Next-generation Communication Networks Research Articles

Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights

By adjusting the propagation environment using reconfigurable reflecting elements, intelligent reflecting surfaces (IRSs) have become potential techniques used to improve the efficiency of wireless communication networks. In IRS-assisted communication systems, accurate channel estimation is crucial for optimizing signal transmission and achieving high spectral efficiency. As mobile data traffic continues to surge and the demand for high-capacity and low-latency wireless connectivity grows, IRSs are becoming pivotal technologies in the development of next-generation communication networks. IRSs offer the potential to revolutionize wireless propagation environments, improving network capacity and coverage, particularly in high-frequency wave scenarios where traditional signals encounter obstacles. Amidst this evolving landscape, machine learning (ML) emerges as a powerful tool to harness the full potential of IRS-assisted communication systems, particularly given the escalating computational complexity associated with deploying and operating IRSs in dynamic environments. This paper presents an overview of preliminary results for IRS-assisted communication using recurrent neural networks (RNNs). We first implement single- and double-layer LSTM, BiLSTM, and GRU techniques for an IRS-based communication system. In the next phase, we explore a hybrid approach, combining different RNN techniques, including LSTM-BiLSTM, LSTM-GRU, and BiLSTM-GRU, as well as their reverse configurations. These RNN algorithms were evaluated with respect to bit error rate (BER) and symbol error rate (SER) for IRS-enhanced communication. According to the experimental results, the BiLSTM double-layer model and the BiLSTM-GRU combination demonstrated the highest BER and SER accuracy compared to other approaches.

Toward next-generation networks: A blockchain-based approach for core network architecture and roaming identity verification

With the evolution of next-generation communication networks, ensuring robust Core Network (CN) architecture and data security has become paramount. This paper addresses critical vulnerabilities in the architecture of CN and data security by proposing a novel framework based on blockchain technology that is specifically designed for communication networks. Traditional centralized network architectures are vulnerable to Distributed Denial of Service (DDoS) attacks, particularly in roaming scenarios where there is also a risk of private data leakage, which imposes significant operational demands. To address these issues, we introduce the Blockchain-Enhanced Core Network Architecture (BECNA) and the Secure Decentralized Identity Authentication Scheme (SDIDAS). The BECNA utilizes blockchain technology to decentralize data storage, enhancing network security, stability, and reliability by mitigating Single Points of Failure (SPoF). The SDIDAS utilizes Decentralized Identity (DID) technology to secure user identity data and streamline authentication in roaming scenarios, significantly reducing the risk of data breaches during cross-network transmissions. Our framework employs Ethereum, free5GC, Wireshark, and UERANSIM tools to create a robust, tamper-evident system model. A comprehensive security analysis confirms substantial improvements in user privacy and network security. Simulation results indicate that our approach enhances communication CNs security and reliability, while also ensuring data security.

AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems.

Traditional heterogeneous networks (HetNets) are constrained by their hardware design and configuration. These HetNets have a limited ability to adapt to variations in network dynamics. Software-defined radio technology has the potential to address this adaptability issue. In this paper, we introduce a software-defined radio (SDR)-based long-term evolution licensed assisted access (LTE-LAA) architecture for next-generation communication networks. We show that with proper design and tuning of the proposed architecture, high-level adaptability in HetNets becomes feasible with a higher throughput and lower power consumption. Firstly, maximizing the throughput and minimizing power consumption are formulated as a constrained optimization problem. Then, the obtained solution, alongside a heuristic solution, is compared against the solutions to existing approaches, showing our proposed strategy is drastically superior in terms of both power efficiency and system throughput. This study is then concluded by employing artificial intelligence techniques in multi-objective optimization, namely random forest regression, particle swarm, and genetic algorithms, to balance out the trade-offs between maximizing the throughput and power efficiency and minimizing energy consumption. These investigations demonstrate the potential of employing the proposed LTE-LAA architecture in addressing the requirements of next-generation HetNets in terms of power, throughput, and green scalability.

Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks.

The integrated satellite-terrestrial network (ISTN) provides a promising solution to achieve high-data-rate and ubiquitous connectivity in next-generation communication networks. Considering the scarce spectrum resources and unevenly distributed traffic demands, we investigate the resource allocation algorithms for ISTNs, where the beam-hopping (BH)-based satellite system and terrestrial systems share the same frequency band. Taking advantage of the scheduling flexibility of BH technology, the dynamical protection zones are constructed to avoid co-channel interference and improve the spectrum efficiency. Since both spectrum efficiency and user fairness are the key optimization indexes in practical systems, two resource allocation problems are formulated to maximize the weighted sum of capacity (MWSC) and maximize the minimum capacity-to-demand ratio (MMCDR) of ISTNs, respectively. By reformulating the problems as mixed-integer linear programming problems, optimal solutions are obtained. To reduce the computational complexity, two greedy suboptimal algorithms are proposed for the MWSC and MMCDR, respectively. The simulation results show that the proposed algorithms achieve higher spectrum efficiency and guarantee fairness between the satellite and terrestrial systems. It is also shown that both the greedy algorithms perform similarly to the optimal algorithms while having much lower complexity.

Load-aware continuous-time optimization for multi-agent systems: toward dynamic resource allocation and real-time adaptability

In the realm of next-generation mobile communication networks, characterized by dynamic and evolving workloads, the efficient resource allocation becomes paramount for achieving optimal performance. This paper addresses the intricate challenges of multiagent resource management through the integration of stochastic learning automata and continuous-time optimization techniques, presenting an innovative solution for real-time adaptability and dynamic load balancing. The central aim is to achieve optimal values for local time-varying cost functions while taking into account resource constraints and the feasibility of local allocation. In the context of multi-agent resource allocation systems, where conditions vary over time, the algorithm exhibits a dynamic adaptability, continuously adjusting to the evolving optimal solutions tailored for each agent. Utilizing stochastic learning automata, the algorithm effectively addresses the time-varying load-balancing function in response to dynamic user demands. Furthermore, we introduced an scalable solution for mobility robustness optimization based on deep reinforcement learning (DRL-MRO). This method dynamically identifies network-wide optimal parameter values across the entire network to accommodate diverse mobility patterns, ensuring the effectiveness of load balancing parameters that seamlessly adapt to the dynamic configuration of the network. The overarching goal is to maintain a consistent quality of service level for each agent, thereby enhancing the overall system performance. Simulation outcomes unequivocally affirm the superior performance of the proposed load balancing approach, rooted in continuous-time optimization and stochastic learning automata, surpassing existing schemes across diverse system configurations.

Delay and Fairness Analysis of C-RAN for Single and Multi Scheduling Domain Strategies

Background: Centralized Radio Access Network (C-RAN) is the most promising network architecture for next-generation communication networks. It meets the need for flexibility on fronthaul as well as large bandwidth on backhaul of the network. All along, scheduling is very important for the transmission of information in an organized manner. C-RAN has not been studied with the scheduling domain strategies yet in the literature. Objective: In this work, packet transmission duration, overall transmission time, wait time, and fairness index parameters have been calculated and analysed for C-RAN architecture for two different scheduling domains. The total transmission cycle time parameter is calculated for the three upper functional split options of C-RAN. The overall transmission time is a parameter calculated for the entire uplink channel. Methods: To implement the network scenario, extensive scripting is done on MATLAB Editor for single scheduling domain (SSD) and multi-scheduling domain (MSD) for three higher functional split options of C-RAN. The data traffic generated in the network is considered random. Results: A closer examination of results reveals the advantages and disadvantages of both algorithms, as well as trade-offs between them. Conclusion: For quicker data transmission, SSD should be preferred whereas MSD should be preferred if multiple users want to access resources simultaneously. Lower functional split options of C-RAN require less transmission cycle time. The MSD technique is fairer than SSD.

Microwave Antenna Optimization for Low Latency and High Throughput Communication Systems

This paper presents a comprehensive exploration of microwave antenna optimization strategies aimed at achieving low latency and high throughput in modern communication systems. With the escalating demand for real-time applications and data-intensive services, the optimization of microwave antennas has become imperative to meet the stringent requirements of responsiveness and efficiency. The abstract delves into various facets of antenna optimization, including design parameters, signal processing techniques, and deployment considerations, all geared towards minimizing latency and maximizing throughput. Through a thorough review of existing literature and practical insights from real-world deployments, this research aims to provide valuable guidance for engineers and researchers in designing and implementing optimized microwave antennas for next-generation communication networks. By examining the intricate interplay between antenna design, signal processing algorithms, and deployment strategies, this paper elucidates the challenges and opportunities in achieving low latency and high throughput in microwave communication systems. Furthermore, it highlights the significance of factors such as gain, beamwidth, polarization, frequency band selection, multiple-input multiple-output (MIMO) systems, beamforming, adaptive coding and modulation (ACM), antenna placement, tower height, and line-of-sight considerations in optimizing antenna performance. The findings presented in this paper contribute to advancing the understanding of microwave antenna optimization and offer practical insights into enhancing the responsiveness, efficiency, and reliability of modern communication networks.

Optical Add-Drop Multiplexers: Enhancing High Transmission Bit Rates in Next-Generation Communication Networks

The development of optical networks in the telecommunications sector is becoming much closer by considering the help of an Optical Add Drop Multiplexer (OADM) based on a novel technology called Wavelength Division Multiplexing (DWDM). The objective of the current study to examine high transmission bit rates for next-generation optical communication networks using the technology of OADM Based on DWDM. Artificial neural networks (ANNs) were developed via MATLAB software to predict three main parameters in this filed such as transmitted signal power (PT), transmitted signal bandwidth (B.Wsig), and transmission bit rate capacity (Bsh) at different fiber cable lengths, such as L=200, 250, and 300 km. The ANNs results showed that, standard error (SE) for predicting PT as a function of the number of transmitted channels (Nch) was 0.115 mW, 0.095 mW and 0.077 mW, for 200, 250 and 300 km, respectively. Additionally, the SE for predicting B.Wsig was 0.067 GHz, 0.051 GHz and 0.040 GHz for 200, 250 and 300 km, respectively. Lastly, the SE for predicting Bsh was 1.665, 1.311 Gbit/sec and 1.076Gbit/sec for 200, 250 and 300 km, respectively. The SE for predicting PT as a function of the Signal Wavelength (λ) was 0.116, 0.096 and 0.079 mW for 200, 250 and 300 km, respectively. Additionally, the SE for predicting B.Wsig was 0.067, 0.052 and 0.052 GHz for 200, 250 and 300 km, respectively. Lastly, the SE for predicting Bsh was 1.688, 1.417 and 1.110 Gbit/sec for 200, 250 and 300 km, respectively. The low SE in ANNs demonstrated the efficiency, motivating further advancements in optimizing network performance for high-bit-rate transmission.

A Comprehensive Review of UAV-Assisted FSO Relay Systems

The evolving requirements of next-generation mobile communications networks can be met by leveraging vertically deployed Unmanned Aerial Vehicle (UAV) platforms integrated with Free Space Optical communications (FSO). This integration offers a flexible and scalable architecture capable of delivering high-rate communication without requiring licenses while aligning with the multi-gigabit paradigm. In recent times, the increasing availability of commercial aerial platforms has facilitated experimental demonstrations of UAV-enabled FSO systems, which play a crucial role in proposed backhaul networks and point-to-point communications by overcoming Line-of-Sight (LOS) challenges. These systems can be rapidly deployed to meet sudden demand scenarios. This document provides a comprehensive review of relevant field demonstrations of UAV-enabled FSO relay systems, with a particular focus on commercially available, free-flying platforms that are driving advancements in this domain. It categorizes the different platforms by considering the operational altitudes of these systems and their payload actuation capacity, which determines their adaptability to variables. The analysis aims to distill the design considerations that lead to optimal performance regarding communications throughput and other relevant metrics. Moreover, it also attempts to highlight areas where design choices have fallen short, indicating gaps in current research efforts toward the widespread adoption of UAV-enabled FSO relay systems. Finally, this work endeavors to outline effective design considerations, guidelines, and recommendations to bridge these identified gaps. It serves as a valuable reference guide for researchers involved in developing UAV-enabled FSO relay systems, enabling them to make informed decisions and pave the way for the successful implementation of such systems.

Bayesian Regularization (BR) Optimization Based DDoS Detection for SDN and Next Generation Communication Network

The rapid evolution of communication networks, particularly Software-Defined Networking (SDN) and next-generation communication infrastructures, has introduced new challenges in securing these dynamic and complex environments. Among the most persistent threats are Distributed Denial of Service (DDoS) attacks, which can disrupt critical services and inflict severe economic and operational damages. To combat these threats, novel and adaptive DDoS detection mechanisms are crucial. This paper proposes a Bayesian Regularization (BR) optimization-based approach for DDoS detection in SDN and next-generation communication networks. Bayesian Regularization is a statistical technique that combines the strength of Bayesian analysis with optimization methodologies, enabling the model to adapt to changing network conditions and attack strategies. This approach leverages the inherent advantages of SDN, such as centralized control and real-time network monitoring, to enhance the accuracy and timeliness of DDoS detection.

An Overview of the Implications of Artificial Intelligence (AI) in Sixth Generation (6G) Communication Network

The 6G communication network is anticipated to be a cutting-edge next-generation communication network that will enhance the value of the intelligent Internet of Things (IoT). The emergence of many domains within artificial intelligence (AI) has paved the way for significant opportunities in the development of 6G technology. These opportunities include the enhancement of human intelligence, the integration of various devices and systems through the Internet of Everything (IoE), improvements in the quality of experiences, and enhancements in the overall quality of life. The integration of AI and 6G networking technology is anticipated to bring about a transformative shift, transitioning from a focus on interconnected devices to a paradigm centered around interconnected intelligent systems. This article provides an overview of the extent to which AI is poised to revolutionize 6G networking technologies. This study is primarily concerned with the implementation of appropriate applications that address human needs and challenges. Furthermore, this research highlights the significance of technology that has the potential to provide value for emerging technologies.

DRIVE: Dynamic Resource Introspection and VNF Embedding for 5G Using Machine Learning

Network Slicing (NS) technique is comprehensively reshaping the next-generation communication networks (e.g. 5G, 6G). Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) predominantly control the flow of service functions on NS to incorporate versatile applications as per user demands. In the virtualized-Software Defined Networking vSDN environment, a chain of well-defined virtual network functions (VNFs) are installed on Service Function Chains (SFCs) by multiple Internet Service Providers (ISPs) concurrently. Generation, allocation, re-allocation, release and destroying associative VNFs on SFC is an extremely difficult task while keeping high selection accuracy. Towards solving this fundamental issue, in this work, we have proposed a multi-layered SFC formation for adaptive VNF allocation on dynamic slices. We have formulated an ILP to address the VNF-EAP (VNF-Embedding and Allocation Problem) over real network topology (AT&T Topology). Leveraging machine learning techniques we have shown an intelligent VNF selection mechanism to optimize resource utilization. The performance evaluation shows remarkable efficiency on ML-driven dynamic VNF selections over static allocations on SFCs by halving resource usage. Further, we have also studied a VNF typecasting technique for service backup on outage slices in the field of disaster management activities.

Securing NextG networks with physical-layer key generation: A survey

As the development of next-generation (NextG) communication networks continues, tremendous devices are accessing the network and the amount of information is exploding. However, with the increase of sensitive data that requires confidentiality to be transmitted and stored in the network, wireless network security risks are further amplified. Physical-layer key generation (PKG) has received extensive attention in security research due to its solid information-theoretic security proof, ease of implementation, and low cost. Nevertheless, the applications of PKG in the NextG networks are still in the preliminary exploration stage. Therefore, we survey existing research and discuss (1) the performance advantages of PKG compared to cryptography schemes, (2) the principles and processes of PKG, as well as research progresses in previous network environments, and (3) new application scenarios and development potential for PKG in NextG communication networks, particularly analyzing the effect and prospects of PKG in massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RISs), artificial intelligence (AI) enabled networks, integrated space-air-ground network, and quantum communication. Moreover, we summarize open issues and provide new insights into the development trends of PKG in NextG networks.

Novel Energy Optimized LDPC Codes for Next-Generation MIMO OFDM Systems

With the increasing prevalence of internet services in our daily lives, there is a growing demand for spectrum, resulting in a shortage of available frequencies. This is primarily due to the recent surge in subscribers seeking faster and more dependable data rates. Researchers globally have suggested various solutions to combat the spectrum shortage, such as technology, modulation schemes, beamforming, intelligent reflective surfaces, and channel coding schemes. With respect to channel coding, multiple codes have been proposed to overcome challenging and uncertain channel conditions. The paper introduces a DWT-incorporated LDPC STBC system as a solution to meet the rigorous demands of 5G deployment and use cases. A comparative analysis of the proposed codes is conducted to assess their suitability for next-generation communication networks, considering SNR and BER. Results indicate that the suggested code performs optimally, achieving a BER of 10-4 at 7dB SNR for Rayleigh Faded wireless channel. Therefore, the suggested code is deemed the most suitable option for the deployment of 5G and beyond systems.

Space Communication Network in the New Era: Architecture, Key Technologies, and Challenges

Space communication network (SCN) is a feasible and flexible solution to enable global seamless coverage, as well as service orchestration in a wide range. It's emerged as a promising perspective for the next-generation communication network. However, the design of SCN architecture with both efficiency and security remains an open challenge. In this paper, we propose a new system architecture for SCN, along with new hardware components, such as the collaborative edge gateway, the reconfigurable label-based switch, etc. Meanwhile, three core capabilities of the proposed architecture are elaborated, including structural collaborative security, efficient adaptive communication, and distributed service orchestration. Firstly, as for single node of SCN has weak security capability, the structural collaborative security aims to establish a strengthened security structure among nodes. Through security associations and strategy collaborations, nodes in the structure can cooperate to resist large-scale distributed attacks. Secondly, instead of fixed coverage and limited protocol exchange, the efficient adaptive communication introduces communication resource adaptation to realize on-demand resource scheduling, and introduces transmission protocol adaptation to support more heterogeneous connections. Thirdly, to solve the efficiency problem of centralized orchestration in a wide area, a smart agent with a learning-based Pattern-Matching Library is constructed, namely, a distributed component to execute near real-time service orchestration, which uses decomposing-based orchestration optimization to accomplish a lower service latency. In addition to the key technologies, some future research directions are also provided for enlightening the researchers.

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.

A Novel Two-Routers Model Based on Category Constraints Secure Data-Dissemination-Aware Scheduling in Next-Generation Communication Networks

A Novel Two-Routers Model Based on Category Constraints Secure Data-Dissemination-Aware Scheduling in Next-Generation Communication Networks

Identification of Indoor Radio Environment Properties from Channel Impulse Response with Machine Learning Models

The design and optimization of next-generation indoor wireless communication networks require detailed and precise descriptions of the indoor environments. Environmental awareness can serve as a fundamental basis for the dynamic adaptation of the wireless system to channel conditions and can improve the system’s performance. Methods that combine wireless technology with machine learning are promising for identifying the properties of the indoor radio environment (RE) without requiring specialized equipment or manual intervention. In the paper, we propose an approach for identifying the materials of the surfaces using channel impulse response (CIR) and RE identification models built with machine learning. To train the models and assess their performance, we acquired radio propagation data from rooms with different sizes and materials using ray tracing. We explored tree-based methods, ensemble-based methods, kernel-based methods, and neural networks for training the models. The performance of the models is evaluated in three realistic scenarios defined by the location of the radio nodes and the room sizes. The multilayer perceptron models performed best in most of the evaluation settings. The results show that the models are capable of accurately predicting the materials in rooms with sizes that were not included in the training procedure. Including CIRs from a large number of rooms with different sizes and surface materials estimated with different radio node positions in the training process results in models with wider practical applicability.

Social-Aware Graph-Based Collaborative Caching in Edge-User Networks

The soaring data traffic brings tremendous pressure on core networks. Collaborative caching among edge servers and user devices has been regarded as a promising technology for releasing backhaul pressure and reducing content download delay in the next-generation communication network. However, given the capacity constraint of edge servers and caching devices, a challenge is how to exploit these storage resources efficiently. In this paper, we introduce a three-tier collaborative caching architecture for edge-user networks and propose a social-aware graph-based collaborative caching (SGCC) strategy to minimize content download delay. Specifically, the small base station tier and user equipment tier are modeled as a one-tier directed graph. Moreover, we model the physical attributes and social attributes as the weight of the directed edges in the physical link graph and social graph, respectively. Then, we construct a social-aware graph by merging the two-tier topology of the physical link graph and social graph to simplify the caching decision. Further, we design SGCC to cache the most popular content according to the weighted content popularity. Simulation results demonstrate that the proposed SGCC can effectively reduce the average download delay and has a 26.8% lower average download delay, compared with the uniform popular caching baseline.

Joint dynamic routing and resource allocation in satellite-terrestrial integrated networks

The Satellite-Terrestrial Integrated Networks (STINs) is considered as a reliable and agile next-generation communication network scheme, because it is flexible to deploy and robust to disasters (e.g., earthquakes, floods, and volcanic eruptions). The routing and resource allocation directly affect the power consumption of satellites in the STINs. However, most of existing research works study these two issues separately, and neglect the impact of path selection on the satellite power consumption. This paper investigates the joint optimization problem of routing, bandwidth allocation, user association, channel allocation, and power allocation of the STINs. We aim at minimizing satellites’ power consumption while satisfying user's Quality of Service (QoS). Because the power consumption of satellites for routing in the space segment is involved with the data rate through satellite gateways in the user segment, this optimization problem is a Multi-Objective Optimization (MOO) problem. To this end, we convert the MOO problem to a Single-Objective Optimization (SOO) problem by linear weighted method. Considering that the SOO problem is a Mixed Integer Nonlinear Programming (MINLP) problem, we decompose it into the resource allocation sub-problem in the user segment and the routing optimization sub-problem in the space segment. And we solve these two sub-problems iteratively. Specifically, we convert the first sub-problem into a convex optimization problem and obtain the current user association and channel allocation. Then we formulate the second sub-problem into a multi-commodity flow problem and solve it based on the obtained user association and channel allocation. The SOO problem is solved by iteratively optimizing these two sub-problems. Experimental results demonstrate that our proposed algorithm is able to get an approximate optimal solution quickly and effectively reduce the power consumption of satellites compared with benchmark algorithms in the STINs.