Radio Networks Research Articles

Formal dependability analysis of fault tolerant Virtual Machine allocation strategies in Cloud Radio Access Network

Cloud Radio Access Network (C-RAN) has been proposed as a fifth generation (5G) cellular network that is designed to physically separate the Baseband Units (BBUs) from the Remote Radio Heads (RRHs). The BBUs are placed in the BBU pool/hotel, while the RRHs are located in the cells. This separation enables sharing baseband processing resources among RRHs by generating Virtual Machines (VMs) in the BBU pool, leading to radio coordination and energy saving opportunities. Nevertheless, the failure of a BBU can affect several RRHs, causing outages in the radio network. Therefore, designing a reliable and resilient C-RAN is essential to provide high availability and service continuity in case of BBU failure. This paper deals with using formal verification techniques to analyze the performance and the dependability of C-RAN architecture. We propose fault tolerant VM allocation strategies that can handle BBU failures, thus improving the system’s overall reliability. Based on redundancy techniques, these strategies ensure call recovery through migration to functional BBUs in the case of a BBU failure. In order to evaluate the mutual impact of performance measures on the system’s dependability, we develop performability (combination of performance and dependability) Markov Reward Models (MRMs) for the proposed strategies. We use Probabilistic Model Checking (PMC) to check the performability requirements written with Continuous Stochastic Reward Logic (CSRL). The proposed models are implemented and checked using the PRISM model checker, which supports solving MRMs and checking CSRL properties.

A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks

Multi-Access Edge Computing (MEC) reduces latency, provides high-bandwidth applications with real-time performance and reliability, supporting new applications and services for the present and future Beyond the Fifth Generation (B5G). Radio Network Information Service (RNIS) plays a crucial role in obtaining information from the Radio Access Network (RAN). With the advent of 5G, RNIS requires improvements to handle information from the new generations of RAN. In this scenario, improving the RNIS is essential to boost new applications according to the strict requirements imposed. Hence, this work proposes a new RNIS as a service to the MEC framework in B5G networks to improve MEC applications. The service is validated and evaluated, and demonstrates the ability to adequately serve a large number of MEC apps (two, four, six and eight) and from 100 to 2000 types of User Equipment (UE).

Spectral Efficiency Maximization for Mixed-Structure Cognitive Radio Hybrid Wideband Millimeter-Wave Transceivers in Relay-Assisted Multi-User Multiple-Input Multiple-Output Systems.

This paper proposes a cognitive radio network (CRN)-based hybrid wideband precoding for maximizing spectral efficiency in millimeter-wave relay-assisted multi-user (MU) multiple-input multiple-output (MIMO) systems. The underlying problem is NP-hard and non-convex due to the joint optimization of hybrid processing components and the constant amplitude constraint imposed by the analog beamformer in the radio frequency (RF) domain. Furthermore, the analog beamforming solution common to all sub-carriers adds another layer of design complexity. Two hybrid beamforming architectures, i.e., mixed and fully connected ones, are taken into account to tackle this problem, considering the decode-and-forward (DF) relay node. To reduce the complexity of the original optimization problem, an attempt is made to decompose it into sub-problems. Leveraging this, each sub-problem is addressed by following a decoupled design methodology. The phase-only beamforming solution is derived to maximize the sum of spectral efficiency, while digital baseband processing components are designed to keep interference within a predefined limit. Computer simulations are conducted by changing system parameters under different accuracy levels of channel-state information (CSI), and the obtained results demonstrate the effectiveness of the proposed technique. Additionally, the mixed structure shows better energy efficiency performance compared to its counterparts and outperforms benchmarks.

Digital antenna array based on programmable logic integrated circuits for mobile radio networks

Currently, there is a need to develop new technical solutions for antenna systems for central (base) stations of mobile radio networks, which not only have improved electrical characteristics, but also allow them to adjust from intentional and unintended interference, while providing a reliable communication channel between several correspondents. The purpose of the work is to develop methods for constructing digital antenna arrays with adaptive DN control for VHF mobile radio networks. The presented solution of the ring car allows you to form the DN in the specified directions, to work in multipath mode, as well as the proposed solutions of ring cars can be used both in the center (base) stations of mobile radio communication systems and on mobile objects operating, including in the relay mode.

Small-sized active phased array antenna for base stations of mobile radio networks in limited deployment conditions

Today, most telecommunications structures are oversaturated with antenna technology. There is a need to use high-rise buildings and facilities that are not equipped for communication. However, the use of such objects imposes a number of restrictions on the installation locations of antenna systems. Thus, the problem arises of developing an antenna system that makes it possible to achieve a circular radiation pattern using small-sized antennas. A model of a small-sized UHF AFAR has been developed. A distinctive feature of this AFAR is the use of a ring active phased antenna array with an electrically controlled radiation pattern as an antenna system. Such solutions have not been used in domestic antenna technology, including military radio communications. The antenna complex is proposed to be used for installation on mobile objects, as well as on the upper platforms of towers and masts to provide a circular radiation pattern in places where it is impossible to implement a ring antenna array around the perimeter of the object.

A Compact C-Band Multiple-Input Multiple-Output Circular Microstrip Patch Antenna Array with Octagonal Slotted Ground Plane and Neutralization Line for Improved Port Isolation in 5G Handheld Devices

In this paper, an eight-port antenna array is presented for 5G handheld terminals to support multiple-input multiple-output (MIMO) operations. The reported design involves three layers: the top contains eight circular microstrip feed elements; the middle is a low-cost FR-4 substrate, and the bottom layer is a ground plane with four etched octagonal slots. Each resonating element is fed by 50-ohm sub-miniature connectors. To mitigate the detrimental effects of mutual coupling of ports and enhance overall isolation between the adjacent microstrip-fed circular patch elements, a neutralization line is strategically implemented between the feed lines of the antenna array. The design configuration involves two elements at each vertex of the printed circuit board (PCB). The overall dimensions of the PCB are 150 × 75 mm2. Each slot and its corresponding radiating elements exhibit linear dual polarization and diverse radiation patterns. The proposed antenna design achieves the required operating bandwidth of more than 1000 MHz spanning from 3 to 4.2 GHz, effectively covering all the upper C-band frequency range of 3.3 GHz to 4.2 GHz allocated for 5G n77 and n78 frequency range 1 (FR1). Required port isolation and lower envelop correlation coefficient (ECC) are achieved for the band of interest. The proposed design gives a peak gain of up to 4 dB for the said band. In addition to these results, degradation in the performance of the antenna array is also investigated during different operating modes of the handheld device. Measured results from the fabricated unit cell and whole array also have a good match with simulated results. On the whole, the proposed antenna possesses the potential to be used in 5G and the open radio access network (ORAN) compliant handheld devices.

A policy configured resource management scheme for AHNS using link reliability K‐means clustering algorithm and Weibull distribution‐based blue monkey optimization

SummaryCognitive Radio Ad Hoc Networks (CRAHNs) are an essential method for resolving conflicts between extreme spectrum scarcity and rapid traffic increase while maintaining high‐quality service for consumers. However, the coexistence of primary and secondary users represents a critical challenge for reasonable resource allocation in order to provide a sustaining system performance. Many approaches have been developed to efficiently allocate resources; however, these methods are currently limited by things like user collision, strange traffic networks, and high data transmission error rates. To address these constraints, this paper proposes a policy‐configured reinforcement learning‐based ad hoc network (AHN) model. To obtain the ideal policy configuration for the network, the system first models the cognitive radio (CR) network, in which nodes are initialised and grouped employing the Link Reliability K‐Means clustering Algorithm (LR‐KMA). The available spectrum was then detected and separated into multiple bands utilizing coherent‐based detection (CBD) and signal source identification employing the Parzen–Rosenblatt Window‐based Restricted Boltzmann Machine (PRW‐RBM). Next, the learning model for the resource allocation process employs the Weibull Distribution‐based Blue Monkey Optimization (WD‐BMO) approach to pick the relevant bands. Finally, the experimental results were analyzed in order to evaluate the proposed resource allocation model's performance in CRAHNs. When compared with previous findings, the proposed method improves resource utilization by 5%, the proposed model achieves a 7% higher throughput, and the PRW‐RBM's accuracy improves classification accuracy by 1.07%.

Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey

SummaryThe emergence of 5G networks has increased the demand for network resources, making efficient resource management crucial. Slice admission control (SAC) is a process that governs the creation and allocation of virtualized network environments, known as “network slices,” which can be tailored to meet specific user requirements. However, traditional SAC methods face dynamic and heterogeneous challenges in wireless networks, especially in cloud radio access networks (C‐RANs). To address this issue, machine learning (ML) techniques, particularly deep reinforcement learning (DRL), have been proposed as powerful tools for optimizing SAC. DRL‐based approaches enable SAC systems to learn from previous interactions with the network environment and dynamically adapt to changing network conditions. This review article comprehensively explains the current state‐of‐the‐art DRL‐based SAC, focusing on C‐RANs. The article identifies key challenges and future research directions and highlights the potential benefits of using DRL for SAC, including improved network performance and efficiency. However, deploying these systems in real‐world scenarios presents several challenges and trade‐offs that need to be carefully considered. Further research and development are required to address these challenges and ensure the successful deployment of DRL‐based SAC systems in wireless networks.

Cognitive radio network architecture for GEO and LEO satellites shared downlink spectrum

The fixed spectrum assignment policy in the space sector and large constellations of Low Earth Orbit (LEO) satellites left little or no spectrum available for future LEO satellite communications services. Cognitive Radio (CR) technology enables spectrum sharing between primary and secondary users without limiting the transmission power, and thus is of great interest to commercial and defense entities. A number of Radio Environment Map (REM) techniques have been making Cognitive Radio Networks (CRNs) practical by constructing a comprehensive map of the CRN by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations. In this paper, we investigate spectrum sharing for a network comprised of a Geostationary Orbit (GEO) and a LEO satellite with a multibeam antenna array. A CRN architecture of GEO and LEO satellites shared downlink spectrum is proposed and details are provided covering its architecture, REM structure and channel utilization data aggregation, as well as a frequency slot assignment mechanism.

Sum rate maximization in UAV-assisted data harvesting network supported by CF-mMIMO system exploiting statistical CSI

Unmanned Aerial Vehicles (UAVs) have been considered to have great potential in supporting reliable and timely data harvesting for Sensor Nodes (SNs) from an Internet of Things (IoT) perspective. However, due to physical limitations, UAVs are unable to further process the harvested data and have to rely on terrestrial servers, thus extra spectrum resource is needed to convey the harvested data. To avoid the cost of extra servers and spectrum resources, in this paper, we consider a UAV-based data harvesting network supported by a Cell-Free massive Multiple-Input-Multiple-Output (CF-mMIMO) system, where a UAV is used to collect and transmit data from SNs to the central processing unit of CF-mMIMO system for processing. In order to avoid using additional spectrum resources, the entire bandwidth is shared among radio access networks and wireless fronthaul links. Moreover, considering the limited capacity of the fronthaul links, the compress-and-forward scheme is adopted. In this work, in order to maximize the ergodically achievable sum rate of SNs, the power allocation of ground access points, the compression of fronthaul links, and also the bandwidth fraction between radio access networks and wireless fronthaul links are jointly optimized. To avoid the high overhead introduced by computing ergodically achievable rates, we introduce an approximate problem, using the large-dimensional random matrix theory, which relies only on statistical channel state information. We solve the nontrivial problem in three steps and propose an algorithm based on weighted minimum mean square error and Dinkelbach's methods to find solutions. Finally, simulation results show that the proposed algorithm converges quickly and outperforms the baseline algorithms.

A Binary Artificial Rabbit Optimization Algorithm for Improved Cognitive Radio Spectrum Allocation

A binary artificial rabbit optimization algorithm is proposed to maximize the cognitive radio (CR) network performance and fairness among users by addressing the issue of low spectrum resource utilization during the CR spectrum allocation process. Building upon the graph coloring spectrum allocation model, Sigmoid is introduced to transform the algorithm solution space. Incorporating the Lévy flight strategy for adaptive step size adjustment enhances the algorithm’s flexibility and convergence accuracy. Moreover, a selective opposition strategy based on Spearman’s coefficient is integrated into the algorithm to improve population diversity and convergence accuracy through reverse learning. Applying the binary artificial rabbit optimization algorithm to the CR spectrum allocation problem in the same CR network environment, we compare network efficiency and inter-user fairness through simulation experiments with the artificial rabbits optimization algorithm seagull optimization algorithm and particle swarm optimization algorithms. The experimental results show that the binary artificial rabbit optimization algorithm has higher convergence performance and global exploration ability, improves the overall network efficiency and user fairness of CR networks, and alleviates the problem of low spectrum resource utilization.

Cooperative Spectrum Sensing Deployment for Cognitive Radio Networks for Internet of Things 5G Wireless Communication

Cooperative Spectrum Sensing Deployment for Cognitive Radio Networks for Internet of Things 5G Wireless Communication

QoS Awareness Transmit Beamforming for Secure Backscattering in Symbiotic Radio Networks

QoS Awareness Transmit Beamforming for Secure Backscattering in Symbiotic Radio Networks

Thin-film lithium niobate-based electro-optic comb cloning for self-homodyne coherent communication.

As the optical communication industry advances, metropolitan area networks (MANs) and radio access networks (RANs) are extensively deployed on a large scale, demanding energy-efficient integrated light sources and simplified digital signal processing (DSP) technologies. The emergence of thin-film lithium niobate (TFLN) has given rise to high-performance, energy-efficient on-chip modulators, making on-chip optical frequency comb (OFC) more appealing. Owing to the phase uniformity and stability of this chip-scale device, it has been possible to eliminate the carrier frequency phase estimation (CPE) in DSP stacks using comb-clone-enabled self-homodyne detection. Here we report the first use, to our knowledge, of a TFLN on-chip electro-optic (EO) frequency comb to realize comb cloning and self-homodyne coherent detection. We transmit three optical pilot tones and eight data channels encoded with 20 Gbaud polarization-multiplexed 16-ary quadrature amplitude modulation (PM-16-QAM) over 10 km and 80 km standard single-mode fibers. The bit error ratios (BERs) of the eight channels reach below 10-3, a result made possible by our on-chip comb. The scalability and mass producibility of on-chip EO combs, combined with the simplified DSP, show potential in our proposed fifth-generation (5G) RAN and MAN transmission scheme.

Implementing green optical waveform system using hybrid cognitive methods for QAM transmission scheme

Abstract This study proposes a hybrid approach combining Energy Detection (ED) and Matched Filter (MF) spectrum sensing techniques to enhance power spectrum density (PSD) in optical Nonorthogonal Multiple Access (NOMA) systems. Optical NOMA has emerged as a key technology for boosting spectral efficiency in optical communication networks. However, optimizing PSD remains a critical challenge due to various factors including signal detection and noise interference. The hybrid ED–MF spectrum sensing method aims to address these challenges by leveraging the strengths of both techniques. Energy Detection (ED) offers simplicity and robustness in detecting primary users, making it suitable for initial spectrum sensing in cognitive radio networks. Matched Filter (MF) spectrum sensing, on the other hand, provides superior signal detection and noise rejection capabilities, particularly in low signal-to-noise ratio (SNR) environments. By integrating these two techniques, we aim to achieve improved sensitivity and accuracy in spectrum sensing, thus enhancing spectral efficiency and system performance in optical NOMA networks. The effectiveness of the proposed hybrid approach is evaluated through theoretical analysis and simulation experiments. Results demonstrate significant enhancements in spectral efficiency and system reliability compared to conventional spectrum sensing methods, highlighting the potential of the hybrid ED–MF approach for enhancing PSD in optical NOMA systems. This research contributes to advancing the design and optimization of optical communication systems for future high-capacity and high-speed data transmission applications. The PSD values −920 are obtained and it confirmed that the proposed algorithm outperforms the standard algorithms.

Optimizing Priority Queuing Systems with Server Reservation and Temporal Blocking for Cognitive Radio Networks

In the domain of cognitive radio (CR), unlicensed users have the opportunity to efficiently use available spectrum bands without interfering with licensed primary users (PUs). Our study addresses the challenge of secondary user (SU) spectrum shortage due to high arrival rates of licensed users. We propose two models aimed at improving the average total waiting time for SUs in such scenarios. These models incorporate non-acquired and preemptive priority mechanisms within the M/D/1 model of a PU delay system. Through quantitative evaluations and Monte Carlo simulations, we evaluate the performance of these models. Our findings show significant improvements in average waiting time for both PUs and SUs, especially under the priority scheme. Furthermore, we explore these models in the context of real-time systems, considering the limited buffer capacity for both user types. This further improves the average waiting time for PUs and SUs in both priority schemes. Our contribution lies in providing effective solutions to mitigate SU shortages in CR networks, providing insight into priority-based approaches and real-time system considerations.

Cost dynamics of converged optical-wireless networks: enabling low-latency xRANs through a reconfigurable hybrid split

Future 6G and beyond wireless networks are anticipated to be highly versatile, accommodating a wide range of services, from ultra-low-latency applications like autonomous vehicles and extended reality to enhanced mobile broadband and massive connectivity for the Internet of Things. In tackling this, xRANs (cloud/virtualized/open radio access networks) encounter significant challenges, including automation, interoperability, scalability, reconfigurability, and standardization, within crosshaul (comprising fronthaul, midhaul, and backhaul) networks. Therefore, the development of programmable converged optical-wireless networks with exceptional flexibility is crucial. This study concentrates on the design of integrated optical and wireless networks to achieve the reconfigurability necessary for automation and to fulfill diverse latency requirements. Initially, we analyze the latency contributions from different network segments and traffic factors in the xRAN, followed by a comprehensive examination of the associated cost dynamics. Subsequently, we investigate the feasibility of integrating high-layer and low-layer splits within the same network to achieve different latency levels. Finally, our study delves into the relationship between latency and cost for converged optical-wireless networks with varying mixed split scenarios and throughput levels. Overall, this article aims to assist network planners in making well-informed decisions that balance throughput performance, cost, and latency requirements in upcoming network deployments.

Rendezvous Sequence Generation Algorithm for Cognitive Radio Networks in Post-Disaster Scenario

Recent natural disasters have inflicted tremendous damage on humanity, with their scale progressively increasing and leading to numerous casualties. Events such as earthquakes can trigger secondary disasters, such as tsunamis, further complicating the situation by destroying communication infrastructures. This destruction impedes the dissemination of information about secondary disasters and complicates postdisaster rescue efforts. Consequently, there is an urgent demand for technologies capable of substituting for these destroyed communication infrastructures. This paper proposes a technique for generating rendezvous sequences to swiftly reconnect communication infrastructures in post-disaster scenarios. We compare the time required for rendezvous using the proposed technique against existing methods and analyze the average time taken to establish links with the rendezvous technique, discussing its significance. This research presents a novel approach enabling rapid recovery of destroyed communication infrastructures in disaster environments through Cognitive Radio Network (CRN) technology, showcasing the potential to significantly improve disaster response and recovery efforts. The proposed method reduces the time for the rendezvous compared to existing methods, suggesting that it can enhance the efficiency of rescue operations in postdisaster scenarios and contribute to life-saving efforts.

Nominal GSM-R Radio Network Design for Signalling and Train Control Systems: Case Study Dar-Moro Electrified SGR Line in Tanzania

The recent development of High-Speed Trains (HST) operations requires real-time information transfer, high network capacity, and reliable communication for train signalling and control purposes. Rail operators face challenges as the high-speed railroad traffic grows. In a high-speed driving profile, the communication must be able to overcome fast handover, large Doppler shift, high penetration losses, limited visibility in tunnels, and the harsh electromagnetic environment. Global System for Mobile Communications for Railway (GSM-R) is still a standard-bearer for railway signalling communications. The link capacity and coverage prediction of the predefined Base Transceiver Station (BTS) locations are simulated using the Radio Planner RF planning tool and Matlab®. The terrain map and the signal levels along the railway corridor are observed. The analysis of the GSM-R for a fast-moving train is performed. Signal level coverage prediction, Doppler-shift effect and the received carrier frequencies due to Doppler shift along Dar–Pugu stations have been presented. The critical sites (locations) with weak signals of the first three BTSs have been identified. It is also observed that the Doppler effect may limit the proposed speed.

Enabling cost-effective wireless THz communication through semiconductor optical amplifier’s direct/cross gain modulation for seamless integration of hybrid fiber-coaxial connectivity

We propose a cost-effective THz wireless communication scheme for future femtocell networks that leverages either direct gain modulation (DGM) or cross gain modulation (XGM) of a semiconductor optical amplifier (SOA) to convert coaxially or optically carried data into intensity-modulated THz waves, respectively. This architecture, based on an SOA and a uni-traveling-carrier photodiode (UTC-PD), has advantages over typical THz modulation schemes involving a resonant tunneling diode or an electro-optic modulator combined with a UTC-PD. Consequently, it facilitates the seamless convergence of hybrid fiber-coaxial networks to THz radio access networks with low cost, straightforward monolithic integration, and a more compact footprint. Furthermore, we experimentally verified the possibility of non-return-to-zero on–off keying wireless communication over 0.5 m in the 300 GHz band, achieving real-time transmission of 4 Gbit/s for coaxial access via an SOA’s DGM and 10 Gbit/s for fiber access via the SOA’s XGM. Notably, these data rates were attained by simple non-coherent THz intensity demodulation based on a Fermi-level managed barrier diode while meeting the 7% hard-decision forward error correction (HD-FEC) threshold (3.4×10−3). Finally, we conducted an analysis of the merits and drawbacks of the proposed THz communication scheme based on the combination of an SOA and a UTC-PD, discussed bottlenecks that hinder further rate improvement, and explored possibilities for optimization. In conclusion, this scheme is crucial for realizing future ultra-high-speed wireless networks that seamlessly connect to fiber-coaxial networks.