Future Networks Research Articles

Integrating spatial and ecological information into comprehensive biodiversity monitoring on agricultural land

Biodiversity loss on agricultural land is a major concern. Comprehensive monitoring is needed to quantify the ongoing changes and assess the effectiveness of agri-environmental measures. However, current approaches to monitoring biodiversity on agricultural land are limited in their ability to capture the complex pattern of species and habitats. Using a real-world example of plant and habitat monitoring on Swiss agricultural land, we show how meaningful and efficient sampling can be achieved at the relevant scales. The multi-stage sampling design of this approach uses unequal probability sampling in combination with intermediate small-scale habitat sampling to ensure broad representation of regions, landscape types, and plant species. To achieve broad coverage of temporary agri-environmental measures, the baseline survey on permanent plots is complemented by dynamic sampling of these specific areas. Sampling efficiency and practicality are ensured at all stages of sampling through modern sampling techniques, such as unequal probability sampling with fixed sample size, self-weighting, spatial spreading, balancing on additional information, and stratified balancing. In this way, the samples are well distributed across ecological and geographic space. Despite the high complexity of the sampling design, simple estimators are provided. The effects of stratified balancing and clustering of samples are demonstrated in Monte Carlo simulations using modelled habitat data. A power analysis based on actual survey data is also presented. Overall, the study could serve as a useful example for improving future biodiversity monitoring networks on agricultural land at multiple scales.

Free-Space and Fiber-Integrated Measurement-Device-Independent Quantum Key Distribution under High Background Noise.

Measurement-device-independent quantum key distribution (MDI QKD) provides immunity against all attacks targeting measurement devices. It is essential to implement MDI QKD in the future global-scale quantum communication network. Toward this goal, we demonstrate a robust MDI QKD fully covering daytime, overcoming the high background noise that prevents BB84 protocol even when using a perfect single-photon source. Based on this, we establish a hybrid quantum communication network that integrates free-space and fiber channels through Hong-Ou-Mandle (HOM) interference. Additionally, we investigate the feasibility of implementing HOM interference with moving satellites. Our results serve as a significant cornerstone for future integrated space-ground quantum communication networks that incorporate measurement-device-independent security.

Fabry-Pérot optical frequency comb based mm-wave RoF system using pilot-assisted equalizer.

The emergence of the millimeter wave (mm-Wave; 30 GHz to 300 GHz) frequency band holds a lot of promise for addressing the congestion at low frequency in future mobile networks. Among many mm-Wave generation schemes, optical heterodyning is considered one of the most promising approaches due to its scalability and potential for integration on chip. Employing optical frequency combs (OFC) for optical heterodyning alleviates the significant phase distortions/noise introduced by the optical sources. However, any residual phase noise in these systems can deteriorate the transmission performance. Here we demonstrate a high-capacity mm-Wave radio-over-fiber (RoF) system using Fabry-Pérot (FP) laser comb overcoming the typical limitations of this source. The temporal phase perturbation induced by the frequency fluctuation of the FP laser is theoretically analyzed, and then estimated and compensated by a pilot-based phase equalizer. Performance evaluation of the proposed phase equalizer is conducted through experiment and simulation. Enabled by the proposed compensation scheme, ten 200 MHz filtered orthogonal frequency division multiplexing (f-OFDM) signal bands modulated by 16-quadrature amplitude modulation (QAM) are transmitted over 10 km fiber, with the ability to serve multiple users. The transmission of 16-QAM modulated single carrier signals with 2 GBd and 8 Gbps data rate is also performed for comparison, which offers better resilience to phase noise, demonstrating the first commercial Quantum Well FP laser-based optical heterodyning mm-Wave RoF system for both multi-carrier and single carrier signals.

Joint Communication and Radar Sensing: RF Hardware Opportunities and Challenges-A Circuits and Systems Perspective.

This paper focuses on the topic of joint communication and radar sensing (JCRS) and its applications in the scope of upcoming sixth-generation (6G) technology. While the fifth-generation applications have reached the consumer market in the last few years, JCRS has been identified as one of the key technologies for next generation networks. The role of JCRS will vary, ranging from tasks such as radar coordination, context awareness for communication, enhanced security, increased availability, and improving the resilience and trustworthiness of future networks. In this work, the niche of JCRS technology in the future 6G ecosystem, as well as several potential applications, are discussed with a focus on RF hardware. The use of centimeter (cmWave) and millimeter (mmWave) frequency spectrums in the context of JCRS system implementation have been further elaborated. After presenting the near-term application scenarios, the circuit implementation perspectives are investigated in terms of radio frequency (RF) front-end architectures, antenna implementation, and phased arrays. Different communication and radar antenna options are compared, and the best candidates are identified. The packaging options are also presented. From circuit and system perspectives, link budget and self-interference cancellation (SIC) are highlighted. Furthermore, future directions including the next steps on the path to enabling JCRS technology are presented throughout this article. Prior works focused more on physical layers and network capabilities of JCRS systems, with less focus on hardware possibilities; to fill this gap, this article aims to contribute to this exciting research topic with a holistic review of RF hardware, highlighting the diversity of applications and the available technologies to tackle the near- and long-term needs of consumer applications.

SFC-based multi-domain service customization and deployment

In order to satisfy diversified service demands, future networks are expected to provide higher quality network services, especially in 5G scenarios. Network Function Virtualization (NFV) is a promising architecture to achieve flexible service provisioning by introducing Service Function Chain (SFC) technology. With explosive expansion of network devices and end users, the complex multi-domain network environment and personalized service demands of different users pose serious challenges to efficient service provisioning. To this end, this paper presents a novel SFC-based Multi-domain Service Customization and Deployment framework, MSCD, by blending merits of Software Defined Networking (SDN) and NFV technologies. MSCD framework can automatically customize multi-domain network services based on users’ personalized service preferences, and deploy them in multi-domain networks, to provide customizable multi-domain services for different users. Taking into account user personalized service preferences, resource allocation and VNF dependency constraints, the multi-domain service customization and deployment problem is mathematically formulated as an optimization model with the target of service satisfaction degree maximization. Furthermore, a Greedy strategy based Heuristic Service Customization and dePloyment (GHSCP) algorithm is proposed to customize and deploy the multi-domain services efficiently. Simulation results demonstrate that the proposed GHSCP algorithm is efficient and outperforms comparison algorithms in terms of service acceptance ratio and service satisfaction degree.

Frequency-insensitive spatiotemporal shaping of single photon in multiuser quantum network

Exploiting the fundamental features of quantum mechanics, an entanglement-based quantum network offers a promising platform for many dramatic applications such as multi-user cryptography. Nevertheless, the implementation of a large-scale quantum network in real-world scenarios remains challenging owing to the multiple scattering events in complex environment, particularly those frequency-sensitive scatterings that disturb quantum correlation both spatially and temporally. Here, we demonstrate the frequency-insensitive spatiotemporal control of entangled photons in a fully connected network by leveraging a Fourier-transform setup and the genetic algorithm. Such an approach can effectively improve the entanglement distribution process through a multimode fiber while the quantum characteristic of the network can be maintained well after the spatiotemporal shaping. Our scheme can serve as a bridging technology to establish entanglement between remote nodes of spectrally interconnected quantum systems and has great potential applications in future real-world quantum networks.

Research on Aggregation Flexibility Method of AC/DC Distribution Network Considering Flexibility Balance and Its Application

The large number of connected renewable energy sources has greatly increased the flexibility demand in the system. At the same time, the ac/dc active distribution network (ADN) will also become the main form of the future distribution network. Therefore, it is very meaningful to study how the ac/dc ADN aggregates flexibility and participates in the dispatching of the transmission network as a flexibility resource. To this end, this article aggregates the ac/dc ADN into a virtual generator with ramp and power parameters. In particular, the flexibility in the ac/dc ADN is subdivided into ramp and power flexibility and incorporated into the aggregation model. Then, an energy-based flexibility cost function is constructed for the aggregated distribution network. Therefore, without relying on the electricity market, the distribution network can be used as a flexible resource to participate in the flexible dispatching of the transmission network. Finally, the effectiveness of the proposed method and model is verified by several case studies.

Hybrid Transmission Scheme for Improving Link Reliability in mmWave URLLC Communications

Ultra-Reliable and Low-Latency Communication (URLLC) represents a key ingredient of current 5G and future 6G mobile communication networks. The demanding reliability requirement set for URLLC claims for novel techniques that can deliver the necessary level of reliability without sacrificing the overall system capacity. In this context, this work presents and analyses a hybrid transmission scheme for improved reliability in millimetre wave bands with adaptive diversity combining. The proposed scheme is based on a hybrid approach that combines two links, one in the FR1 band (characterised by lower capacity but higher reliability due to more favourable propagation) and one in the FR2 band (offering higher capacity but experiencing a less reliable connectivity). The proposed scheme dynamically adapts the usage of both links in order to exploit the complementary characteristics of both bands (reliability of FR1 bands and capacity of FR2 bands) by switching between FR2-only and joint FR1-FR2 transmission according to the instantaneous channel quality in the main FR2 link. The performance is evaluated under two popular and well-known diversity combining techniques, namely Selection Combining (SC) and Maximal Ratio Combining (MRC). The obtained results demonstrate that the proposed scheme can achieve the same level of reliability as a continuous dual-link transmission scheme but with a much lower level of links usage and without sacrificing (and indeed enhancing) the capacity, thus making it a suitable candidate to deliver URLLC services in a resource-efficient manner.

Path loss modeling based on neural networks and ensemble method for future wireless networks

In light of the technological advancements that require faster data speeds, there has been an increasing demand for higher frequency bands. Consequently, numerous path loss prediction models have been developed for 5G and beyond communication networks, particularly in the millimeter-wave and subterahertz frequency ranges. Despite these efforts, there is a pressing need for more sophisticated models that offer greater flexibility and accuracy, particularly in challenging environments. These advanced models will help in deploying wireless networks with the guarantee of covering communication environments with optimum quality of service. This paper presents path loss prediction models based on machine learning algorithms, namely artificial neural network (ANN), artificial recurrent neural network (RNN) based on long short-term memory (LSTM), shortly known as RNN-LSTM, and convolutional neural network (CNN). Moreover, an ensemble-method-based neural network path loss model is proposed in this paper. Finally, an extensive performance analysis of the four models is provided regarding prediction accuracy, stability, the contribution of input features, and the time needed to run the model. The data used for training and testing in this study were obtained from measurement campaigns conducted in an indoor corridor setting, covering both line-of-sight and non-line-of-sight communication scenarios. The main result of this study demonstrates that the ensemble-method-based model outperforms the other models (ANN, RNN-LSTM, and CNN) in terms of efficiency and high prediction accuracy, and could be trusted as a promising model for path loss in complex environments at high-frequency bands.

V-Band Geodesic Generalized Luneburg Lens Antenna With High Beam Crossover Gain

Quasi-optical beamformers provide attractive properties for antenna applications at millimetre-wave frequencies. Antennas implemented with these beamformers have demonstrated wide angle switching of directive beams, making them suitable as base station antennas for future communication networks. For these applications, it is essential to ensure a high beam crossover gain to provide a robust service to end users within the steering range. Here, we propose a geodesic generalized Luneburg lens antenna operating from 57 to 67 GHz that provides increased crossover gain compared to previously reported geodesic Luneburg lens antennas. The focal curve of the generalized Luneburg lens can be displaced from the beamformer, allowing for a higher angular resolution in the placement of the feed array along the focal curve. The lens is fed with 21 ridge waveguides with an angular separation of 5.1 degrees, thus providing beam steering in a 102-degree range. The peak realized gain varies from 19 to 21 dBi throughout the steering and frequency ranges and the beam crossover gain is roughly 3 dB below the peak gain. The simulations are experimentally validated.

Vehicular Networking: Ultra-Low Latency and Reliable Communications For Future Wireless Networks

Vehicular Networking: Ultra-Low Latency and Reliable Communications For Future Wireless Networks

Sub-Array-Based Millimeter Wave Massive MIMO Channel Estimation

Combination of millimeter wave (mmWave) and massive multiple input multiple output (MIMO) forms a promising technology for future sixth generation networks. As the number of antennas increases, propagation channel starts yielding spherical wavefronts, for which the traditional channel estimation algorithms are no longer applicable. To overcome this technical gap, a novel MIMO channel estimation scheme based on the assumption of spherical wavefront is proposed in this work. The large antenna array is first divided into several sub-arrays, and then the channel estimation is performed for each sub-array based on the orthogonal matching pursuit algorithm. In addition, the joint estimation of angle of arrivals and departures (AoAs and AoDs) of each uniform planar sub-array is transformed into separate estimation by dimensionality reduction. Next, an iterative estimation is performed using Taylor expansion to continuously approximate the real grid points to recover the AoAs/AoDs. Lastly, the superiority of the proposed algorithm is verified by numerical simulations, where the proposed algorithm exhibits the best NMSE performance, compared with the existing MIMO channel estimation methods.

Data Perspectives in AI-Assisted Fiber-Optic Communication Networks

Machine learning (ML) is widely being regarded as a powerful technology to solve various complex problems in fiberoptic communication networks. Recently, ML algorithms have been considered for a variety of use-cases in optical networks ranging from the design of network devices to the compensation of transmission impairments to predicting network-wide traffic flow patterns. As ML techniques are inherently data driven, the availability of relevant data sets largely dictates the performance of developed ML models. Presently, majority of ML-aided methods in the literature rely either on synthetic data from certain simulation tools or exploit data from some simple laboratory setups. However, developing robust ML-based solutions for real fiber-optic networks demands assembling big data from various parts as well as across different layers and domains of the network. Unfortunately, there is currently a lack of understanding among the research community about various crucial data aspects in practical optical networks as well as key challenges faced. While there has been a plethora of works discussing numerous ML applications in fiber-optic communications and networking, to the best of our knowledge, there is presently no paper which particularly focuses on data perspectives. In this paper, we aim to shed light on various critical data factors by first describing the tools and technologies imperative for adequate network monitoring and data acquisition. We also elucidate different data types, their physical sources, and their applications. Several useful data handling and pre-processing techniques for efficient network data collection, storage, presentation, analysis, etc., are discussed. Major challenges in the generation, transmission, and sharing of the data are highlighted and some potential solutions to the outstanding problems are proposed, thus paving the way for seamless availability of pertinent data sets essential for realizing intelligent data-driven operations in future fiber-optic networks.

IRS-enabled NOMA communication systems: A network architecture primer with future trends and challenges

Non-Orthogonal Multiple Access (NOMA) has already proven to be an effective multiple access scheme for 5th Generation (5G) wireless networks. It provides improved performance in terms of system throughput, spectral efficiency, fairness, and energy efficiency (EE). However, in conventional NOMA networks, performance degradation still exists because of the stochastic behavior of wireless channels. To combat this challenge, the concept of Intelligent Reflecting Surface (IRS) has risen to prominence as a low-cost intelligent solution for Beyond 5G (B5G) networks. In this paper, a modeling primer based on the integration of these two cutting-edge technologies, i.e., IRS and NOMA, for B5G wireless networks is presented. An in-depth comparative analysis of IRS-assisted Power Domain (PD)-NOMA networks is provided through 3-fold investigations. First, a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems, and parallels are drawn with conventional network configurations, i.e., conventional NOMA, Orthogonal Multiple Access (OMA), and IRS-assisted OMA networks. Followed by this, a comparative analysis of these network configurations is showcased in terms of significant performance metrics, namely, individual users' achievable rate, sum rate, ergodic rate, EE, and outage probability. Moreover, for multi-antenna IRS-enabled NOMA networks, we exploit the active Beamforming (BF) technique by employing a greedy algorithm using a state-of-the-art branch-reduce-and-bound (BRB) method. The optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques, i.e., minimum-mean-square-error, zero-forcing-BF, and maximum-ratio-transmission. Furthermore, we present an outlook on future envisioned NOMA networks, aided by IRSs, i.e., with a variety of potential applications for 6G wireless networks. This work presents a generic performance assessment toolkit for wireless networks, focusing on IRS-assisted NOMA networks. This comparative analysis provides a solid foundation for the development of future IRS-enabled, energy-efficient wireless communication systems.

Integrated sensing and communication for future wireless networks

Integrated sensing and communication for future wireless networks

IQ-Impaired Wireless-Powered Modify-and-Forward Relaying for IoT Networks: An In-Depth Physical-Layer Security Analysis

With the large-scale commercialization of 5G networks, the era of Internet-of-Things (IoT), which is oriented towards the internet-of-everything (IoE), is coming. Under the circumstance, reliable and secure communication are the great challenges for future wireless network because of the broadcasting characteristics of electromagnetic wave. Physical layer security (PLS) is an effective way to ensure secure communication by exploiting random nature of fading channels. Motivated by this, we investigate PLS of the wireless-powered cooperative multi-relaying for IoT networks in the presence of eavesdropper with the estimation errors of channel (EEC) and imbalance between in-phase and quadrature-phase (IIQ). Specifically, the relays can be charged by the source with the aid of energy harvesters, and a novel more secure modify-and-forward (MF) relay protocol is proposed. For further improving energy efficiency and reducing extra interference, the K-th superior relay selection scheme is proposed since some best ones are not available due to some scheduling or failure. Based on the system under study, we derive the analytical expressions for the outage probability (OP), intercept probability (IP) and secrecy outage probability (SOP) to evaluate the reliable and secure performance of this consideration system. Particularly, we then analyze the asymptotic behaviors of the OP, IP and SOP, respectively. Through computer simulation, we show that: i) With EEC, the error floors of the OP and SOP are of presence; ii) Multiple relays lead to better OP and SOP performance; and iii) IIQ improves the security and is detrimental to the system reliability.

Research on compression pruning methods based on deep learning

In the past decade or so, deep neural networks have continuously refreshed the best performance in tasks such as computer vision and natural language processing, and have become the most concerned research directions. Although deep neural networks have significant performance, they are difficult to deploy on hardware-constrained embedded and mobile devices due to their huge number of parameters, computational and storage costs. Therefore, in recent years, the lightweight design of network structure has gradually become a cutting-edge and popular direction, improving the operation speed and optimizing the storage space under the premise of maintaining the accuracy of the neural network. In this paper, the results achieved by domestic and foreign scholars in deep learning model compression are sorted and classified, and the methods of network pruning, model quantification, knowledge distillation, and lightweight network design are summarized, and the compression effect of related methods on known public models is summarized. Finally, the possible directions, application directions and development trends of future neural network model lightweight research are prospected.

Deep Reinforcement Learning for Multi-Objective Resource Allocation in Multi-Platoon Cooperative Vehicular Networks

Grouping vehicles into platoons is a promising cooperative driving scenario to enhance the traffic safety and capacity of future vehicular networks. However, fast changing channel conditions in multi-platoon vehicular networks cause tremendous uncertainty for resource allocation. In addition, the unprecedented proliferation of various emerging vehicle-to-infrastructure (V2I) applications may result in some service demands with conflicting quality of experience. In this paper, we formulate a multi-objective resource allocation problem, which maximizes the transmission success rate of intra-platoon communications and the mean opinion score (MOS) of V2I communication links. To efficiently solve this multi-objective optimization problem, we resort to a deep reinforcement learning (DRL) framework. Specifically, we divide it into a set of scalar optimization subproblems based on the weighted sum approach and model each one as a partially observable stochastic game (P-OSG), where each platoon acts as an agent and the actions taken by all platoons correspond to the resource allocation solution. We further propose a Contribution-based Dual-Clip Proximal Policy Optimization (CD-PPO) algorithm to deal with each subproblem, which is a DRL algorithm based on the actor-critic framework. The network parameters of all subproblems are then optimized collaboratively by using the proposed training algorithm and the neighborhood parameter transfer strategy. The desired Pareto front is obtained when all the subproblems are solved. Simulation results reveal that proposed algorithm can outperform the other algorithms in terms of the MOS and transmission success rate.

Collaborative Satellite-Terrestrial Edge Computing Network for Everyone-Centric Customized Services

The collaborative Satellite-Terrestrial network plays an important role in the future 6G mobile communication network, which can realize the global ubiquitous connection and support everyonecentric customized services. Taking into account the multi-layer heterogeneity of the Satellite-Terrestrial network, Mobile Edge Computing (MEC) technology can provide users with support for various data business computing services. Most of the existing satellite edge computing regards satellites as relay networks and neglects the deployment of MEC servers on satellites. We first propose a collaborative Satellite-Terrestrial edge computing network that deploys edge servers on LEO satellites. Then, we introduce the collaborative Satellite-Terrestrial network architecture and design a central-edge multiagent collaboration model. Furthermore, it integrates SDN and NFV to achieve optimal control for the collaborative network. Finally, extensive simulations show that the proposed strategy that jointly tasks offloading and resource allocation can achieve higher performance with low complexity.

Code Index Modulation-Aided Spatial Media-Based Modulation System for Future Wireless Networks

Future wireless networks, increment in mobile users, and Internet-connected devices have brought many needs, such as high data rate, low transmission energy, high error performance, and low latency. Index modulation-based systems have been considered as one of the strong candidates for meeting these demands and needs. In this article, a novel wireless communication system that combines code index modulation (CIM)-aided spatial modulation (SM) and media-based modulation (MBM) techniques, briefly called CIM-SMBM, is proposed for future wireless networks. In the CIM-SMBM system, source information bits are carried in transmitted symbols in addition to three indices: active antenna index selected by SM, indices of different channel states that vary depending on the propagation pattern with MBM, and selected spreading code indices determined by CIM. In order to further reduce the complexity of optimal maximum-likelihood (ML) and despreading-based ML detectors for the CIM-SMBM scheme, an enhanced low-complexity detector that can achieve near-ML error performance is proposed. The average bit error rate (ABER), throughput, energy efficiency, and complexity analyses of the CIM-SMBM system are derived over Rayleigh fading channels for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> -quadrature amplitude modulation technique. It has been observed that the ABER and simulation results are quite close to each other.