Interference In Networks Research Articles

Deep Reinforcement Learning-Assisted Optimization for Resource Allocation in Downlink OFDMA Cooperative Systems.

This paper considers a downlink resource-allocation problem in distributed interference orthogonal frequency-division multiple access (OFDMA) systems under maximal power constraints. As the upcoming fifth-generation (5G) wireless networks are increasingly complex and heterogeneous, it is challenging for resource allocation tasks to optimize the system performance metrics and guarantee user service requests simultaneously. Because of the non-convex optimization problems, using existing approaches to find the optimal resource allocation is computationally expensive. Recently, model-free reinforcement learning (RL) techniques have become alternative approaches in wireless networks to solve non-convex and NP-hard optimization problems. In this paper, we study a deep Q-learning (DQL)-based approach to address the optimization of transmit power control for users in multi-cell interference networks. In particular, we have applied a DQL algorithm for resource allocation to maximize the overall system throughput subject to the maximum power and SINR constraints in a flat frequency channel. We first formulate the optimization problem as a non-cooperative game model, where the multiple BSs compete for spectral efficiencies by improving their achievable utility functions while ensuring the quality of service (QoS) requirements to the corresponding receivers. Then, we develop a DRL-based resource allocation model to maximize the system throughput while satisfying the power and spectral efficiency requirements. In this setting, we define the state-action spaces and the reward function to explore the possible actions and learning outcomes. The numerical simulations demonstrate that the proposed DQL-based scheme outperforms the traditional model-based solution.

Development of New Spectral Amplitude Coding OCDMA Code by Using Polarization Encoding Technique

OCDMA is an optical access technology that has a lot of potential because it can be asynchronously accessed and provides a higher level of security. The authors presented a new DW family code, a flexible double weight (FDW) code, and a novel polarization encoding approach in this paper. The new code is applicable to both odd- and even-weighted codes. The novel polarization encoding approach may be used for numerous wavelengths that overlap. Based on analytic principles, a comparison of two widely used spectrum amplitude-coding SAC-based OCDMA codes, notably modified frequency hopping (MFH), Hadamard, and the double weight (DW) code family. The comparison was based on observing the bit error rate (BER) in each situation. The DW code has a fixed weight of two. The FDW code was introduced to reduce phase-induced intensity noise and multiple access interference (MAI) in transmission networks. FDW codes are versions of the DW code family with weights larger than two. The FDW code outperforms the Hadamard, MFH, DW, modified double weight (MDW), and enhanced double weight (EDW) algorithms. FDW has the capacity to support up to 220 concurrent users. With the new polarization encoding technology, the FDW code can travel up to 60 km at a bit rate of 2.5 Gb/s and 40 km for a 10 Gb/s bit rate.

A Review on Cell-Free Massive MIMO Systems

Cell-free massive multiple-input multiple-output (CF mMIMO) can be considered as a potential physical layer technology for future wireless networks since it can benefit from all the advantages of distributed antenna systems (DASs) and network MIMOs, such as macro-diversity gain, high channel capacity, and link reliability. CF mMIMO systems offer remarkable spatial degrees of freedom and array gains to mitigate the inherent inter-cell interference (ICI) of cellular networks. In such networks, several distributed access points (APs) together with precoding/detection processing can serve many users while sharing the same time-frequency resources. Each AP can be equipped with single or multiple antennas, and hence, can provide a consistently adequate service to all users regardless of their locations in the network. This paper presents a detailed overview of the current state-of-the-art on CF systems. First, it performs a literature review of the conventional CF and scalable user-centric (UC) CF mMIMO systems in terms of the limited capacity of the fronthaul links and the connection between APs and user equipments (UEs). As beyond networks will rely on higher frequency bands, it is of paramount importance to discuss the impact of beamforming techniques that are being investigated. Finally, some of the CF promising enabling technologies are presented to emphasize the main applications in these networks.

Machine learning-optimized relay selection method for mitigating interference in next generation communication networks

Machine learning-optimized relay selection method for mitigating interference in next generation communication networks

Modified Heuristic Computational Techniques for the Resource Optimization in Cognitive Radio Networks (CRNs)

With the advancement of internet technologies and multimedia applications, the spectrum scarcity problem is becoming more acute. Thus, spectral-efficient schemes with minimal interference for IoT networks are required. Device-to-device communication (D2D) technology has the potential to solve the issue of spectrum scarcity in future wireless networks. Additionally, throughput is considered a non-convex and NP-hard problem, and heuristic approaches are effective in these scenarios. This paper presents two novel heuristic approaches for throughput optimization for D2D users with quality of service (QoS)-aware wireless communication for mobile users (MU): the modified whale colony optimization algorithm (MWOA) and modified non-domination sorted genetic algorithm (MNSGA). The performance of the proposed algorithms is analyzed to show that the proposed mode selection technique efficiently fulfills the QoS requirements. Simulation results show the performance of the proposed heuristic algorithms compared to other understudied approaches.

Impact Analysis of Intercell Interference in Cellular Networks for Navigation Applications

This article studies the intercell interference (ICI) effects in cellular networks for navigation applications. This is achieved through the derivation of an analytical expression for a cellular navigation receiver postcorrelation signal-to-noise power ratio (SNR) in the presence of multiple asynchronous cells. It reveals that in addition to the channel power, the cell loading rate and data modulation order for the interfering cells also play important roles in affecting the received signal quality of the desired cell. Furthermore, the time-of-arrival (TOA) estimation and positioning accuracy degradation due to the ICI is characterized by the Cramer–Rao and Ziv–Zakai lower bounds based on the derived postcorrelation SNR. Simulations are performed to verify the theoretical expressions and the results indicate that the ICI term can be treated as an additional Gaussian disturbance for characterizing the TOA estimation accuracy in cellular navigation receivers.

Throughput Analysis on Slotted Underwater Acoustic Sensor Networks With Guard Time

A guard time is a short time interval that is added between packet transmissions, and has been applied in time- slotted underwater acoustic sensor networks (UASNs) to balance the transmission rate and the inter-slot interference. In this article, we take a physical-layer approach and investigate the throughput performance in time-slotted UASN with guard time. Based on the geographical node distribution, we first derive the probability distribution of the arrival time of an interfering packet and further the distribution of the packet overlap duration. We then derive the expression of the probability density functions (pdfs) of the signal-to-interference-plus-noise ratio (SINR). Expressions for the outage probability and the normalized throughput of a typical link between two nodes are also obtained. We show that when the length of guard time is properly selected, time-slotted network with guard time has higher throughput than those without guard time. Simulation results validate the theoretical results and illustrate typical features of the interference in an underwater network.

Study of social network interference of memory processes in adolescence

Memory is the fundamental basis of human learning. Conceptual assessment and study of the memory mechanisms, its formation, consolidation and preservation at different levels of the structural and functional organization of the nervous system constitutes the necessary knowledge for solving many problems of practical and fundamental psychology. This article presents the study of memory, namely the process of its transition from a short-term to a more stable long-term form in a digital environment under the influence of the interference factor of social networks. The aim of the work is to study the influence of the social network interference factor on the processes of consolidation and reconsolidation of memory in adolescence. The total sample size was 68 adolescents aged 12–17 years. The authors conducted an experiment to achieve this goal. Its results showed that adolescents have difficulty remembering information in the form of short, successive videos in the digital space. It was found that videos that are characterized not only by visual accompaniment, but also by speech production are subject to better memorization. Under the influence of the interfering factor of social networks, adolescents faced difficulties in linking individual elements of stimuli with each other – they connected elements of one video with elements of another. According to one of the provisions of the theory of interference, information storage is characterized by the “unbinding” of representations of objects, followed by their reverse linking in an arbitrary combination during further reproduction.

Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications.

Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention. Vehicle-to-vehicle (V2V) communication should provide low-latency and highly reliable services through direct communication between vehicles, improving safety. In particular, as the number of vehicles increases, efficient radio resource management becomes more important. In this paper, we propose a deep reinforcement learning (DRL)-based decentralized resource allocation scheme in the V2X communication network in which the radio resources are shared between the V2V and vehicle-to-infrastructure (V2I) networks. Here, a deep Q-network (DQN) is utilized to find the resource blocks and transmit power of vehicles in the V2V network to maximize the sum rate of the V2I and V2V links while reducing the power consumption and latency of V2V links. The DQN also uses the channel state information, the signal-to-interference-plus-noise ratio (SINR) of V2I and V2V links, and the latency constraints of vehicles to find the optimal resource allocation scheme. The proposed DQN-based resource allocation scheme ensures energy-efficient transmissions that satisfy the latency constraints for V2V links while reducing the interference of the V2V network to the V2I network. We evaluate the performance of the proposed scheme in terms of the sum rate of the V2X network, the average power consumption of V2V links, and the average outage probability of V2V links using a case study in Manhattan with nine blocks of 3GPP TR 36.885. The simulation results show that the proposed scheme greatly reduces the transmit power of V2V links when compared to the conventional reinforcement learning-based resource allocation scheme without sacrificing the sum rate of the V2X network or the outage probability of V2V links.

Assessing and mitigating the radar–radar interference in the German C-band weather radar network

Abstract. The national German weather radar network operates in C-band between 5.6 and 5.65 GHz. In a radar network, individual transmit frequencies have to be chosen such that radar–radar-induced interferences are avoided. In a unique experiment the Hohenpeißenberg research radar and five operational systems from the radar network were used to characterize radar–radar-induced interferences as a function of the radar frequency. The results allow assessment of the possibility of adding additional C-band radars with magnetron transmitters into the existing network. Based on the experiment, at least a 15 MHz separation of the nominal radar frequency is needed to avoid a radar–radar interference. The most efficient mitigation of radar–radar interference is achieved by the “Radar Tango”, which refers to the synchronized scanning of all radar systems in the network. Based on those results, additional C-band radar systems can be added to the German weather radar network if a further improvement of the radar coverage is needed.

Estimating Causal Effects of Multi-Valued Treatments Accounting for Network Interference: Immigration Policies and Crime Rates

Policy evaluation studies, which assess the effect of an intervention, face statistical challenges: in real-world settings treatments are not randomly assigned and the analysis might be complicated by the presence of interference among units. Researchers have started to develop methods that allow to manage spillovers in observational studies; recent works focus primarily on binary treatments. However, many studies deal with more complex interventions. For instance, in political science, evaluating the impact of policies implemented by administrative entities often implies a multi-valued approach, as a policy towards a specific issue operates at many levels and can be defined along multiple dimensions. In this work, we extend the statistical framework about causal inference under network interference in observational studies, allowing for a multi-valued individual treatment and an interference structure shaped by a weighted network. The estimation strategy relies on a joint multiple generalized propensity score and allows one to estimate direct effects, controlling for both individual and network covariates. We follow this methodology to analyze the impact of the national immigration policy on the crime rate, analyzing data of 22 OECD countries over a thirty-years time frame. We define a multi-valued characterization of political attitude towards migrants and we assume that the extent to which each country can be influenced by another country is modeled by an indicator, summarizing their cultural and geographical proximity. Results suggest that implementing a highly restrictive immigration policy leads to an increase of the crime rate and the estimated effect is larger if we account for interference.

D2D Communication Network Interference Coordination Scheme Based on Improved Stackelberg

The sudden explosive growth of data in intelligent devices and existing communication networks has brought great challenges to existing communication networks. On the one hand, D2D (device to device) technology greatly improves the utilization of spectrum resources; on the other hand, it improves the communication quality of users. It has become an important part of the future communication network. Aiming at the problem that the existing D2D communication network system has complex user interference, and the communication quality of cellular users is difficult to guarantee, a D2D communication network interference coordination scheme based on improved Stackelberg is proposed. Using resource allocation and power control to solve the interference coordination problem, this paper proposes an improved Stackelberg model based on DQN (deep Q network), establishes the master–slave game between cellular users and multiplexing resource users (D2D users; relay communication users), optimizes the cost parameters in the Stackelberg mode and improves the transmission power and resource allocation scheme of multiplexing resource users. The simulation results show that compared with similar algorithms, the algorithm proposed in this paper has the best performance in guaranteeing the QoS of cellular users in the system and has good interference management capability for D2D communication networks.

Interference Alignment for Physical Layer Security in Multi-User Networks With Passive Eavesdroppers

We investigate the physical layer security (PLS) in multi-user interference networks. In particular, we consider secure transmission from a legitimate source (Alice) to a legitimate destination (Bob), coexisting with multiple passive eavesdroppers (Eves), as well as multiple legitimate transceivers. By assuming that only statistical channel state informations (CSIs) of Eves and local CSIs of legitimate users are available, we propose an artificial noise (AN) assisted interference alignment (IA) for security enhancement. Unlike traditional IA based security approaches which may result in secret signal cancellation, we design a modified alternating minimization (AM) scheme to overcome this threat, by incorporating the max-eigenmode beamforming (MEB) for secure transmission. Moreover, by partitioning the IA equation into three independent subsets and their combinations, a much tighter necessary condition for IA feasibility is established. We also provide guiding insights into the practical system designs, including useful guidelines for the selection of the dimension of AN. Furthermore, the power allocation ratio between the secret signal and the AN signal is optimized to minimize the secrecy outage probability (SOP), subject to a minimum secrecy rate constraint. Numerical results demonstrate that our design can enhance both quality and security of the secret signal, and thus is suitable and reliable for PLS in interference networks.

Resource Allocation Based on SFLA Algorithm for D2D Multicast Communications

Multicast device-to-device (D2D) communication technology is considered as one of the new technologies in the fifth generation (5G) networks that directly addresses the need for content sharing among internet users. In fact, when direct communication is available between devices, the spectral efficiency is improved by reusing the licensed cellular spectrum. The current studies show that D2D communication increases network capacity and reduces latency. In order to achieve the alternate capabilities, coordination is required to implement interference management. We considered subcarrier allocation for the uplink, in addition to the power control that takes place on the underlay network. The completed data rate in single multicast communication is significantly reduced and limited by nodes with lower channel quality. In this paper, we used Shuffled Frog Leaping Algorithm (SFLA) for resource allocation (RA) in D2D multicast communications. We compared the results of the SFLA algorithm with the Firefly Algorithm (FA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO); in terms of D2D user throughput, Cellular User (CU) throughput, network average throughput, network interference and signal interference noise ratio (SINR) target. The simulation results show that SFLA clearly outperforms other algorithms in terms of data rate under the high pressure of infeasibility.

УДОСКОНАЛЕННЯ МЕТОДУ ВИЯВЛЕННЯ ТА ЛОКАЛІЗАЦІЇ НЕЛЕГАЛЬНИХ ТОЧОК ДОСТУПУ ДО БЕЗДРОТОВОЇ МЕРЕЖІ ОБ'ЄКТІВ ІНФОРМАЦІЙНОЇ ДІЯЛЬНОСТІ

Extensive use of mobile devices has led to increased Internet connections and the deployment of new wireless LANs. According to the latest Cisco research, by 2023, 66% of the world's population will be Internet users worldwide. More than 28 billion devices will be connected to the global network. In the last two decades, we have witnessed the birth and development of a technology that has significantly changed our work and life - IEEE 802.11, also known as Wi-Fi. Wi-Fi is a favorite way to connect to the Internet because of its ease of use and flexibility. To connect to a wireless network, you only need to be within range. That is, consumers and businesses will increasingly rely on mobile networks. However, it should be noted that each new opportunity of digitalization also gives new opportunities to cybercriminals and therefore, the problem of security of wireless networks today is one of the main problems of IT technologies. The inevitable proliferation of wireless networks and the growing traffic in these networks can lead to many information security incidents. The main threats are aimed at interception, breach of confidentiality and integrity of transmitted data, attacks on the availability of transmission channel nodes and their substitution. The article analyzes the existing methods of detecting unauthorized access points to information. The method of detection and localization of unauthorized access points to information circulating in the wireless network at the objects of information activities has been improved. Natural modeling of detection of unauthorized interference in the information wireless network of the enterprise was carried out. Full-scale simulation confirmed the accuracy of localization of an unauthorized point of access to information in the Wi-Fi network – up to 2 m. This will allow timely detection and localization of unauthorized access points to information in the wireless network of enterprises and institutions.

Aperture-dynamic properties of the sensor information in mobile object management systems

This article discusses the aperture-dynamic properties of sensor devices used in control systems for unmanned devices, in particular, automatic manipulators operating as part of robotic complexes (RTCS). The effects of interference affecting the software control systems of automatic manipulators using high-speed algorithms with interference-resistant coding under operating conditions in production are considered. It is known that in production conditions, there are sources of network interference, interference of an informational nature through communication channels, wired channels, radio engineering channels in a large radio engineering range, from vibration processes, interference and distortion in optoelectronic networks, interference that violates the safe working conditions of radioelectronic equipment. Examples of implemented algorithms based on aperture operators for controlling automatic manipulators with various program cycles, taking into account the peculiarities of production, are given. A comparative analysis was carried out with analogues having similar technological characteristics. control of automatic manipulators operating as part of robotic complexes.

Performance Analysis of Scalable User-Centric HC-RAN Over Rician Fading Channels

In this paper, we analyze the uplink performance of a scalable user-centric heterogeneous cloud-radio access network (HC-RAN) implemented by using dynamic cooperative clustering (DCC) framework over a Rician fading channel with phase shifts. This channel describes various practical aspects, such as a deterministic line of sight (LoS) component and random non-LoS (N-LoS) components. To account for the phase shifts due to user mobility, the phase of the LoS component is modeled as a uniformly distributed random variable. We assume that phase information is available at each remote radio head (RRH). We derived the phase aware-minimum mean square error (PA-MMSE) and phase unaware Linear-MMSE estimators and obtained the channel state information (CSI). We derived a closed-form expression for the achievable spectral efficiency (SE) to evaluate the system performance with both estimators. To address the effect of coherent interference in the ultra-dense networks, we developed a two-layer decoding scheme in uplink in which maximum ratio (MR) combining is performed at the RRH and large-scale fading decoding is performed at the base-band unit (BBU) pool. Based on the obtained results, the proposed method enhanced the uplink performance in an ultra-dense scenario. It is validated by comparing it with the simulation results. Moreover, the performance loss caused by the lack of phase knowledge will depend on the pilot sequence length.

Quantum Computing Based Optimization for Intelligent Reflecting Surface (IRS)-Aided Cell-Free Network

Intelligent reflecting surface (IRS) enables the control of propagation characteristics and is attracting considerable attention as a technology to improve energy utilization efficiency in 6th generation mobile communication systems. As cell-free networks with multiple distributed base stations (BSs) can communicate in a coordinated manner, they are being actively researched as a new network architecture to resolve the problem of inter-cell interference in conventional cellular networks. The introduction of the IRS into the cell-free network can avoid shadowing at a lower cost with less power consumption. Thus, in this study, we considered the case of communication with user equipment (UE) in a shadowing environment using IRS in a cell-free network that contained distributed BSs with a single antenna. Moreover, the selection of multiple access methods was derived according to the numbers of BSs, IRSs, and UEs. In addition, we proposed a quadratic unconstrained binary optimization formulation to optimize the IRS reflection coefficient using quantum computing. The simulation results verified that the application of the proposed method resulted in a more efficient communication. Thus, this study clarifies that the optimum control method in every communication environment and aims to act as a stepping stone to optimize the entire cell-free system.

Real-Time Investigation of Cross-Technology Interference in Heterogeneous IoT Networks

Real-Time Investigation of Cross-Technology Interference in Heterogeneous IoT Networks

Channel Training & Estimation for Reconfigurable Intelligent Surfaces: Exposition of Principles, Approaches, and Open Problems

Reconfigurable intelligent surfaces (RIS) are passive controllable arrays of small reflectors that direct electromagnetic energy towards or away from the target nodes, thereby allowing better management of signals and interference in a wireless network. The RIS has the potential for significantly improving the performance of wireless networks. Unfortunately, RIS also multiplies the number of Channel State Information (CSI) coefficients between the transmitter and receiver, which magnifies the challenges in estimating and communicating the channel state information. Furthermore, the simplicity and cost-effectiveness of the passive RIS also implies that the incoming links are not locally estimated at the RIS, and fresh pilots are not inserted into outgoing RIS links. This introduces new challenges for training and estimation of channel state information. The rapid growth of the literature on CSI acquisition in RIS-aided systems has been accompanied by variations in the underlying assumptions, models, and notation, which can obscure the similarities and differences of various techniques, and their relative merits. This paper presents a comprehensive exposition of principles and approaches in RIS channel estimation. The basic ideas underlying each class of techniques are reduced to their simplest form under a unified model and notation, and various approaches within each class are discussed. Several open problems in this area are identified and highlighted.