 Subjects -> ELECTRONICS (Total: 207 journals)
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- 5G, 6G, and Beyond: Recent advances and future challenges
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Abstract: Abstract With the high demand for advanced services and the increase in the number of connected devices, current wireless communication systems are required to expand to meet the users’ needs in terms of quality of service, throughput, latency, connectivity, and security. 5G, 6G, and Beyond (xG) aim at bringing new radical changes to shake the wireless communication networks where everything will be fully connected fulfilling the requirements of ubiquitous connectivity over the wireless networks. This rapid revolution will transform the world of communication with more intelligent and sophisticated services and devices leading to new technologies operating over very high frequencies and broader bands. To achieve the objectives of the xG networks, several key technology enablers need to be performed, including massive MIMO, software-defined networking, network function virtualization, vehicular to everything, mobile edge computing, network slicing, terahertz, visible light communication, virtualization of the network infrastructure, and intelligent communication environment. In this paper, we investigated the recent advancements in the 5G/6G and Beyond systems. We highlighted and analyzed their different key technology enablers and use cases. We also discussed potential issues and future challenges facing the new wireless networks.
PubDate: 2023-10-01
- Hidden Markov Model for early prediction of the elderly’s dependency
evolution in ambient assisted living-
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Abstract: Abstract The integration of information and communication technologies (ICT) can be of great utility in monitoring and evaluating the elderly’s health condition and its behavior in performing Activities of Daily Living (ADL) in the perspective to avoid, as long as possible, the delays of recourse to health care institutions (e.g., nursing homes and hospitals). In this research, we propose a predictive model for detecting behavioral and health-related changes in a patient who is monitored continuously in an assisted living environment. We focus on keeping track of the dependency level evolution and detecting the loss of autonomy for an elderly person using a Hidden Markov Model based approach. In this predictive process, we were interested in including the correlation between cardiovascular history and hypertension as it is considered the primary risk factor for cardiovascular diseases, stroke, kidney failure and many other diseases. Our simulation was applied to an empirical dataset that concerned 3046 elderly persons monitored over 9 years. The results show that our model accurately evaluates person’s dependency, follows his autonomy evolution over time and thus predicts moments of important changes.
PubDate: 2023-10-01
- Backscatter communication system efficiency with diffusing surfaces
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Abstract: Abstract In an ambient backscatter communication system, the waves generated by a source are reflected by a tag, in a variable manner in time. Therefore, the tag can transmit a message to a reader, without generating any radio wave and without battery. As a consequence, such a communication system is a promising technology for ultra-low energy wireless communications. In the simplest implementation of such a system, the tag sends a binary message by oscillating between two states and the reader detects the bits by comparing the two distinct received powers. In this paper, for the first time, we propose to analyze the impact of the shape of diffusing flat panel surfaces that diffuse in all directions, on an ambient backscatter communication system. We establish the analytical closed form expression of the power contrast in the presence of flat panels, by considering a rectangular surface and a disk-shaped surface, and we show that diffusing surfaces improve the power contrast. Moreover, our approach allows us to express the contrast to noise ratio, and therefore to establish the BER performance. Furthermore, we show that it makes it possible to improve the energetic performance, thanks to diffusing surfaces. For any configuration characterized by a fixed source, tag and reader, we moreover determine the precise locations of diffusing surfaces, which induce a maximum efficiency of the surfaces, whatever the wavelength. Furthermore, we show that it becomes possible to easily determine an optimal frequency which maximizes the contrast power, thanks to the expression of the contrast power.
PubDate: 2023-10-01
- Analysis of SNR penalty in coherent WDM receiver system for detection of
QPSK signal with component crosstalk-
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Abstract: Abstract We propose analytical modelling of component crosstalk on the performance of wavelength division multiplexed (WDM) receiver with generalized quadrature phase shift keying (QPSK) signal and coherent detection. A comprehensive study on the signal-to-noise ratio (SNR) penalty is conducted, which can be used to balance SNR values and the system's power budget in the presence of finite crosstalk sources. Results express that, in the presence of five crosstalk interferers, the crosstalk level leading to 1-dB of SNR penalty must be less than -23.8 to -26.5 dB for bit error rate (BER) from 10–7 to 10–13. For the BER of 10–9, the QPSK signal has a component crosstalk tolerance of -21.7 dB for a 1 dB SNR penalty with a single interferer. Furthermore, the study of spectral efficiency reveals that crosstalk level, SNR, and the number of active interferers perform a vital role in determining the bandwidth efficiency of the system. The analysis exploits the characteristic function method and Maclaurin series expansion to compute a closed form expression of BER over additive white Gaussian noise (AWGN) channel. Following the analysis, the SNR and bandwidth expenses of the system are examined numerically through the estimated BER and binary entropy function. The estimated values of the BER using the proposed model are in close agreement with a similar theoretical investigation for a single interferer.
PubDate: 2023-10-01
- Telephony speech system performance based on the codec effect
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Abstract: Abstract This paper is a part of our contribution to research on the enhancement of network automatic speech recognition system performance. We built a highly configurable platform by using hidden Markov models, Gaussian mixture models, and Mel frequency spectral coefficients, in addition to VoIP G.711-u and GSM codecs. To determine the optimal values for maximum performance, different acoustic models are prepared by varying the hidden Markov models (from 3 to 5) and Gaussian mixture models (8–16-32) with 13 feature extraction coefficients. Additionally, our generated acoustic models are tested by unencoded and encoded speech data based on G.711 and GSM codecs. The best parameterization performance is obtained for 3 HMM, 8–16 GMMs, and G.711 codecs.
PubDate: 2023-10-01
- DLT architectures for trust anchors in 6G
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Abstract: Abstract This manuscript investigates viable Distributed Ledger Technology (DLT) architecture approaches to be used as basis for the distribution of integrity verification data. We discuss what can be a Trust Anchor and how the property of trust can be enabled as a service for mobile communications infrastructures. This follows up on a preceding publication, in the course of which a service was developed that can be utilized to create trust and traceability in transactions between other services. Crucial for the integrity of such an audit trail is proof for which side was committing, in case a tampering was detected. For such verification in the aftermath, mechanisms for the distribution of meta data are necessary. Where our ultimate goal is to develop a versatile framework for Trust as a Service (TaaS), the work at hand contributes the investigation on header distribution. We put a major focus on providing Trust as a Service (TaaS) especially in the mobile communications domain since a reliable concept for trustworthiness is indispensable for the vision of organic infrastructures beyond 5G, which means that such networks are flexible regarding their composition and open for stakeholders.
PubDate: 2023-10-01
- Deep unfolding for energy-efficient resource allocation in mmWave networks
with multi-connectivity-
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Abstract: Abstract In millimeter-wave (mmWave) communications, multi-connectivity can enhance the communication capacity while at the cost of increased power consumption. In this paper, we investigate a deep-unfolding-based approach for joint user association and power allocation to maximize the energy efficiency of mmWave networks with multi-connectivity. The problem is formulated as a mixed integer nonlinear fractional optimization problem. First, we develop a three-stage iterative algorithm to achieve an upper bound of the original problem. Then, we unfold the iterative algorithm with a convolutional neural network (CNN)-based accelerator and trainable parameters. Moreover, we propose a CNN-aided greedy algorithm to obtain a feasible solution. The simulation results show that the proposed algorithm can achieve good performance and strong robustness but with much reduced computational complexity.
PubDate: 2023-10-01
- Social network malicious insider detection using time-based trust
evaluation-
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Abstract: Abstract In recent years, malicious insider attacks have become a common fraudulent activity in which an attacker is often perceived as a trusted entity in Social Networks (SNs). At present, machine learning (ML) approaches are widely used to identify the behavior of users in the network. From this perspective, this paper presents an integrated approach, namely, Social network malicious insider detection (SID), which consists of long short-term memory (LSTM) and time-based trust evaluation (TBTE). The proposed SID aims to identify deviations in SN user behavior by monitoring their data. The proposed SID uses LSTM, an advanced version of the recurrent neural network (RNN), which precisely predicts the behavior of users and identifies the anomaly pattern in SNs. A time-based trust evaluation method is integrated with LSTM, which not only differentiates the abnormal behavior of SN users but also precisely categorizes an anomaly node as a malicious node, a new user or a broken node. Moreover, the proposed SID detects insiders accurately and reduces false alarms by providing a novel quantitative analysis for computing the balancing factor according to time, which avoids the misinterpretation of normal user patterns as anomalies. The performance of the proposed SID is evaluated in real time, which demonstrates that the detection accuracy for attacks is 96% for normal users and 98% for new users with a smaller time span.
PubDate: 2023-10-01
- Robust adaptive beamforming algorithm for coherent signals based on
virtual array-
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Abstract: Abstract Aiming at the problem of beamforming performance degradation under the coherent signals model, this paper proposes an adaptive beamforming algorithm based on the virtual array. Compared with previous work, the creative construction of virtual arrays in this paper allows the algorithm to ensure strong coherent signal processing and superior output performance with no degradation in coherence capability. The proposed algorithm firstly constructs a virtual array symmetric to the physical array to form a virtual antenna array model; secondly, a full-rank covariance matrix is obtained by matrix reconstruction; then, the direction vector and power of the signals are estimated; finally, the estimated parameters are used to reconstruct the interference plus noise covariance matrix (INCM) and calculate the weight vector. Simulation analysis verifies the superiority of the algorithm and the validity of theoretical analysis.
PubDate: 2023-10-01
- Deep learning-based sequential models for multi-user detection with M-PSK
for downlink NOMA wireless communication systems-
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Abstract: Abstract Non-orthogonal multiple access (NOMA) techniques have the potential to achieve large connectivity requirements for future-generation wireless communication. NOMA detection techniques require conventional successive interference cancellation (SIC) techniques for uplink and downlink transmissions on the receiver side to decode the transmitted signals. Multipath fading significantly impacts the SIC process and correct signal detection due to propagation delay and fading channel. Deep learning (DL) techniques can overcome conventional SIC detection limitations. Signal detection for a multi-user NOMA wireless communication system that relies on orthogonal frequency-division multiplexing (OFDM) is discussed using various DL approaches in this paper. For multi-user signal detection, different deep learning-based sequential model neural networks, gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional long short-term memory (Bi-LSTM) are applied. The deep neural network is initially trained offline with multi-user NOMA signals in the OFDM system and used to recover transmitted signals directly. DL-based sequential models with different cyclic prefixes and fast Fourier transforms with various M-phase shift keying (M-PSK) modulation schemes are discussed with deep learning optimization algorithms. In simulation results, the conventional SIC technique with minimum mean square error approach is compared to the effectiveness of DL-based models for signal detection of multi-user NOMA systems by their bit error rate performances. The root mean square error performance of different deep learning-based sequence models with other optimizers is also discussed. Moreover, the robustness of the Bi-LSTM is evaluated with the reliability of other DL-based sequential model applications in the multi-user downlink NOMA wireless communication systems.
PubDate: 2023-09-25
- Experimental assessment of SDR-based 5G positioning: methodologies and
insights-
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Abstract: Abstract While GPS has traditionally been the primary positioning technology, 3GPP has more recently begun to include positioning services as native, built-in features of future-generation cellular networks. With Release 16 of the 3GPP, finalized in 2021, a significant standardization effort has taken place for positioning in 5G networks, especially in terms of physical layer signals, measurements, schemes, and architecture to meet the requirements of a wide range of regulatory, commercial and industrial use cases. However, experimentally driven research aiming to assess the real-world performance of 5G positioning is still lagging behind, root causes being (i) the slow integration of positioning technologies in open-source 5G frameworks, (ii) the complexity in setting up and properly configuring a 5G positioning testbed, and (iii) the cost of a multi-BS deployment. This paper sheds some light on all such aspects. After a brief overview of state of the art in 5G positioning and its support in open-source platforms based on software-defined radios (SDRs), we provide advice on how to set up positioning testbeds, and we demonstrate, via a set of real-world measurements, how to assess aspects such as reference signal configurations, localization algorithms, and network deployments. Our contribution further includes an assessment of the efficacy of utilizing measurements obtained from a single-link limited-size testbed to forecast localization performance in more elaborate (and hence more expensive) multi-node network settings. We posit that our methodological insights can assist in lowering the entry cost barriers associated with conducting 5G positioning experiments and, consequently, promote additional experimental research in this domain.
PubDate: 2023-09-15
- Semi-blind AF transmission in secure NOMA systems
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Abstract: Abstract In the wireless channel state information (CSI)-assisted amplify-and-forward (AF) networks, an instantaneous CSI of the first hop is required to scale the amplification gain. However, the deployment of instantaneous CSI always remains difficult in real applications because it increases the CSI overhead and causes resources wastage such as power and bandwidth. In order to reduce the CSI overhead and the system complexity, we suggest the integration of semi-blind relay in secure non-orthogonal multiple access (NOMA) systems where only a statistical CSI of the first hop is used to generate the amplification gain. This paper addresses the performance analysis of the secure semi-blind AF-NOMA (S-SBAF-NOMA) schemes in which the base station communicates with a pair of users via a semi-blind relay node in the presence of one eavesdropper. First, we provide the expressions for the end-to-end signal-to-noise ratio (SNR) at each receiver node. We then derive new analytical and asymptotic expressions for strictly positive secrecy capacity (SPSC) and secrecy outage probability (SOP). To ensure the exactness and the tractability of mathematical analysis, we provide some numerical results obtained through simulation rounds in Matlab, and we compare them with those of secure CSI-assisted AF-NOMA (S-CSIAF-NOMA) networks. Our results show that the proposed S-SBAF-NOMA scheme achieves comparable secrecy performance/same performance bounds as compared to S-CSIAF-NOMA scheme at the gain of a decrease in processing complexity and system overhead. Numerical results also demonstrate that S-SBAF-NOMA networks achieve superior secrecy performance for lower values of target data rates and SNR of the illegal link.
PubDate: 2023-09-13
- Using packet trimming at the edge for in-network video quality adaption
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Abstract: Abstract This paper describes the effects of running in-network quality adaption by trimming the packets of layered video streams at the edge. The video stream is transmitted using the BPP transport protocol, which is like UDP, but has been designed to be both amenable to trimming and to provide low-latency and high reliability. The traffic adaption uses the Packet Wash process of Big Packet Protocol (BPP) on the transmitted Scalable Video Coding (SVC) video streams as they pass through a network function which is BPP-aware and embedded at the edge. Our previous work has either demonstrated the use of Software Defined Networking (SDN) controllers to implement Packet Wash directly, or the use of a network function in the core of the network to do the same task. This paper presents our effort to deploy and evaluate such a process at the edge, highlighting the packet trimming algorithm and showing the packet trimming effects on the streams. We compare the performance of transmitting video using BPP and the Packet Wash trimming, against alternative transmission schemes, namely UDP and HTTP adaptive streaming (HAS), presenting a number of quality parameters. The results demonstrate that providing traffic engineering using in-network quality adaption using packet trimming, provides high quality at the receiver.
PubDate: 2023-09-11
- Two level data centric aggregation scheme for wireless sensor networks
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Abstract: Abstract Wireless sensor networks (WSNs) sense and collect information from a desired phenomenon with the help of sensor nodes that have limited computational power, battery, and memory. Several data aggregation approaches are proposed to make the sensor networks energy-efficient, increasing the network’s lifetime by controlling data redundancy at aggregator nodes. Redundant data is suppressed before transmission to the sink. In this work, our aim is to enhance the network lifetime by efficiently utilizing the network’s energy through controlled data redundancy and minimizing data transmission to the sink. Data aggregation occurs in two steps: firstly, within clusters where the cluster-head serves as the aggregation point, and secondly, at a central point in the network where the gateway node acts as the aggregation point. Experiments demonstrate that our proposed approach yields better results compared to a benchmark clustering protocol in terms of network stability, the number of data packets transferred to the destination, energy dissipation of nodes, and overall network lifetime.
PubDate: 2023-09-08
- On estimating the interest satisfaction ratio in IEEE 802.15.4-based
named-data networks-
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Abstract: Abstract Named-Data Networking (NDN) over Low Power and Lossy Networks (LLNs), employing IEEE 802.15.4 communication technology, is projected to provide native support for mobility and efficient content delivery for the emerging Internet of Things (IoT). While many interest forwarding strategies have been proposed for NDNs over LLNs, most existing studies have relied on software simulations to evaluate their performance due to the lack of analytical modeling tools. This paper introduces the first analytical model for estimating the Interest Satisfaction Ratio (ISR) in NDN over LLNs, which is a crucial metric for assessing the effectiveness of interest forwarding strategies. We develop the analytical model specifically for the broadcast forwarding strategy, which has been extensively studied due to its simplicity and ease of implementation. Simulation results confirm that the proposed model predicts the ISR with reasonable accuracy. The model is then used to elucidate the strong interaction between the CSMA/CA parameters of the IEEE 802.15.4 standard and the achieved ISR.
PubDate: 2023-09-06
- Distributed congestion control method for sending safety messages to
vehicles at a set target distance-
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Abstract: Abstract In the present paper, we propose a method for controlling the interval of safety message transmissions in a fully distributed manner that maximizes the number of successful transmissions to vehicles located a set target distance away. In the proposed method, each vehicle estimates the density of vehicles in its vicinity, and, based on the estimated vehicle density, each vehicle calculates an optimal message transmission interval in order to maximize the number of successful message transmissions to vehicles located a set target distance away. The optimal message transmission interval can be analytically obtained as a simple expression when it is assumed that the vehicles are positioned according to a two-dimensional Poisson point process, which is appropriate for downtown scenarios. In addition, we propose two different methods for a vehicle by which to estimate the density of other vehicles in its vicinity. The first method is based on the measured channel busy ratio, and the second method relies on counting the number of distinct IDs of vehicles in the vicinity. We validate the effectiveness of the proposed methods using several simulations.
PubDate: 2023-09-05
- On the performance and scalability of consensus mechanisms in
privacy-enabled decentralized renewable energy marketplace-
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Abstract: Abstract Renewable energy sources were introduced as an alternative to fossil fuel sources to make electricity generation cleaner. However, today’s renewable energy markets face a number of limitations, such as inflexible pricing models and inaccurate consumption information. These limitations can be addressed with a decentralized marketplace architecture. Such architecture requires a mechanism to guarantee that all marketplace operations are executed according to predefined rules and regulations. One of the ways to establish such a mechanism is blockchain technology. This work defines a decentralized blockchain-based peer-to-peer (P2P) energy marketplace which addresses actors’ privacy and the performance of consensus mechanisms. The defined marketplace utilizes private permissioned Ethereum-based blockchain client Hyperledger Besu (HB) and its smart contracts to automate the P2P trade settlement process. Also, to make the marketplace compliant with energy trade regulations, it includes the regulator actor, which manages the issue and consumption of guarantees of origin and certifies the renewable energy sources used to generate traded electricity. Finally, the proposed marketplace incorporates privacy-preserving features, allowing it to generate private transactions and store them within a designated group of actors. Performance evaluation results of HB-based marketplace with three main consensus mechanisms for private networks, i.e., Clique, IBFT 2.0, and QBFT, demonstrate a lower throughput than another popular private permissioned blockchain platform Hyperledger Fabric (HF). However, the lower throughput is a side effect of the Byzantine Fault Tolerant characteristics of HB’s consensus mechanisms, i.e., IBFT 2.0 and QBFT, which provide increased security compared to HF’s Crash Fault Tolerant consensus RAFT.
PubDate: 2023-09-02
- Federated deep Q-learning networks for service-based anomaly detection and
classification in edge-to-cloud ecosystems-
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Abstract: Abstract The diversity of services and infrastructure in metropolitan edge-to-cloud network(s) is rising to unprecedented levels. This is causing a rising threat of a wider range of cyber attacks coupled with a growing integration of a constrained range of infrastructure, particularly seen at the network edge. Deep reinforcement-based learning is an attractive approach to detecting attacks, as it allows less dependency on labeled data with better ability to classify different attacks. However, current approaches to learning are known to be computationally expensive (cost), and the learning experience can be negatively impacted by the presence of outliers and noise (quality). This work tackles both the cost and quality challenges with a novel service-based federated deep reinforcement learning solution, enabling anomaly detection and attack classification at a reduced data cost and with better quality. The federated settings in the proposed approach enable multiple edge units to create clusters that follow a bottom-up learning approach. The proposed solution adapts a deep Q-learning network (DQN) for service-tunable flow classification and introduces a novel federated DQN (FDQN) for federated learning. Through such targeted training and validation, variation in data patterns and noise is reduced. This leads to improved performance per service with lower training cost. Performance and cost of the solution, along with sensitivity to exploration parameters, are evaluated using examples of publicly available datasets (UNSW-NB15 and CIC-IDS2018). Evaluation results show the proposed solution to maintain detection accuracy in the range of ≈75–85% with lower data supply while improving the classification rate by a factor of ≈2.
PubDate: 2023-08-31
- Publisher Correction: Towards programmable IoT with ActiveNDN
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PubDate: 2023-08-19
- Automated slow-start detection for anomaly root cause analysis and BBR
identification-
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Abstract: Abstract Network troubleshooting usually requires packet level traffic capturing and analyzing. Indeed, the observation of emission patterns sheds some light on the kind of degradation experienced by a connection. In the case of reliable transport traffic where congestion control is performed, such as TCP and QUIC traffic, these patterns are the fruit of decisions made by the congestion control algorithm (CCA), according to its own perception of network conditions. The CCA estimates the bottleneck’s capacity via an exponential probing, during the so-called “Slow-Start” (SS) state. The bottleneck is considered reached upon reception of congestion signs, typically lost packets or abnormal packet delays depending on the version of CCA used. The SS state duration is thus a key indicator for the diagnosis of faults; this indicator is estimated empirically by human experts today, which is time-consuming and a cumbersome task with large error margins. This paper proposes a method to automatically identify the slow-start state from actively and passively obtained bidirectional packet traces. It relies on an innovative timeless representation of the observed packets series. We implemented our method in our active and passive probes and tested it with CUBIC and BBR under different network conditions. We then picked a few real-life examples to illustrate the value of our representation for easy discrimination between typical faults and for identifying BBR among CCAs variants.
PubDate: 2023-08-18
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