Enhance Communication Reliability Research Articles

A series‐fed frequency scanning antenna for millimeter wave wireless communication

AbstractIn the realms of 5G and the upcoming 6G communications, the millimeter‐wave bands offer substantially greater bandwidth. However, due to their propagation characteristics, aligning antennas becomes a challenging issue, particularly at the node side in industrial and vehicular network applications. Angle‐scanning antennas can significantly enhance communication reliability or provide new possibilities for spatial multiplexing. Previous studies have predominantly concentrated on frequency‐scanning antennas characterized by either low‐gain individual units or expensive digital arrays. Consequently, there has been a scarcity of solutions that are both cost‐effective and high‐gain. In this study, a method is proposed for achieving frequency scanning by extending the feed lines between elements. Based on this approach, a millimeter‐wave high‐gain serial‐feed frequency‐scanning antenna is developed. This antenna operates within the 60–64 GHz millimeter‐wave ISM (Industrial, Scientific, and Medical) band and is capable of performing a 10 scan in the E‐plane. Moreover, the antenna maintains a width of less than half a wavelength, allowing for the assembly of multiple antennas into a MIMO array.

Enhancing Communication Reliability: Designing Microwave Links for Bahir Dar-Woretta Connectivity

This paper explores the need for establishing a microwave link between Bahir Dar and Woretta as an alternative communication solution to the existing optical fiber infrastructure. Microwave links offer an effective way to overcome challenges posed by rugged terrains and unfavorable environmental conditions that hinder the deployment of fiber optics. As Woretta emerges as a key economic and investment hub within the Amhara Region, demand for reliable and efficient communication is expected to grow significantly. The study encompasses various aspects of planning and designing the microwave link, including site surveys, consideration of fade margins, frequency planning, link budget calculations, and assessing the feasibility and reliability of the proposed link. The paper employs LINKPlanner 5.4.1 software to simulate and validate the results. Due to terrain constraints, a direct link between Bahir Dar and Woretta is not feasible. Instead, a two-hop link is proposed, involving transmission from Bahir Dar to Zege, and then from Zege to Woretta. This alternative configuration ensures optimal connectivity while addressing the terrain limitations. By presenting a comprehensive analysis and simulation of the microwave link, this paper provides valuable insights into the planning and implementation of a robust communication infrastructure. The proposed microwave link will offer a reliable and efficient alternative to the existing optical fiber network, ensuring uninterrupted connectivity to support the region’s growth and development.

IoT Based Antenna Positioning System

An IOT-based antenna’s position system aimed at optimizing connectivity. Through IoT-enabled actuators, the system enables remote adjustment of antenna orientation, managed by a central control unit. Real-time monitoring ensures accurate alignment for optimal signal reception, enhancing communication reliability. Key advantages include remote accessibility, allowing users to control antennas from anywhere with internet connectivity, and scalability, facilitating seamless integration into existing infrastructure. By leveraging IoT technology, the system empowers users with enhanced control over wireless communication networks, offering efficient and reliable antenna positioning solutions across diverse applications.

Machine learning-Based Energy Efficient and Enhancing Communication Reliability for MANETs of Balanced Less Loss Routing Protocol

The aim of the presented research was the utilization of machine learning methods, for example, SOM, MARL, and DQN, to promote the increase of energy efficiency and communication reliability in MANET. For the purpose of the research conduction, 10 trials of the experiment were carried out with the view to establish the performance of the proposed approaches from various perspectives. DQN demonstrated superior performance throughout all experiment trials as compared to both SOM and MARL. The model recorded an average energy efficiency of 95%, indicating that it was highly successful in optimizing routing strategies and communication policies. Furthermore, the average packet delivery ratio was shown to be 96%, meaning that DQN provided guarantees of timely and reliable data exchange across the MANET infrastructure. Finally, the average delay was determined to be from 4 ms to 9 ms, evidencing the quick delivery of packets with little temporal latency. The results obtained demonstrate that DQN is able to alleviate the challenges associated with the energy efficiency and communication reliability of MANETs. Notably, deep reinforcement learning with DQN appears to offer viable solutions for more energy-efficient routing, minimized energy consumption in these networks, and improvement to their communication reliability. Thus, the study under consideration has contributed to the development of the existing knowledge as it has expanded the existing understanding of the fact that machine learning, in general, and DQN, in particular, can be utilized to optimize the operation of MANETs.

Channel-aware low Earth orbit satellite cluster networking for space-to-Earth laser communication: reliability and bandwidth advantages.

The reliability of the space-to-Earth laser communication plays a crucial role in providing uninterrupted real-time services in satellite optical networks. In traditional satellite optical networks, the space-to-Earth laser communication is carried out using a monolithic satellite in close proximity to the target optical ground station. However, the reliability of the communication in this approach is heavily influenced by the atmospheric environment. For instance, variations in cloud thickness can cause fluctuations in the link quality of the space-to-Earth laser communication, significantly reducing its reliability. This study proposes an innovative channel-adaptive space-to-Earth laser communication (CA-S2E-LC) architecture based on satellite cluster optical networking (SCON). SCON provides space-diversity link sets, reducing the probability of space-to-Earth laser communications affected by clouds. By leveraging the perception of link quality, the CA-S2E-LC architecture can adaptively choose the better space-to-Earth laser communication links established by member satellites within a satellite cluster under different environments, and properly schedule the resource, ensuring reliable space-to-Earth laser communication. The principles of the SCON is analyzed and the implementation of the CA-S2E-LC architecture is demonstrated through the explanation of hardware and functional modules, workflows, finite state machines, and strategies. Simulation results demonstrate that the CA-S2E-LC architecture can significantly enhance communication reliability and capacity compared with the traditional monolithic satellite. Furthermore, the workflow of the architecture is demonstrated to validate the feasibility.

Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks.

With the rapid development of the intelligent transportation system (ITS), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. The high mobility of vehicles in urban streets poses serious challenges to routing protocols and has a significant impact on network performance. Existing topology-based routing is not suitable for highly dynamic VANETs, thereby making location-based routing protocols the preferred choice due to their scalability. However, the working environment of VANETs is complex and interference-prone. In wireless-network communication, the channel contention introduced by the high density of vehicles, coupled with urban structures, significantly increases the difficulty of designing high-quality communication protocols. In this context, compared to topology-based routing protocols, location-based geographic routing is widely employed in VANETs due to its avoidance of the route construction and maintenance phases. Considering the characteristics of VANETs, this paper proposes a novel environment-aware adaptive reinforcement routing (EARR) protocol aimed at establishing reliable connections between source and destination nodes. The protocol adopts periodic beacons to perceive and explore the surrounding environment, thereby constructing a local topology. By applying reinforcement learning to the vehicle network's route selection, it adaptively adjusts the Q table through the perception of multiple metrics from beacons, including vehicle speed, available bandwidth, signal-reception strength, etc., thereby assisting the selection of relay vehicles and alleviating the challenges posed by the high dynamics, shadow fading, and limited bandwidth in VANETs. The combination of reinforcement learning and beacons accelerates the establishment of end-to-end routes, thereby guiding each vehicle to choose the optimal next hop and forming suboptimal routes throughout the entire communication process. The adaptive adjustment feature of the protocol enables it to address sudden link interruptions, thereby enhancing communication reliability. In experiments, the EARR protocol demonstrates significant improvements across various performance metrics compared to existing routing protocols. Throughout the simulation process, the EARR protocol maintains a consistently high packet-delivery rate and throughput compared to other protocols, as well as demonstrates stable performance across various scenarios. Finally, the proposed protocol demonstrates relatively consistent standardized latency and low overhead in all experiments.

Resource allocation and energy trading in the short blocklength regime for radio access networks

AbstractWith the large‐scale deployment of communication and computation units, the conventional radio access network has been gradually transformed into a sophisticated Cyber‐Physical System. Since the access network is put forward higher expectations for ultra‐reliable and low‐latency services, Short‐Packet Communications (SPC) based on Finite Blocklength Codes can be adopted to further decrease network latency and enhance communication reliability. Considering the interaction among the service provider, the network operator, and users, a three‐stage Stackelberg game is constructed to dynamically arrange communication and computation resources. In this paper, the tripartite interaction problem is modelled as a generalized Nash equilibrium problem, and the reverse induction method is employed to achieve the sub‐game equilibrium. To solve the problem of maximizing tripartite utilities, the authors propose an iterative algorithm for jointly managing resource allocation and tripartite pricing. Simulation results show that compared with the benchmark, the proposed scheme can effectively improve the utilities of users, the service provider, and the network operator and achieve a win–win situation.

An Adaptive Hybrid Automatic Repeat Request (A-HARQ) Scheme Based on Reinforcement Learning

V2X communication is susceptible to attenuation and fading caused by external interference. This interference often leads to bit error and poor quality and stability of the wireless link, and it can easily disrupt packet transmission. In order to enhance communication reliability, the 3rd Generation Partnership Project (3GPP) introduced the Hybrid Automatic Repeat Request (HARQ) technology for both 4G and 5G systems. Nevertheless, it can be improved for poor communication conditions (e.g., heavy traffic flow, long-distance transmission), especially in advanced or cooperative driving scenarios. In this paper, we propose an Adaptive Hybrid Automatic Repeat Request (A-HARQ) scheme that can reduce the average block error rate, the average number of retransmissions, and the round-trip time (RTT). It adapts the Q-learning model to select the timing and frequency of retransmission to enhance the transmission reliability. We also design some transmission schemes—K-repetition, T-delay and [T, K]-overlap—which are used to shorten latency and avoid packet collision. Compared with the conventional 5G HARQ, our simulation results show that the proposed A-HARQ scheme decreases the system’s average BLER, the number of retransmissions, and the RTT to 5.55%, 1.55 ms, and 0.97 ms, respectively.

A Comprehensive Study on the Advancements of Man and Machine in Network Security and Coding Theory

The article offers a comprehensive analysis of network coding, communications security, and coding theory, examining their applications and advancements. It evaluates the fundamental concepts and methodologies utilized in these fields while shedding light on current progress and potential future research directions. The implications of the study discussed in this article extend widely across the communication sector, with immediate practical applications across various disciplines. One of the key areas covered in the article is the development of novel error-correcting codes and coding algorithms, which contribute to enhancing communication reliability. Additionally, the integration of machine learning and artificial intelligence (AI) techniques into network communications security is explored, highlighting their potential to bolster safeguarding measures. Furthermore, the incorporation of security controls into connected devices and Internet of Things (IoT) networks is addressed, acknowledging the need to ensure security in these interconnected systems. To ensure the reliability and security of network communications and foster innovation and growth within the communication sector, the article concludes that coding theory and network communications security must continue to evolve and progress. By pushing the boundaries of these fields, researchers can address emerging challenges, improve existing systems, and pave the way for future advancements in communication technology.

Multichannel Power Constraints-Based SNR Estimation for the non-cooperative and cooperative scenarios of multi-user Wireless Network

Cooperative communication holds a significant task in Wireless Networks (WNs) by enhancing communication reliability, power, efficiency, and network connectivity. In addition, cooperative communication also enables the progression of a well-organized method for improving the WN quality. Moreover, it facilitates the exploitation of communicating resources by permitting the pathways and nodes in a network to assist in the transmission of data. In this research, a Multi-Channel Power Constraints-Based SNR Estimation (MCPC-SNR Estimation) is considered with source node to destination node and relay node to destination node. Also, three probabilistic models like joint entropy, mutual information, and correlation are considered for SNR parameters. Rather than considering the signals with the current information of signal and channel, we consider signal-to-noise ratio combining the method with static and dynamic Signal to Noise Ratio (SNR) estimation for the non-cooperative and cooperative scenarios. Finally, an assessment of the Multi-Channel Power Constraints-Based SNR Estimation (MCPC-SNR) model shows that the power allocations at the source, as well as relay nodes for transmissions, and the destination and relay nodes, have a notable effect on the Bit Error Rate (BER) performance for power-constrained cooperative communications. The analysis proves that the proposed work ensures a much lower Bit Error Rate (BER) for almost all timing error variations, which probably reaches 0.005.

Reliable Semantic Communication System Enabled by Knowledge Graph.

Semantic communication is a promising technology used to overcome the challenges of large bandwidth and power requirements caused by the data explosion. Semantic representation is an important issue in semantic communication. The knowledge graph, powered by deep learning, can improve the accuracy of semantic representation while removing semantic ambiguity. Therefore, we propose a semantic communication system based on the knowledge graph. Specifically, in our system, the transmitted sentences are converted into triplets by using the knowledge graph. Triplets can be viewed as basic semantic symbols for semantic extraction and restoration and can be sorted based on semantic importance. Moreover, the proposed communication system adaptively adjusts the transmitted contents according to channel quality and allocates more transmission resources to important triplets to enhance communication reliability. Simulation results show that the proposed system significantly enhances the reliability of the communication in the low signal-to-noise regime compared to the traditional schemes.

Enhancing Communication Reliability from the Semantic Level under Low SNR

In the low signal-to-noise ratio region, a large number of bit errors occur, and it may exceed the channel error correction capability of the receiver. Traditional communication system may use the technology of automatic repeat-request to deal with this problem, which is time consuming and a waste of resources. To enhance the reliability of the communication system, we investigate reasoning and decoding at the semantic level instead of the grammar level. In particular, we propose a semantic communication model for text transmission, assisting the communication system to be more robust in terrible channel environments. Based on the traditional communication system, the language model BERT, part of speech tagging, and prior information concerning bit-flipping are introduced to enhance the semantic reasoning ability of the transceiver. Furthermore, this paper analyzes the effects of the sub-strategies on the performances of the improved communication model, such as the existence of a candidate set and language model. The numerical results show the effectiveness of our model in terms of improving the semantic accuracy measured by BLUE, the METEOR score, and the similarity score based on BERT between transmitted messages and recovered messages.

Understanding commercial 5G and its implications to (Multipath) TCP

In this paper, we perform an empirical study on commercial 5G. Unlike previous works studying 5G in early deployments, we focus on fully deployed 5G networks that already serve massive numbers of users, and in particular, we perform controlled experiments to analyze TCP and multipath TCP (MPTCP) performances and behaviors over 5G. Our results show that 5G faces a dilemma in maintaining in-network buffer for TCP traffic: on one hand, TCP packets are less likely to accumulate within the network, thanks to the high data rate of 5G radio; but on the other hand, with TCP performance highly sensitive to signal quality, when signal becomes poor abruptly in mobile environment, the in-network buffer easily overflows, leading to packet loss bursts. We also confirm that employing MPTCP can aggregate capacities on 5G and 4G paths, but under the current deployment model where 5G and 4G base stations co-locate and share the same core network, MPTCP does not necessarily enhance communication reliability. In addition to active measurement experiments, we leverage a speedtest service to evaluate commercial 5G from a nationwide perspective. By exploiting over 13k crowdsourced speedtest results reported from 197 cities in China, we find that there exist considerable regional differences on 5G performances across the nation, and with the 4G co-locating deployment model, the 5G networks in some populous metropolitan areas have inferior performances. Our study provides insights for people to understand commercial 5G and to optimize upper-layer protocols and applications over 5G.

Design optimization of WirelessHART networks in Cyber-Physical Systems

Abstract Recently, more and more factories begin to employ wireless mesh networks for process control. The high dynamics of wireless mesh network leads to a tight interaction between control performance and communication reliability. As a result, these wireless Cyber-Physical Systems need a holistic co-design approach to integrate the cyber space and physical space. In this paper, we establish a regulation of co-design strategies for wireless Cyber-Physical Systems. We also propose a heuristic algorithm to seek the feasible values of co-design parameters in the huge design space. A testbed is deployed for exploration and evaluation in industrial environment. The results show that our proposed co-design method provides an approach to enhance communication reliability and control performance even under harsh industrial environment.

Optimized Low-Power Flexible Magnetic-Coupling Antenna for Wearable Devices

Flexibility and low-power consumption are desirable for wearable magnetic-coupling antennas. In order to achieve these configurable designs, in this paper, we proposed an antenna architecture in which a coaxial distributed diameter coil with twisted loops antenna (DDC-TLA) is integrated with a flexible ferromagnetic core and a rubber-nature substrate. Through adjusting the diameter distribution of the DDC-TLA, the magnetic field distribution is adjusted to enhance communication reliability. While through moderating the weight ratio of the nanostructured magnetodielectric material, the concentration of the polymer hybrid solution and the filler content of the rubber substrate, the quality factor of the antenna is tailed to meet performance requirements. Furthermore, through an optimized algorithm, the preferable structure of the DDC-TLA and the suitable ingredient of the ferromagnetic core are determined, while the power loss are minimized. Finally, simulations and experiments are conducted to corroborate our studies.

Enabling D2D Transmission Mode with Energy Harvesting and Information Transfer in Heterogeneous Networks

The concept of energy harvesting-assisted relay has been introduced to support the relaying transmission using Device-to-Device (D2D) communications for enhancing communication reliability. Motivated by the recent advance in Heterogeneous Network (HetNet) using relaying techniques, we consider the D2D communication provided by Energy Harvesting (EH) assisted relay where signal is forwarded from a Base Station (BS) to the conventional cellular user (non-D2D user) and D2D user. We first derive the outage probability by taking into account the SNR and power allocation parameters, and propose the transmission mode for D2D link as well as non-D2D link. After deriving the outage probability of the D2D-HetNet, we explore the effects of the network parameters on the outage probability and throughput.

A Pragmatic Relay Placement Approach in 3-D Space and Q-Learning-Based Transmission Scheme for Reliable Factory Automation Applications

The biggest challenge in adopting industrial wireless sensor networks (IWSNs) for factory automation applications is to provide low latency and highly reliable communication in harsh factory environments. IEEE 802.15.4e low latency deterministic network (LLDN) mode attempts to address this requirement at the medium access control (MAC) layer. However, the measures offered by this mode are inadequate considering realistic factory environments, suffering from noise, interference, multipath fading, and resulting in frequent packet losses. Cooperative diversity using relay nodes and incorporation of forward error correction (FEC) techniques are the two conventional ways to enhance communication reliability. However, the challenge lies in the placement of relay nodes considering a realistic three-dimensional (3-D) factory space and satisfying various physical, performance, and energy-related constraints. Moreover, the versatile and dynamic behavior of factory environment demand that the solutions offered to enhance communication reliability must be generic and adaptive, thereby eliminating the need for unnecessary redesigns. This paper proposes a twofold solution to enhance the communication reliability offered by 802.15.4e LLDN. First, an efficient and pragmatic relay-placement strategy based on rainbow product ranking algorithm for a 3-D factory space. Second, an adaptive transmission scheme (ATS) inspired from reinforcement learning (RL) technique called Q-learning is proposed, which incorporates cooperative diversity and Reed Solomon (RS) block codes. The effectiveness of the proposed solution is established and demonstrated using a real-world case study.

Heterogeneous Cellular Network With Energy Harvesting-Based D2D Communication

The concept of mobile user equipment (UE) relay (UER) has been introduced to support device-to-device (D2D) communications for enhancing communication reliability. However, as the UER needs to use its own power for other UE’s data transmission, relaying information in D2D communication may be undesirable for the UER. To overcome this issue, motivated by the recent advances in energy harvesting (EH) techniques, we propose a D2D communication provided EH heterogeneous cellular network (D2D-EHHN), where UERs harvest energy from an access point (AP) and use the harvested energy for D2D communication. We develop a framework for the design and analysis of D2D-EHHN by introducing the EH region (EHR) and modeling the status of harvested energy using Markov chain. The UER distribution is derived, and a transmission mode selection scheme including the efficient UER selection method is proposed. The network outage probability is derived in close form to measure the performance of D2D-EHHN. Based on our analysis results, we explore the effects of network parameters on the outage probability and the optimal offloading bias in terms of the outage probability. Particularly, we show that having a high EH efficiency enhances the performance of D2D-EHHN, but can also degrade, especially for dense network.

Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

Lossy transmission is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without retransmitting the data; the promise of this approach is to reduce communication and thus power savings. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. Specifically, this paper targets to provide accurate compensation for stationary and compressible acceleration signals obtained from structural health monitoring (SHM) systems with data loss ratio below 20%. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.

Using Mobile Data Collectors to Enhance Energy Efficiency a nd Reliability in Delay Tolerant Wireless Sensor Networks

A primary task in wireless sensor networks (WSNs) is data collection. The main objective of this task is to collect sensor readings from sensor fields at predetermined sinks using routing protocols without conducting network processing at intermediate nodes, which have been proved as being inefficient in many research studies using a static sink. The major drawback is that sensor nodes near a data sink are prone to dissipate more energy power than those far away due to their role as relay nodes. Recently, novel WSN architectures based on mobile sinks and mobile relay nodes, which are able to move inside the region of a deployed WSN, which has been developed in most research works related to mobile WSN mainly exploit mobility to reduce and balance energy consumption to enhance communication reliability among sensor nodes. Our main purpose in this paper is to propose a solution to the problem of deploying mobile data collectors for alleviating the high traffic load and resulting bottleneck in a sink`s vicinity, which are caused by static approaches. For this reason, several WSNs based on mobile elements have been proposed. We studied two key issues in WSN mobility: the impact of the mobile element (sink or relay nodes) and the impact of the mobility model on WSN based on its performance expressed in terms of energy efficiency and reliability. We conducted an extensive set of simulation experiments. The results obtained reveal that the collection approach based on relay nodes and the mobility model based on stochastic perform better.