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The advent of 5G and interactive live broadcasting has led to a growing trend of people preferring real-time interactive video services on mobile devices, particularly mobile phones. In this work, we measure the performance of Google congestion control (GCC) in cellular networks, which is the default congestion control algorithm for Web Real-Time Communication (WebRTC). Our measurements show that GCC sometimes makes bitrate decisions which are harmful to quality of experience (QoE) in cellular networks with high jitter. We further find that the frame delivery time (FDT) in the player can mitigate network jitter and maintain QoE. Moreover, the receiving rate is better to reflect the network congestion than RTT in cellular networks. Based on these measurements and findings, we propose Mustang, an algorithm designed to overcome the jitter in cellular networks. Mustang makes use of the FDT and receiving rate as feedback information to the sender. Then the sender adjusts its sending rate based on the information to guarantee QoE. We have implemented Mustang in WebRTC and evaluated it in both emulated and real cellular networks. The experimental results show that Mustang can improve WebRTC's QoS and QoE performance. For QoS, Mustang increases the sending rate by 72.1% and has similar RTT and packet loss when compared with GCC, while it is about 30% better for QoE. [ABSTRACT FROM AUTHOR]

We address the strategic deployment of base stations (BSs) or access points (APs) within a smart building to form ultra-dense, small-cell-based 5G or beyond 5G networks. We consider the coverage of an arbitrarily shaped service area (convex or non-convex) in an indoor environment by placing BSs/APs with varying transmit powers at appropriate locations such that every point in the indoor region under consideration will be covered by at least one BS. Towards this goal, we propose a Voronoi diagram-based BS placement framework that can completely cover a given convex/non-convex region without generating any coverage hole. Our placement technique considers the presence of different obstacles, partition walls, etc., of varying attenuation factors inside a building affecting the LOS propagation of signals in the mmWave range. Our proposed strategy simultaneously minimizes the total wastage of transmission powers of all BSs by minimizing the total area of overlapped regions and out-of-region coverages. The unique feature of our approach is that it is applicable for fully covering any convex or non-convex region in contrast to the existing coverage techniques in the literature, which are applicable only for convex regions. We demonstrate that our proposed algorithm outperforms existing techniques for covering convex geometries in terms of the total transmission power requirement of all BSs/APs. Our proposed framework for the placement of access points will also be applicable for upcoming Wi-Fi standards such as the 802.11ad and the 802.11ay standards that can operate in the 60 GHz band. [ABSTRACT FROM AUTHOR]

The millimeter-Wave (mmWave) communication with the advantages of abundant bandwidth and immunity to interference has been deemed a promising technology to greatly improve network capacity. However, due to such characteristics of mmWave, as short transmission distance, high sensitivity to the blockage, and large propagation path loss, handover issues (including trigger condition and target beam selection) become much complicated. In this paper, we design a novel handover scheme to optimize the overall system throughput as well as the total system delay while guaranteeing the Quality of Service (QoS) of each User Equipment (UE). Specifically, the proposed handover scheme called O-MAPPO integrates the Reinforcement Learning (RL) algorithm and optimization theory. The RL algorithm known as Multi-Agent Proximal Policy Optimization (MAPPO) plays a role in determining handover trigger conditions. Further, we propose an optimization problem in conjunction with MAPPO to select the target base station. The aim is to evaluate and optimize the system performance of total throughput and delay while guaranteeing the QoS of each UE after the handover decision is made. The numerical results show the overall system throughput and delay with our method are slightly worse than that with the exhaustive search method but much better than that using another typical RL algorithm Deep Deterministic Policy Gradient (DDPG).

Device-to-device (D2D) communication is a short-range communication technology that can effectively improve the spectral efficiency of cellular networks. A two-stage subchannel and power joint allocation scheme was proposed to address the complex scenario of "many-to-many" communication in cellular networks (one subchannel could be assigned to multiple pairs of D2D user equipment (DUE), and one pair of DUE could also use multiple subchannels at the same time), considering the full frequency domain resource reuse in both uplink and downlink subchannels. In the first stage, a weighted bipartite graph matching-based resource allocation (WBGM-RA) algorithm was introduced. This algorithm allocated all subchannels to all cellular user equipment (CUE) to maximize CUE sum rate. In the second stage, an interference clustering-based resource allocation (IC-RA) algorithm was proposed, and the interference matrix was constructed according to the interference relations among UE sharing the same subchannel. Resources allocated to CUE were reallocated to DUE. Moreover, the transmit power of DUE was optimized to maximize the system sum rate while ensured that DUE did not cause serious interference to CUE. This study established a novel joint resource allocation for uplink and downlink subchannels, coupled with a mechanism for "many-to-many" channel reuse. This led to a substantial increase in spectrum access opportunities for DUE and overall spectrum efficiency in the network. Simulation results show that compared with the existing typical algorithm, this algorithm can effectively improve the system sum rate, increase the number of communication links in the system and increase the DUE access rate.

The Internet of things (IoT) is the third evolution of the traditional Internet, enabling interaction and communication among machines. IoT platforms and networks have been developed, and market sectors have recently started developing specific IoT applications and services. Integrating heterogeneous IoT networks with the existing ones, mainly cellular networks, is in great demand. IoT represents one of the prominent use cases of the sixth-generation (6G) cellular system, as announced by the Third Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU). This study focuses on Heterogeneous IoT networks over 6G networks with ultra-dense deployment using Mobile edge computing and software-defined wide-area network (MEC/SDWAN). This software-defined wide-area network is used for designing systematic Wireless Inventory Trackers (WIT). Wireless inventory trackers identify the exact location. However, the heterogeneous network consists of interconnected nodes and links of different types with a software-defined Wide Area Network(SD-WAN). To address the heterogeneous network system capacity issue, the HIoT and Ultra-Dense deployment are employed for making the promising technologies in the 6G to manage them, enhancing the spatial reuse through flexible, intensive deployment. This procedure helps to improve the prolonged lifetime of up to 2.5 h with low energy consumption of up to (3%) on efficient communications in the 6G cellular network. [ABSTRACT FROM AUTHOR]

With the growing adoption of battery energy storage systems in renewable energy sources, electric vehicles (EVs), and portable electronic devices, the effective management of battery systems has become increasingly critical. The advent of wireless battery management systems (wBMSs) represents a significant innovation in battery management technology. Traditional wired battery management systems (BMSs) face challenges, including complexity, increased weight, maintenance difficulties, and a higher chance of connection failure. In contrast, wBMSs offer a robust solution, eliminating physical connections. wBMSs offer enhanced flexibility, reduced packaging complexity, and improved reliability. Given that wBMSs are still in a preliminary stage, this review paper explores their evolution, current state, and future directions. A comprehensive survey of state-of-the-art wBMS technologies, including academic and commercial solutions, is elaborated in this paper. We compare wireless communication technologies like Bluetooth Low Energy (BLE), Zigbee, Near-Field Communication (NFC), Wi-Fi, and cellular networks in the context of wBMSs. We discuss their performance in terms of efficiency, reliability, scalability, and security. Despite its promising outlook, wBMSs still face challenges such as data security, signal interference, regulatory and standardization issues, and competition from the continued advancement of wired BMS technologies, making the advantages of wBMSs less evident. This paper concludes with guidelines for future research and development of wBMSs, aiming to address these challenges and pave the way for a broad adoption of wBMSs across various applications. This paper aims to inspire further research and innovation in the field, contributing to developing an industry-ready wBMS. [ABSTRACT FROM AUTHOR]

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Wireless sensor networks (WSNs) are gaining traction in the realm of network communication, renowned for their adaptability, configuration, and flexibility. The forthcoming network traffic within WSNs can be forecasted through temporal sequence models. In this correspondence, we present a method (TSENet) that can accurately predict the traffic in the cellular network. TSENet is composed of transformers and self-attention network. We have designed a temporal transformer module specifically for extracting temporal features. This module accomplishes this by modeling the traffic flow within each grid of the communication network at both near-term and periodical intervals. Simultaneously, we amalgamate the spatial features of each grid with information from its correlated grids, generating spatial predictions within the spatial transformer. Furthermore, we employ self-attention aggregation to capture dependencies between external factor features and cellular data features. Empirical assessments performed on a genuine cellular traffic dataset offer compelling evidence substantiating the efficacy of TSENet. [ABSTRACT FROM AUTHOR]

Blockchain has shown significant potential as a key enabler in privacy and security in the forthcoming 6G wireless network, due to its distributed and decentralized characteristics. Practical Byzantine fault tolerance (PBFT) emerges as a prominent technology for deployment in wireless networks due to its attributes of low latency, high throughput, and minimal computational requirements. However, the high complexity of communication is the bottleneck of PBFT for achieving high scalability. To tackle this problem, this paper proposes a novel framework of PBFT, where the inter-node communication during the normal case operation is completed through base stations. The uplink and downlink communication between the base station and nodes are modelled based on the signal-to-interference-plus-noise ratio (SINR) threshold. A novel ‘timeout’ mechanism is incorporated to reduce the communication complexity. The performance is evaluated by metrics including consensus success probability, communication complexity, view change delay, view change occurrence probability, consensus delay, consensus throughput and energy consumption. The numerical results show that the proposed scheme achieves higher consensus success probability and throughput, lower communication complexity and consensus delay compared to the conventional PBFT. The results of view change delay and view change occurrence probability and the optimal configuration provide analytical guidance for the deployment of wireless PBFT networks.

The importance of an efficient network resource allocation strategy has grown significantly with the rapid advancement of cellular network technology and the widespread use of mobile devices. Efficient resource allocation is crucial for enhancing user services and optimizing network performance. The primary objective is to optimize the power distribution method to maximize the total aggregate rate for all customers within the network. In recent years, graph-based deep learning approaches have shown great promise in addressing the challenge of network resource allocation. Graph neural networks (GNNs) have particularly excelled in handling graph-structured data, benefiting from the inherent topological characteristics of mobile networks. However, many of these methodologies tend to focus predominantly on node characteristics during the learning phase, occasionally overlooking or oversimplifying the importance of edge attributes, which are equally vital as nodes in network modeling. To tackle this limitation, we introduce a novel framework known as the Heterogeneous Edge Feature Enhanced Graph Attention Network (HEGAT). This framework establishes a direct connection between the evolving network topology and the optimal power distribution strategy throughout the learning process. Our proposed HEGAT approach exhibits improved performance and demonstrates significant generalization capabilities, as evidenced by extensive simulation results.

In this study, we explore a downlink cellular network where each base station (BS) engages in simultaneous communication with multiple users through spatial division multiple access (SDMA). The positions of both BSs and users are established through independent random point processes, effectively representing the cellular network. SDMA utilizes block diagonalization (BD) at each BS, employing multiple receive antennas for each user. To implement BD, users quantize and provide feedback on their downlink channels to their respective BSs. The net spectral efficiency, measuring the effective rate accounting for both downlink and uplink resource usage, serves as a performance metric. In prior research, the optimal feedback rate in terms of maximizing net spectral efficiency has been approximated in this scenario. The corresponding approximations effectively illustrate the asymptotic behavior of the optimal number as a function of the length of the coherent channel block. However, the accuracy of the approximation diminishes when the length of the coherent channel block is relatively small. Given that the length of the coherent channel block can assume relatively small values depending on wireless environments, achieving a precise estimate across the entire range of the coherent block length holds significant importance. Consequently, this paper focuses primarily on enhancing the accuracy of the approximation for the optimal feedback rate. In order to achieve a more precise estimation, we analyze the derivative of the net spectral efficiency, which encompasses two functions that demonstrate distinct growth rates. In contrast to prior studies, both functions are rigorously approximated through mathematical analysis. As a result, the proposed approximation significantly improves the accuracy compared to previous studies, particularly when dealing with short coherent channel block lengths. Moreover, this approximation generally achieves near-optimal performance, regardless of system parameters. [ABSTRACT FROM AUTHOR]

Caller ID spoofing is a global industry problem and often acts as a critical enabler for telephone fraud. To address this problem, the Federal Communications Commission has mandated telecom providers in the U.S. to implement STIR/SHAKEN, an industry-driven solution based on digital signatures. STIR/SHAKEN relies on a public key infrastructure (PKI) to manage digital certificates, but scaling up this PKI for the global telecom industry is extremely difficult, if not impossible. Furthermore, it only works with IP-based systems (e.g., SIP), leaving the traditional non-IP systems (e.g., SS7) unprotected. So far the alternatives to the STIR/SHAKEN have not been sufficiently studied. In this article, we propose a PKI-free solution, called Caller ID Verification (CIV). CIV authenticates the caller ID based on a challenge-response process instead of digital signatures, hence requiring no PKI. It supports both IP and non-IP systems. Perhaps counter-intuitively, we show that number spoofing can be leveraged, in conjunction with Dual-tone Multi-frequency, to efficiently implement the challenge-response process, i.e., using spoofing to fight against spoofing. We implement CIV for Voice over Internet Protocol, cellular, and landline phones across heterogeneous networks (SS7/SIP) by only updating the software on the user's phone. This is the first caller ID authentication solution with working prototypes for all three types of telephone systems in the current telecom architecture. Finally, we show how the implementation of CIV can be optimized by integrating it into telecom clouds as a service, which users may subscribe to. [ABSTRACT FROM AUTHOR]

Recently, the Non-Standalone Architecture option 3 standard is proposed to facilitate the deployment of 5th Generation (5G) New Radio (NR) cellular systems. According to this standard, 5G NR base stations can utilize the 4th Generation (4G) Long-Term Evolution (LTE) core network to provide network services. Additionally, the network can be configured to support LTE-NR dual connectivity enhancement, which means that a User Equipment (UE) can connect to a 4G LTE and a 5G NR base station at the same time. In such a dual connectivity network, the 4G LTE base station dispatches downlink traffic flows between the 4G LTE and 5G NR base stations for UEs. As investigated in previous works, traffic flow controls between 4G LTE and 5G NR base stations will affect network performance. In this work, we propose a downlink flow control algorithm based on the concept of Genetic Algorithm, with the objectives of maximizing throughput and fairness of throughput. We verify our designs through simulation programs and experiments on real platforms. The results indicate that the proposed solution effectively achieves the designed objectives. [ABSTRACT FROM AUTHOR]

The present study aims to design a low-cost smart AI-powered node to serve as a flood Early Warning System with a complete solution. The node is designed to predict forthcoming flood events by capturing and combining critical data related to such phenomena. Such data are the water levels at rivers or other water discharge basins, rainfall, soil moisture, and material displacement at river slopes. The node will autonomously monitor the above quantities at a high frequency rate and selectively upload them to a server only when verified conditions for a forthcoming flood will exist. These conditions will be evaluated by the local ML model. This will allow each node to reliably predict flood events and issue local and remote alarms. The combination of several nodes at an area of interest will form a robust and reliable Early Warning System. [ABSTRACT FROM AUTHOR]

Cooperative intelligent transport systems (C-ITS) continue to be developed to enhance transportation safety and sustainability. However, the communication of vehicle-to-everything (V2X) systems is inherently open, leading to vulnerabilities that attackers can exploit. This represents a threat to all road users, as security failures can lead to privacy violations or even fatalities. Moreover, a high fatality rate is correlated with soft-mobility road users. Therefore, when developing C-ITS systems, it is important to broaden the focus beyond connected vehicles to include soft-mobility users and legacy vehicles. This work presents a new approach developed in the context of emerging hybrid networks, combining intelligent transport systems operating in 5.9 GHz (ITS-G5) and radio-mobile cellular technologies. Two protocols were implemented and evaluated to introduce security guarantees (such as privacy and integrity) in communications within the developed C-ITS hybrid environment. As a result, this work securely integrates G5-connected ITS stations and soft-mobility users through a smartphone application via cellular networks. Commercial equipment was used for this goal, including on-board and roadside units. Computational, transmission and end-to-end latency were used to assess the system's performance. Implemented protocols introduce an additional 11% end-to-end latency in hybrid communications. Moreover, workflows employing hybrid communications impose, on average, an extra 28.29 ms of end-to-end latency. The proposal shows promise, as it reaches end-to-end times below the latency requirements imposed in most C-ITS use cases. [ABSTRACT FROM AUTHOR]

Now days MIMO–OFDM is highly preferred in mobile communication, includes the 5th generation (long term evaluation). The objectives of current research include proposing and designing a high quality 5G cellular network using minimum resources and handover mechanism. The handover mechanism is implemented for the minimization of the time in the performance of the cellular network. The performance of the proposed simulation model for the 5G cellular network is extracted based resource allocation and handover mechanism. This implementation is reducing the handover preparation time and execution time. For the three selected schemes of transmission as − 5 dB, 0 dB, and 5 dB the experiment acts as an active, passive, and clustered. For the 0 dB the transmission acts as an active transmission, for the − 5 dB it acts as a passive, and for the 5db it acts as a clustered mode for the transmission. The present study clearly demonstrated that the 5G network can be communicated within minimum resources, time, space, and usability of the white spectrum with maximum speed. Due to the increasing complexity of network topology in 5G Heterogeneous with the integration of many different base station types, in the 5G architecture mobility management has many challenges. The intense deployment of small cells, along with many advantages it provides, brings important mobility management problems such as frequent Handover (HO), HO failure, HO delays, ping pong HO and high energy consumption which will result in lower user experience and heavy signal loads. The simulation framework was efficiently designed towards the minimum resources and maximum speed objective. The proposed model allowed to reduce the time required for handover mechanism time and its execution time. [ABSTRACT FROM AUTHOR]

In this paper, a probe for measuring radio-frequency electric fields in the environment is designed and presented. These electric fields consist of multi cellular technology (2G, 3G and 4G), including four bands: GSM900, GSM1800, 3G2100 and LTE2600. This device, called the MCT electric probe, is realized by three orthogonal antennas, in connection to frequency multiplexer circuits and detectors. The proposed antenna is a 3-D multi-branch monopole antenna, and these orthogonal antennas can receive the electric fields in all directions uniformly and isotopically. The proposed multiplexer can separate the received signals into four narrowband and has the ability to remove out-of-band signals. The detector is able to convert the fields received from the antenna and multiplexer sections to suitable DC voltages for amplifying and digital processing. Finally, the designed MCT electric probe is fabricated and tested. The measurements confirm the proper operation of the probe in terms of dynamic range, accuracy, sensitivity, and the linearity and isotropicity of the received electric fields.

With the growing adoption of battery energy storage systems in renewable energy sources, electric vehicles (EVs), and portable electronic devices, the effective management of battery systems has become increasingly critical. The advent of wireless battery management systems (wBMSs) represents a significant innovation in battery management technology. Traditional wired battery management systems (BMSs) face challenges, including complexity, increased weight, maintenance difficulties, and a higher chance of connection failure. In contrast, wBMSs offer a robust solution, eliminating physical connections. wBMSs offer enhanced flexibility, reduced packaging complexity, and improved reliability. Given that wBMSs are still in a preliminary stage, this review paper explores their evolution, current state, and future directions. A comprehensive survey of state-of-the-art wBMS technologies, including academic and commercial solutions, is elaborated in this paper. We compare wireless communication technologies like Bluetooth Low Energy (BLE), Zigbee, Near-Field Communication (NFC), Wi-Fi, and cellular networks in the context of wBMSs. We discuss their performance in terms of efficiency, reliability, scalability, and security. Despite its promising outlook, wBMSs still face challenges such as data security, signal interference, regulatory and standardization issues, and competition from the continued advancement of wired BMS technologies, making the advantages of wBMSs less evident. This paper concludes with guidelines for future research and development of wBMSs, aiming to address these challenges and pave the way for a broad adoption of wBMSs across various applications. This paper aims to inspire further research and innovation in the field, contributing to developing an industry-ready wBMS.

The proposed work offers a concise review of the application of machine learning (ML) to cellular network handovers (HO) via the Systematic Mapping Study (SMS) methodology, emphasizing the problem areas and requirements. The key points include the paramount role of high-quality data, with meticulous data acquisition and preprocessing as vital steps in ML dataset construction. The article identifies prevalent parameters for HO enhancement and underscores the diversity of ML algorithms, aligning them with specific data input and tasks. This study establishes a robust basis for forthcoming research in applying machine learning to cellular network HOs. [ABSTRACT FROM AUTHOR]

Cellular networks have been widely deployed and are under ever-growing pressure from increasing energy consumption. Identifying the spatio-temporal properties of network traffic is crucial to effectively manage large-scale cellular networks and to optimize energy control strategies. However, traditional methods need to construct complex mathematical models to describe the properties of traffic, making the process inefficient and not automatic. Recent methods based on deep learning techniques are unexplainable and untraceable. In this paper, we propose a novel modeling and analysis approach by applying the spatio-temporal model checking technique to the identification of network traffic properties. First, we model the spatial structure of the cellular network by a closure space and the temporal structure of network traffic by the Kripke structure. Second, we provide logical characterizations of the spatio-temporal properties of network traffic by suitable spatio-temporal logic of closure space (STLCS) formulas. Third, we use model checking algorithms to detect the spatio-temporal properties of network traffic and to visualize the results. The experiments are illustrated with the Milan network traffic dataset and indicate that our approach can automatically and effectively detect desirable spatio-temporal properties of cellular network traffic. [ABSTRACT FROM AUTHOR]

In this paper, a probe for measuring radio-frequency electric fields in the environment is designed and presented. These electric fields consist of multi cellular technology (2G, 3G and 4G), including four bands: GSM900, GSM1800, 3G2100 and LTE2600. This device, called the MCT electric probe, is realized by three orthogonal antennas, in connection to frequency multiplexer circuits and detectors. The proposed antenna is a 3-D multi-branch monopole antenna, and these orthogonal antennas can receive the electric fields in all directions uniformly and isotopically. The proposed multiplexer can separate the received signals into four narrowband and has the ability to remove out-of-band signals. The detector is able to convert the fields received from the antenna and multiplexer sections to suitable DC voltages for amplifying and digital processing. Finally, the designed MCT electric probe is fabricated and tested. The measurements confirm the proper operation of the probe in terms of dynamic range, accuracy, sensitivity, and the linearity and isotropicity of the received electric fields. [ABSTRACT FROM AUTHOR]

Particularly, for real time monitoring-IOT has just evolved to support massive capacity Cellular LTE networks. The fundamental issue is the constrained bandwidth that NB-IOT channels allow. Therefore, goal of paper is to enhance the capacity of the NB-IOT physical layer shared channels for uplink and down links. The primary objective of this article is to improve the efficiency of an LTE-OFDM framework for NB-IOT-WAN via BER and BLER assessment. The proposed approach improves performance by selecting suitable cyclic prefix sampling with FFT size. It is planned to adopt M-QAM for increased data transmission capacity and reducing OFDM BER. In order to enhance the capacity approach of diversity reception is considered along with the modified modulation parameters. Increased FFT size is offered for OFDM capacity enhancement for narrowband physical downlink shared channel (NPDSCH) and narrowband physical uplink shared channel (NPUSCH) transceivers. The block error rate (BLER) is used as the performance evaluation parameters for the LTE-OFDM based NB-IOT system. The impact of the higher block transmission length is assessed for the improvement in the BLER rates. It is proposed to use M-QAM for NPUSCH which may offer better capacity of data transmission. The performance is compared with the standard QPSK modulation. For the uplink design under AWGN and frequency selective channels are considered for evaluation. The numbers of receiver antenna are increased for offering the better diversity reception to further enhance the channel capacity. The optimal design parameter selection might improve the performance of the NB-IOT cellular networks. The BLER is evaluated for different case of uplink and downlink designs against the wide range of the SNR. The entire document takes into account all factors in order to get the greatest system efficiency, and it offers capacity enhancement. [ABSTRACT FROM AUTHOR]

To analyze the key performance indicators of cellular network communication and improve the reliability of communication systems, the user terminals uniformly distributed in cellular networks were analyzed. The random geometric modeling of user terminals in the downlink of ultra dense networks in 5G communication systems was conducted, and the situation of user equipment interfered by a base station outside the cell was analyzed. The probability density function of the user equipment's signal-to-interference ratio was given, and the interruption probability based on the signal-to-interference ratio was calculated. The analysis results were verified by Monte Carlo simulation. The results show that the proposed interruption probability analysis method can be applied to the downlink communication scenario of heterogeneous cellular networks. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are gaining traction in the realm of network communication, renowned for their adaptability, configuration, and flexibility. The forthcoming network traffic within WSNs can be forecasted through temporal sequence models. In this correspondence, we present a method (TSENet) that can accurately predict the traffic in the cellular network. TSENet is composed of transformers and self-attention network. We have designed a temporal transformer module specifically for extracting temporal features. This module accomplishes this by modeling the traffic flow within each grid of the communication network at both near-term and periodical intervals. Simultaneously, we amalgamate the spatial features of each grid with information from its correlated grids, generating spatial predictions within the spatial transformer. Furthermore, we employ self-attention aggregation to capture dependencies between external factor features and cellular data features. Empirical assessments performed on a genuine cellular traffic dataset offer compelling evidence substantiating the efficacy of TSENet.

UAV-assisted base stations (UAV-BSs) to provide wireless access for terrestrial users is an alternative solution in emergency communication and disaster scenarios. Multi-UAV-BS networks have greater coverage and flexible networking capability, but face problems such as frequency resource conflicts, inter-base station relay links and deployment costs. It is an open topic for discussion whether multi-UAV-BS networks can adopt the cellular architecture applied in terrestrial base station networks. Due to the mobile deployment of UAV-BSs, static interference cancellation or coordination methods are not practical in dynamic scenarios, resulting in further deterioration of signal quality for the edge users. In this paper, a new dynamic cellular network framework is developed for UAV-BS networks with frequency reuse. First, the coverage radius of UAV-BS cells is maximized by solving the convex optimization problem. Second, a non-convex optimization model is proposed to minimize the number of UAV-BSs required, and quickly solved based on the movable location candidate set. The new solution can form a connected backbone network among UAV-BSs and adapt to the dynamic changes of ground users. Simulations are conducted to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The indispensable need to realize smart cities, the cellular network, and particularly 5G, as the common denominator infrastructure for the coexistence of many other wireless technologies, is the apparent enabler infrastructure. One of the significant challenges in cellular networks is continual connectivity. In the wake of ever-increasing time-dependent demand and high mobility, this challenge cannot be adequately addressed by either conventional solutions such as cell splitting, roaming, and base stations number increase or far-from-practical yet unregulated wireless cognitive networks. The proposed roaming service concept, roaming-on-the-fly, complements the classic roaming in many otherwise non-served situations by providing more efficient spectrum utilization management through obviating high-level telecommunications agreements, employing a shared-economy model, and adoption of Ad-Hoc payment methods. The simulation results clearly show elevated visited operator income and uncompromised primary user satisfaction while establishing traditionally-impossible visiting-user services. The spectrum management scheme and the business model in this proposal are as simplistic as possible to make the concept, not the implementation details, the center of focus. More improvements are in perspective by introducing a more delicate spectrum. [ABSTRACT FROM AUTHOR]

In recent years, cellular communication systems have continued to develop in the direction of intelligence. The demand for cellular networks is increasing as they meet the public's pursuit of a better life. Accurate prediction of cellular network traffic can help operators avoid wasting resources and improve management efficiency. Traditional prediction methods can no longer perfectly cope with the highly complex spatiotemporal relationships of the current cellular networks, and prediction methods based on deep learning are constantly growing. In this paper, a spatial-temporal parallel prediction model based on graph convolution combined with long and short-term memory networks (STP-GLN) is proposed to effectively capture spatial-temporal characteristics and to obtain accurate prediction results. STP-GLN is mainly composed of a spatial module and temporal module. Among them, the spatial module designs dynamic graph data based on the principle of spatial distance and spatial correlation. It uses a graph convolutional neural network to learn the spatial characteristics of cellular network graph data. The temporal module uses three time series based on the principle of temporal proximity and temporal periodicity. It uses three long and short-term memory networks to learn the temporal characteristics of three time series of cellular network data. Finally, the results learned from the two modules are fused with different weights to obtain the final prediction results. The mean absolute error (MAE), root mean square error (RMSE), and R-squared (R2) are used as the performance evaluation metrics of the model in this paper. The experimental results show that STP-GLN can more effectively capture the spatiotemporal characteristics of cellular network data; compared with the most advanced model in the comparison model on the real cellular traffic dataset in one cell, the RMSE can be improved about 81.7%, the MAE is improved about 82.7%, and the R2 is improved about 2.2%. [ABSTRACT FROM AUTHOR]

The One-Tap Authentication (OTAuth) based on the cellular network is a password-less login service provided by Mobile Network Operator (MNO) through the unique communication gateway access technique. The service allows app users to quickly sign up or log in with their mobile phone numbers without entering a password. Due to its convenience, OTAuth has been widely used by various apps. However, some studies have elaborated that OTAuth services are of great drawbacks from the perspective of mobile security and identified several flawed designs, which make the MNO cannot distinguish malicious apps from normal ones and cause impersonation attacks. In this paper, we further analyze OTAuth services from the perspective of 4G and 5G cellular networks and focus on two important procedures in which the cellular network plays an important role in OTAuth services. Not surprisingly, we discover a new fundamental design flaw in determining whether the runtime environment supports OTAuth services. Moreover, we propose a mature attack paradigm by exploiting this flaw, which allows an attacker to login or register one app as a victim. To evaluate the impact of the attack, we have examined 100/90/100 Android/iOS/HarmonyOS apps for OTAuth services of 3 mainstream MNOs in China. The experimental results show that our proposed attack is applicable to almost all the apps that support OTAuth services, and affects more apps than the attacks that have been reported before. Finally, we propose several countermeasures to defend against the attack. Note that, for security’s sake, we have already reported our findings to authorized parties and received their confirmations. [ABSTRACT FROM AUTHOR]

Many 5G solutions have been proposed, with the goal of either increasing the efficiency of existing resources or providing new radio resources or infrastructure. Device-to-Device (D2D) communications are regarded as a promising technique that allows mobile devices to communicate directly with one another without the use of access points or BSs. The implementation of D2D communications in a multi-cellular network environment presents a number of technical challenges that must be addressed. This work focused on interference management, which stands out as a critical and complex issue that must be addressed. In this work, an improved resource allocation algorithm for interference mitigation with improved QoS for cellular and Device-to-Device (D2D) communication was developed. Aheuristic allocation scheme was employed. The system was such that the CUEs were uniformly distributed, and the transmitter (DUE-Tx) and the receiver (DUE-Rx) of each D2D pair were also uniformly distributed in a cluster. Two important metrics namely access rate and D2D throughput gain were used to evaluate the performance and efficiency of the proposed resource allocation scheme. To validate the performance of the developed algorithm, the impact on the D2D throughput gain for different SINR requirement was compared to the result obtained by another research work. Simulations result showed that as the SINR requirement increased, the access rate and D2D throughput gain of the system was reduced. Although the two methods leveraged the greedy heuristic algorithm, the method used in this work increased the achievable throughput by introducing an additional threshold for minimum SINR requirement, such that the throughput was increased as the access rate increased. Comparisons showed that the developed method had a 5.3% improvement over the method by Celik et al (2017). Also, when the maximum distance between the DUE-Tx and DUE-Rx was 100m, the developed method showed about 60.9% improvement. [ABSTRACT FROM AUTHOR]

High-precision sensing is one of the basic capabilities of 6G mobile communication system to fulfill the demands of many application scenarios in the future, and the integrated design of sensing and communication (ISAC) is an important direction of 6G research.The works on ISAC mainly focus on improving the sensing performance.However, besides high sensing accuracy, 6G ISAC network still has a high communication rate requirement.Therefore, joint analysis and design of communication and sensing is necessary.First, three classic sensing algorithms were introduced that could achieve multi-target ranging and speed measurement, and the algorithms were analyzed from three aspects: sensing accuracy, communication performance and computational complexity.It is shown that the optimal sensing accuracy, sensing capacity and communication rate could not be achieved at the same time by using either one algorithm.Second, combined with the characteristics of different sensing algorithms, an adaptive sensing algorithm was proposed that the receiver selected the appropriate sensing algorithm according to the measured receive signal-to-interference-plus-noise ratio to realize the joint optimization of sensing and communication performance.Finally, the link-level simulation was carried out to verify that the proposed algorithm can obtain better sensing accuracy and communication capacity than any single algorithm.

Since the emergence of device-to-device (D2D) communications, an efficient resource allocation (RA) scheme with low-complexity suited for high variability of network environments has been continuously demanded. As a solution, we propose a RA scheme based on deep reinforcement learning (DRL) for D2D communications exploiting cluster-wise non-orthogonal multiple access (NOMA) protocol underlay cellular networks. The goal of RA is allocating transmit power and channel spectrum to D2D links to maximize a benefit. We analyze and formulate the outage of NOMA-enabled D2D links and investigate performance measures. To alleviate system overhead and computational complexity with maintaining high benefit, we propose a sub-optimal RA scheme under a centralized multi-agent DRL framework. Each agent corresponding to each D2D cluster trains its own artificial neural networks in a cyclic manner with a timing-offset. The proposed DRL-based RA scheme enables prompt allocation of resources to D2D links based on the observation of time-varying environments. The proposed RA scheme outperforms other schemes in terms of benefit, energy efficiency, fairness and coordination of D2D users, where the performance gain becomes significant when the mutual interference among user equipments is severe. In a cell of radius 100-meter with target rates for D2D and cellular links of 2 and 8 bits/s/Hz, respectively, the proposed RA scheme improves normalized benefit, energy efficiency, fairness and coordination of D2D users by 18%, 23%, 75% and 80%, respectively, over a greedy scheme. The improvements in these performance measures over a random RA scheme are 152%, 164%, 87% and 77%, respectively.

In this work, we talk about the problem of joint power allocation and user association based on quality-of-service for non-orthogonal multiple access (NOMA) to downlink networks. The problem is especially difficult due to its non-convex form and the large number of optimization variables, which are solved using two different nature-inspired algorithms with low complexity. We investigate the effect of different network parameters on increasing users. Numerical results show that, for a growing number of users, the problem is becoming increasingly difficult, which indicates the increasing network resources required to solve it. The results of the simulations show that using evolutionary algorithms is a fast and effective way to solve this kind of problem. Moreover, the NOMA advantage over OMA becomes clear as the number of users increases. Evolutionary techniques outperform randomly generated solutions, as expected.