Reduce Outage Probability Research Articles

An Innovative Method for Improving the Performance of UAV‐SM‐NOMA for 6G Networks Over Generalized Fading Channels

ABSTRACTThe emergence of 6G wireless communications has brought numerous challenges and opportunities for meeting the growing demands of enhanced system capacity, higher data rates, lower latency, improved security, and stringent quality of service (QoS) requirements. One promising solution to tackle these challenges involves utilizing unmanned aerial vehicles (UAVs) in conjunction with nonorthogonal multiple access (NOMA) and spatial modulation (SM). However, optimizing the performance of UAV‐based SM‐NOMA systems for both perfect and imperfect channel state information (CSI) over generalized fading channels (α–μ, k–μ and η–μ) while simultaneously considering multiple objectives is a complex task. To address these challenges, this paper proposes integrating the monarch butterfly optimization algorithm (MBOA) into UAV‐SM‐NOMA systems for 6G networks. Inspired by the foraging behavior of monarch butterflies, the MBOA aims to enhance UAV‐based SM‐NOMA systems' performance by optimizing resource allocation, increasing system sum rate, reducing outage probability (OP) and bit error rate (BER), improving spectrum efficiency (SE), and ensuring desirable signal‐to‐interference‐plus‐noise ratio (SINR) levels. Key findings include that the MBOA effectively improves performance metrics across generalized fading channels compared to existing UAV‐assisted NOMA and orthogonal multiple access (OMA) models. Simulation results confirm that the proposed UAV‐assisted SM‐NOMA model with MBOA significantly outperforms conventional approaches, demonstrating superior capacity, reliability, and efficiency in 6G network scenarios.

Joint antenna selection and resource allocation for mm‐wave directional D2D communications using distributed deep reinforcement learning

AbstractIn this paper, with the promising assumption of using adaptive directional microstrip antenna on user equipment, the problem of joint antenna selection, spectrum assignment, and transmit power allocation for mm‐wave device‐to‐device communications underlying cellular networks is tackled, with the goal of enhancing system throughput and energy efficiency. To address the complexity of this problem, a method based on multi‐agent distributed deep reinforcement learning in which an autonomous intelligent agent is deployed for each user equipment is proposed. The performance evaluation demonstrates its superiority over existing strategies, resulting in improved system performance, reduced outage probability, and enhanced energy efficiency.

Experimental analysis of reducing outage probability using deep interleaving for long‐distance free space optical systems

AbstractFree space optical communication (FSO) has become a popular research direction due to its high bandwidth, easy deployment, and inherent security. Its channel state is unstable because of atmospheric environment, especially in long‐distance ground transmission system. Interleaving combined with forward error correction coding (FEC) have been utilised to improve the stability of system. However, there is little detailed experimental results of deep interleaving combined with FEC under different atmospheric turbulence intensity over long‐distance FSO system. A deep interleaving with FEC method was designed and implemented on a 7 km long online experiment. The performance of different depth interleaving is analysed under weak and strong atmospheric turbulence state. The experiment results show that deep interleaving performs better under weak turbulence for the larger correlation factor of the channel and the outage probability of the system with deep interleaving can be greatly reduced.

Deep Learning Enabled Slow Fluid Antenna Multiple Access

The increasing interest of fluid antenna systems is reinforced by an unprecedented way of achieving multiple access, by exploiting moments of deep fades in space. This phenomenon, referred to as fluid antenna multiple access (FAMA), allows the fluid antenna at each user to be switched to a location in space (i.e., port) where the sum-interference power collectively suffers from a deep fade, resulting in a decent signal reception without the need of complex signal processing. Nevertheless, selecting the best port is an arduous task, which requires a large number of channel observations to obtain the high performance gain. This letter aims to devise a low-complexity port selection scheme for FAMA where each user has a small number of port observations only. We assume slow FAMA ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> -FAMA) so that the selected port remains unchanged until the channel conditions change. A deep learning approach is proposed to infer the signal-to-interference plus noise ratios (SINR) at all the available ports given only a small number of observations. The simulation results exhibit that the proposed scheme is able to attain significant reductions in outage probability, and improvements in multiplexing gain, from a relatively small number of available port observations, showing great potential for future multiple access technologies.

Statistical Method Based Waveform Optimization in Collocated MIMO Radar Systems

Multiple-input multiple-output (MIMO) radar has acquired considerable attention as it offers an additional degree of freedom which results in performance gains when contrasted with the regular single antenna element radar system. Waveform optimization in MIMO radar is essential as it can offer tremendous improvements in target detection which are quantified in terms of reductions in the symbol error rate and improvements in target detection probability. In this work, we foster a strategy for the optimization of transmitter and receiver waveform in a collocated MIMO radar by only considering the second order statistics, thus relaxing the information of the instantaneous target states. Our contributions are primarily two-fold. First, we find a closed-form expression of the outage probability of an unknown target under clutter environment. For this prospect, we model the signal-to-interference-plus-noise ratio in a canonical quadratic structure, and then utilize the modern residue theory approach to characterize the distribution function. Secondly, we propose constrained and unconstrained optimization problems for the reduction in outage probability using algorithmic techniques such as interior-point, sequential-quadratic programming, and the active-set method for the optimization of the transmitter and receiver waveform. We also provide simulated re-enactments to validate our hypothetical deductions.

Digitally Reconfigurable Metasurface Array for a Multipath Based Wireless Link With Media-Based Modulation

Electromagnetic (EM) windows with a digitally reconfigurable metasurface (DRM) are proposed to experimentally demonstrate a multipath based communication technique known as media-based modulation (MBM). In MBM, the magnitude and phase of a transmitted tone from a single RF chain are modulated in space using different switching combinations of DRM. After transmission, superposition of various multipath components helps diversifying the transmitted symbols across the constellation leading to an improved signal-to-noise ratio (SNR), bandwidth, and energy-efficient multiuser system with enhanced security, reduced outage probability in a slow fade channel, and possibility of achieving capacity of a Gaussian channel. Prototype of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> 8 array of the DRM unit cells with p-i-n diodes for switching and its hardware interface are developed to perform experiments under different scattering environments. Constellations, bit-error-rate (BER) responses, and statistical distributions of the received symbols with the measured data establish the above characteristic features of a wireless link using MBM. A scheme for the data transmission with DRM is also discussed.

Founding AP's IVM and related IHM for subway LiFi network

In this paper, the information value model (IVM) of an access point (AP) and the related intelligent handover model (IHM) are firstly founded in the subway light-fidelity (LiFi) network. At first, based on statistical methods, the IVM is created by analyzing the behavioral characteristics of subway passengers. By the IVM, the probability of user access to each AP can be successfully predicted. Next, based on this probability, the IHM is created by deeply analyzing the alternating blockage problem of handholds. By the IHM, the system can self-adapt to the hysteresis state. This not only effectively avoids ping-pong handovers, but also reduces outage probability. Finally, by simulation, the effectiveness of the above IVM and IHM has been demonstrated. The simulation results indicate that the accuracy of predicting user target AP can be up to 92% by the IVM. Specially, compared with using the standard handover model, the throughput of the network not only increases 11.5%, but also the outage probability of the network decreases 23.6% by using the IHM.

Free-Space Optics Communications Employing Elliptical-Aperture Multimode Diversity Reception Under Anisotropic Turbulence

We experimentally demonstrate an elliptical-aperture multimode diversity receiver to mitigate the impairment from anisotropic turbulence, which is emulated by employing an electrically-controlled heater. The anisotropy gradually decreases as the flow-level of the introduced airflow from fans increases. In contrast to circular-aperture reception, the elliptical-aperture multimode diversity receiver can effectively resist beam wander under anisotropic turbulence, reducing outage probability from 14.63% to 0.38% for detecting a dual-polarization 30-Gbaud QPSK signal. Without the aid of an adaptive optics system, this work demonstrates the feasibility of realizing an anisotropic-turbulence-resistant FSO link using multimode and multi-aperture diversity reception employing digital coherent combining techniques.

Hierarchical Reinforcement Learning for Relay Selection and Power Optimization in Two-Hop Cooperative Relay Network

In this paper, we study the outage probability minimizing problem in a two-hop cooperative relay network. To reduce outage probability, existing studies propose many schemes for relay selection and power allocation, which are usually based on the assumption of exact channel state information (CSI). However, it is difficult to obtain perfect instantaneous CSI in practical situations where channel states change rapidly, and thus traditional methods would not perform well. Considering these factors, we turn to the emerging reinforcement learning (RL) methods for solutions. RL methods do not need any prior knowledge of CSI, but use neural network for approximation and decision after interacting with communication environment. Nevertheless, conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. In addition, non-stationarity is a common problem when using hierarchical reinforcement learning (HRL), which is caused by the changing behavior in different hierarchies. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further design an HRL framework and training algorithm. By decomposing relay selection and power allocation into two hierarchical optimization objectives, and combining on- policy and off-policy methods in the HRL framework, our method successfully address the sparse reward and non-stationary problem. Simulation results reveal that compared with traditional DRL method, the proposed HRL training algorithm can converge faster and reduce the outage probability by 8% in two-hop relay network with the same outage threshold.

Performance Analysis and Beamforming Design of a Secure Cooperative MISO-NOMA Network.

This paper studies the cell-edge user’s performance of a secure multiple-input single-output non-orthogonal multiple-access (MISO-NOMA) system under the Rayleigh fading channel in the presence of an eavesdropper. We suppose a worst-case scenario that an eavesdropper has ideal user detection ability. In particular, we suggest an optimization-based beamforming scheme with MISO-NOMA to improve the security and outage probability of a cell-edge user while maintaining the quality of service of the near-user and degrading the performance of the eavesdropper. To this end, power allocation coefficients are adjusted with the help of target data rates of both the users by utilizing a simultaneous wireless information and power transfer with time switching/power splitting protocol, where the near-user is used to forward the information to cell-edge user. The analytical results demonstrate that our beamformer analysis can achieve reduced outage probability of cell-edge user in the presence of the eavesdropper. Moreover, the provided simulation results validate our theoretical analysis and show that our approach improves the overall performance of a two-user cooperative MISO-NOMA system.

Distributed Video Content Caching Policy With Deep Learning Approaches for D2D Communication

In this paper, we develop a novel active video content caching scheme (RCC) based on a recommendation system, and consistent hash for device-to-device(D2D) communication. The RCC scheme is constructed by a cache placement scheme, a consistent hash algorithm, an optimal video segmentation scheme, and an optimal video library segmentation scheme. To begin with, a well-designed cache placement scheme based on a mobile model with helper notes, and video segmentation is proposed to reduce the redundancy of the videos, and save users’ cache. In order to solve the interruption problem caused by segmentation, a consistent hash algorithm is introduced to improve the success probability of D2D communication. According to the recommendation system, all users’ predictive score for all videos can be calculated, which results that users’ most interesting video files rather than popular video files as previous work can be obtained, and cached in advance to improve the hitting probability. Furthermore, an optimal video segmentation scheme, and an optimal video library segmentation scheme are developed to minimize the transmission delay, and maximize the hitting probability respectively. Simulation results show that compared with other traditional caching schemes, the proposed RCC scheme can reach about 70% reduction in outage probability, 40% reduction in system latency, and 10% improvement in hitting probability, all of which can achieve the best performance.

On the performance of modes diversity combined with multipath-SPGD algorithm over turbulence free-space optical links

The performance of mode diversity combined with adaptive optics (AO) based on a multipath stochastic parallel gradient descent (SPGD) algorithm over free-space optical (FSO) links through atmospheric turbulence is presented. The experimental results indicate that, compared with a single-mode fiber (SMF) combined with a single-path SPGD algorithm, three modes diversity combined with a multipath SPGD algorithm effectively reduces the power fluctuation. When the sensitivity of the receiver is assumed, the outage probability for the FSO system based on mode diversity is superior to that of the FSO system based on a SMF. The reduction of outage probability arising from mode diversity is more significant when AO is implemented. To the best of our knowledge, this is the first time that the performance of modes diversity combined with a multipath SPGD algorithm over a turbulence channel has been studied.

On Interference Aware Power Adjustment and Scheduling in Femtocell Networks

Densely-deployed femtocell networks are used to enhance wireless coverage in public spaces such as office buildings, subways, and academic buildings. These networks can increase user throughput, but edge users can suffer from co-channel interference and service outages. This paper introduces a distributed algorithm for network configuration, called Radius Reduction and Scheduling (RRS), to improve the performance and fairness of the network. RRS works by jointly adapting femtocell transmission power, allocating user to femtocells, and scheduling resource blocks so as to increase fairness and reduce outage probability in dense femtocell networks. RRS produces a network configuration that guarantees either user or area coverage depending on the management needs. A prototype implementation confirms the benefits of RRS in a real environment. Furthermore, extensive simulations show that RRS reduces the outage probability of up to 50%, and provides better fairness, with an increase in Jain’s index of 190%, with respect to a baseline algorithm which works with fixed power and best-effort scheduling and to a previous approach to resource management in femtocell networks.

Fast-Charging Station Deployment Considering Elastic Demand

Electric vehicles (EVs), as part of sustainable transport, are believed to be helpful to reduce global warming. In this article, we focus on the fast-charging station (FCS) deployment problem, which is one of the key issues of the EV ecosystem. Specifically, elastic demand is considered, i.e., charging demand will be suppressed because of either long driving distance to get charging or long waiting time at the station. A fixed-point equation is proposed to capture the nature of the EV users’ charging behavior. It considers both spatial and temporal penalties by establishing a connection between the resulting arrival rate and a combination of driving distance and waiting time. We formulate the FCS deployment problem as a nonlinear integer problem, which seeks to figure out the optimal locations to build the FCSs and the optimal number of charging piles of each selected FCS. A genetic-algorithm-based heuristic algorithm is adopted to tackle the problem. Simulation results prove the effectiveness of our proposed algorithm. The importance of a match between the power grid capacity and the amount of charging demand is revealed, both in terms of increasing profit and reducing outage probability.

Performance Analysis of Opportunistic Relay Selection Cooperative Communication in Cascaded Fading Channels

Cooperative communication enables users to transmit information in a cooperative way, which is one of the key technologies used in wireless communication systems to improve spectral efficiency. A cascade Nakagami-m fading function is proposed to describe the fading characteristics of complex wireless channels, and a communication model of decoding-and-forward opportunistic relay selection (DF-ORS) cooperative communication under total power constraints is established. Theoretical analysis and simulation results show that DF-ORS cooperative communication can effectively reduce outage probability and improve spectrum efficiency under total power constraints and complex cascade Nakagami-m fading channels. The transmission power allocation of source node and relay node has an important impact on communication performance. Equal power allocation scheme cannot guarantee the optimal system performance, and the obtained conclusions have more general applicability.

Impact of Polarization- and Mode-Dependent Gain on the Capacity of Ultra-Long-Haul Systems

Motivated by the recent interest in single-mode semiconductor optical amplifiers and multimode erbium-doped fiber amplifiers, we present a unified, comprehensive treatment of the effect of polarization- and mode-dependent gain (PDG and MDG) on the capacity of ultra-long-haul transmission systems. We study the problem using simulations of a multisection model, including the effects of PDG or MDG and polarization mode dispersion (PMD) or modal dispersion. We also analytically derive exact expressions for the capacity distribution of PDG-impaired single-mode systems. In agreement with previous work, we find that PDG and MDG cause fluctuations in capacity, which reduces average capacity and may cause outage. We show that the multimode systems studied, with at least $D = 14$ spatial/polarization modes, have sufficient modal diversity and frequency diversity to strongly suppress capacity fluctuations and reduce outage probability so that the outage capacity approaches the average capacity. We show that single-mode systems, by contrast, inherently provide low modal and frequency diversity, making them more prone to outage. To alleviate this problem, frequency diversity can be increased by artificially inserting PMD. Finally, we quantify the PDG/MDG requirements of optical amplifiers to ensure that the average capacity is close (within a 1-2 dB effective SNR loss) to the theoretical optimum. We show that these PDG/MDG requirements are stringent, especially considering the minimum-mean-square error linear equalizers implemented in typical multiple-input multiple-output receivers.

Energy harvesting Internet of Things health-based paradigm: Towards outage probability reduction through inter–wireless body area network cooperation

In today’s healthcare environment, the Internet of Things technology provides suitability among physicians and patients, as it is valuable in numerous medicinal fields. Wireless body sensor network technologies are essential technologies in the growth of Internet of Things healthcare paradigm, where every patient is monitored utilising small-powered and lightweight sensor nodes. A dual-hop, inter–wireless body sensor network cooperation and an incremental inter–wireless body sensor network cooperation with energy harvesting in the Internet of Things health-based paradigm have been investigated and designed in this work. The three protocols have been named and abbreviated as follows: energy harvesting–based dual-hop cooperation, energy harvesting–based inter–wireless body sensor network cooperation and energy harvesting–based incremental inter–wireless body sensor network cooperation. Outage probabilities for the three designed protocols were investigated and inspected, and mathematical expressions of the outage probabilities were derived. The simulation and numerical results showed that the energy harvesting–based incremental inter–wireless body sensor network cooperation provided superior performance over the energy harvesting–based inter–wireless body sensor network cooperation and energy harvesting–based dual-hop cooperation by 1.38 times and 5.72 times, respectively; while energy harvesting–based inter–wireless body sensor network cooperation achieved better performance over energy harvesting–based dual-hop cooperation by 1.87 times.

Compensating rain induced impairments in terrestrial FSO links using aperture averaging and receiver diversity

In this paper, we investigate the performance of a free space optical link considering the deteriorations caused by heavy rain. The applicability of receiver diversity and aperture averaging techniques to compensate the rain-induced impairments is analyzed on the basis of outage probability and signal to noise ratio at varying rain intensities. A 10-Mbps on-off keying modulated optical signal is taken as the test signal for which rain specific attenuation is calculated using a laboratory testbed having controlled rainfall conditions up to 250 mm/h. A 15-m single input multi output) optical link with selection combining technique at the receiver is used to observe diversity gain. Significant diversity improvement by a factor of 1.35 is observed by comparing the results with the conventional single input single output link. Experimental observations with and without aperture averaging shows average SNR gain of 1.58 and outage probability reduction of 12.3%.

New Weight Function for Adapting Handover Margin Level over Contiguous Carrier Aggregation Deployment Scenarios in LTE-Advanced System

In this paper, an Adaptive Handover Margin algorithm based on Novel Weight Function (AHOM-NWF) is proposed through Carrier Aggregation operation in Long Term Evolution—Advanced system. The AHOM-NWF algorithm automatically adjusts the Handover Margin level based on three functions, $$f(SINR), \; f(TL)\; {\text{and}}\; f(v)$$ , which are evaluated as functions of Signal-to-Interference-plus-Noise-Ratio (SINR), Traffic Load $$(TL)$$ , and User’s velocity $$(v)$$ respectively. The weight of each function is taken into account in order to estimate an accurate margin level. Furthermore, a mathematical model for estimating the weight of each function is formulated by a simple model. However, AHOM-NWF algorithm will contribute for the perspective of SINR improvement, cell edge spectral efficiency enhancement and outage probability reduction. Simulation results have shown that the AHOM-NWF algorithm enhances system performance more than the other considered algorithms from the literature by 24.4, 14.6 and 17.9%, as average gains over all the considered algorithms in terms of SINR, cell edge spectral efficiency and outage probability reduction respectively.

Cooperative HARQ-Assisted NOMA Scheme in Large-Scale D2D Networks

This paper develops an interference aware design for cooperative hybrid automatic repeat request (HARQ) assisted non-orthogonal multiple access (NOMA) scheme for large-scale device-to-device (D2D) networks. Specifically, interference aware rate selection and power allocation are considered to maximize long term average throughput (LTAT) and area spectral efficiency (ASE). The design framework is based on stochastic geometry that jointly accounts for the spatial interference correlation at the NOMA receivers as well as the temporal interference correlation across HARQ transmissions. It is found that ignoring the effect of the aggregate interference, or overlooking the spatial and temporal correlation in interference, highly overestimates the NOMA performance and produces misleading design insights. An interference oblivious selection for the power and/or transmission rates leads to violating the network outage constraints. To this end, the results demonstrate the effectiveness of NOMA transmission and manifest the importance of the cooperative HARQ to combat the negative effect of the network aggregate interference. For instance, comparing to the non-cooperative HARQ assisted NOMA, the proposed scheme can yield an outage probability reduction by $32$%. Furthermore, an interference aware optimal design that maximizes the LTAT given outage constraints leads to $47$% throughput improvement over HARQ-assisted orthogonal multiple access (OMA) scheme.