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Vehicle-to-vehicle (V2V) communication has been designed to afford improvements in the traffic congestion and traffic accidents by directly exchanging information between nearby vehicles. However, V2V communication may have difficulty providing service reliability due to interference between V2V links and vehicle-to-cellular user equipment links. In this paper, we consider the transmit power control algorithm to minimize interference among cellular users and vehicles in the context of V2V communication underlaid uplink cellular networks. First, we formulate the problem, which is an NP-hard combinatorial optimization problem with linear constraints. Addressing this problem with traditional optimization methods is ineffective; therefore, we design and train a deep neural network to address this optimization problem. Computational complexity analyses and simulation results reveal that the proposed algorithm outperforms weighted minimum mean squared error (WMMSE), fixed transmit power, and Dinkelbach's methods, and achieves near-global optimum with lower computation complexity than the exhaustive search (ES). [ABSTRACT FROM AUTHOR]

The spectrum usage often comes in an online fashion. Considering the selfish behaviors of both primary users(PUs) and sencond users(SUs), we design online double spectrum allocation methods. We propose a truthful online double auction for spectrum allocation. Preempting existing spectrum usage is not allowed. We design a strategyproof mechanism for both the primary user side and the SU side. In previous studies, users who do not interfere with each other have not been reasonably allocated to use channels. There is one and only one user on the used spectrum channel at a certain time, which will result in discarding many user requests. Our model allows multiple users to use a spectrum channel at the same time. Aiming at the shortcomings of previous research, we propose the concept of grouping for online auctions, so that all SUs that do not interfere with each other can use spectrum channels at the same time. This will greatly increase the number of users who use the spectrum at the same time. We thus increase the utilization rate of the user market, and a large number of users will not be idle and abandoned.In the design of our experiments, the grouping model proposed in this paper has obtained at least $43{\%}$ utilization rate on the set channel, and our experiments have obtained good generality for different interference radius. [ABSTRACT FROM AUTHOR]

The Third Generation Partnership Project has standardized cellular vehicle-to-everything (C-V2X) sidelink Mode 4 to support direct communication between vehicles. In Mode 4, the sensing-based semipersistent scheduling (SPS) scheme enables vehicles to autonomously reserve and select radio resources. In particular, SPS has three processes to realize the resource scheduling, including continuously sensing resources, probabilistically reselecting resources, and periodically reserving resources. However, vehicles randomly select resources from the available resource lists in the resource reselection process, resulting in frequent packet collisions especially when radio resources are insufficient. Unlike the traditional SPS, this paper proposes a multiagent deep reinforcement learning-based SPS (RL-SPS) algorithm to help vehicles select appropriate radio resources with the aim of reducing packet collisions. Furthermore, a multi-head attention mechanism is adopted to improve the training efficiency by helping vehicles selectively pay attention to the observations and actions of neighbouring vehicles. It is worth noting that the RL-SPS algorithm fits the characteristics of Mode 4, which selects resources without requiring any global information. Simulation results show that RL-SPS outperforms other decentralized approaches and demonstrate the scalability and robustness of RL-SPS in a dynamic vehicular network. [ABSTRACT FROM AUTHOR]

With low-cost energy consumption, the reconfigurable intelligent surface (RIS) technique is a potential solution to the real-time data processing for intelligent transportation systems (ITSs). In this paper, an intelligent transmissive surface is introduced into the vehicular communications, enabling vehicle-to-infrastructure (V2I) signals to penetrate the intelligent RIS to access the base station (BS) on the opposite side of the vehicle. Considering that the vehicle-to-vehicle (V2V) communication reuses the spectrum spanned for V2I link, we investigate the ergodic capacity optimization problem for the vehicle performing V2I communications with the assistance of RIS, while meeting the low-latency and high-reliability requirements of the V2V link. The RIS transmission coefficients and power allocation of vehicles are jointly optimized, for the management of the desired and undesired vehicular communication links. Moreover, the expression of optimal phase shifts is derived in a closed-form, which reveals that the performance gain brought by RIS is proportional to the number of intelligent elements, while inversely proportional to the distance from vehicle-to-BS, in a quadratic form. Moreover, in the case of discrete phase shifts, an intelligent algorithm is proposed for the beamforming design at RIS. Afterwards, with the objective to maximize the ergodic capacity of the V2I link, the optimal power allocation is also proposed. Simulation results confirm the accuracy of the proposed resource allocation strategy, and that the system performance in terms of the ergodic V2I capacity can be significantly improved by the RIS. [ABSTRACT FROM AUTHOR]

With the full development of intelligent mobile communications, wireless mixed reality (MR) provides a more visually immersive experience and stronger interaction with environments than virtual reality (VR) and augmented reality (AR). However, the asymmetric characteristic of wireless MR traffic creates a huge challenge to current mobile networks. Dynamic time division duplex (D-TDD) is considered as a promising technology to improve wireless MR users’ quality of experience (QoE) due to its potentials and advantages in delivering asymmetric traffic. Therefore, in this paper, we propose a QoE-driven distributed multidimensional resource allocation (MRA) supplemented by inter-cell interference (ICI) mitigation scheme for wireless MR in multi-cell D-TDD systems. First, to improve QoE of MR users, we formulate the joint optimization of subframe configuration, channel assignment and computation offloading as a mixed-integer nonlinear programming problem. A novel fully-decentralized multi-agent deep Q-network (DQN) algorithm is developed to solve the problem. Then, to mitigate ICI, a water filling based power control algorithm is investigated to minimize the total power of each small base station and its associated MR users. Simulation results demonstrate that our proposed scheme improves QoE of MR users in a realizable way as compared to existing schemes. [ABSTRACT FROM AUTHOR]

The cell-free massive multiple-input multiple-output (CFmMIMO) aims to provide uniform quality of service (QoS) for all users, and can be used in small-area scenarios such as indoor industrial networks. This paper studies the CFmMIMO system for indoor industrial scenarios. Firstly, an access point (AP) grouping based hierarchical network topology is proposed. Based on this, we propose an effective AP selection method. To reduce pilot contamination, a pilot assignment scheme based on inspection robot (IR) location is proposed. Considering the high reliability requirement of industrial data transmission, the power control and backhaul combining are jointly optimized to maximize the minimum signal to interference plus noise ratio (SINR). The scalability of the proposed CFmMIMO system is analyzed, and a scalable power control method is proposed. The simulations demonstrate the effectiveness of the AP selection method, pilot assignment scheme, and the joint optimization algorithm for power control and backhaul combining. Moreover, the impact of network scale and network load on system performance is evaluated and analyzed in the simulations. [ABSTRACT FROM AUTHOR]

The topological interference management (TIM) problem refers to the study of the $K$ -user partially connected interference networks with no channel state information at the transmitters (CSIT), except for the knowledge of network topology. In this paper, we study the TIM problem with confidential messages (TIM-CM), where message confidentiality must be satisfied in addition to reliability constraints. In particular, each transmitted message must be decodable at its intended receiver and remain confidential at the remaining $(K-1)$ receivers. Our main contribution is to present a comprehensive set of results for the TIM-CM problem by studying the symmetric secure degrees of freedom (SDoF). To this end, we first characterize necessary and sufficient conditions for feasibility of positive symmetric SDoF for any arbitrary topology. We next present two achievable schemes for the TIM-CM problem: For the first scheme, we use the concept of secure partition and, for the second one, we use the concept of secure independent sets. We also present outer bounds on symmetric SDoF for any arbitrary network topology. Using these bounds, we characterize the optimal symmetric SDoF of all $K=2$ -user and $K=3$ -user network topologies. [ABSTRACT FROM AUTHOR]

Visible Light Communication (VLC) is a rapidly growing technology which can supplement the current radio-frequency (RF) based wireless communication systems. VLC can play a huge part in solving the ever-increasing problem of spectrum scarcity because of the growing availability of Light Emitting Diodes (LEDs). One of the biggest advantages of VLC over other communication systems is that it can provide illumination and data communication simultaneously without needing any extra deployment. Although it is essential to provide data rate at a blazing speed to all the users nowadays, maintaining a satisfactory level in the distribution of lighting is also important. In this paper, we present a novel approach of using mirrors to enhance the illumination uniformity and throughput of an indoor multi-element VLC system architecture. In this approach, we improve the Signal-to-Interference plus Noise Ratio (SINR) of the system and overall illumination uniformity of the room by redirecting the reflected LED beams on the walls to darker spots with the use of mirrors. We formulate a joint optimization problem focusing on maximization of the SINR while maintaining a reasonable illumination uniformity across the room. We propose a two-stage solution of the optimization problem: design solution and communication solution. In the design optimization, we formulate an equivalent binary linear optimization to achieve the best illumination quality by optimizing the mirror placements and the LEDs’ transmit powers. In the communication problem, however, we aim to improve the throughput of the system using a fair utility metric based on maximizing the minimum user’s data rate. Due to non-convexity of the communication problem, we propose three different heuristic solutions and analyze their performance. We also show that about threefold increase in average illumination and fourfold increase in average throughput can be achieved when the mirror placement is applied which is a significant performance improvement. [ABSTRACT FROM AUTHOR]

The unprecedented growth in wireless Internet-of-Things and WiFi devices has renewed interest in mechanisms for efficient spectrum reuse. Existing schemes require some level of primary-secondary coordination, cross-channel state estimation and tracking, or activity detection– which complicate implementation. For low-power short-range secondary communication, the main impediment is strong and time-varying (e.g., intermittent) interference from the primary system. This paper proposes a practical underlay scheme that permits reliable secondary communication in this regime. The secondary transmitter merely has to send its signal twice, at very low power - a few dBs above the noise floor, but far below the primary’s interference. Exploiting the repetition structure, reliable and computationally efficient recovery of the secondary signal is possible via canonical correlation analysis (CCA). Experiments using a software radio testbed reveal that, for a secondary user with only two receive antennas, reliable detection of the secondary signal is possible for signal to interference plus noise ratio (SINR) in the range of −20 to −40 dB. The approach works with unknown time-varying channels, digital or analog modulation, it is immune to carrier frequency offset, and, as a side-benefit, it provides means for accurate synchronization of the secondary user even at very low SINR. [ABSTRACT FROM AUTHOR]

In this paper, we consider an integrated data and energy network and D2D communication coexistence (DED2D) system. The DED2D system allows a base station (BS) to transfer data to information-demanded users (IUs) and energy to energy-demanded users (EUs), i.e., using a time-fraction-based information and energy transfer (TFIET) scheme. Furthermore, the DED2D system enables D2D communications to share spectrum with the BS. Therefore, the DED2D system addresses the growth of energy and spectrum demands of the next generation networks. However, the interference caused by the D2D communications and propagation loss of wireless links can significantly degrade the data throughput of IUs. To deal with the issues, we propose to deploy an intelligent reflecting surface (IRS) in the DED2D system. Then, we formulate an optimization problem that aims to optimize the information beamformer for the IUs, energy beamformer for EUs, time fractions of the TFIET, transmit power of D2D transmitters, and reflection coefficients of the IRS to maximize IUs’ worse throughput while satisfying the harvested energy requirement of EUs and D2D rate threshold. The max-min throughput optimization problem is computationally intractable, and we develop an alternating descent algorithm to resolve it with low computational complexity. The simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate secure transmissions in integrated satellite-terrestrial communications and the green interference based symbiotic security scheme is proposed. Particularly, the co-channel interference induced by the spectrum sharing between satellite and terrestrial networks and the inter-beam interference due to frequency reuse among satellite multi-beam serve as the green interference to assist the symbiotic secure transmission, where the secure transmissions of both satellite and terrestrial links are guaranteed simultaneously. Specifically, to realize the symbiotic security, we formulate a problem to maximize the sum secrecy rate of satellite users by cooperatively beamforming optimizing and a constraint of secrecy rate of each terrestrial user is guaranteed. Since the formulated problem is non-convex and intractable, the Taylor expansion and semi-definite relaxation (SDR) are adopted to further reformulate this problem, and the successive convex approximation (SCA) algorithm is designed to solve it. Finally, the tightness of the relaxation is proved. In addition, numerical results verify the efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

Federated learning (FL) has recently become one of the hottest focuses in wireless edge networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs train local machine learning models and transmit them to an aggregator, where a global model is formed and then sent back to UEs. In wireless networks, local training and model transmission can be unsuccessful due to constrained computing resources, wireless channel impairments, bandwidth limitations, etc., which degrades FL performance in model accuracy and/or training time. Moreover, we need to quantify the benefits and cost of deploying edge intelligence, as model training and transmission consume certain amount of resources. Therefore, it is imperative to deeply understand the relationship between FL performance and multiple-dimensional resources. In this paper, we construct an analytical model to investigate the relationship between the FL model accuracy and consumed resources in FL empowered wireless edge networks. Based on the analytical model, we explicitly quantify the model accuracy, available computing resources and communication resources. Numerical results validate the effectiveness of our theoretical modeling and analysis, and demonstrate the trade-off between the communication and computing resources for achieving a certain model accuracy. [ABSTRACT FROM AUTHOR]

In order to evaluate the impacts of swarm distribution characteristics of unmanned aerial vehicles (UAVs) and the complexity of millimeter wave (mmWave) propagations on the coverage performance of UAV networks, this paper develops a stochastic geometry-based approach for the modeling and analysis of single- and multi-swarm UAV networks with mmWave communications. We first utilize binomial point processes (BPPs) to model the locations of UAVs in a finite area as a single-swarm UAV network and allow for arbitrary user locations. Then, we define multi-BPPs and extend our model to multi-swarm UAV networks. We consider an open-access strategy, where users access the closest UAV in all swarms, and derive the distance distributions of serving and interfering UAVs. Moreover, a three-dimensional (3D) blockage model and a 3D sectorized antenna model are introduced to completely depict the characteristics of air-to-ground channel in mmWave band. Based on the distance distributions and the Laplace transform of interference, coverage probabilities in considered networks are derived. Our results reveal that, there exists optimal height and density of UAV swarms that maximize the coverage probability. When densely deployed, the interference from inter-swarm cannot be negligible in the multi-swarm UAV network. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC), as an emerging and prospective mobile computing paradigm, allows a content provider to serve its users by allocating their mobile devices to nearby edge servers to lower the latency in the delivery of its content to those mobile services. From the content provider's perspective, a cost-effective mobile device allocation (MDA) aims to allocate maximum mobile devices to minimum edge servers. However, the allocation of excessive mobile devices to an edge server may result in severe communication interference and consequently, impact mobile devices’ data rates. Sometimes, not all the mobile devices can be allocated to edge servers and thus have to retrieve content from the remote cloud through base stations with high latency. The connection between these mobile devices and base stations also incur communication interference. In this paper, we formally model this Interference-aware mobile edge device allocation (I-MEDA) problem, and propose a game-theoretic based approach named I-MEDAGame to formulate the I-MEDA problem as an I-MEDA game. In the I-MEDA game, allocation decisions are made for individual mobile devices in parallel to alleviate the need for centralized optimization. Our theoretical analysis of I-MEDAGame shows that it admits at least one Nash equilibrium. To solve the I-MEDA problem, I-MEDAGame employs a novel decentralized algorithm to find the Nash equilibrium of the IMEDA game. The performance of I-MEDAGame is theoretically analyzed and experimentally evaluated. The results show that I-MEDAGame can solve the I-MEDA problem effectively and efficiently, outperforming four representative approaches significantly. [ABSTRACT FROM AUTHOR]

Licensed Shared Access (LSA) is a new concept proposed by the radio spectrum policy group in order to optimize spectrum usage: a Mobile Network Operator (MNO) can access temporarily to other incumbent's spectrum after obtaining a license. The licensing process is made via an auction mechanism. The mechanisms proposed in the literature for the LSA context are one-shot auction mechanisms which allocate all the available spectrum as a unique block. In this paper, we first show how to increase the performance of those auctions (in terms of revenue, efficiency and fairness of the allocation) while preserving truthful bidding, by splitting spectrum and converting single block auctions into multi-block auctions. Simulation results illustrate how appropriately choosing the number of blocks allows to increase the aforementioned metrics. Second, we show how to convert one-shot mechanisms to equivalent ascending mechanisms (in terms of allocations and payments) so that we add transparency and privacy to the auction. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) has the potential to provide frequency regulation services by aggregating demand-side distributed energy resources (DERs). Cellular communication networks are commonly utilized for connecting DERs. Nevertheless, conventional cellular networks prior to 5G were unreliable, while private cellular networks incur huge investment costs. Radio access network (RAN) slicing in 5G communications can address these issues by providing the VPP with virtual private networks in public cellular networks. The VPP must evaluate the benefits of 5G RAN slicing and determine the size of slices before implementing it. This paper proposes to apply 5G RAN slicing to assist the VPP in participating in frequency regulation services. First, an optimization model is developed for the VPP to maximize the profit of engaging DERs in various frequency regulation services. Its solution determines the VPP’s DER portfolio schedule of regulation services and communication needs. Then, the effects of communication reliability on the regulation performance are quantified. 5G RAN slicing enhances the predictability of communication reliability, and its benefit is explicitly modeled as its contribution to reducing VPP’s risk profit loss. Next, based on the 5G RAN slicing benefit, an optimization model for determining the size of slices is further established, considering the cost of purchasing slices from mobile service providers. Additionally, an online solution method is developed, such that DERs’ regulation service portfolio can be determined in real-time. Numerical case studies are conducted to demonstrate the benefit of 5G RAN slicing for the VPP’s frequency regulation services. [ABSTRACT FROM AUTHOR]

Recently, a new approach called Morlet wavelet-based two-point FIR (MW-FIR) is proposed for the phasor estimation. Although the approach shows several advantages, it is still not fully compliant with all standard requirements. In this case, the paper proposes the enhanced version of the MW-FIR method to improve immunity against disturbing signals. The estimators are tested under IEC/IEEE 60255-118-1, and compared with other state-of-the-art methods. Results show that the enhanced P-class estimator improves almost all circumstances while the M-class one improves under the most challenging tests, out-of-band interference (OOBI), by around 10% and 20%. Additionally, the enhanced algorithms can reach the best performance without iterations. [ABSTRACT FROM AUTHOR]

Effective testing of new sensing technologies requires realistic yet deterministic test ranges. The advancements in customizable, adaptive unmanned surface vessels (USV) have contributed to the increasing presence of USVs completing maritime site characterizations of test ranges using commercial off-the-shelf (COTS) sensors such as the Tritech Starfish 990F side-scan sonar. This paper aims to present recommendations for the electromechanical integration of a COTS sonar system onto a capable USV through passive techniques such that underwater objects within the littoral zone of Sand Island, Hawaii, are characterized. Results demonstrated a 49% improvement in the sonar images. Therefore, designers are recommended to fully isolate the sensing system from the USV, physically separate the sonar and USV components, and to establish a baseline performance for the system prior to operation. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) ground-penetrating radar (GPR) is an important research topic for target detection in many fields. In this paper, we develop a UAV-mounted GPR system with a frequency band at 150 MHz–309 MHz. However, the received signal in the complex background is covered by various clutter and interference, leading to the serious obscuring of the target. To meet this challenge, a cross-correlation-based background subtraction (CCBS) method and an interference suppression technique are adopted in combination for more accurate detection. The CCBS method processes the raw echo by establishing a background-removal model and using the similarity between each A−Scan and a reference wave. In addition, a Butterworth filter is adopted to get rid of the active electromagnetic interference beyond the working frequencies of the system; then, a lateral Doppler filtering (LDF) technique is introduced to suppress the passive interference generated by the rotation of the UAV rotor itself. Moreover, a practical method for estimating the dielectric constant is introduced by the calibration process of the measured radar echo. Numerical simulations and experimental results by our UAV-GPR system demonstrate that the proposed method has presented a better performance than the traditional methods, and the system has great potential in detecting deeply buried targets. [ABSTRACT FROM AUTHOR]

Interference, the dependency of an individual's potential outcome on the exposure of other individuals, is a common occurrence in medicine and public health. Recently, targeted maximum likelihood estimation (TMLE) has been extended to settings of interference, including in the context of estimation of the mean of an outcome under a specified distribution of exposure, referred to as a policy. This paper summarizes how TMLE for independent data is extended to general interference (network-TMLE). An extensive simulation study is presented of network-TMLE, consisting of four data generating mechanisms (unit-treatment effect only, spillover effects only, unit-treatment and spillover effects, infection transmission) in networks of varying structures. Simulations show that network-TMLE performs well across scenarios with interference, but issues manifest when policies are not well-supported by the observed data, potentially leading to poor confidence interval coverage. Guidance for practical application, freely available software, and areas of future work are provided. [ABSTRACT FROM AUTHOR]

With the ease of appending new nodes without re-installing the whole network, the Internet of Things (IoT) builds several smart applications on Wireless Mesh Network (WMN). One of the important aspects of integrating WMN and smart IoT applications is to provide an energy-efficient and reliable routing protocol. Seeking the communication route that delivers the high-quality stream quickly over WMN is an important issue, but the maximum utilization of a single high-quality path leads to poor throughput and large communication delay, including route discovery and data forwarding delay. The broadcasting mechanism creates redundant transmissions of control packets into the network and reinitializes the blind route discovery process due to link disconnections leading to network resource constraints and high delay during the route discovery process. Moreover, the congestion in the communication route incurs data transmission latency. This paper proposes the Multiple Disjoint Path Determination (MDPD) mechanism based on-demand routing in WMN to formulate the path discovery and data transmission latency. Reducing the neighbor list into the uncommon neighbor set reduces the unnecessary latency in route discovery, and deriving high capacity multiple disjoint communication routes reduce the communication delay in the proposed work. The proposed work employs the queue dynamics in queuing delay, which mainly provides adaptability to the dynamics in network capacity and efficient diversity paths to the gateway node to infer the available bandwidth and optimize the network traffic. To fully utilize the advantage of heterogeneous routers, it disables the flooding of control packets across the stable mesh routers, excluding the initial route discovery process, because it enables the available route storage system in each mesh router. Hence, the proposed work efficiently supports wireless broadband internet access with reduced delay and control overhead. The simulation results demonstrate the fast detection of the multiple disjoint routes and data traffic optimization over the discovered disjoint routes in the proposed MDPD mechanism over WMN. [ABSTRACT FROM AUTHOR]

Current multi-core processors implement sophisticated hardware prefetchers, that can be configured by application (PID), to improve the system performance. When running multiple applications, each application can present different prefetch requirements, hence different configurations can be used. Setting the optimal prefetch configuration for each application is a complex task since it does not only depend on the application characteristics but also on the interference at the shared memory resources (e.g., memory bandwidth). In his paper, we propose DeepP, a deep learning approach for the IBM POWER8 that identifies at run-time the best prefetch configuration for each application in a workload. To this end, the neural network predicts the performance of each application under the studied prefetch configurations by using a set of performance events. The prediction accuracy of the network is improved thanks to a dynamic training methodology that allows learning the impact of dynamic changes of the prefetch configuration on performance. At run-time, the devised network infers the best prefetch configuration for each application and adjusts it dynamically. Experimental results show that the proposed approach improves performance, on average, by 5.8%, 6.7%, and 15.8% compared to the default prefetch configuration across different 6-, 8-, and 10-application workloads, respectively. [ABSTRACT FROM AUTHOR]

Objective: In this paper, we aim to address the problem of subspace detection in the presence of locally-correlated complex Gaussian noise and interference. For applications like brain activity detection using functional magnetic resonance imaging (fMRI) data where the noise is possibly locally correlated, using the sample covariance estimator is not a suitable choice due to significant dependency of its accuracy on the number of observations. Methods: In this study, we take advantage of an assumed banded structure in the covariance matrix to model the local dependence in the noise and propose a new covariance estimation approach. In particular, we use the idea of factorizing the joint likelihood function into a few conditional likelihood terms and maximizing each term independently of the others. This process leads to an explicit estimator for banded covariance matrices which requires fewer observations to achieve the same accuracy as the sample covariance. This estimate is then fed into an adaptive matched filter, two-step Rao and two-step Wald tests for detection. Results: Simulation results reveal the superiority of the proposed methods over well known classical detectors. Finally, the proposed methods are applied to functional magnetic resonance imaging (fMRI) data to localize neural activities in the brain. Conclusion: The proposed method can offer better activation maps in terms of accuracy and spatial smoothness. Significance: The proposed methods can be seen as alternatives for standard detection approaches which are not perfectly aligned with the properties of fMRI data. [ABSTRACT FROM AUTHOR]

Randomized trials of infectious disease interventions, such as vaccines, often focus on groups of connected or potentially interacting individuals. When the pathogen of interest is transmissible between study subjects, interference may occur: individual infection outcomes may depend on treatments received by others. Epidemiologists have defined the primary parameter of interest—called the “susceptibility effect”—as a contrast in infection risk under treatment versus no treatment, while holding exposure to infectiousness constant. A related quantity—the “direct effect”—is defined as an unconditional contrast between the infection risk under treatment versus no treatment. The purpose of this paper is to show that under a widely recommended randomization design, the direct effect may fail to recover the sign of the true susceptibility effect of the intervention in a randomized trial when outcomes are contagious. The analytical approach uses structural features of infectious disease transmission to define the susceptibility effect. A new probabilistic coupling argument reveals stochastic dominance relations between potential infection outcomes under different treatment allocations. The results suggest that estimating the direct effect under randomization may provide misleading conclusions about the effect of an intervention—such as a vaccine—when outcomes are contagious. Investigators who estimate the direct effect may wrongly conclude an intervention that protects treated individuals from infection is harmful, or that a harmful treatment is beneficial. [ABSTRACT FROM AUTHOR]

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Next-generation satellite systems require more flexibility in resource management such that available radio resources can be dynamically allocated to meet time-varying and non-uniform traffic demands. Considering potential benefits of beam hopping (BH) and non-orthogonal multiple access (NOMA), we exploit the time-domain flexibility in multi-beam satellite systems by optimizing BH design, and enhance the power-domain flexibility via NOMA. In this paper, we investigate the synergy and mutual influence of beam hopping and NOMA. We jointly optimize power allocation, beam scheduling, and terminal-timeslot assignment to minimize the gap between requested traffic demand and offered capacity. In the solution development, we formally prove the NP-hardness of the optimization problem. Next, we develop a bounding scheme to tightly gauge the global optimum and propose a suboptimal algorithm to enable efficient resource assignment. Numerical results demonstrate the benefits of combining NOMA and BH, and validate the superiority of the proposed BH-NOMA schemes over benchmarks. [ABSTRACT FROM AUTHOR]

Signal-to-interference-plus-noise ratio (SINR) evaluates the quality of the link between the base station (BS) and the user equipment (UE), taking into account the interference coming from neighboring cells and the thermal noise. Predicting the SINR with good accuracy ensures proper planning and optimization of radio coverage and good estimation of end-user throughput. In this paper, SINR is predicted at any user location based on UE geo-located measurements, using the Kriging technique and assuming that the received signals from serving and interfering cells undergo shadowing fading. Unlike previous works where only the prediction of the received powers is studied, the current functionality also considers the prediction error on the received signals. We consider both spatial correlation of shadowing signals for each cell and also inter-cell correlation and we propose a new model to generate multiple links shadowing signals for the downlink of cellular networks. For validation, we perform Monte-Carlo network simulations in which the shadowing samples are generated based on the proposed model. Results show a good prediction accuracy of the SINR at a new location resulting in a good estimation of the end-user perceived average data rate. [ABSTRACT FROM AUTHOR]

The next-generation cellular system operating in millimeter-wave (mm-Wave) frequencies requires different signal processing schemes from the legacy LTE system. Among these, beam management aligns the transmitter-receiver pairs with narrow beams and serves as an essential initial acquisition procedure. This paper introduces a novel angle-based downlink precoding strategy, including beam selection and user scheduling for a full dimension (FD) MIMO-OFDM system. The highly directional mm-Wave channel allows the system to perform user/beam scheduling rather than pure user scheduling during the precoding process. A beam-based receive matrix eliminates the remaining intra-cell interference caused by overlapped beams. The performance analysis proves the superiority of the proposed precoding and scheduling strategies over the conventional technique. [ABSTRACT FROM AUTHOR]

This paper proposes a cascade blockage model for analyzing the vision that a user has of a wireless network. This model, inspired by the classical multiplicative cascade models, has a radial structure meant to analyze blockages seen by the receiver at the origin in different angular sectors. The main novelty is that it is based on the geometry of obstacles and takes the joint blockage phenomenon into account. We show on a couple of simple instances that the Laplace transforms of total interference satisfies a functional equation that can be solved efficiently by an iterative scheme. This is used to analyze the coverage probability of the receiver and the effect of blockage correlation and penetration loss in both dense and sparse blockage environments. Furthermore, this model is used to investigate the effect of blockage correlation on user beamforming techniques. Another functional equation and its associated iterative algorithm are proposed to derive the coverage performance of the best beam selection in this context. In addition, the conditional coverage probability is also derived to evaluate the effect of beam switching. The results not only show that beam selection is quite efficient for multi-beam terminals, but also show how the correlation brought by blockages can be leveraged to accelerate beam sweeping and pairing. [ABSTRACT FROM AUTHOR]

Traditional link adaptation (LA) schemes in cellular network must be revised for networks beyond the fifth generation (b5G), to guarantee the strict latency and reliability requirements advocated by ultra reliable low latency communications (URLLC). In particular, a poor error rate prediction potentially increases retransmissions, which in turn increase latency and reduce reliability. In this paper, we present an interference prediction method to enhance LA for URLLC. To develop our prediction method, we propose a kernel based probability density estimation algorithm, and provide an in depth analysis of its statistical performance. We also provide a low complexity version, suitable for practical scenarios. The proposed scheme is compared with state-of-the-art LA solutions over fully compliant 3rd generation partnership project (3GPP) calibrated channels, showing the validity of our proposal. [ABSTRACT FROM AUTHOR]

Channel state information (CSI) estimation is one of the key techniques for improving the performance of wireless communication systems. Meanwhile, the fifth generation wireless communication systems require higher accuracy and lower latency for CSI estimation. In this paper, the methods of noise modeling and online learning are combined to improve the accuracy and reduce the latency. The complex noise environment (considering noise and interference together) is modeled as a specific mixture of Gaussian (MoG) distribution because of its widely approximation capability to any continuous distribution. The MoG CSI estimation (MoG-CE) model and expectation maximization (EM) algorithm are introduced as one of the baseline methods. Further, the parameters of the model can be updated in real time based on the prior knowledge of historical information. Therefore, the online MoG CSI estimation (O-MoG-CE) model and online MoG dynamic CSI estimation (O-MoG-D-CE) model are proposed for time-invariant and time-varying CSI estimations, respectively. The above models can not only self-adapt to various complex communication scenarios robustly but also achieve online and dynamic CSI estimation to improve the accuracy and reduce the latency significantly. In addition, the proposed models can be formulated as standard maximum a posteriori estimations and efficient online expectation maximization (OEM) algorithms are applied for the estimations in a pure machine learning fashion. Comparing with baseline methods, the simulation results demonstrate the superiority of the proposed methods in terms of the accuracy, latency and computation consumption. [ABSTRACT FROM AUTHOR]

The accelerated convergence of digital and real-world lifestyles has imposed unprecedented demands on today’s wireless network architectures, as it is highly desirable for such architectures to support wireless devices everywhere with high capacity and minimal signaling overhead. Conventional architectures, such as cellular architectures, are not able to satisfy these requirements simultaneously, and are thus no longer suitable for the future era. In this paper, we propose a capacity-centric (C2) architecture for future wireless networking. It is designed based on the principles of maximizing the number of non-overlapping clusters with the average cluster capacity guaranteed to be higher than a certain threshold, and thus provides a flexible way to balance the capacity requirement against the signaling overhead. Our analytical results reveal that C2 has superior generality, wherein both the cellular and the fully coordinated architectures can be viewed as its extreme cases. Simulation results show that the average capacity of C2 is at least three times higher compared to that of the cellular architecture. More importantly, different from the widely adopted conventional wisdom that base-station distributions dominate architecture designs, we find that the C2 architecture is not over-reliant on base-station distributions, and instead the user-side information plays a vital role and cannot be ignored. [ABSTRACT FROM AUTHOR]

Network virtualization allows the coexistence of multiple network slices over a shared physical infrastructure, each delivering a service with own requirements in terms of quality of service, coverage, and time span. Spectrum is an expensive commodity, so it must be managed among these slices in the most efficient way. However, current spectrum sharing techniques are too rigid to address all combinations of service requirements and properly exploit the new air interface flexibility and network deployment options introduced by the Fifth Generation (5G) mobile networks. In this paper, we extend the state of the art first by proposing a novel spectrum sharing scheme that supports an unlimited number of radio networks. Second, we propose a 5G compliant novel service-based network management architecture to integrate Machine Learning (ML) algorithms for Radio Resource Management (RRM), including spectrum sharing. Finally, we propose a new three-stage ML framework that exploits forecasting, clustering and reinforcement learning algorithms to implement the proposed spectrum sharing scheme. Our proposed solution can allow an arbitrary number of 5G network slices to share spectrum more effectively and more dynamically either with each other or with other radio networks. [ABSTRACT FROM AUTHOR]

Fifth-Generation mobile networks for Railway (5G-R) is a promising train-ground communication solution to deal with the design challenges on ubiquitous connections and high reliability transmission for smart railways. Considering the linear coverage scenario along the railway lines, deep overlapping coverage can enhance the coverage reliability, but the capacity of cell edge users is plagued by severe inter-cell interference. In this paper, the fundamental performance of 5G-R systems with linear redundant coverage is investigated. We quantitatively analyze the impact of inter-cell interference on the capacity of cell edge users, in which the exponential effective signal to interference plus noise (SINR) mapping method is utilized to analyze SINR distribution of 5G-R users. Then, we investigate the coverage reliability considering the base station (BS) failure. Finally, taking cell edge user capacity as the optimization objective, we analyze the cell edge user capacity-coverage reliability tradeoff for 5G-R systems, and then provide useful insights into BS deployment for 5G-R systems through performance simulations and numerical results. [ABSTRACT FROM AUTHOR]

Existing vehicle re-identification (Re-ID) methods usually suffer from intra-instance discrepancy and inter-instance similarity. The key to solving this problem lies in filtering out identity-irrelevant interference and collecting identity-relevant vehicle details. In this paper, we aim to design a robust vehicle Re-ID framework that trains a model guided by knowledge vectors yet is able to disentangle the identity-relevant features and identity-irrelevant features. Toward this end, we propose a novel Multi-scale Knowledge-Aware Transformer (MsKAT) to build a knowledge-guided multi-scale feature alignment framework. First, we construct a Knowledge-Aware Transformer (KAT) to interact with semantic knowledge and visual feature. KAT mainly includes State elimination Transformer (SeT) to eliminate state (camera, viewpoint) interference and Attribute aggregation Transformer (AaT) to gather attribute (color, type) information. Second, to learn the knowledge-guided sample differences, we propose to encourage the separation of identity-relevant features and identity-irrelevant features by a Knowledge-Guided Alignment loss ($\mathcal {L}_{KGA}$). Specifically, $\mathcal {L}_{KGA}$ suppresses the difference between knowledge-guided positive pairs and the similarity between knowledge-guided negative pairs. Third, with the multi-scale settings of KAT and $\mathcal {L}_{KGA}$ , our model can capture knowledge-guided visual consistency features at different scales. Extensive evidence demonstrates our approach achieves new state-of-the-art on three widely-used vehicle re-identification benchmarks. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) transceivers suffer from optical interference and ambient light saturation, which severely affect the reception of optical signals. In this paper, we discuss various techniques for the suppression of ambient light in electronic, optical, communication and wavelength domains with focus on VLC systems for vehicular communication. Designing transceivers for outdoor VLC systems is challenging due to the dynamic environment and the time-varying nature of interference from different types of ambient light sources. This is a unique study that addresses the problem of optical interference in multiple physical domains and offers qualitative insights in a systematic manner. [ABSTRACT FROM AUTHOR]

Indoor vehicle localization is an underlying technology for realizing Autonomous Valet Parking (AVP), which demands high accuracy and reliability. However, existing localization technologies, such as GPS, WiFi, Bluetooth, suffer from either low availability or high cost, which are not practical in the real world. In order to put AVP into practice, We desperately need an efficient and reliable indoor vehicle localization technology. In this paper, we propose a Deep learning and FM fingerprint map based indoor vehicle Localization method, namely DeFLoc, which leverages FM signals to achieve accurate and practical indoor localization. In order to reduce the workload of the FM fingerprints collecting process, DeFLoc uses partially uniform sampling to decrease sample data volume and reconstructs the FM fingerprint map from collected incomplete fingerprints precisely using a dedicated deep Convolutional Neural Network (CNN). To alleviate the influence of signal distortions in some FM frequencies, we further design smooth layers in the neural network for improving the accuracy of map reconstruction. Moreover, we devise a continuous vehicle localization algorithm by considering the preferences of vehicle movements to assist us to calibrate localization. We implemented a prototype of DeFLoc and conducted extensive experiments both in simulation and practice. Evaluation results show that our proposed reconstruction model improves accuracy by 40% over conventional matrix completion methods even under the 60% data missing rate. With the precisely reconstructed fingerprint map, DeFLoc achieves over 90% localization accuracy, which indicates DeFLoc can realize accurate and practical indoor vehicle localization. [ABSTRACT FROM AUTHOR]

In visible light communication systems, the ability to suppress interference caused by other light sources is a major benefit towards performance improvements. Especially for large transmitter arrays or even multi-cell arrangements, the interference problem needs to be handled. In previous work, we have presented a liquid crystal display (LCD) used as an adaptive interference-suppression filter mounted in front of each photodetector. The display elements are switched on and off in such a way that light emitted by unwanted light sources ideally is blocked, but light emitted by desired light sources reaches the detector. The pattern generated by the LC display has strong impact on the system performance. In this paper, we propose combined precoding in conjunction with LCD-based interference suppression in order to increase the signal-to-interference-plus-noise ratio and to ensure user fairness in massive MIMO scenarios. The suggested precoding strategy uses a new heuristic optimization approach based on the Santa Claus problem on unrelated machines known from computer sciences, and employs only binary entries in the weighting matrix. Corresponding results are compared with a genetic evolutionary optimization strategy and with conventional zero-forcing precoding. Regarding performance evaluation, we perform numerical ray-tracing simulations and present a room-scale VLC testbed for experimental verification. [ABSTRACT FROM AUTHOR]

This paper raises the question of whether the secondary user can attain a nonzero achievable rate without causing capacity loss at the primary user, even when it is utilizing the channel in cognitive radio networks (CRNs). It is assumed that secondary nodes are aware of the channel state information (CSI) of primary nodes and of how the primary nodes operate, whereas the primary nodes operate with no prior knowledge about the secondary nodes. We first propose a full-duplex relaying protocol for the cognitive radio (CR) network with a single relay node shared by both the primary and the secondary networks. In this case, the shared relay is assumed to have full CSI of the primary nodes and to know how the primary and secondary nodes operate. The proposed relaying protocol enables the secondary network to achieve nonzero rates without causing capacity loss at the primary network in a certain channel condition. Then, we also propose three half-duplex relaying protocols for the CR network with two relays, each of which is dedicated to the primary or the secondary network, respectively. It is shown that appropriate combinations of the proposed three half-duplex relaying protocols make it always possible for the secondary network to achieve nonzero rates without causing capacity loss at the primary network. Achievable rates of the proposed full- and half-duplex relaying protocols are evaluated through extensive computer simulations. Simulation results show that the proposed relaying protocols provide nonzero achievable rates of the secondary network over a wide range of signal-to-noise ratios (SNRs). [ABSTRACT FROM PUBLISHER]

Maintaining a good trajectory and the transmission of signal towards the target, is a very delicate subject in the field of transmission. In particular, wireless transmission which is permitted on a sensible physical medium to guide the trajectory of the wave, is the greatest concern of radio transmission. Following this challenge, we propose approaches to the applicability of the Beamforming (BF) techniques and Convolutional Neural Network (CNN) on a signal received from an antenna array. The approaches consist of monitoring and driving the signal trajectory from the antenna output to the device target detecting and removing all interferences which may deflect the signal from the desired direction by pivoting it in another one (around 45 degrees). The work is based on experimentation with BF approaches such as the MVDR (minimum-variance distortionless-response), and the LCMV (linear-constraint minimum-variance) to optimize the transmission phase. We chose this BF technique above others because; the LCMV technique detects and reduces interference signals efficiently by utilizing calculated weight vectors to direct the powerful beam-based signal towards its intended target. The LCMV technique also ensure that each sensor outcomes are filtered with a finite impulse response (FIR) filter to satisfy narrow-band signal restrictions. For accurate signal transmission and reception with noise and interference suppression, LCMV seems promising. We use LCMV to cancel out interference and maintain the desired signal. When noise is not separable from the data (signal), an estimate of the sample covariance matrix is obtained from the data. Some approaches are taken to specify constraints, such as amplitude and derivative constraints. For example, specifying weights that suppress spurious signals from a particular direction while transmitting signals from a different direction without distortion. To avoid self-cancellation of signals, the LCMV permits multiple constraints to be placed along the target direction (directional vector). The last developed (marginal) approach is about the application of a 24-layer CNN model exploiting in depth all features and components of the Device-To-Device (D2D) signal. Three different sizes of convolution kernels are using (32, 64 and 128) to exploit the original signal which is noisy to remove the noise on the signal and to recover the desired signal. The scientific merit and novelty value of this paper is to allow us to analyse and understand how MVDR and LCMV filters identify and reduce interference in new expressions for their output signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR). We showed the trade-off between noise reduction and interference rejection. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the performance of an intelligent reflecting surface (IRS) aided multiple-input single-output (MISO) wireless system in the absence of direct link. We focus on the practical scenario where the N reflecting elements of the IRS have a spatial correlation between them, and both the destination and IRS are subject to interferences. Moreover, the IRS design with discrete phase shifting is considered. In order to quantify the impact of spatial correlation, phase error and interference on the system performance, we provide closed-form and asymptotic expressions for the system outage probability. Additionally, approximations for the average channel capacity are also derived. To gain further insight, the achievable diversity gain of both partial and full correlation cases is quantified. The analytical and simulation results reveal that spatial correlation has a dominant effect and reduces the system diversity order, but it is beneficial to the average channel capacity. Phase error results in performance degradation. Interference degrades the system performance only by reducing the coding gain. Compared to the direct path, interference from the reflected path plays a major role in reducing the system performance. [ABSTRACT FROM AUTHOR]

Efficiently scanning for space signals and accurately detecting them from noisy environment is essential in space communication. Various unwanted interferences also present in space that may hamper the perfect detection process. This paper proposes a novel near-field circular beamformer (NCB) that will perfectly detect the desired source signal from any direction and position in space. To improve the robustness of NCB against Direction of Arrival (DOA) error, distance error, unwanted interferences and noises, this work also offers robust NCBs (RNCB) using robust Optimal Diagonal Loading (ODL) and Variable Diagonal Loading (VDL) techniques. While searching for wanted signal, the beamformer provides a primary lobe at the look direction and shows some secondary unwanted side lobes for noise and interference. Sometimes these undesired side lobe levels (SLL) become so severe that it may create conflict in locating the precise position of the desired source. To reduce these SLL, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) techniques have been applied to RNCB. The simulation results show that the optimized RNCB significantly diminishes the objectionable SLL of non-optimized RNCB by choosing appropriate weight vector of antenna array without affecting the other antenna parameters. Artificial Neural Network (ANN) have also been used to predict the weight vector for minimum SLL. [ABSTRACT FROM AUTHOR]