. We use tools from stochastic geometry, and model interferer vehicles locations as a Poisson point process. First, we calculate the outage probability for a direct transmission when the receiving nodes can be on the road (as vehicles), or outside the road (as infrastructures). Then, we analyze the outage probability performance of a cooperative transmission scheme. The analysis takes into account two dimensions: the decoding strategy at the receiving nodes, and the vehicles mobility. We determine the optimal relay position under different traffic densities and for different vehicles mobility models. We also show that the outage probability does not improve after the number of infrastructure relays reach a threshold value. Finally, we show that the outage probability performance of VSCs is higher at intersections than on highways. We validate our analytical results by Monte-Carlo simulations.

Modeling and Analysis of Stochastic Mobile-Edge Computing Wireless Networks

To realize the vision of the Internet of Things (IoT), mobile-edge computing (MEC) has recently emerged as a promising paradigm to meet the computation demand from mobile users (MUs). In this article, we study the network performance in large-scale stochastic MEC wireless networks, where the tasks can be computed locally by the local computation capabilities (LCCs) or be offloaded to MEC servers for edge computing. To this end, a MEC network is modeled featuring random node distribution, dynamic task requests, orthogonal frequency-division multiple access, task retransmission, and parallel computing in MEC servers. Given the model, a 2-D discrete-time Markov chain is first adopted to characterize the task execution process, including local computing and task offloading. Based on the coupling between communication and computing, the average outage probability of the task transmission and the average MEC computation load are derived by integrating the stochastic geometry and queuing theory. Furthermore, by jointly analyzing the local computation latency, transmission latency, and edge computation latency, we derive the average end-to-end latency of the task execution. Our results show that the LCCs in MUs can improve the network performance, including communication and computation performance, in stochastic MEC networks. In addition, useful guidelines for MEC network provisioning and planning are provided to avoid either the local computing or the task offloading being the latency performance bottleneck.

Spectrum Sharing Incentive for Legitimate Wireless Information Surveillance

In this paper, spectrum sharing incentive for legitimate wireless information surveillance at the physical layer is investigated. The concept behind this is that authorized government agencies want to eavesdrop the communication between a suspicious source-destination pair, and stimulate the help of another source-destination pair (referred to as secondary users) looking for transmission opportunities. We thus propose to let the secondary receiver act as a monitor to eavesdrop the suspicious communication and let the secondary transmitter act as a friendly jammer to improve the eavesdropping performance, such that they are granted to share the spectrum of the suspicious users, as a reward. To guarantee the eavesdropping performance, the successful eavesdropping probability is required to be larger than that with passive eavesdropping. The problems of jointly optimizing the transmit power of the secondary transmitter and the successive interference cancellation (SIC) decoding order at the secondary receiver to maximize the average secondary transmission rate and minimize the secondary transmission outage probability are investigated. Two scenarios are considered for the availability of the channel state information (CSI). The first scenario assumes perfect CSI, and the optimal strategies for the transmit power control and the SIC decoding ordering are derived. The second scenario assumes partial CSI, where the instantaneous CSI of the secondary transmission link and the statistical CSI of the links related to the suspicious communication are available, and the optimal strategies for maximizing an approximate average secondary transmission rate and minimizing an approximate secondary transmission outage probability are proposed. Extensive simulation results validate the effectiveness of the proposed strategies.

Q-Learning-Based Power Allocation for Secure Wireless Communication in UAV-Aided Relay Network

Unmanned aerial vehicle (UAV)-aided wireless relay networks are at risk of eavesdropping activities due to their open nature. In this paper, we study the security of a UAV-aided selective relaying wireless network in which $N$ UAVs are employed as decode-and-forward (DF) relays linking a ground base station (BS) with $L$ legitimate users on the ground in the presence of a passive eavesdropper ( $Eave$ ). Direct links between the ground BS and both the ground users and the eavesdropper are assumed to be blocked. The ground-to-air and air-to-ground channels are assumed to follow Rician fading model with opportunistic scheduling scheme for UAVs and users selection. In order to secure data transmissions against such an interception action, the UAV of the worst UAV-selected user link transmits a jamming artificial noise (AN) signal to degrade $Eave$ ability in decoding the confidential information successfully. The transmission outage probability, intercept probability, and hybrid outage probability are derived and analyzed. Due to the heavy computation burden raised by increasing the number of UAVs and users as well as the difficulty in estimating the instantaneous channel state information (CSI), existing traditional optimization methods are not highly efficient in solving the considered power allocation problem. Therefore, we propose a dynamic power control scheme based on Q-learning algorithm combined with statistical CSI where the hybrid outage probability is minimized. Simulation results show that the proposed algorithm efficiently reduces the hybrid outage probability with a noticeable reduction in the computational time.