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Broadcasting is one of the essential features in the Internet Protocol Ver 4 (IPv4). Attackers often exploit this feature of the IP protocol to launch several attacks against a network or an individual host. Attackers may either be a part of a Local Area Network (LAN) or outside a LAN to launch these attacks. There are numerous papers available in the literature to solve problems resulting from IP broadcasting. However, all these solutions target a specific problem that results from IP broadcasting. Furthermore, these solutions use either a computationally-intensive cryptographic scheme, the a priori relation between the host and the network or a modified protocol stack at every host. In this paper, we provide a seamless and transparent solution to eliminate IP broadcasting and thus eliminate all problems related to IP broadcasting. Our proposed solution is crypto-free and does not need any modification to the protocol stack.

Optical wireless communications (OWC) has emerged as a strong candidate for wireless communications, due to the capacity limitation in the radio frequency (RF) spectrum. Especially visible light communication (VLC) has great potential for short-range outdoor vehicular communications, as vehicle LED lights also transmit data. However, outdoor VLC channels vary fast and, experience multipath scattering and reflection resulting in time domain dispersion. Outdoor VLC links are also subjected to high levels of ambient noise, especially from the sun. Orthogonal frequency-division multiplexing (OFDM), which has proven robustness to multi path fading and noise effects in RF links can also be deployed in VLC links. In this paper, optical OFDM (O-OFDM) along with adaptive modulation scheme is investigated in VLC for vehicle to vehicle (V2V) communications. A (2 x 2) multiple input multiple output (MIMO) channel, with multiple polarimetric bidirectional reflections and realistic sunlight interference is considered. Two schemes of O-OFDM; direct current biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM) are investigated. Simulation results of the proposed model show increase in data rates up to 50 Mbps along with reduced bit error rate (BER) under both line of sight (LOS) and non-LOS and high noise conditions.

Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.

Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.

"Time-frequency analysis has been intensively investigated and developed in the last two decades. A variety of time-frequency distributions have been developed to provide efficient analysis of signals with a time-varying spectral content. In most cases, signal analyses in the joint time-frequency domain outperform the traditional frequency-domain approaches. Generally, the time-frequency distributions have found fruitful applications in many important fields dealing with nonstationary signals, such as biomedical, radar, seismic, telecommunications, and mechanical engineering. Additionally, a large number of applications are related to multimedia signals in speech, audio/music, image, and video signal processing, where time-frequency analysis can be employed to broaden and enhance the signal processing capabilities. Because each type of multimedia signals has its specific nature that may significantly differ from others, the applicability and method of time-frequency analysis depend on the multimedia data to be processed. This fact opens a number of challenging directions for research in the field of time-frequency analysis and its applications to multimedia signals. For instance, having the different dimensionalities of multimedia signals in mind, time-frequency analysis for one-, two-, and three-dimensional signals should be used." -- from p1.

Accurate detection of white spaces is crucial in cognitive radio networks. Initial investigations show that the accurate detection in a multiple primary users environment is challenging, especially under severe multipath conditions. Among many techniques, recently proposed eigenvalue-based detectors that use random matrix theories to eliminate the need of prior knowledge of the signals proved to be a solid approach. In this work, we study the effect of Rayleigh multipath fading channels on spectrum sensing in a multiple primary user environment for a pre-proposed detector called the spherical detector using the eigenvalue approach. Simulation results show interesting outcomes.

Massive multiple-input-multiple-output (MIMO) systems use few hundred antennas to simultaneously serve large number of wireless broadband terminals. It has been incorporated into standards like long term evolution (LTE) and IEEE802.11 (Wi-Fi). Basically, the more the antennas, the better shall be the performance. Massive MIMO systems envision accurate beamforming and decoding with simpler and possibly linear algorithms. However, efficient signal processing techniques have to be used at both ends to overcome the signaling overhead complexity. There are few fundamental issues about massive MIMO networks that need to be better understood before their successful deployment. In this paper, we present a detailed review of massive MIMO homogeneous, and heterogeneous systems, highlighting key system components, pros, cons, and research directions. In addition, we emphasize the advantage of employing millimeter wave (mmWave) frequency in the beamforming, and precoding operations in single, and multi-tier massive MIMO systems. Keywords: 5G wireless networks; massive MIMO; linear precoding; encoding; channel estimation; pilot contamination; beamforming; HetNets

Previous approaches to texture analysis and segmentation use multichannel filtering by applying a set of filters in the frequency domain or a set of masks in the spatial domain. This paper presents two new texture segmentation algorithms based on multichannel filtering in conjunction with neural networks for feature extraction and segmentation. The features extracted by Gabor filters have been applied for image segmentation and analysis. Suitable choices of filter parameters and filter bank coverage in the frequency domain to optimize the filters are discussed. Here we introduce two methods to optimize Gabor filter bank. First, a Gabor filter bank with a flat response is implemented and the optimal feature dimension is extracted by competitive networks. Second, a subset of Gabor filter bank is selected to compose the best discriminative filters, so that each filter in this small set can discriminate a pair of textures in a given image. In both approaches, multilayer perceptrons are employed to segment the extracted features. The comparisons of segmentation results generated using the proposed methods and previous research using Gabor, discrete cosine transform (DCT), and Laws filters are presented. Finally, the segmentation results generated by applying the optimized filter banks to textured images are presented and discussed.

Optimizing the antecedent part of neurofuzzy system is an active research topic, for which different approaches have been developed. However, current approaches typically suffer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this paper, we introduce a novel incremental training algorithm for the class of neurofuzzy systems that are structured based on local linear classifiers. Linear discriminant analysis is utilized to transform the data into a space in which linear discriminancy of training samples is maximized. The neurofuzzy classifier is then built in the transformed space, starting from the simplest form (a global linear classifier). If the overall performance of the classifier was not satisfactory, it would be iteratively refined by incorporating additional local classifiers. In addition, rule consequent parameters are optimized using a local least square approach. Our refinement strategy is motivated by LOLIMOT, which is a greedy partition algorithm for structure training and has been successfully applied in a number of identification problems. The proposed classifier is compared to several benchmark classifiers on a number of well-known datasets. The results prove the efficacy of the proposed classifier in achieving high performance while incurring low computational effort.

It is difficult to predict to do a random sampling technique with the consideration that the existing population is very large, so it is not possible to study the existing population. Thus, in the form of a population representative, this research is part of the total customers who stay at Graha Buana Hotel Medan. The decisions taken must consider well based on the data that is held, especially those that are closely related to the hotel service system. The stages of research carried out in research are to conduct interviews by direct questioning to the section related to research, direct observation of events that occur at the research site, analyzing and designing and making applications and testing applications. The Rough Set method is one of the methods above that allows us to make decisions in hotel services because in this method there are formulations or stages of problem mechanics and there is a result (decision) from a possible combination of the above criteria. From the results (decisions) that come from the processed data mining, it can be used as a reference for decision making. The results obtained in this study are finding a rule with the rough set method used to obtain the results of each criterion for service satisfaction results. The results obtained in this study are finding a rule with the rough set method used to obtain the results of each criterion for service satisfaction results.

Disease is very susceptible to occur in children under five because the immune system in children under five has not been fully developed. Lack of knowledge about the diseases of children under five and the symptoms they experience makes parents fearful. The lack of knowledge of children's diseases from experts can result in delayed treatment. Problems that occur can be overcome by utilizing artificial intelligence technology. One of the artificial intelligence technologies is an expert system. Information needs very quickly from an expert to deal with problems or diseases of children under five that are expected by parents or society. So that is what drives the development of a software application, namely an expert system for the identification of diseases of children under five. An expert system for the identification of toddlers' diseases is made as a tool to diagnose diseases experienced by toddlers by using the symptoms experienced by toddlers as a tool to detect diseases experienced by children under five. The system can identify 5 types of disease with 23 symptoms of disease. This expert system uses the development method of problem identification, system design, implementation and testing. Inference in this expert system uses the Bayes theorem method. This system is built with Visual Basic and Microsoft Access as the database. The results of consulting tests with this system show that the system is able to determine the disease along with the initial treatment and treatment solutions that must be carried out, based on the symptoms previously selected by the user.

Wireless internet service in educational buildings plays a crucial role in telecommunications for the knowledge sharing process. Therefore, various factors that may limit internet services coverage in the building should be eliminated or reduced. One such factor is path losses. Path losses are caused by multiple obstacles between the transmitting and receiving antennas. The problem of path losses in the education building can be solved by providing signal booster devices or Wireless Fidelity (Wi-Fi). But not all college buildings have such tools. Besides, WiFi devices also have limitations on bandwidth and the number of users. Thus, mobile communication devices or smartphones located inside the education buildings still need internet service coverage from the transmitter antenna outside the building. An accurate propagation model is required so that the transmitter antenna outside the building can provide internet service coverage to the inside of the building. This paper had been analyzed the selection of propagation models using three validation formulas, namely Mean Error (ME), Root Mean Square Error (RMSE), and Standard Deviation Error (SDE). This paper used several propagation models, namely the 3GPP Model, Winner+ Model, and COST231 Model. Based on the analysis of calculation and measurement data, it is known that the COST231 model is the most accurate because it has the lowest ME, RMSE, and SDE values.

With so many hotels and other accommodation facilities in the city of Medan. More and more people are coming and staying and using the facilities at each hotel. Whether it's rooms, meeting room facilities, facilities to hold events, and much more are available in every hotel. Every hotel wants to give satisfaction to guests who come to use the hotel facilities and services. Likewise with SwissbeliNN hotels which have a 3 (three) star classification. Therefore, the opinions of guests and complaints given are very useful to improve the quality of hotel services. To find out, we will look at the satisfaction level of each guest. In this case, the writer applies the servqual method to see the level of guest satisfaction that has been obtained. To find out what is still lacking in the services provided. The purpose of this research is to find out the shortcomings of the hotel for the services provided to hotel visitors, as well as to fulfill the complaints of each visitor to the hotel from the results of the research carried out, with the objectives described later to be able to provide solutions to hotel leaders to fulfill the research results. obtained. The research methods carried out include collecting data directly from the hotel related to the problems that arise to be researched, literature studies, analyzing the problems to be studied based on the data collection that has been obtained, designing applications to overcome problems that arise and making it easier to find out the level of satisfaction of visitors. against managed hotels, as well as testing applications that are made to test the level of success obtained from the application being built. Based on the research conducted, the average perceived service quality score was 3.54. This score is in the somewhat unsatisfactory category when compared with the average score of service quality score which is perceived to be lower than expected. This indicates a gap. The highest average score is given to responsiveness with a value of 3.62 while the lowest score is given to the dimension of empathy with a value of 3.44.

Depth is essential information for autonomous robotics applications that need environmental depth values. The depth could be acquired by finding the matching pixels between stereo image pairs. Depth information is an inference from a matching cost volume that is composed of the distances between the possible pixel points on the pre-aligned horizontal axis of stereo images. Most approaches use matching costs to identify matches between stereo images and obtain depth information. Recently, researchers have been using convolutional neural network-based solutions to handle this matching problem. In this paper, a novel method has been proposed for the refinement of matching costs by using recurrent neural networks. Our motivation is to enhance the depth values obtained from matching costs. For this purpose, to attain an enhanced disparity map by utilizing the sequential information of matching costs in the horizontal space, recurrent neural networks are used. Exploiting this sequential information, we aimed to determine the position of the correct matching point by using recurrent neural networks, as in the case of speech processing problems. We used existing stereo algorithms to obtain the initial matching costs and then improved the results by utilizing recurrent neural networks. The results are evaluated on the KITTI 2012 and KITTI 2015 datasets. The results show that the matching cost three-pixel error is decreased by an average of 14.5% in both datasets.

In order to solve the problem between low power of Internet of Things devices and the high cost of cryptography, lightweight cryptography is required. The improvement of the scalar multiplication can effectively reduce the complexity of elliptic curve cryptography (ECC). In this paper, we propose a fast formula for point septupling on elliptic curves over binary fields using division polynomial and multiplexing of intermediate values to accelerate the computation by more than 14%. We also propose a scalar multiplication algorithm based on the step multi-base representation using point halving and the septuple formula we proposed, which significantly reduces the computational cost. The experimental results show that our method is more efficient over binary fields and contributes to reducing the complexity of ECC.

The more rapid growth of information technology in the world, so that more and more people are taking advantage of this advancement in information technology in various activities of life or to solve various problems. One of them is filling positions that have not been filled for promotion processes often experience problems because the submission of prospective candidates who can occupy the position by matching employee profiles and job profiles is not resolved properly. To minimize these constraints, a decision support system is needed that can analyze employee performance in accordance with existing job profiles. The Profile Matching process is carried out to determine career planning recommendations by calculating sub-criteria from each aspect (work attitude and intellectual capacity) which consists of 4 stages including the calculation of the GAP value, the calculation of the core and secondary factor values ​​and the calculation of the total value and ranking. The results of this study will produce a form in the form of employee recommendations with the highest score of 5.4163 on behalf of Ariz and Widi which has the same numerical value as the number from the calculation method used., Semakin pesatnya perkembangan teknologi informasi di dunia, maka semakin banyak pula orang yang memanfaatkan kemajuan teknologi informasi ini dalam berbagai kegiatan kehidupan atau untuk menyelesaikan berbagai permasalahan. Salah satunya adalah pengisian posisi yang belum diisi untuk proses promosi sering mengalami kendala karena pengajuan calon yang dapat menempati posisi tersebut dengan cara mencocokkan profil karyawan dan profil pekerjaan tidak terselesaikan dengan baik. Untuk meminimalisir kendala tersebut maka diperlukan suatu sistem pendukung keputusan yang dapat menganalisis kinerja karyawan sesuai dengan profil pekerjaan yang ada. Proses Profile Matching dilakukan untuk menentukan rekomendasi perencanaan karir dengan menghitung sub kriteria dari masing-masing aspek (sikap kerja dan kemampuan intelektual) yang terdiri dari 4 tahapan yaitu penghitungan nilai GAP, penghitungan nilai core dan secondary factor dan perhitungan nilai total dan peringkat. Hasil penelitian ini akan menghasilkan formulir berupa rekomendasi pegawai dengan skor tertinggi sebesar 5,4163 atas nama Ariz dan Widi yang memiliki nilai numerik yang sama dengan angka dari metode perhitungan yang digunakan.

As the PHY/MAC-layer IR-HARQ and RLC-layer ARQ error recovery procedures, adopted in LTE, may impose additional delay when their code-block retransmissions occur, the arising question is whether these significantly contribute to IP and consequently RTP packet delays, and finally degrade the overall application-layer end-to-end QoE, especially when voice is transmitted over LTE? With this regard, we propose and demonstrate a VoLTE QoS and QoE test procedure based on PHY/MAC/RLC/IP/TCP-UDP/RTP cross-layer protocol analysis and perceptual speech quality QoE measurements. We identified monotonic relationship between the paired observations: QoE and HARQ RTT, i.e. between the PESQ voice quality rating and the IP/RTP packet latency, for given BLER of the received MAC/RLC code-blocks. Specifically, we found out that, for the HARQ RTT value of about 8 ms, only up to 2 HARQ retransmissions (and consequently no RLC-ARQ one) is appropriate during any voice packet, otherwise delay accumulation might not be accordingly “smoothed out” by jitter/playback buffers along the propagation path.

The emergence of 5G communication systems will not replace existing radio access networks but will gradually merge to form ultra-dense heterogeneous networks. In heterogeneous networks, the design of efficient vertical handoff (VHO) algorithms for 5G infrastructures is necessary to improve quality of service (QoS) and system resource utilization. In this paper, an optimized algorithm based on a multi-objective optimization model is proposed to solve the lack of a comprehensive consideration of user and network impacts during the handoff process in existing VHO algorithms. The Markov chain model of each base station (BS) is built to calculate a more accurate value of the network state that reflects the network performance. Then, a multi-objective optimization model is derived to maximize the value of the network state and the user data receiving rate. The multi-objective genetic algorithm NSGA-II is finally employed to turn the model into a final VHO strategy. The results of the simulation for the throughput and blocking rate of networks demonstrate that our algorithm significantly increases the system throughput and reduces the blocking rate compared to the existing VHO strategies.

In the next generation of mobile communication networks, unprecedented challenges are required to be met, such as much higher data rates and spectrum efficiency, lower latency, and massive connectivity. Non-orthogonal multiple access (NOMA) has recently been proposed as a promising technology to achieve much superior spectral efficiency compared to conventional orthogonal multiple access techniques employed in present communication systems. A salient feature of NOMA is its use of successive interference cancellation (SIC) to decode users’ information when multiple users are allowed to transmit in same time/frequency/code domain. In this paper, we aim to exploit an aspect of SIC, namely the availability of other users’ data to realize a cooperative NOMA system. EXtrinsic information transfer (EXIT) charts are utilized to examine the performance of proposed system in terms of user fairness while employing IRregular convolutional codes (IRCC)s. The EXIT chart using IRCC evaluates the convergence analysis for the proposed system. Further, to evaluate the system performances in cooperative NOMA system, we have derived the expressions for the achievable rates which are obtained independently and utilized them in evaluating the experimental data for the proposed NOMA model.

Students' interest in a field is usually seen from the grades resulting from students' learning in the classroom. This is a common thing for parents and teachers to do. In the research conducted by researchers this time is in the field of design studied by students in the classroom in one semester, namely the subjects Photoshop and CorelDraw. The grades taken are grades that include the value of theory, practical grades, assignments, and quiz assignments obtained by students in the classroom. The four grades will be calculated until they get a provision on whether or not the student graduates in the subject. These values will be studied by researchers using the Naïve Bayes method, so that it can be known how much can be said to pass in this design lesson. Researchers conducted research using Rapid Miner program, where the data will be divided into 2 parts, namely by using some existing data as learning samples and the rest as test data. The results obtained from the experiment were 146 students graduated in the field of design, and 119 students failed. This suggests the experiment using the Naïve Bayes method was successful if the experiment data was entered a lot

With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure generation process is initially employed to transform RF fingerprint features from time-domain waveforms to two-dimensional figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A universal software radio peripheral receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95% identification accuracy in a real environment.

Multi-kernel polar codes have recently received considerable attention since they can provide more flexible code lengths than do the original ones. The construction process of them can be simplified by obtaining the Bhattacharyya parameter bounds of the kernels employed. However, there has been currently no generic method for seeking such bounds. In this paper, therefore, we focus on the upper Bhattacharyya parameter bounds of the standard binary polar code kernels with an arbitrary dimension of $$l\ge 2$$ l ≥ 2 . A calculation process composing of four steps, the common column binary tree construction for the channel inputs, the common factor extraction, the calculation feasibility testing, and the upper bound calculation based on pattern matching, is formulated with a computational complexity of $$O(2^l)$$ O ( 2 l ) . It is theoretically proved that the upper bounds obtained by the proposed method are tight, which can lay the foundation to compare the reliability of the synthesized channels in polar codes.

The transmission performance of spatial multiplexing cross-polarized MIMO is studied to determine how to increase the capacity of digital terrestrial broadcasting and make it more robust. In this paper, the performance was evaluated in laboratory experiments and large-scale field experiments in central Tokyo under three deployment scenarios. The results showed that the required received power can be improved by 7.3 dB when MIMO is introduced to enhance transmission robustness, i.e., maintaining the same transmission capacity as SISO. Alternatively, the transmission capacity can be doubled by introducing MIMO for the same robustness as SISO, requiring a slight increase in received power of 0.9 dB compared with SISO. The field experiments were performed by using a prototype transmission system for advanced digital terrestrial TV broadcasting in Japan, but the results obtained are also valid for 5G broadcasting. Regarding the feasibility of the advanced terrestrial broadcasting system operating at a target bit rate of about 60 Mbps to distribute a VVC-based 8 K program within a 6-MHz channel bandwidth, an increased received power of 3.7 dB compared with SISO was required for MIMO to achieve a higher transmission capacity in the worst case for 28 reception points evaluated in urban areas.

The security of wireless routers receives much attention given by the increasing security threats. In the era of Internet of Things, many devices pose security vulnerabilities, and there is a significant need to analyze the current security status of devices. In this paper, we develop WNV-Detector, a universal and scalable framework for detecting wireless network vulnerabilities. Based on semantic analysis and named entities recognition, we design rules for automatic device identification of wireless access points and routers. The rules are automatically generated based on the information extracted from the admin webpages, and can be updated with a semi-automated method. To detect the security status of devices, WNV-Detector aims to extract the critical identity information and retrieve known vulnerabilities. In the evaluation, we collect information through web crawlers and build a comprehensive vulnerability database. We also build a prototype system based on WNV-Detector and evaluate it with routers from various vendors on the market. Our results indicate that the effectiveness of our WNV-Detector, i.e., the success rate of vulnerability detection could achieve 95.5%.

Considering the issue with respect to the high data redundancy and high cost of information collection in wireless sensor nodes, this paper proposes a data fusion method based on belief structure to reduce attribution in multi-granulation rough set. By introducing belief structure, attribute reduction is carried out for multi-granulation rough sets. From the view of granular computing, this paper studies the evidential characteristics of incomplete multi-granulation ordered information systems. On this basis, the positive region reduction, belief reduction and plausibility reduction are put forward in incomplete multi-granulation ordered information system and analyze the consistency in the same level and transitivity in different levels. The positive region reduction and belief reduction are equivalent, and the positive region reduction and belief reduction are unnecessary and sufficient conditional plausibility reduction in the same level, if the cover structure order of different levels are the same the corresponding equivalent positive region reduction. The algorithm proposed in this paper not only performs three reductions, but also reduces the time complexity largely. The above study fuses the node data which reduces the amount of data that needs to be transmitted and effectively improves the information processing efficiency.

The area is the center of problems in the administrative record management of Kebayoran District, because of its dense condition and it is difficult to determine land measurements due to the density of residential areas. The problem in Indonesia to this day is that the administrative boundaries of the kelurahan already exist, but the administrative boundaries for the Rukun Warga / Rukun Tetangga (RW / RT) do not yet exist. The local government of DKI already has a large scale map (1: 1,000) to map RW administrative boundaries. Large-scale mapping (Batas RW) is useful for accurate information on incidence of dengue fever or other diseases, thereby eliminating information bias due to the use of village boundary maps. Another benefit is the accuracy of address management for customers, for example PDAM customers, to facilitate verification of customer data with large-scale maps, especially those that only include RT / RW addresses, without mentioning street names and household numbers. The method used is data mining K-Means Clustering. By using this method, the data that has been obtained can be grouped into several clusters, where the application of the KMeans Clustering process uses Excel calculations. The processed data is divided into 3 clusters, namely: high cluster (C1), medium cluster (C2) and low cluster (C3). The iteration process of this research occurs 2 times so that an assessment is obtained in classifying the household / neighborhood unit based on the Kelurahan. The results obtained are that there is 1 neighborhood unit with the highest cluster (C1), there are 4 neighborhood units with 4 medium clusters (C2), and 5 neighborhood units with the lowest cluster (C3). This data can be input to the sub-district to disseminate information about dengue fever, health education, and for the accuracy of PDAM customer address management and others., Kawasan merupakan pusat permasalahan dalam pengelolaan pencatatan administrasi Kabupaten Kebayoran Baru, karena kondisinya yang padat dan sulit untuk menentukan pengukuran lahan akibat padatnya kawasan pemukiman. Persoalan di Indonesia hingga saat ini adalah batas administratif kelurahan sudah ada, namun batas administratif untuk Rukun Warga / Rukun Tetangga (RW / RT) belum ada. Pemerintah Daerah DKI sudah memiliki peta skala besar (1: 1.000) untuk memetakan batas administrasi RW. Pemetaan skala besar (Batas RW) berguna untuk mendapatkan informasi yang akurat tentang kejadian demam berdarah atau penyakit lain, sehingga menghilangkan bias informasi akibat penggunaan peta batas desa. Manfaat lainnya adalah keakuratan pengelolaan alamat untuk pelanggan, misalnya pelanggan PDAM, untuk memudahkan verifikasi data pelanggan dengan peta skala besar, terutama yang hanya mencantumkan alamat RT / RW, tanpa menyebutkan nama jalan dan nomor rumah tangga. Metode yang digunakan adalah data mining K-Means Clustering. Dengan menggunakan metode ini maka data yang telah diperoleh dapat dikelompokkan menjadi beberapa cluster, dimana penerapan proses KMeans Clustering menggunakan perhitungan Excel. Data yang diolah dibagi menjadi 3 cluster, yaitu: cluster tinggi (C1), cluster sedang (C2) dan cluster rendah (C3). Proses iterasi penelitian ini dilakukan sebanyak 2 kali sehingga diperoleh penilaian dalam mengelompokkan ruta / rukun tetangga berdasarkan kelurahan. Hasil yang diperoleh adalah terdapat 1 RT dengan klaster tertinggi (C1), ada 4 RT dengan 4 klaster sedang (C2), dan 5 RT dengan klaster paling rendah (C3). Data ini dapat menjadi masukan bagi kelurahan untuk menyebarluaskan informasi tentang demam berdarah, penyuluhan kesehatan, ketepatan pengelolaan alamat pelanggan PDAM dan lain-lain

This paper presents the concepts for an anti-collision system intended for trams. The purpose of the anti-collision system is to develop and provide information to support the driver’s decision to initiate the braking of a tram. The anti-collision system is based on the processing of data from multiple sources (obstacle detection, image processing, and visual light communication system) and an expert system. The information about the road situation is visually presented on HUD (Head-up Display) of the driver.

This paper describes a concept for the construction and application of the Counter-UAV Defence System as developed by the Polish consortium of Zakłady Mechaniczne “Tarnów” S.A. (ZMT) and Military University of Technology (MUT). The article describes a system designed to counteract unmanned aerial vehicles, which consists of a multi-barrel machine gun and a radiolocation system. The Counter-UAV Defence System enables the combating of aerial targets, including unmanned aerial vehicles (UAVs).

In order to improve the connection rate and transmission efficiency of field network for power distribution grid, a dual-mode heterogeneous field network with high-speed power line broadband carrier and micro-power radio frequency wireless communication capabilities was designed. First, the topological structure of the field network, the networking process of the central node and the free nodes and the dynamic maintenance mechanism of the network were discussed in detail. Secondly, the routing measurement mechanism for creating a hybrid routing table and the improved layer limit shortest path routing algorithm were presented. On this basis, each node in the network could choose the optimal communication media at any given moment to create communication links with the adaptive data transfer speed according to the real-time hybrid routing table. Finally, the dual-mode heterogeneous field network was applied to the electricity consumption information collection system and tested in the laboratory and jobsite. The test results show that the dual-mode field network was more effective than the single-mode field network in shortening the reading meter time and increasing networking success rate.

Subject and Purpose. The paper is devoted to the microwave propagation over the sea in the nearshore region. Emphasis is on microwave attenuation measurements in the semi-shade and deep-shade areas with a view to study conditions of the microwave propagation on a beyond-the-horizon path in the nearshore area using a radar method. Methods and Methodology. A radar technique, developed and tested, provides measuring a microwave attenuation coefficient along a 60 km long beyond-the-horizon path in the tropospheric surface layer. High-grade radar and Doppler radar beacons (racons) are employed. Of interest are experimentally obtained temporal dependences of signals from racons situated at different heights and, also, spatial field distribution curves from a steadily moving racon running a height 1 to 27 m for 25 minutes. Results. A prototype of high-grade X-band coherent continuous-wave (Doppler) radar has been designed and fabricated. A radar technique has been developed and tested for the analysis of microwave beyond-the-horizon propagation conditions in the shadow region, which involves continuous-wave (Doppler) radar and Doppler racons with a radar cross section (RCS) of about 60 m2. Conclusions. The technique proposed enables reliable tracking of racon responses up to a double radiohorizon range on over-sea paths. This technique can be used for radar calibrations when dealing with various location problems in the nearshore area, including detection and tracking small-size and low-flying targets and their radar identification.

This paper presents the general principle of operation of delayed blowback small arms, their classification by the applied blowback delay, and a discussion of the existing designs. An analysis was carried out to rate the specific design solutions. The results of this work will be used in further investigations into and testing of delayed blowback firearms.

This paper presents a preliminary physical and mathematical model which describes the specific action of an automatic, short recoil operated firearm with an accelerator. The model includes the characteristic stages of the automatic action for a short recoil operated firearm (during one half of a single shot cycle), which enables simulation and assessment of the effect of the firearm’s system design parameters on the recoil velocities in specific recoil assembly components.

We consider the design of distributed detection algorithms for single-hop, single-channel wireless sensor networks in which sensor nodes send their local decisions to a fusion center (FC) by using a random access protocol. There is also limited time to collect local decisions before a final decision must be made. We thus propose and analyze a modified random access protocol in which the FC combines slotted ALOHA with a population-splitting algorithm called population-splitting-based random access (PSRA) and collision-aware distributed detection according to an estimate-then-fuse approach. Under the PSRA, only sensor nodes whose observations fall in a particular range of reliability will send their decisions in a specific frame by using slotted ALOHA. At the end of the collection time, the FC applies the collision-aware distributed detection to make a final decision. Here, the FC will first observe the state of each time slot—idle, successful, collision—in each frame, use this information to estimate the number of sensor nodes participating in each frame, and, then, compute a final decision using a population-based fusion rule. An approximation of the optimal transmission probability of the slotted ALOHA is determined to minimize the probability of error. Numerical results show that, unlike slotted-ALOHA-based data networks, the transmission probability maximizing the number of successful time slots does not optimize the performance of the proposed distributed detection. Instead, the proposed distributed detection performs best with a transmission probability that induces many collisions.

The paper contains a literature review of studies on several firearms operation systems and a review of articles on modern methods of calculating the kinematic characteristics of weapons. It includes the presentation of the short recoil operated 9 mm PW INKA pistol numerical model. The model was created in the MCS Adams software which uses the multibody systems. The boundary conditions for each part were specified and their implementation was determined. As the preliminary results of numerical investigations, the slide and barrel velocity and displacement curves were obtained. Comparison of a numerical solution with the literature data allowed for its qualitative verification.

We consider a continuous-time bandlimited additive white Gaussian noise channel with 1-bit output quantization. On such a channel the information is carried by the temporal distances of the zero-crossings of the transmit signal. The set of input signals is constrained by the bandwidth of the channel and an average power constraint. We derive a lower bound on the capacity by lower-bounding the mutual information rate for a given set of waveforms with exponentially distributed zero-crossing distances, where we focus on the behavior in the mid to high signal-to-noise ratio regime. We find that in case the input randomness scales appropriately with the available bandwidth, the mutual information rate grows linearly with the channel bandwidth for constant signal-to-noise ratios. Furthermore, for a given bandwidth the lower bound saturates with the signal-to-noise ratio growing to infinity. The ratio between the lower bound on the mutual information rate and the capacity of the additive white Gaussian noise channel without quantization is a constant independent of the channel bandwidth for an appropriately chosen randomness of the channel input and a given signal-to-noise ratio. We complement those findings with an upper bound on the mutual information rate for the specific signaling scheme. We show that both bounds are close in the mid to high SNR domain.

With the advent of the Internet of things era, many of the original power equipment and its fault information acquisition system have the problems of low efficiency, slow and inaccurate fault information collection speed, so the design of intelligent system is very important. The purpose of this study is to design the power equipment fault information acquisition system of the Internet of things, and analyze the collection speed and accuracy of the system. In this study, a power company in our city was taken as the experimental object to collect the fault information reported by the company, and put the new IOT fault information acquisition system into use in another company. After using for one month, the data collected by the power equipment fault information acquisition system of the Internet of things are compared with the original system, and the information collection amount, accuracy and acquisition speed are calculated. The research results show that the company's low-voltage user acquisition success rate has reached more than 99%, and the F1 value of the power equipment information acquisition system of the Internet of things is 96.4%, which is much higher than 86.3% and 75.7% of other systems. With the increase of time, the fault information collection efficiency can approach 99%. It shows that the power equipment information acquisition system of the Internet of things has higher accuracy and speed of information acquisition than the traditional system and other systems, and greatly improves the working efficiency of the system. The conclusion is that the function of this research system is better, the economic loss of the company is reduced, and the management is optimized. It makes contribution to the optimization and intelligent development of power equipment fault information acquisition system.

It has been widely acknowledged that network slicing is a key architectural technology to accommodate diversified services for the next generation network (5G). By partitioning the underlying network into multiple dedicated logical networks, 5G can support a variety of extreme business service needs. As network slicing is implemented in radio access networks (RAN), user handoff becomes much more complicated than that in traditional mobile networks. As both physical resource constraints of base stations and logical connection constraints of network slices should be considered in handoff decision, an intelligent handoff policy becomes imperative. In this paper, we model the handoff in RAN slicing as a Markov decision process and resort to deep reinforcement learning to pursue long-term performance improvement in terms of user quality of service and network throughput. The effectiveness of our proposed handoff policy is validated via simulation experiments.

Internet of things (IoT) is a concept that is currently gaining a lot of popularity as a result of its potential to be incorporated into many heterogeneous systems. Because of its diversity, integrating IoT is conceivable in almost all fields, including the healthcare sector. For instance, a promising technology in the healthcare sector known as wireless body area network (WBAN) could be integrated with the IoT to enhance its productivity. However, in order to guarantee the optimization of the operation of the healthcare applications facilitated by the WBAN-enabled IoT technology, there must be enough support from all the different protocol stack layers so as to satisfy the critical quality-of-service (QoS) requirements of the WBAN systems. Consequently, the medium access control (MAC) protocol has recently been gaining lots of attention in the area of WBANs due to its ability to manage and coordinate when a shared communication channel can be accessed. For the purpose of achieving efficient MAC protocols for WBAN-enabled IoT technology, this paper investigates some key MAC protocols that could be exploited in WBANs based on their characteristics, service specifications, technical issues such as energy wastage issues, and possible technical solutions were provided to enhance energy efficiency, channel utilization, data transmission rate, and dealy rate. Also, these MAC protocols were grouped and compared based on short- and long-range communication standards. Following this, future directions and open research issues are pointed out.

Subject and Purpose. The paper addresses interaction processes going in the ocean–atmosphere system and is concerned with their research by the method of radar remote sensing. Specifically, the matter of concern is the detection and parameter estimation of long waves, including nonlinear ones, on the ocean surface. Methods and Methodology. In August 1988, a series of successive radar surveys of long surface wave manifestations on the Pacific Ocean surface was carried out in the 3 cm wave range by means of an airborne X-band radar system “Analog”. The analysis of the results includes estimation of both spatial and frequency features of the detected long-wave packets and, also, a comparison of the measurement results with model calculations performed in the framework of theory of radio wave scattering by the sea surface in the presence of seismic wave effects. Results. Radar images of wave packets of long surface waves in the open ocean have been obtained. From the imaging data, the spatial scale (5…10 km) of these waves, the lengths (1…5 km) of wave packet components and the wave packet velocity (6.1 m/s) have been derived. Analysis has been given to the nonlinear form of wave packet components, and their amplitudes have been estimated by comparing the experimental and theoretically obtained radio contrasts. The bathymetry of the surface-wave track has been performed to suggest that the observed wave packet represents a set of solitons generated by a seismic impact with the further underwater collapse. Conclusions. A possibility has been demonstrated for monitoring wave packets of long surface waves in their propagation dynamics. The experiments of the sort for gaining a deeper insight into the ocean–atmosphere interaction physics can be conducted by means of not only airborne but also spaceborne radar systems with allowance made for the rate of surveys in both time and space.

In the era of the 5G network, network traffic grows rapidly. Edge computing will face the challenges of high bandwidth, low latency, high reliability and other requirements of 5G network services. Due to the limited resources of node communication, computing and storage, under the sudden, intensive and high-traffic task request, edge computing will suffer from network jitter, excessive delay, access congestion, service failure and low distribution efficiency. In order to ensure network service quality and improve service efficiency, it is necessary to construct an effective collaborative service mechanism, motivate nodes to participate in cooperation, integrate network service resources and self-adapt to manage collaborative services. Therefore, establishing an effective node and service evaluation system to identify reliable resources and nodes is an effective way to improve the overall availability and reliable services of edge computing network. In this paper, we summarize and analyze the key technologies of the current collaborative service organization incentive and trust mechanism. This paper presents an attack-resistant node and service evaluation system based on a trust network. The system includes a voting collection mechanism, a trustable node selection mechanism, a questioning response mechanism and a punishment mechanism. The experiments prove that the system has a strong ability to resist attacks and is superior to the existing reputation evaluation model in terms of its performance. It can effectively improve the collaborative efficiency of edge computing and guarantee the quality of network service.

Subject and Purpose. The development and prototype making of a laser rangefinder operating in the 1.50…1.70 μm spectral region is reported. This wavelength region is attractive to both laser producers and laser users for, first of all, relative eye-safety of radiation. Methods and Methodology. The paraxial scheme of rangefinder construction is used, involving a software-controlled power supply of laser radiation with technical arrangements providing its adaptation to varying operating conditions. The alignment of the transceiver channels is provided with laser beam visualization methods. Results. A pulsed laser rangefinder operating at a 1.54 μm wavelength has been developed, a prototype has been made. The rangefinder essentially consists of the transmitting and receiving channels and the visual channel for targeting. The radiation source is a pulsed laser on ytterbium-erbium glass with semiconductor diode pumping and modulated Q-factor. The laser provides a 6 mJ power pulse of 25 ns duration and 5 mrad radiation divergence. A laser light spot of a required aperture is formed using a Galilean telescope system. For the photodetector of the reflected radiation, a pin-photodiode with a photosensitive area diagonal of 0.3 mm and a 2.5 ns time resolution is used. The echo signal processing module has been developed and performed, providing a high-precision registration of a time delay between the starting and reflected pulses. An effective method with the use of a charge-coupled device and an LCD monitor has been proposed and implemented for the alignment of all the three rangefinder channels. The rangefinder can operate in a single-pulse or repetitive-pulse mode with a probing pulse repetition rate of 1 Hz. Conclusion. A pulsed laser rangefinder operating in a relatively eye-safe spectrum region has been developed, a prototype has been made. The field tests have shown that the created rangefinder measures an object distance within 140…7 000 m with a measurement error no worse than 3 m.

Subject and Purpose. The paper investigates tropospherically caused fluctuations of signals from the GPS navigation system while the satellite is beyond the radio horizon. The intensity of GPS signal fluctuations under the influence of meteorological factors is studied, too. The purpose of the work is to determine relationship between the intensity of GPS signal fluctuations and the meteorological conditions when satellite elevation angles are small. Methods and Methodology. The methodology of the work is based on the search for the intensity of amplitude fluctuations of GPS satellite’s signals depending on the meteorological situation. The measurement approach is registration of the GPS satellite’s signal level just before the satellite crosses the horizon. The data processing after a series of experimental studies is based on the extraction of tropospheric fluctuations from the complete GPS signal by the moving average method. Results. Experimental studies of the turbulent component of signals from GPS satellites at low elevation angles have been carried out. It has been revealed that a conventional elevation boundary below which the tropospheric influence is predominant can be established due to the synchronism property which the orbits of the GPS satellites hold. It has been shown that variations in the mean square deviation (MSD) of GPS signal fluctuations caused by the troposphere and extracted from the complete signal are consistent with meteorological parameter changes. Conclusion. Analysis has been carried out to show that a meteorological dependence of the MSD statistics of GPS signal tropospheric fluctuations exists. The intensity of fluctuations rises with the convective activity in the troposphere. The conducted experiments suggest that periods of increased turbulence in the atmosphere can be detected with the use of GPS signals. For this purpose, the optical methods will not do.

Recent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.

Wireless sensor network (WSN)-based Internet of Things (IoT) applications suffer from issues including limited battery capacity, frequent disconnections due to multihop communication and a shorter transmission range. Researchers propose different but isolated clustering and routing solutions that are inefficient in terms of energy efficiency and network connectivity in IoT-based WSNs. In this work, we emphasize the importance of considering the context of IoT applications that have further requirements for dedicated data collection per node. We address two interlinked issues, clustering and routing, in a large-scale IoT-based WSN. We propose an improved clustering and routing (ICR) protocol to jointly solve both of these issues. Improved clustering and routing provide area-based clustering derived from the transmission range of network nodes. This clustering also develops a strong network backbone that provides fail-over-proof routing. An efficient routing path is achieved by finding the minimal hop count with the availability of alternate routing paths. The results are compared with state-of-the-art benchmark protocols, Joint Clustering and Routing (JCR), Low Energy Adaptive Hierarchical Clustering (LEACH) and other recent protocols. Theoretical and simulation results demonstrate reliable network topology, improved network lifetime, efficient node density management and improved overall network capacity.

Perfect image compositing can harmonize the appearance between the foreground and background effectively so that the composite result looks seamless and natural. However, the traditional convolutional neural network (CNN)-based methods often fail to yield highly realistic composite results due to overdependence on scene parsing while ignoring the coherence of semantic and structural between foreground and background. In this paper, we propose a framework to solve this problem by training a stacked generative adversarial network with attention guidance, which can efficiently create a high-resolution, realistic-looking composite. To this end, we develop a diverse adversarial loss in addition to perceptual and guidance loss to train the proposed generative network. Moreover, we construct a multi-scenario dataset for high-resolution image compositing, which contains high-quality images with different styles and object masks. Experiments on the synthesized and real images demonstrate the efficiency and effectiveness of our network in producing seamless, natural, and realistic results. Ablation studies show that our proposed network can improve the visual performance of composite results compared with the application of existing methods.

In this article, a novel two-way double-relay selection strategy with its bit error rate (BER) performance analysis is proposed. In this novel strategy, as a first step we choose two relays out of a set of relay-nodes in a way to maximize the system performance in terms of BER and complexity. In the second step, the selected relays apply orthogonal space-time coding scheme using the three-phase protocol to establish a two-way communication between the transceivers, which will lead to a significant improvement in the achievable diversity and coding gain with a very low decoding complexity by using a symbol-wise decoder. Furthermore, the performance of the overall system is further enhanced through the use of a network coding method at the selected relay-nodes. Moreover, this paper proposes the analytical approximation of the BER performance. As well, we show that the analytical results match perfectly the simulated ones. In addition, we prove that our strategy outperforms the current state-of-the-art ones by proposing a better cooperative communication system in terms of BER.

Subject and Purpose. The mechanism of destruction of the S-state of a nonlinear high-temperature superconductor as part of a coplanar waveguide has not been properly elucidated as the effect of avalanche-type transition to a highly dissipative state, which was experimentally detected by the authors, takes place. The present work is concerned with the development of an appropriate approach describing kinetics of destruction of the S-state of a nonlinear high-temperature superconductor in a coplanar waveguide with allowances made for an inhomogeneous distribution of the microwave current in the superconducting film strip. Methods and Methodology. Use of I.B. and O.G. Vendiks’ reasoning [2] is made on kinetics of the destruction of the superconducting state of a wide film when a direct current governed by the time-dependent Ginzburg-Landau equation is applied. Keeping unchanged their idea as to the S–N boundary forming in the film strip with the boundary movement to the middle of the strip, the S–N boundary motion equation is obtained for a coplanar waveguide, proceeding in doing this from the motion equation of magnetic flux vortices under certain restrictions specified. Results. The time of S-state destruction has been numerically estimated: 1) for a wide superconducting film of YBa2Cu3O7–d composition, the destruction is by the direct current and 2) for a coplanar waveguide based on the same film, the destruction is by the microwave current. When the superconductivity is small (I / I c ³ 1), the destruction time values in both cases are close to each other within the order of magnitude. Conclusion. It is for the first time that the S-state destruction time in a coplanar waveguide has been expressed in terms of the microwave current distribution in the waveguide. It has been shown that this characteristic linearly depends on the ratio between the critical current and the microwave current amplitude in contrast to a quadratic dependence obtained for a superconducting strip with a direct current.

With the development of the wireless network, increasing mobile applications are emerging and receiving great popularity. These applications cover a wide area, such as traffic monitoring, smart homes, real-time vision processing, objective tracking, and so on, and typically require computation-intensive resources to achieve a high quality of experience. Although the performance of mobile devices (MDs) has been continuously enhanced, running all the applications on a single MD still causes high energy consumption and latency. Fortunately, mobile edge computing (MEC) allows MDs to offload their computation-intensive tasks to proximal eNodeBs (eNBs) to augment computational capabilities. However, the current task offloading schemes mainly concentrate on average-based performance metrics, failing to meet the deadline constraint of the tasks. Based on the deep reinforcement learning (DRL) approach, this paper proposes an Energy-aware Task Offloading with Deadline constraint (DRL-E2D) algorithm for a multi-eNB MEC environment, which is to maximize the reward under the deadline constraint of the tasks. In terms of the actor-critic framework, we integrate the action representation into DRL-E2D to handle the large discrete action space problem, i.e., using the low-complexity k-nearest neighbor as an approximate approach to extract optimal discrete actions from the continuous action space. The extensive experimental results show that DRL-E2D achieves better performance than the comparison algorithms on all parameter settings, indicating that DRL-E2D is robust to the state changes in the MEC environment.

Reliability and user compliance of the applied sensor system are two key issues of digital healthcare and biomedical informatics. For gait assessment applications, accurate joint angle measurements are important. Inertial measurement units (IMUs) have been used in a variety of applications and can also provide significant information on gait kinematics. However, the nonlinear mechanism of human locomotion results in moderate estimation accuracy of the gait kinematics and thus joint angles. To develop “digital twins” as a digital counterpart of body lower limb joint angles, three-dimensional gait kinematic data were collected. This work investigates the estimation accuracy of different neural networks in modeling lower body joint angles in the sagittal plane using the kinematic records of a single IMU attached to the foot. The evaluation results based on the root mean square error (RMSE) show that long short-term memory (LSTM) networks deliver superior performance in nonlinear modeling of the lower limb joint angles compared to other machine learning (ML) approaches. Accordingly, deep learning based on the LSTM architecture is a promising approach in modeling of gait kinematics using a single IMU, and thus can reduce the required physical IMUs attached on the subject and improve the practical application of the sensor system.

This article presents an experimental demonstration of a high-capacity millimeter-wave 5G NR signal transmission with analog radio-over-fiber (ARoF) fronthaul over multi-core fiber and full real-time processing. The demonstration validates the core of the blueSPACE fronthaul architecture which combines ARoF fronthaul with space division multiplexing in the optical distribution network to alleviate the fronthaul capacity bottleneck and maintain a centralized radio access network with fully centralized signal processing. The introduction of optical beamforming in the blueSPACE architecture brings true multi-beam transmission and enables full spatial control over the RF signal. The proposed ARoF architecture features a transmitter that generates the ARoF signal and an optical signal carrying a reference local oscillator employed for downconversion at the remote unit from a single RF reference at the central office. A space division multiplexing based radio access network with multi-core fibre allows parallel transport of the uplink ARoF signal and reference local oscillator at the same wavelength over separate cores. A complete description of the real-time signal processing and experimental setup is provided and system performance is evaluated. Transmission of an 800 MHz wide extended 5G NR fronthaul signal over a 7-core fibre is shown with full real-time signal processing, achieving 1.4 Gbit/s with a bit error rate $$ < 3.8 × 10 - 3 and thus below the limit for hard-decision forward error correction with 7% overhead.