Annals of Telecommunications最新文献

Non-orthogonal multiple access (NOMA) is paramount in modern wireless communication systems since it enables efficient multiple access schemes, allowing multiple users to share the same spectrum resources and thus improving overall network capacity. Multiple-input multiple-output (MIMO) technology is crucial in wireless communication as it leverages multiple antennas to enhance data throughput, increase link reliability, and mitigate signal interference, resulting in improved communication performance. The combination of MIMO and NOMA represents a transformative synergy that harnesses the benefits of both technologies, facilitating efficient spectrum utilization, higher data rates, and improved reliability in wireless networks. This makes it particularly valuable in the fifth-generation (5G) era and beyond. This paper investigates the performance of majority-based transmit antenna selection and maximal ratio combining (TAS-maj/MRC) in MIMO-NOMA networks. We derive a closed-form expression for the exact bit error rate (BER) for binary phase shift keying (BPSK) modulation in Nakagami-m fading channels. Moreover, asymptotic expressions are obtained in the high signal-to-noise ratio (SNR) region to get further insight into the BER behavior of the system. Finally, we verify the analytical results’ accuracy through simulations. The results demonstrate that diversity and code gains are achieved. In addition, the BER performance is significantly improved as the number of receive antennas increases or channel condition enhances.

In the current era of agricultural robotization, it is necessary to use a suitable automated data collection system for constant plant, animal, and machine monitoring. In this context, cloud computing (CC) is a well-established paradigm for building service-centric farming applications. However, the huge amount of data has put an important burden on data centers and network bandwidth and pointed out issues that cloud-based applications face such as large latency, bottlenecks because of central processing, compromised security, and lack of offline processing. Fog computing (FC), edge computing (EC), and mobile edge computing (MEC) (or flying edge computing FEC) are gaining exponential attention and becoming attractive solutions to bring CC processes within reach of users and address computation-intensive offloading and latency issues. These paradigms from cloud to mobile edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations and challenges. This paper carries out a systematic review of the latest high-quality literature and aims to identify similarities, differences, and the main use cases in the mentioned computing paradigms, particularly when using drones. Our expectation from this work is to become a good reference for researchers and help them address hot topics and challenging issues related to this scope.

The underwater green transport system (UwGTs) is a network of connected, intelligent underwater sensors or Internet of Things (IoT) devices. The sensor networks used in UwGTs are distinct from traditional territorial wireless sensor networks in many ways, including their lengthy propagation delays, restricted bandwidths, and poor reliability. UwGTs would face significant security difficulties due to these unique traits. The main goal of this paper is to resolve authentication issues across the entire UwGTs network. The more significant challenge in developing an authentication scheme for UwGTs is to develop a simple, efficient, and secure system that considers the sensor nodes’ resource limitations. In light of this, we suggest an identity-based signature-based authentication scheme based on elliptic curve cryptography that enhances network lifetime by reducing sensor node energy consumption. The security of the suggested scheme has also been confirmed using a formal security assessment technique like the Random Oracle Model (ROM) and Automated Validation of Internet Security Protocols and Applications (AVISPA) software tools. In addition, the proposed scheme is more effective and lightweight regarding computation costs, communication costs, energy consumption, and comparative energy efficiency than the existing identity-based authentication schemes.

The significant role of multiple antenna techniques is vital to enable wireless systems to support the ever-rising demand for higher data rates and reliability. Thus, investigating these systems is continually important, and one of the essential aspects of this study is analyzing the capacity of such systems to gain insight into their performance. This paper presents several closed-form formulae to express the capacity of the multiple antenna system, by introducing newly derived finite and unconditionally valid solutions. It is also mathematically describing the outage probability of multiple antenna system in several scenarios. The numerical results show the tight fit between the obtained formulae and the Monte Carlo simulation outcomes.

Over the last three decades, we have become more dependent on wireless connectivity to access services and applications from nearly anywhere. The overstated emergence of the all-encompassing fifth generation (5G) of mobile systems begs the question of the future of the new generation of IEEE 802.11 (Wi-Fi) solutions. However, Wi-Fi has certain advantages compared to cellular systems in different ways: (i) a fast-paced standardization process; (ii) a diverse, agile, and highly competitive manufacturer base; and (iii) a broad base of early adopters for both office and house wireless networks. In addition, the rise of enabling technologies, such as software-defined wireless networks, may allow more robust and reliable Wi-Fi networks to bridge gaps in Wi-Fi technology to reach several vertical sectors. This review provides a technical analysis of the relationship between broadband wireless and Wi-Fi technologies. Wi-Fi has taken decisive steps with the evolution of several standards, and there is already evidence that Wi-Fi may partially (or completely) fulfill 5G’s strict service requirements. Next, we discussed the Wi-Fi and 5G convergence, which allow more control over user experiences and provide better service. This review concludes with an analysis of open challenges in the convergence of 5G and Wi-Fi systems. We conclude that Wi-Fi technology has and will continue to have a decisive role as an access technology in the new ecosystem of wireless networks.

Specific workloads are increasingly offloaded to accelerators such as a graphic processing unit (GPU) and field-programmable gate array (FPGA) for real-time processing and computing efficiency. Because accelerators are expensive and consume much power, it is desirable to increase the efficiency of accelerator utilization by sharing accelerators among multiple servers over a network. However, task offloading over a network has the problem of latency due to network processing overhead in remote offloading. This paper proposes a low-latency system for accelerator offloading over a network. To reduce the overhead of remote offloading, we propose a system composed of (1) fast recombination processing of chunked data with a simple protocol to reduce the number of memory copies, (2) polling-based packet receiving check to reduce overhead due to interrupts in interaction with a network interface card, and (3) a run-to-completion model in network processing and accelerator offloading to reduce overhead with context switching. We show that the system can improve performance by 66.40% compared with a simple implementation using kernel protocol stack and confirmed the performance improvement with a virtual radio access network use case as a low-latency application. Furthermore, we show that this performance can also be achieved in practical usage in data center networks.

The interplay of physical layer enhancements and classic random access protocols is the objective of this paper. Successive interference cancellation (SIC) is among the major enhancements of the physical layer. Considering the classic representatives of random access protocols, Slotted ALOHA and Channel Sensing Multiple Access (CSMA), we show that two regimes can be identified as a function of the communication link spectral efficiency. In case of high levels of spectral efficiency, multi-packet reception enabled by SIC is of limited benefit. Sum-rate performance is dominated by the effectiveness of the Medium Access Control (MAC) protocol. On the contrary, for low spectral efficiency levels, sum-rate performance is essentially dependent on physical layer SIC capability, while the MAC protocol has a marginal impact. Limitations due to transmission power dynamic range are shown to induce unfairness among nodes. However, the unfairness issue fades away when the system is driven to work around the sum-rate peak achieved for low spectral efficiency. This can also be confirmed by looking at Age of Information (AoI) metric. The major finding of this work is that SIC can boost performance, while still maintaining a fair sharing of the communication channel among nodes. In this regime, the MAC protocol appears to play a marginal role, while multi-packet reception endowed by SIC is prominent to provide high sum-rate, low energy consumption, and low AoI.

We present Soteria, a data processing pipeline for detecting multi-institution attacks. Soteria uses a set of machine learning techniques to detect future attacks, predict their future targets, and rank attacks based on their predicted severity. Our evaluation with real data from Canada-wide academic institution networks shows that Soteria can predict future attacks with 95% recall rate, predict the next targets of an attack with 97% recall rate, and detect attacks in the first 20% of their life span. Soteria is deployed in production and is in use by tens of Canadian academic institutions that are part of the CANARIE IDS project.

The increasing development of cryptocurrencies has brought cryptojacking as a new security threat in which attackers steal computing resources for cryptomining. The digitization of the supply chain is a potential major target for cryptojacking due to the large number of different infrastructures involved. These different infrastructures provide information sources that can be useful to detect cryptojacking, but with a wide variety of data formats and encodings. This paper describes the semantic data aggregator (SDA), a normalization and aggregation system based on data modelling and low-latency processing of data streams that facilitates the integration of heterogeneous information sources. As a use case, the paper describes a cryptomining detection system (CDS) based on network traffic flows processed by a machine learning engine. The results show how the SDA is leveraged in this use case to obtain aggregated information that improves the performance of the CDS.

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.