Next-generation Communication Systems Research Articles

Analysis of time-weighted LoRa-based positioning using machine learning

Localization systems have gained attention owing to the paradigm shift from human-centric communication systems (1G to 4G) to the machine-to-machine architectures (5G and beyond). The commercial localization applications standardized for 5G systems have served as a precursor to the cardinality of positioning technologies in the next-generation communication systems. The stringent requirements of these use-cases have motivated researchers to propose novel architectures and techniques to develop scalable, accurate, reliable, robust, and low-power positioning and tracking systems. Low-power wide-area network (LPWAN) technologies have found their niche in the Internet-of-thing (IoT)-focused industrial and research communities, since they promise wide area coverage to many battery-operated devices. LoRaWAN, with regulatory features and high network density, has emerged as the widely adopted long-range, low-power solution for scheduled IoT applications. This paper explores the feasibility of LoRa technology for satellite navigation-independent positioning, using received signal strength indicator (RSSI) fingerprinting. We explore traditional path-loss models, machine learning and deep learning techniques to develop an accurate RSSI-to-distance mapping. We further use the analytically optimal model as the underlying ranging function for trilateration-based deterministic positioning. The results indicate that LoRa technology is a feasible alternate for fingerprinting-based positioning in line-of-sight and non-line-of-sight scenarios, with accuracies ranging from 6 to 15 m.

Open network structure and smart network to sharing cybersecurity within the 5G network

The <span>next-generation communication system incorporates information technology (IT) and operations technology (OT) for generating, delivering, and collecting, and obtaining communication power. We plan to include a brief outline of internet of thing (IoT) communication and its context, along with security concerns that arise for IoT data on the network and some methods for detecting and avoiding cyber security threats. With the rise of the 5G networks, we introduce the smart network's emergent technology and its opportunities and more cybersecurity issues. Whereas, finding or responding to a power outage is an essential part of system security That is why we will discuss the innumerable advantages of 5G networks and we must also cover the inevitable problems that we will encounter in power delivery. The use of smart IoT communication technologies is becoming more common in the energy sector, particularly with the network (5G. The smart network and energy flow integration Real-time data on generation, electricity distribution, and energy consumption is measured using computers and cutting-edge technologies. This information aids utility companies in managing electricity supply and demand, as well as price. While enhanced communication and information technologies are unquestionably crucial to </span>the smart network.

ML-based reconfigurable symbol decoder: An alternative for next-generation communication systems

Modern Machine Learning (ML) techniques offer numerous opportunities to enable intelligent communication designs while addressing a wide range of problems in communication systems. A wide majority of communication systems ubiquitously employ the Maximum Likelihood (MLH) decoder in the symbol decoding process with QPSK modulation, thereby providing a non-reconfigurable solution. This work addresses the application of an ML-based reconfigurable solution for such systems. The proposed decoder can be considered a strong candidate for future communication systems, owing to its upgradable functionality, lower complexity, faster response, and reconfigurability. First, a novel low-complexity dataset for model training/testing is generated, that uses only the received symbols. Subsequently, three predictors are extracted from each of the received noisy symbols for model training/testing. The model is then trained/tested using nineteen standard ML-based classifiers, and the computations of various performance metrics indicate the suitability of Naïve Bayes (NB), and Ensemble Bagged Decision Tree (EBDT) classifiers for the model. The simulation results show that the model respectively delivers significant decoding accuracies and error rates of about 93% and 7% during testing, even for a low SNR of 5 dB. Moreover, the statistical analysis of simulation results shows the marginal superiority of the Gaussian Naïve Bayes (GNB) classifier. Further, the model reconfiguration is validated using a BPSK modulated dataset. Finally, a user-separation scheme that eliminates Successive Interference Cancellation (SIC) in the next-generation Power-Domain (PD) Non-Orthogonal Multiple Access (NOMA) networks is suggested by employing the proposed decoder.

Quantum Stream Cipher Based on Holevo-Yuen Theory.

In this review paper, we first introduce the basic concept of quantum computer-resistant cryptography, which is the cornerstone of security technology for the network of a new era. Then, we will describe the positioning of mathematical cryptography and quantum cryptography, that are currently being researched and developed. Quantum cryptography includes QKD and quantum stream cipher, but we point out that the latter is expected as the core technology of next-generation communication systems. Various ideas have been proposed for QKD quantum cryptography, but most of them use a single-photon or similar signal. Then, although such technologies are applicable to special situations, these methods still have several difficulties to provide functions that surpass conventional technologies for social systems in the real environment. Thus, the quantum stream cipher has come to be expected as one promising countermeasure, which artificially creates quantum properties using special modulation techniques based on the macroscopic coherent state. In addition, it has the possibility to provide superior security performance than one-time pad cipher. Finally, we introduce detailed research activity aimed at putting the quantum stream cipher into practical use in social network technology.

Graphdiyne-Coated Microfiber All-Optical Temporal Modulator Based on Saturable Absorption

A high-speed all-optical modulator is a key device in next-generation communication systems. Due to easy fabrication and an effective modulation effect, two-dimensional (2D) material–microfiber structure all-optical modulators have aroused much attention. Graphdiyne (GDY) is an excellent nonlinear optical material and is expected to be utilized in all-optical modulators. In this work, we demonstrate a GDY-coated all-optical temporal modulator according to its saturable absorption. Under the pump pulse light of 1,064 nm, the fabricated modulator successfully modulates the continuous-wave (CW) light of 1,550 nm to the waveform of pump light with a pulse width of 4 ns and a repetition rate of 5 kHz. Our results show that GDY could be used in high-speed all-optical modulators and pave a way for the research of GDY in all-optical information processing applications.

19-ring-air-core fiber supporting thousands of OAM modes for spatial division multiplexing.

We propose and design a 19-ring-air-core fiber that can support about 3000 orbital angular momentum (OAM) modes (156 modes in each ring) with <-80 dB inter-ring cross talk across the entire C and L bands after 100-km fiber propagation. Moreover, the eigenmodes are all separated from their adjacent modes by effective index differences >2.67 × 10-4 and mode groups by > 1.90 × 10-2, which can guarantee the stable transmission of OAM modes. This designed fiber is a potential candidate for applications in spatial division multiplexing (SDM) of optical channels to improve the capacity of next-generation high-speed optical communication systems, especially in short-distance applications. In this Letter, we also show the relationship between supported OAM mode numbers, total cross talk, and effective refractive index of intra-ring modes during the optimization of fiber through numerical simulations. It can provide a related reference for the future design of multi-ring-core fibers.

Enabling URLLC Applications Through Reconfigurable Intelligent Surfaces: Challenges and Potential

Next-generation communication systems, including the sixth generation (6G) cellular systems, are expected to support a wide range of new ultra-reliable low-latency communications (URLLC). These emerging applications strictly demand high data rates and/or massive connectivity besides their strict reliability and latency requirements. To enable such URLLC applications alongside the enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC), it is imperative to develop spectrally efficient methods, and non-conventional technologies and networking architectures. To this end, reconfigurable intelligent surfaces (RISs) have recently emerged as a key promising technology to enhance the capabilities of 6G wireless networks. The RIS technology offers a wide range of advantages, including offering a low-cost and energy-efficient solution for controlling the propagation environments. We study the potential of deploying RIS in 6G networks with an emphasis on accommodating the coexistence of URLLC and eMBB traffic. Specifically, we demonstrate through several case studies the potential of integrating RIS into 6G networks with the objective of meeting the requirements of URLLC without compromising the performance of eMBB users. Motivated by this and the fact that the RIS technology has not been fully investigated, we propose a number of relevant research directions and highlight what kind of challenges such directions may entail. We also elaborate on how such challenges, once tackled, may lead to realizing the full potential of integrating RIS into future wireless networks.

Generalized 3-D Spatial Scattering Modulation

Three-dimensional (3-D) massive multiple-input-and-multiple-output (MIMO) systems, which explore degrees of freedom in both the vertical and the horizontal dimensions, are a promising technology to enhance spectral efficiency in the next-generation communication systems. As an emerging modulation technology with millimetre wave (mm-wave) communication in massive MIMO systems with limited radio-frequency (RF) chains, the spatial scattering modulation (SSM) makes use of beamspace domain resources to further improve the spectral efficiency. However, most published works on the SSM only focus on two-dimensional (2-D) MIMO systems. In this paper, we generalize the SSM system to 3-D space. First, we design a novel generalized 3-D SSM system by considering both vertical and horizontal angles to determine scattering paths, which are selected to convey information bits. Then, we propose a whitening filter based optimal detection algorithm to detect the received symbols with correlated noise, where the correlation is generated by the combination of the received signals from the large-scale planar receiving antenna array. Next, we design a 2-D fast Fourier transform (FFT) based transceiving approach to improve the hardware friendliness. After that, we propose a low-complexity detector based on the linear minimum mean square error (MMSE) detection algorithm. Moreover, we derive the union upper bound on average bit error probability (ABEP) in a closed form and the asymptotic performance expression for the generalized 3-D SSM system. Finally, we analyse the impact of transmission environment on the ABEP performance. Numerical results show that the generalized 3-D SSM system outperforms the conventional 2-D SSM system, which reduces the ABEP by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> with the same signal-to-noise ratio (SNR) level under the typical indoor environment.

Optimal hybrid precoder design for millimeter-wave massive MIMO systems

Millimeter-waves (mmWave) have emerged as a promising candidate for next-generation communication systems. However, they have severe degradation issues that can be overcome by multiple-input multiple-output (MIMO) technology. Still, high-power consumption and budget issues are associated with multiple antennas. Therefore, hybrid precoders are exploited to combat these inevitable issues. The prime objective of this manuscript is to develop a hybrid precoder for massive MIMO systems that can provide better performance than the existing ones in terms of bit error rate (BER) versus signal-to-noise ratio (SNR). In this context, we exploit iterative geometric mean decomposition (IGMD) method that presents asymptotically optimum performance not only in traditional MIMO systems but also in massive MIMO systems. Later, the outcomes of the proposed scheme are compared with a recent design exploiting generalized triangular decomposition (GTD) scheme. The simulation studies show that the proposed method provides more than 7 dB improvement according to other designs.

Analytical Guarantees for Hyperparameter Free RFF Based Deep Learning in the Low-Data Regime

Deep-learning (DL) has emerged as promising for diverse signal-processing tasks concerning the next-generation of communication-systems, Internet of Things (IoT), and in circuit-design. However, existing DL based signal-processing methods are well-known to require large datasets to achieve good generalization. Moreover, the existing DL-based solutions are known for their sensitivity to the hyperparameter-choices and the DL-architecture. In this regard, existing DL-paradigms, such as meta-learning are useful to find optimal hyperparameter-values and architectures. In this work, generalized analytical results are provided for random Fourier feature-based DL (RFF-DL) that guarantee a degree of hyperparameter-independence and significantly improved generalization under data-limited applications. The benefits of RFF-DL promised by the presented analytical contributions are validated by a case study that improves low-pilot training for the next-generation IoT using the hyperparameter-free RFF based DL.

Pulse Compression Photoconductive Switching Using Negative Differential Mobility

This work demonstrates a novel optoelectronic device with the potential for use as a high-frequency, high-power RF source or amplifier. The device is a gallium–arsenide coplanar waveguide with a small gap in the signal trace for optical illumination. A confined charge cloud is generated by illumination through an aperture in an opaque mask over this gap. An electric field above the threshold for negative differential mobility (NDM) enables pulse compression, which prevents the charge cloud from spreading temporally during the drift process. Due to the NDM phenomenon, the output electrical pulse is temporally compressed compared to the input optical pulse. This phenomenon is demonstrated using three different experiments with varied laser pulsewidth (28–700 ps) and device geometry (50- and 100- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> -length gaps). A 66% reduction in the full-width at half-maximum of the electrical pulse relative to the input optical pulse was demonstrated. This novel coupled optoelectronic device opens avenues for high-frequency, high-power, compact devices that could enable next-generation satellite communication systems with faster data rates and longer ranges.

Guest editorial: Brain-computer-interface inspired communications

Following the massive commercialization of 5G mobile communication systems, both academia and industry are initiating research activities to shape the next-generation communication systems, namely, 6G. Although the detailed killer-applications and key technologies of 6G have not been clearly defined yet, it is commonly expected that 6G will provide hyper-coverage and hyper-connectivity. Enabled by these capabilities, the 6G communication systems are especially aiming at improving the user's experience greatly, or more ambitiously, to change the way of human's everyday life. So far, many new services with more stringent requirements, such as truly immersive extended reality (XR), high-fidelity mobile hologram, and digital replica, are expected to be satisfied by the 6G communication systems.

A Road towards 6G Communication—A Review of 5G Antennas, Arrays, and Wearable Devices

Next-generation communication systems and wearable technologies aim to achieve high data rates, low energy consumption, and massive connections because of the extensive increase in the number of Internet-of-Things (IoT) and wearable devices. These devices will be employed for many services such as cellular, environment monitoring, telemedicine, biomedical, and smart traffic, etc. Therefore, it is challenging for the current communication devices to accommodate such a high number of services. This article summarizes the motivation and potential of the 6G communication system and discusses its key features. Afterward, the current state-of-the-art of 5G antenna technology, which includes existing 5G antennas and arrays and 5G wearable antennas, are summarized. The article also described the useful methods and techniques of exiting antenna design works that could mitigate the challenges and concerns of the emerging 5G and 6G applications. The key features and requirements of the wearable antennas for next-generation technology are also presented at the end of the paper.

Online multimedia traffic classification from the QoS perspective using deep learning

In the next generation of communication systems, data traffic is expected to increase dramatically and continuously. Particularly for multimedia traffic, it has a dominant share in the increasing traffic. Therefore, there is an urgent need to develop an effective and accurate scheme to achieve online and automatic traffic management. To this end, this paper proposes an online multimedia traffic classification framework based on a Convolutional Neural Network (CNN), capable of conducting fast and early classification as well as class incremental learning. First, the sliding window technique is applied to capture the flow slices for further feature extraction. Then, the 3-dimensional flow representation is extracted based on the probability distribution function. After that, according to the specific structure of features, a deeply adapted structure of CNN is devised to better learn the knowledge from the representation. Besides, to better support the addition of new services, a class incremental learning model is developed with the techniques of knowledge distillation and bias correction to achieve continuous learning without retraining from scratch. Our experimental results reveal that the proposed method achieves faster and more accurate traffic classification compared with the state-of-the-art. Additionally, the deployed scheme using incremental learning achieves drops by about 50% in both time and memory consumptions compared with existing methods, while guaranteeing the accurate classification after adding new classes.

X2-Based Signalling Mechanisms for Downlink Uplink Decoupling in Next Generation Communication Systems

Cell selection in cellular networks is an important aspect that impacts the quality of service. The traditional cell selection mechanism is based on downlink received power. Despite the dense deployment of macrocells, mobile network operators are still confronting the daunting challenge of providing capacity and coverage. Deployment of a large number of small cells has emerged as a promising solution towards addressing this problem. However, this success expands the heterogeneous cellular networks where there is a significant disparity in the transmit power of the different base station types. Downlink and Uplink Decoupling (DUDe) can improve efficiency by associating the downlink cell based on the downlink received power and the uplink based on the pathloss. While the higher layer signalling has not been proposed in detail for the DUDe mechanism yet, we aim to propose a solution for the problem. This work addresses four different signalling mechanisms to realise decoupled up/downlinks connections in the radio access network for the next-generation communication systems with handling mobility. Our proposed signalling mechanisms cover uplink decoupling, downlink coupling, downlink decoupling, and uplink coupling scenarios. We analyse the proposed signalling mechanisms using ns3 simulation and present the impact of applying the DUDe mechanism, which mainly shows improvements for the uplink. For the selected mobility scenario, delay and lost packets are reduced by 30% and 26%, respectively. Delay and lost packets are reduced by 36% and 27% for the fix location scenario, respectively. The improvements imply that it reaches particular demand considering next-generation communication systems, with a massive number of smart devices demanding high quality of service requirements.

High Spectral Efficiency and Low Error Rate MIMO-GFDM for Next-Generation Communication Systems

Multiple-input multiple-output (MIMO) technology with high order signal modulation method has been proposed recently as a scheme to achieve high throughput requirements of next-generation communication applications. However, the use of high-order modulation signals degrades the error rate performance. Generalized frequency division multiplexing (GFDM) has attracted enormous consideration for deploying in new communication scenarios due to its advantages in terms of out-of-band (OOB) emission, spectral efficiency (SE), and latency. Index modulation (IM) methods convey digital information by utilizing transmission entities with adjustable index parameters to provide flexible system design and offer attractive advantages such as energy and SE without increasing the computational complexity. The Multiple-Mode (MM) method permits multiple subcarriers to transmit multiple modes and employs the whole permutation of these modes to convey additional information bits. The proposed MIMO MM-GFDM-IM scheme improves the bit error rate and the spectral efficiency compared to the existing schemes. The proposed method can select the modulation order and the number of modes to achieve the design goals. Furthermore, a system with high modulation order can improve the spectral efficiency when the constellation point is fixed. This paper also proposes a practical and effective MM selection strategy and two low-complexity detectors.

Empowering 6G Communication Systems With Digital Twin Technology: A Comprehensive Survey

The global roll-out phase of the fifth-generation of mobile communication systems is currently underway. The industry and academia have already begun research on potential sixth-generation (6G) communication systems. The 6G communication system is anticipated to provide network connectivity for an extensive range of use cases in a variety of emerging vertical industries. Consequently, a new set of challenging requirements and more stringent key performance indicators have to be considered, a novel architecture has to be designed, and unique enabling technologies shall be developed in order to fulfill the technical, regulatory, and business demands of the communication service customers. These requirements place enormous pressure on the players in the telecommunications industry, including network operators, service providers, hardware suppliers, standards development organizations (SDOs), and regulatory authorities aimed at developing, standardizing, and regulating an energy-efficient, cost-effective, performing, and sustainable 6G communication ecosystem. One area of focus for 6G communication systems is the digital twin (DT) technology, which is a well-defined set of tools designed to create virtual representations of physical objects that serve as their digital counterparts. This article explores the applicability of the DT technology in the context of 6G communication systems by viewing it as a promising tool to make research, development, operation, and optimization of the next-generation communication systems highly efficient. The major contribution of this article is fivefold. Firstly, we provide critical analysis of the state-of-the-art literature in the field of DT technology in order to capture its essence in several application areas since its inception. Secondly, we conduct a comprehensive survey of the research concerning the deployment of DT technology in 6G communication systems. Thirdly, we discuss potential use cases and key areas of applications (along with detailed examples) of 6G communication systems that can benefit from DT technology. Fourthly, we present an overview of the activities of several SDOs that are active in the field of DT technology. Finally, we identify several open research challenges and future directions that need to be addressed before the end-to-end deployment of DT technology in 6G communication systems.

Securing Privacy Using Optimization and Statistical Models in Cognitive Radio Networks

Cognitive Radio Networks (CRN) are the possible and ideal solution for meeting the spectrum needs of next-generation communication systems. CRN is a promising alternative approach that allows spectrum sharing in many applications. The licensed users considered Primary Users (PU) and unlicensed users as Secondary Users (SU). Time and power consumption on security issues are considered degrading factors in performance for improving the Quality of Service (QoS). Irrespective of using different optimization techniques, the same methodology is to be updated for the task. So that, learning and optimization go hand in hand. It ensures the security in CRN, risk factors in spectrum sharing to SU for secure communication. The objective of the proposed work is to preserve the location of the SU from attackers and attain the clustering of SU to utilize the resource. Ant Colony Optimization (ACO) is implemented to increase the overall efficiency and utilization of the CRN. ACO is used to form clusters of SUs in the co-operative spectrum sensing technique. This paper deals with threat detection and classifying threats using parameters such as unlikability, context privacy, anonymity, conditional traceability, and trade-off. In this privacy-preserving model, overall accuracy is 97.4%, and it is 9% higher than the conventional models without Privacy-Preserving Architecture (PPA).

Sub-Band Assignment and Power Control for IoT Cellular Networks via Deep Learning

As various Internet of things (IoT) communication services have recently received great attention, the development of resource allocation scheme that can support the connection of a number of IoT devices becomes an important task for next-generation communication systems. Motivated this challenge, we propose deep learning-based optimization algorithms for a joint resource allocation problem in uplink IoT cellular networks, in which the base station uses multiple sub-bands to serve IoT users and inter sub-band interference exists due to spectral leakage. Specifically, to maximize the achievable sum rate of IoT users with low complexity, we develop a two-stage optimization method built on convolutional neural networks (CNNs) that sequentially optimizes sub-band assignment and transmit power control. Moreover, in order to examine the performance according to the neural network structure, the proposed scheme is also implemented through fully-connected neural networks (FNNs) and compared with the CNN-based scheme. Simulation results show that our proposed CNN-based algorithm significantly improves the sum rate and reduces the required computation time compared to previous schemes without deep learning.

Waveguide Manufacturing Technologies for Next-Generation Millimeter-Wave Antennas.

Some recent waveguide-based antennas are presented in this paper, designed for the next generation of communication systems operating at the millimeter-wave band. The presented prototypes have been conceived to be manufactured using different state-of-the-art techniques, involving subtractive and additive approaches. All the designs have used the latest developments in the field of manufacturing to guarantee the required accuracy for operation at millimeter-wave frequencies, where tolerances are extremely tight. Different designs will be presented, including a monopulse antenna combining a comparator network, a mode converter, and a spline profile horn; a tunable phase shifter that is integrated into an array to implement reconfigurability of the main lobe direction; and a conformal array antenna. These prototypes were manufactured by diverse approaches taking into account the waveguide configuration, combining parts with high-precision milling, electrical discharge machining, direct metal laser sintering, or stereolithography with spray metallization, showing very competitive performances at the millimeter-wave band till 40 GHz.