Dynamic Communication Environment Research Articles

Sparse representation for massive MIMO satellite channel based on joint dictionary learning

AbstractA constrained joint dictionary learning (CJDL) algorithm for high‐precision channel representation in massive multiple input multiple output (MIMO) satellite systems is proposed. Furthermore, taking into account the angular reciprocity of massive MIMO satellite systems, joint dictionary learning can establish a common support basis for both uplink and downlink. Previous deterministic dictionary has utilized deterministic basis, such as discrete Fourier transform (DFT) or orthogonal DFT (ODFT) basis, which tend to represent noise interference as part of channel characteristics. Furthermore, this deterministic dictionary is not able to adapt to dynamic communication environments. However, dictionary learning has shown the potential to significantly improve the accuracy of channel representation. Nevertheless, current research on training dictionary lacks analysis regarding constraints and boundary requirements, resulting in a suboptimal basis. To address this issue, conditional constraints associated with joint dictionary for channel representation are analysed. To screen for optimal basis, the joint dictionary is subject to constraints, including uplink and downlink constraints. Furthermore, the authors aim to quantify the maximum boundary of joint dictionary learning. Additionally, a joint dictionary updating method with singular value decomposition under constraint boundary conditions is proposed. Simulation results demonstrate that the proposed CJDL algorithm provides a more accurate and robust channel representation.

Open Set Domain Adaptation for Automatic Modulation Classification in Dynamic Communication Environments

Open Set Domain Adaptation for Automatic Modulation Classification in Dynamic Communication Environments

Contactless Diseases Diagnoses Using Wireless Communication Sensing: Methods and Challenges Survey

Respiratory illness diagnosis and continuous monitoring are becoming popular as sensitive markers of chronic diseases. This interest has motivated the increased development of respiratory illness diagnosis by exploiting wireless communication as a sensing system. Several methods for diagnosing a respiratory illness are based on multiple sensors and techniques. Depending on whether the device embeds the sensor in contact with the body or not, these techniques are commonly categorized as contact based or contactless. Contactless methods have gained increasing popularity due to their ubiquitous nature, non-intrusiveness, and low cost. However, contactless methods are difficult to implement, with several challenges such as dynamic wireless communication environments. This article comprehensively reviews all contactless respiratory illnesses using wireless communication sensing methods, their associated challenges, and issues. In addition, applications of respiratory illness diagnosis methods using wireless communication are provided to investigate each method's potential development and applicability. Continuous and accurate diagnosis of respiratory illness using wireless communication sensing systems can assist caregivers in enhancing the care quality and bestowing patients with more freedom for both inpatients and outpatients. Furthermore, wireless communication monitoring systems could lead to treatment plans remotely more effectively, decrease the duration of patient stays in medical facilities, and reduce overall treatment costs.

Recurrent neural network and federated learning based channel estimation approach in mmWave massive MIMO systems

AbstractSo far, various data‐driven approaches have been presented to obtain channel state information (CSI) in millimeter wave multiple‐input‐multiple‐output wireless networks. In almost all previous works, training and testing channels were assumed to have the same distribution, which may not be the case in practice. In this article, we address this challenge by proposing a learning framework that is a combination of a recurrent neural network (RNN) model and a deep neural network (DNN) for estimating CSI in a dynamic wireless communication environment. Furthermore, we use federated learning to train the learning‐based channel estimation model. More specifically, we introduce a two‐stage downlink pilot transmission procedure, where in the initial stage, long frame length downlink pilot signals are used to train the introduced RNN‐DNN model. Following that, users will receive shorter‐frame‐length pilot signals that can be used for CSI estimation. To speed up the training procedure of the proposed network, we first generate a pre‐trained model and then modify it according to the collected data samples. Simulation results demonstrate that, when the channel distribution is unavailable, the proposed approach performs significantly better than the most recent channel estimation algorithms in terms of estimation performance and computational complexity.

Learning-Based Intelligent Reflecting Surface-Aided Cell-Free Massive MIMO Systems

Intelligent reflecting surface (IRS) is amenable to assisting cell-free wireless networks because of its low cost and high spectrum efficiency. However, solving the IRS optimization problem usually requires high computational complexity. Therefore, we propose a novel multi-agent reinforcement learning (MARL) based joint optimization scheme to improve the system performance. First, we model the MARL framework to portray the dynamic wireless communication environment and interactive agent entities, which interprets the relationship between reinforcement learning and communication strategies. Moreover, in an IRS-aided cell-free massive MIMO system, entries are collaborated by the CPU for joint optimization, and the actions are continuous. Thus we propose a multi-agent deep deterministic policy gradient scheme for jointly optimizing the user's power control and IRS's passive beamforming (MADDPG-JPA-PB). Via centralized training and decentralized execution, the system gradually converges into an optimized policy associated with the maximization of the success transmission rate. Simulation results verify that the performance of the proposed scheme outperforms various independent deep deterministic policy gradient (DDPG) schemes and traditional optimization methods.

Deep Reinforcement Learning and NOMA-Based Multi-Objective RIS-Assisted IS-UAV-TNs: Trajectory Optimization and Beamforming Design

In this paper, we discuss the co-optimized performance of multi-reconfigurable intelligent surface (RIS)-assisted integrated satellite-unmanned aerial vehicle-terrestrial network (IS-UAV-TN), where the multiple vehicle users are applied to the network under consideration. The performance optimization of IS-UAV-TNs faces two major challenges: one is the obstacles in the transmission path and the other is the highly dynamic communication environment caused by the UAV movement for the multiple ground vehicle users. To tackle these above issues efficiently, we will install RIS on the UAV for the purpose of reshaping the wireless transmission path. In addition, non-orthogonal multiple access (NOMA) protocols are considered as a new paradigm to address spectrum shortage and enhance connection quality. Considering the UAV energy consumption, the satellite transmission beamforming matrix and RIS phase shift configuration, a multi-objective optimization problem is proposed to maximize the system achievable rate and minimize the UAV energy consumption during a specific mission. On this foundation, to facilitate the online decision problem, the deep reinforcement learning (DRL) algorithm is utilized to achieve real-time interaction with the communication environment. A multi-objective deep deterministic policy gradient (MO-DDPG) algorithm is proposed to search for sub-optimal solutions about the learning problem of multi-objective control policies in IS-UAV-TNs. Experimental results show that the method can simultaneously consider three optimization objectives and effectively adjust the optimal update policy according to the settings of different weight parameters.

OF-FSE: An Efficient Adaptive Equalization for QAM-Based UAV Modulation Systems

Quadrature amplitude modulation (QAM) is one of the essential components of unmanned 1 aerial vehicle (UAV) communications. However, the output signal accuracy of QAM deteriorates dramatically and even collapses in the case of UAVs in a harsh channel environment. This is due to the fractionally spaced equalization based on the multi-modulus blind equalization algorithm being implemented prior to carrier synchronization in QAM-based UAV modulation systems. The carrier frequency offset from the harsh channel signal thus contributes to the significantly degraded performance of MMA by suffering the fractionally spaced equalization. Therefore, in this paper, a novel offset feedback fractionally spaced equalization architecture for UAVs to eliminate the carrier frequency offset is first proposed. In this architecture, the carrier frequency offset allows estimated and incorporation into the input signal of fractionally spaced equalization to compensate for the offset. Moreover, a new multi-modulus decision-directed algorithm is presented for the novel architecture to improve the received signal accuracy of UAVs further. It enables adaptive optimization of the convergence process in accordance with the dynamic UAV communication environment employing the multi-modulus blind equalization algorithm and decision-directed blind equalization algorithm (MDD). Simulation results demonstrate the effectiveness of the OF-FSE framework in enabling the QAM-based UAV modulation systems operation in harsh channel scenarios. Moreover, the performance of the presented new MDD algorithm compared with baseline approaches is also confirmed.

Efficient Allocation of Resource for Energy Optimization in Heterogeneous V2X with Imperfect CSI

In heterogeneous V2X networks, the channel state information (CSI) undergoes rapid fluctuations owing to the dynamic mobility of vehicles. Therefore, it is important to redevelop a more suitable channel description for resource allocation in vehicular communications. In this paper, we concentrate on the effect of imperfect CSI with practical channel description to achieve maximum energy efficiency (EE) of vehicle-to-vehicle (V2V) links by jointly performing power-control and channel-allocation. A fractional nonlinear programming model is constructed to address power-control while satisfying link reliability and interference threshold requirements. Since the target function and restrictions are quasi-concave, the problem can be tackled via Dinkelbach and Lagrangian duality. In addition, to further improve the spectrum utilization for channel-allocation, we propose an improved Gale-Shapley strategy so that multiple V2V pairs can reuse the spectrum resource of a single cellular vehicular user (C-VUE). Finally, the numerical experiments simulations validate the performance of our offered strategy and the employed strategy effectively boosts energy-efficiency in dynamic communication environments.

Exploiting Security Issues in Human Activity Recognition Systems (HARSs)

Human activity recognition systems (HARSs) are vital in a wide range of real-life applications and are a vibrant academic research area. Although they are adopted in many fields, such as the environment, agriculture, and healthcare and they are considered assistive technology, they seem to neglect the aspects of security and privacy. This problem occurs due to the pervasive nature of sensor-based HARSs. Sensors are devices with low power and computational capabilities, joining a machine learning application that lies in a dynamic and heterogeneous communication environment, and there is no generalized unified approach to evaluate their security/privacy, but rather only individual solutions. In this work, we studied HARSs in particular and tried to extend existing techniques for these systems considering the security/privacy of all participating components. Initially, in this work, we present the architecture of a real-life medical IoT application and the data flow across the participating entities. Then, we briefly review security and privacy issues and present possible vulnerabilities of each system layer. We introduce an architecture over the communication layer that offers mutual authentication, solving many security and privacy issues, particularly the man-in-the-middle attack (MitM). Relying on the proposed solutions, we manage to prevent unauthorized access to critical information by providing a trustworthy application.

A REM Update Methodology Based on Clustering and Random Forest

In this paper, we propose a radio environment map (REM) update methodology based on clustering and machine learning for indoor coverage. We use real measurements collected by the TurtleBot3 mobile robot using the received signal strength indicator (RSSI) as a measure of link quality between transmitter and receiver. We propose a practical framework for timely updates to the REM for dynamic wireless communication environments where we need to deal with variations in physical element distributions, environmental factors, movements of people and devices, and so on. In the proposed approach, we first rely on a historical dataset from the area of interest, which is used to determine the number of clusters via the K-means algorithm. Next, we divide the samples from the historical dataset into clusters, and we train one random forest (RF) model with the corresponding historical data from each cluster. Then, when new data measurements are collected, these new samples are assigned to one cluster for a timely update of the RF model. Simulation results validate the superior performance of the proposed scheme, compared with several well-known ML algorithms and a baseline scheme without clustering.

ALPS: An Adaptive Link-State Perception Scheme for Software-Defined Vehicular Networks

The software-defined vehicular networking (SDVN) paradigm alleviates the deficiencies brought on by distributed vehicular. The separation of the control plane and the data plane allows the controller to manage the network based on global information. Most existing routing schemes in SDVN obtain the link-state through which vehicles periodically send beacon messages to the controller. However, due to the high mobility of the vehicles and the dynamic communication environment, the link-state changes within the beacon interval. In this case, the controller may select an expired link to transmit data during routing calculation, which will undoubtedly result in packet loss. Therefore, it is important for the controller to timely obtain the link-state during the beacon interval. If the controller can timely obtain the information after a link becomes unavailable, the risk of selecting unavailable links can be significantly reduced. In this paper, we propose an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> daptive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</u> ink-state <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> erception <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> cheme (ALPS) for SDVN, which enables the controller timely obtain the link-state within the beacon interval. We obtain the link-state by detecting the loss of packets on a link. A link quality evaluation method based on fuzzy logic is present to evaluate the possibility of link failure. After the link evaluation, we present an adaptive threshold adjustment method to dynamically adjust the detection range to decrease the detection cost. Simulation results demonstrate that ALPS can effectively reduce the packet loss ratio at a low cost.

An efficient federated learning framework for multi-channeled mobile edge network with layered gradient compression

A fundamental issue for federated learning (FL) is how to achieve efficient training performance under complex dynamic communication environments. This issue can be alleviated by the fact that modern edge devices usually can connect to the edge server via multiple communication channels (e.g., 4G, LTE, and 5G) because multi-channel communication can increase the communication bandwidth and has lower communication costs and energy consumption than a single high-speed communication channel. However, if the communication data cannot be properly allocated to multiple channels in a complex dynamic communication network, multi-channel communication will still waste resources (e.g., bandwidth, battery life, and monetary cost). In this paper, we propose an efficient FL framework called, which consists of two parts, the layered gradient compression (LGC), and a learning-driven control algorithm. Specifically, with LGC, local gradients from a device are coded into several layers, and each layer is sent to the server along a different channel. The FL server aggregates the received layers of local gradients from devices to update the global model and sends the result back to the devices. Furthermore, we prove the convergence of LGC and formally define the problem of resource-efficient with LGC. We then propose a learning-driven algorithm for each device to dynamically adjust its local computation (i.e., the number of local stochastic descent) and communication decisions (i.e., the compression level of different layers and the layer-to-channel mapping) in each iteration. Results from extensive experiments show that significantly reduces the training time and improves the resource utilization (energy consumption and money cost) while achieving a similar test accuracy compared with well-known FL baselines.

Думи на водещите

Issue 53 includes publications on topics related to the manifestations of communication and rhetoric in dynamically developing fields and spheres in the 21st century: politics, social processes, media, the internet, business, education, etc. Some articles are more narrowly thematic, while others present the results of interdisciplinary research. The methods and methodologies used by the authors are both quantitative and qualitative. The analyses include inferring phenomena and processes in different areas of society, identifying trends, and presenting predictions from a scientific perspective. Bulgarian reality is one of the objects of study. Katya Mihailova analyses the campaigns for early parliamentary elections in Bulgaria in 2021 and draws rhetorical and discursive features based on an in-depth analysis. The presidential election candidates in Bulgaria in 2021 are analysed through the prism of visual communication and in particular based on posters by Anelia Georgieva. A broader scope is presented in the research of Ivelina Boevska, who presents digital democracy not only in a national context but also reaches to bring out the essence and trends in the dynamic communication environment from the perspective of political communication, political science and sociology. Again in discursive terms, but this time the dominant one is the online space as the object of study, with the authors (Natalia Ryabchenko, Olga Malysheva, Veronika Katermina, Anna Gnedash) making a study on the American reality from a linguistic perspective. Media and contemporary phenomena and manifestations are studied by Iliya Valkov, who analyzes four media or platforms that are part of the media ecosystem and media market in the country. Blogs and blogging are the subject of a theoretical overview by Rosalina Bozhilova, who presents traditional and new types and identifies specific manifestations. Lavinia Enache finds intersections between virtual communities, branding, ecotourism and consumer behaviour, with a focus on Romania. Education, academic communication and changes in the attitudes of teachers and students provoke the interest of researchers. Antoaneta Getova informs about some results of an analysis of changes in attitudes and communication in higher education during the pandemic in Bulgaria. Zeyneb Khadi, Hana Aissaoui present results of hybrid learning in an virtual environment in Algeria. Randy Harris’s book “Linguistic Wars: Chomsky, Lakoff, and the Battle for Deep Structure” is presented by Simeon Vassilev. The text has the metaphorical yet informative and appealing title of Randy Harris’s Linguistic Shakespeare. The publications written by students from the University of Silesia in Katowice, Poland (Armin Kuźmiński and Agnieszka Budzisz), are related to public and media communication. The internationality of the authors and the broad range of the subject matter, the high quality of the research, and the topicality of the issues are the features of Issue 53. Traditionally, there is a section introducing the authors.

Дигитална демокрация – същност и тенденции в динамичната комуникационна среда

Abstract: This report focuses on the term ‘digital democracy’ and the new technologies being developed in the context of an exceedingly dynamic communication environment. It presents concepts and predominant para¬digms which exemplify the main political communication practices and the functioning of digital democracy. The report offers a theoretical and analy-tical framework based on a study of the connections between democracy, the Internet, public opinion and political communication. This study allows for an original taxonomy and conceptualization of the challenges faced by the political communication environment, which, as of 2021, seem in¬surmoun¬table. The pandemic has brought to life a number of viruses that no one is likely to overcome in the near future - neither in Bulgaria, nor in Europe or on other continents. The revolutionary technological and commu¬nication changes taking place, the scientific breakthroughs as well as the versatile political play are prerequisites for a modification of the political scene in Bulgaria and abroad. There is an urgent need for re¬freshing the concept of political processes and accumulating deeper knowledge of politics and communication. This, in turn, will lead to the need for profound heuristic studies of digital democracy and political communication. Кeywords: digital democracy, political communication, trends, information, the Internet.

A multi-channel and multi-user dynamic spectrum access algorithm based on deep reinforcement learning in Cognitive Vehicular Networks with sensing error

In this paper, a spectrum access problem is proposed to improve the spectrum access rates of secondary vehicles in Cognitive Vehicular Networks, where the channel capacity mathematic model is established under the conditions of spectrum sensing errors rates and the dynamic occupancy spectrum rates. Meanwhile, an improved Q-learning method is proposed to conform the dynamic communication under the different conditions of the reward functions. In this function, a Deep Q Network method with a modified reward function (IDQN) is proposed to deal with the situation of multi-vehicle in multi-channel. In order to verify the effectiveness of the IDQN method, the Myopic method, the improved Q-learning method, and the traditional DQN method are compared on Python. The simulation results shown that the proposed IDQN method not only outperforms the compared methods in terms of channel utilization and channel capacity but also improves the ability that the vehicle adapts to the dynamic communication environment.

Adaptive multi‐layer clustering strategies based on capacity weight for Internet of Things

SummaryDue to the requirements brought by diversification of IoT applications, differentiation of nodes' capability, dynamic communication environment and demands, the real‐time information of nodes (actual energy consumption, living nodes' density, pairwise nodes' communication radius) should be considered comprehensively for the clustering strategies of wireless sensor networks to achieve efficient, stable, and flexible performance with limited energy and different quality of service (QoS). This article proposes an improved dynamic multi‐layer clustering strategy for various IoT applications with heterogeneous nodes' energy, unpredictable or fast‐changing distribution of alive nodes, and dynamic scenarios. In addition, an adaptive adjustment strategy based on capability weight for multi‐layer clustering network is proposed to reduce the impact of unreasonable head selection cycle of clustering. By analyzing the node energy, the change of node locations and historical data transmission of cluster head, different capability weights are assigned to each node to adaptively re‐cluster the clusters with heavy load and poor performance, further make the network topology better match current situation and specified QoS requirements. Experimental results have demonstrated that proposed strategy can achieve less energy consumption, longer network lifetime, and better load balancing, especially for the cases with heterogeneous initial energy, nonuniform distribution, and higher density of nodes.

Using Humor to Promote Social Distancing on Tiktok During the COVID-19 Pandemic.

BackgroundDuring the COVID-19 pandemic, many humorous videos on how to practice social distancing appeared on social media. However, the effect of using humor as a crisis communication strategy to persuade people to conform to social distancing rules is not known.ObjectiveDrawing on the literature on humorous message framing and crisis communication, this research explores the effectiveness of a humorous message in communicating social distancing rules in two crisis severity phases (low vs. high severity) and also evaluates how humor affects individuals’ online and offline engagement intentions during the COVID-19 pandemic.MethodsA 2 (message framing: humorous vs. non-humorous) x 2 (crisis severity phase: low vs. high) between-subjects design experiment was conducted to test the research questions during the first weeks of the COVID-19 pandemic in China from January 30 to February 2, 2020.ResultsThe results showed that the severity of the phase of a health crisis can significantly affect stakeholders’ online and offline responses toward the disease. More specifically, in a low severity phase, humor led to increased source likability for the message, and more online and offline engagement intentions. However, no differences between a humorous and non-humorous message in perceived risk were observed. Whereas, in a high severity crisis phase, humor reduced individuals’ offline engagement intentions and a decrease in perceived risk, no significant difference was found between a humorous and non-humorous message on source likeability.ConclusionHumor can motivate both more online engagement and offline protective action intention when the crisis severity phase is low, while when crisis severity soars, a non-humorous message should be more desirable. More specifically, using humor in communicating information about an infectious disease can enhance the spokesperson’s likeability in a low severity phase, and also helps to spread health information to a larger audience. While, the negative side of using humor in communicating an infectious disease appears in severe crisis phases, as it then decreased the public’s perception of risk, and triggers less protective actions. Going beyond previous research, this study recognized that crisis severity changes in different phases of the spread of infectious disease, thereby providing actionable strategy selections for crisis practitioners in a dynamic communication environment.

QoE Optimization for Live Video Streaming in UAV-to-UAV Communications via Deep Reinforcement Learning

A challenge for rescue teams when fighting against wildfire in remote areas is the lack of information, such as the size and images of fire areas. As such, live streaming from Unmanned Aerial Vehicles (UAVs), capturing videos of dynamic fire areas, is crucial for firefighter commanders in any location to monitor the fire situation with quick response. The 5G network is a promising wireless technology to support such scenarios. In this paper, we consider a UAV-to-UAV (U2U) communication scenario, where a UAV at a high altitude acts as a mobile base station (UAV-BS) to stream videos from other flying UAV-users (UAV-UEs) through the uplink. Due to the mobility of the UAV-BS and UAV-UEs, it is important to determine the optimal movements and transmission powers for UAV-BSs and UAV-UEs in real-time, so as to maximize the data rate of video transmission with smoothness and low latency, while mitigating the interference according to the dynamics in fire areas and wireless channel conditions. In this paper, we co-design the video resolution, the movement, and the power control of UAV-BS and UAV-UEs to maximize the Quality of Experience (QoE) of real-time video streaming. We applied the Deep Q-Network (DQN) and Actor-Critic (AC) to maximize the QoE of video transmission from all UAV-UEs to a single UAV-BS to learn the dynamic fire areas and communication environment. Simulation results show the effectiveness of our proposed algorithm in terms of the QoE, delay and video smoothness compared to the Greedy algorithm.

Contextual information aware optimal communication in radio networks in considering the pervasive computing -A literature review

The modern tactical network demands a highly dynamic communications environment that involves autonomous platforms and systems. In order to achieve the full potential of modern technologies, it is critical to ensure the critical data finds the optimum path at the right time in a highly aggressive environment employing secure and best possible available communication path or network resources. Moreover, Contextual information aware optimal communication, such as the power of the radios, battery level, distance among the radios, and maximum and minimum data rates of the radio aware optimal path selection, remains an unresolvable research area. The mentioned limitation engenders various complex challenges in mission-critical networks. Therefore, contextual information-aware optimal communication in radio networks is a priority demand raised as a primary objective for military communication research. In this paper, we have performed a comprehensive review of various proposed methodologies to achieve the objective, followed by a critical comparative analysis to anticipate the state of the mentioned research area that leads to a future research direction for military researchers.

SARSA-based delay-aware route selection for SDN-enabled wireless-PLC power distribution IoT

The integration of power line communications (PLC) and wireless communications in power distribution internet of things (PD-IoT) provides a cost-efficient and easy-to-access solution to solve the problems of limited range of access and signal fading due to the influence of distance and obstacles. The traditional route selection methods are difficult to adapt to uncertain global state information (GSI) under dynamic network environment, the integration of PLC and wireless communications, and strong electromagnetic interference. In this paper, we put forward a state-action-reward-state-action (SARSA)-based Delay-aware Route Selection (SDRS) algorithm, which adopts software-defined networking (SDN) to simplify the network configuration and management in wireless-PLC PD-IoT. The proposed algorithm utilizes SARSA to evaluate and learn the optimal route selection strategy that can reduce transmission delay and improve reliability. Moreover, it can also adapt to the dynamic and complex communication environment. Compared with existing shortest route selection (SRS) algorithm and random route selection (RRS) algorithm, numerical results verify that SDRS has excellent performances in terms of transmission delay and reliability.