Deployment Of Wireless Networks Research Articles

Exploring the Role of Wireless Networking in Uganda’s Socio-Economic Development: Deployment, Impact, and Challenges

In Uganda, wireless networking technologies are considered an indispensable destination for advancement in connectivity, digital inclusion, and socio-economic progress. Wi-Fi connectivity is the technology that uses radio waves to send data without cables, permitting devices to share files wirelessly. Resolving the linkage problem and creating a chance for a digital economy are what matter most in Uganda’s economic development. Because of the high cost of setting up and operating wireless infrastructure in rural areas, we could use cellular networks, Wi-Fi, and satellite internet in tandem to bridge the digital divide. Such an infrastructure also provides an enabling environment for innovations, start-ups, job creation, and education. This paper will examine the deployment of wireless networks in Uganda, specifically focusing on sectors like telecommunications, education, healthcare, and agriculture. It emphasized the importance of Wi-Fi and its various benefits, including bridging the digital divide, promoting digital inclusion, and fostering innovation. Nevertheless, these problems of infrastructure constraints, spectrum allocation problems, interference issues, and socioeconomic hurdles are still there and remain. The content also includes sections on power supply, safety, staffing deficiencies, and regulatory compliance. It is capable of demonstrating the current level of cellular networking as well as its future growth prospects. We will employ pertinent recently published data (2014–2024) from multiple reliable databases. Uganda’s wireless technology future is promising for growth, innovation, and social-economic development. Accelerating 5G deployment, developing rural connectivity, and investing in IoT and smart infrastructure are essential. The government should provide financial incentives, strengthen cybersecurity, raise public awareness, and collaborate on modernised curriculum and certifications. Keywords: wireless networking, Uganda, telecommunications, digital inclusion, innovation, challenges, opportunities.

BCEWN: Design of a Hybrid Bioinspired Clustering Model for Deployment of Energy-Aware Wireless Networks

BCEWN: Design of a Hybrid Bioinspired Clustering Model for Deployment of Energy-Aware Wireless Networks

Next-Generation Threat Detection and Mitigation in 6G Wireless Networks Using IAM, ZTNA and Advanced Security Mechanisms

As the deployment of 6G wireless networks looms on the horizon, the imperative to fortify their security infrastructure becomes increasingly pressing. One way to achieve this is through Identity and Access Management (IAM) frameworks to implement strong authentication and authorization mechanisms. Zero Trust Network Access (ZTNA) architectures advocate for a shift towards continuous verification and least-privileged access principles. Secure network segmentation and behavior anomaly detection systems limit the scope of potential breaches. Intrusion Detection and Prevention Systems (IDPS) detect and block cyber threats. These advanced security mechanisms improve the resilience of 6G networks and ensure the integrity, confidentiality, and availability of critical network assets. IAM protocols exhibit fast user authentication speeds with average authentication time of 0.5 seconds. ZTNA frameworks ensure high network security with a 98% detection rate of abnormal network behaviors

Next-Generation Threat Detection and Mitigation in 6G Wireless Networks Using IAM, ZTNA and Advanced Security Mechanisms

As the deployment of 6G wireless networks looms on the horizon, the imperative to fortify their security infrastructure becomes increasingly pressing. One way to achieve this is through Identity and Access Management (IAM) frameworks to implement strong authentication and authorization mechanisms. Zero Trust Network Access (ZTNA) architectures advocate for a shift towards continuous verification and least-privileged access principles. Secure network segmentation and behavior anomaly detection systems limit the scope of potential breaches. Intrusion Detection and Prevention Systems (IDPS) detect and block cyber threats. These advanced security mechanisms improve the resilience of 6G networks and ensure the integrity, confidentiality, and availability of critical network assets. IAM protocols exhibit fast user authentication speeds with average authentication time of 0.5 seconds. ZTNA frameworks ensure high network security with a 98% detection rate of abnormal network behaviors.

A Torpor-based Enhanced Security Model for CSMA/CA Protocol in Wireless Networks

Mobile wireless networks enable the connection of devices to a network with minimal or no infrastructure. This comes with the advantages of ease and cost-effectiveness, thus largely popularizing the network. Notwithstanding these merits, the open physical media, infrastructural-less attributes, and pervasive deployment of wireless networks make the channel of communication (media access) vulnerable to attacks such as traffic analysis, monitoring, and jamming. This study designed a virtual local area network (VLAN) model to circumvent virtual jamming attacks and other intrusions at the Media Access Control (MAC) layer of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. A Torpor VLAN (TVLAN) Data Frame Encapsulation and the algorithm for T-VLAN security in CSMA/CA were formulated and presented. A simulation experiment was conducted on the model using OMNeT++ software. The performance metrics used to evaluate the model were packet delivery ratio, network throughput, end-to-end channel delay, and channel load. The simulation results show that the TVLAN defence mechanism did not increase the channel load arbitrarily during TVLAN defence. similarly, the system throughput was shown to be 82% during TVLAN defence. Nevertheless, the network delay of the system during TVLAN defence was significantly high but the channel load was 297 when the TVLAN security mechanism was launched. These results demonstrate the model’s ability to provide a survivability mechanism for critical systems when under attack and add a security layer to the CSMA/CA protocol in wireless networks. Such a remarkable performance is required of a CSMA/CA infrastructure for improving the cybersecurity posture of a wireless network.

Automatic Cell Planning Method for Radio Network Optimization

As the first step in building a wireless communication network, wireless network optimization is crucial since it determines how the network will be built scientifically. Numerous challenges remain in the way of the Radio Network's deployment in Indonesia, not the least of which is the still-uneven coverage region. The Kiaracondong region in Bandung is one of the numerous areas in Indonesia that are still considered to be "bad spot areas" as a result. Based on the findings of the driving test conducted in the Kiaracondong sub-district, the KPI target was not fulfilled for the RSRP, SINR, and Throughput parameters. Therefore, this study primarily focuses on the physical tuning optimization using the Automatic Cell Planning (ACP) method for the LTE wireless network optimization. To assess the quality of the LTE network before and after optimization, the results of the ACP optimization simulation will be compared with the results of the existing or non-ACP site simulation and the results of the operator's ACP implementation. As a result, Area 1 has an average RSRP of -72.79 dBm, area 2 -73.17 dBm, and area 3 -68.22 dBm. Additionally, the average SINR in areas 1,2 and 3 is 8 dB, 6.58 dB, and 8.17 dB, respectively. The average downlink throughput in area 1 is 42652.66 Kbps, area 2 is 34420.88 Kbps, and area 3 is 43882.92 Kbps. Finally, the average throughput uplink for areas 1 to 3 is 51651.24 Kbps, 47895.99 Kbps, and 49648.84 Kbps, respectively.

A Tutorial on Cross-layer Optimization Wireless Network System Using TOPSIS Method

Wireless network system. Introduction: The availability and rising demand of ubiquitous connectivity has been one of the most revolutionary technological trends of the last ten years. Today, we anticipate being able can access these internet services at any time, from everywhere, for a variety of purposes, including checking email, using the phone, browsing the web, and a plethora of other uses, such as when travelling by car, subway, airline, or anywhere else. Although we still need to actively seek out connections today (by looking for a Wi-Fi hotspot nearby), the future will undoubtedly feature ubiquitous connectivity and Internet access. Central to this movement are wireless networks. A wireless network is any network that is not linked by cables and provides the user with the comfort and mobility they need. We may anticipate that dozens of various wireless technologies will be available to satisfy the demands, each with distinct performance requirements and each tailored for a particular task and environment, given the enormous variety of use cases and applications. Research signification: The densest wireless network deployments up until recently took place in campus-like settings, with the cell architecture being meticulously planned by professionals. The wireless environment is evolving quickly due to the increasing adoption of low-cost 802.11 equipment as well as other personal wireless devices (2.4 GHz cordless telephones, Bluetooth devices). There are two significant differences between dense installations of wireless networking hardware in locations like neighborhoods, shopping centers, and apartment buildings and campus-type deployments. First, numerous separate people or devices, each installing one or a small number of APs, result in complicated installations that are meticulously planned to enhance coverage and reduce cell overlap. The concentration of wireless sensor nodes and APs produced by this kind of haphazard, unplanned deployment is widely varied (and sometimes quite high). Methodology: Alternative: 1G, 2G, 3G, and 4G. Evaluation Preference: Multiplexing, Implementation, Services, Standards, Core Network. Results: from the result it is seen that Core Network is got the first rank where as is the Services is having the lowest rank. Conclusion: The value of the dataset for Wireless network system in TOPSIS method shows that it results in Core Network and top ranking.

Online Learning to parallel offloading in heterogeneous wireless networks

In the forthcoming era of 6G, the deployment of dense and diverse wireless networks equipped with edge servers makes parallel offloading a crucial technology for multiple access edge computing. However, the effectiveness of parallel offloading is constrained by heterogeneous edge servers (HES) and heterogeneous wireless networks (HWN). Existing solutions often assume that the link state of the wireless network and the computing resources of the edge server are either known or can be estimated as prior knowledge, which is insufficient, especially for HWN. To address this challenge, we propose a novel online learning parallel offloading (OLPO) framework aimed at jointly selecting multiple radio networks and multiple edge servers (MRMS) by learning the unknown and stochastic metrics of wireless links and edge servers. Specifically, we design and compare three OLPO algorithms using multi-armed bandits (MAB) and construct a comparison matrix with the analytic hierarchy process (AHP). Our experimental results demonstrate the effectiveness of the proposed OLPO algorithms and establish their applicability for various traffic types, considering metrics such as delay, delay jitter, packet loss, rate, and energy delay product.

Deployment of UAV-assisted 6G wireless networks using whale optimisation algorithm

Deployment of UAV-assisted 6G wireless networks using whale optimisation algorithm

Autonomous On-Demand Deployment for UAV Assisted Wireless Networks

Unmanned aerial vehicle (UAV) assisted wireless network has been recognized as an effective technology to facilitate the formation of a super flexible low-altitude platform for relieving the strain on traditional ground cellular systems. However, the on-demand deployment of the UAV-assisted wireless networks (OWN) becomes an essential yet challenging issue, as the constraints of UAVs’ location, resource provisioning, and demand distribution should be jointly considered. In this work, we investigate the OWN problem by proposing an autonomous learning framework (ALF) consisting of three sequential stages: demand prediction, proactive deployment, and resource allocation fine-tuning, which can be capable of autonomous network planning without reliance on manual operations in an extremely dynamic environment. In the demand prediction stage, we first design a dual transformer network (DTN) to capture the temporal and spatial dependencies of wireless traffic. We further reduce the computational complexity of DTN from quadratic time complexity to log-linear time complexity. In the proactive deployment stage, we jointly optimize the UAVs’ location and resource provisioning by proposing a modified general benders decomposition algorithm with a Γ-optimal convergence, where a learning-based discerning module is designed to accelerate the algorithm. In the resource allocation fine-tuning stage, we propose a simulated annealing-based algorithm to minimize the transmission rate degradation of users to reduce the bias caused by traffic demand prediction. Extensive numerical results based on an open source dataset demonstrate the effectiveness of the proposed methods in comparison with existing baselines.

Radio Resource Management Satellite Communication Network MCDM Method

Worldwide deployment of heterogeneous wireless networks is growing as a result of consumer demand for connectivity at all times and in all places. These customers' interest in multimedia apps like video streaming and VoIP, which demand tight Quality of Service (QoS) support, is growing at the same time. With such limitations, provisioning network resources is a difficult undertaking. In fact, it might be challenging for a network operator to identify trustworthy criteria to choose the optimum network that ensures user happiness while maximising network utilisation, given the availability of numerous access technologies (WiFi, WiMAX, or cellular networks). To solve this problem, each eNB just needs to learn the traffi c conditions or patterns of its owncell in our proposal. Wireless communication systems depend heavily on radio resource management (RRM). To ensure the efficient and successful operation of wireless networks, it involves the allocation and control of radio frequency spectrum, power, and other resources. RRM is significant because it can use scarce radio resources as efficiently as possible, enhancing capacity, lowering interference, and improving service quality. Successful deployment and operation of wireless communication systems like cellular networks, Wi-Fi, and Bluetooth depend on effective RRM approaches. The need for wireless communication is growing, and new technologies and standards are constantly being developed. The methodology of radio resource management (RRM) involves a variety of techniques and algorithms designed to allocate radio resources in a way that maximizes network performance while minimizing interference. Taken as alternate parameter is Laser communication, optical networks, satellite optical communication, vibrations, satellite networks. Taken as is solar radiation power, thermal bending, micro meteorite impact, solar and lunar gravity, earth oblations method. satellite optical communication has reached near 2000 data set compare other data set. The operation of wireless communication networks depends on radio resource management (RRM). Wireless networks would have interference, congestion, and a lacklustre level of service if effective RRM procedures weren't used. RRM is therefore a key component in ensuring that wireless communication systems can provide users with dependable and high-quality services.

Machine-Learning-Based UAV-Assisted Agricultural Information Security Architecture and Intrusion Detection

In recent years, Unmanned Aerial Vehicle (UAV) remote sensing has developed rapidly in the field of farmland information monitoring. Real-time and accurate access to farmland information and crop growth dynamics is a prerequisite for the implementation of precision agriculture. Machine Learning identifies existing knowledge to acquire new knowledge, promotes the development of Artificial Intelligence, and brings a large number of data training sets for Machine Learning. The present work aims to ensure the safe operation of agricultural information systems and guarantee the data security of intelligent agriculture. The Machine Learning method explores the wireless network deployment of the UAV system. The geographical location deployment of agricultural information security can effectively carry out rapid security detection of agricultural information security. Firstly, the UAV-assisted information acquisition system was studied. Besides, a Double Deep Q-network (DDQN) algorithm for location deployment based on Geography Position Information (GPI) was proposed to quickly optimize the deployment location of UAVs. GPI can avoid the complicated calculation process of channel state information. The DDQN algorithm was introduced to obtain the functional relationship between GPI and the optimal UAV deployment position, forming a new GPI-Learning strategy. In addition, the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are integrated as the CNN-LSTM algorithm to build the intrusion detection system for agricultural IoT for agricultural. In the integrated network structure of the system, LSTM is responsible for data transmission, and CNN is capable of network model building. Combined with the influence of various parameters on the performance of the UAV deployment location algorithm, the simulation experiment set the population size as 36, the discovery probability as 0.25, the step scaling factor as 0.8, and the Levy flight index as 1.25. The network throughput performance of the GPI-Learning algorithm combined with Cuckoo Search was better than other algorithms under different numbers of UAVs. On the KDD-CUP99 data set, the accuracy and detection rate of the agricultural IoT intrusion detection system based on the CNN+LSTM algorithm reached 93.5% and 94.4%, respectively. In general, the agricultural IoT intrusion detection system reported here has crucial practical reference value for the safe operation of agricultural information systems.

Examination of Wireless Body Area Network Using Mobile Sinks

In the last few years, postural mobility has been seen as a significant barrier to the successful deployment of Wireless Body Area Networks (WBAN), and several mobility models have been put out to overcome this problem. Due to the sink node's fixed location in such topologies, WBAN performance was declining. The domains of other wireless networks including MANET, VANET, and FANET have successfully used Mobile Sink as a solution. The network lifespan and other QoS metrics like average end-to-end latency, PDR, throughput and energy consumption are significantly influenced by sink mobility. The random movement of the sink node is taken into account in this study effort to cope with the heterogeneity of network nodes and their movement pattern. Static and mobile sinks (Controlled and Random movements) are used to test both mobility models using Network Simulator NS2.35, and it was shown that mobile sinks improved WBAN performance for all QoS criteria.

Radio Channel Measurements and Characterization in Substation Scenarios for Power Grid Internet of Things

Wireless communication technologies play a key role to support the implementation of power grid Internet of Things (PIoT). An in-depth knowledge of the radio channel is vital to the application of wireless communication technologies in PIoT. This paper investigates the radio channel measurements and characterization for PIoT substation scenarios. A novel channel sounder is designed for achieving omnidirectional measurements and phased array antennas based directional measurements, and a directional multipath components extraction algorithm is proposed. The channel sounding system is verified and is used to perform a series of 3.35 GHz omnidirectional and directional channel measurements in four substation scenarios, involving a 10 kV switch room, a 110 kV GIS room, a semi-indoor 110 kV substation and an outdoor 220 kV substation. Based on the measured channel impulse responses data, both large-scale and small-scale fading characteristics of substation channels are extracted and analyzed. Empirical models of path loss, shadow fading, Ricean K-factor and root-mean-square (RMS) delay spread are proposed. In addition, results of RMS angle spread of departure and RMS angle spread of arrival are presented. These results will provide useful reference for the deployment and optimization of wireless communication networks in PIoT substation scenarios.

A long distance telesurgical demonstration on robotic surgery phantoms over 5G.

Using robotic technology and communications infrastructure to remotely perform surgery has been a persistent goal in medical research in the past three decades. The recent deployment of the Fifth-Generation Wireless Networks has revitalized the research efforts in the telesurgery paradigm. Offering low latency and high bandwidth communication, they are well suited for applications that require real-time data transmission and can allow smoother communication between surgeon and patient, making it possible to remotely perform complex surgeries. In this paper, we investigate the effects of the 5G network on surgical performance during a telesurgical demonstration where the surgeon and the robot are separated by nearly 300km. The surgeon performed surgical exercises on a robotic surgery training phantom using a novel telesurgical platform. The master controllers were connected to the local site on a 5G network, teleoperating the robot remotely in a hospital. A video feed of the remote site was also streamed. The surgeon performed various tasks on the phantom such as cutting, dissection, pick-and-place and ring tower transfer. To assess the usefulness, usability and image quality of the system, the surgeon was subsequently interviewed using three structured questionnaires. All tasks were completed successfully. The low latency and high bandwidth of the network resulted into a latency of 18 ms for the motion commands while the video delay was about 350 ms. This enabled the surgeon to operate smoothly with a high-definition video from about 300km away. The surgeon viewed the system's usability in a neutral to positive way while the video image was rated as of good quality. 5G networks provide significant advancement in the field of telecommunications, offering faster speeds and lower latency than previous generations of wireless technology. They can serve as an enabling technology for telesurgery and further advance its application and adoption.

Analysis and Multiobjective Optimization of a Machine Learning Algorithm for Wireless Telecommunication

There has been a fast deployment of wireless networks in recent years, which has been accompanied by significant impacts on the environment. Among the solutions that have been proven to be effective in reducing the energy consumption of wireless networks is the use of machine learning algorithms in cell traffic management. However, despite promising results, it should be noted that the computations required by machine learning algorithms have increased at an exponential rate. Massive computing has a surprisingly large carbon footprint, which could affect its real-world deployment. Thus, additional attention needs to be paid to the design and parameterization of these algorithms applied in order to reduce the energy consumption of wireless networks. In this article, we analyze the impact of hyperparameters on the energy consumption and performance of machine learning algorithms used for cell traffic prediction. For each hyperparameter (number of layers, number of neurons per layer, optimizer algorithm, batch size, and dropout) we identified a set of feasible values. Then, for each combination of hyperparameters, we trained our model and analyzed energy consumption and the resulting performance. The results from this study reveal a great correlation between hyperparameters and energy consumption, confirming the paramount importance of selecting optimal hyperparameters. A tradeoff between the minimization of energy consumption and the maximization of machine learning performance is suggested.

Genetic Algorithms Optimized Adaptive Wireless Network Deployment

Advancements in UAVs have enabled them to act as flying access points that can be positioned to create an interconnected wireless network in complex environments. The primary aim of such networks is to provide bandwidth coverage to users on the ground in case of an emergency or natural disaster when existing network infrastructure is unavailable. However, optimal UAV placement for creating an ad hoc wireless network is an NP-hard and challenging problem because of the UAV’s communication range, unknown users’ distribution, and differing user bandwidth requirements. Many techniques have been presented in the literature for wireless mesh network deployment, but they lack either generalizability (with different users’ distributions) or real-time adaptability as per users’ requirements. This paper addresses the UAV placement and control problem, where a set of genetic-algorithm-optimized potential fields guide UAVs for creating long-lived ad hoc wireless networks that find all users in a given area of interest (AOI) and serve their bandwidth requirements. The performance of networks deployed using the proposed algorithm was compared with the current state of the art on several experimental simulation scenarios with different levels of communication among UAVs, and the results show that, on average, the proposed algorithm outperforms the state of the art by 5.62% to 121.73%.

Path Loss Modeling in Urban Water–Land Environments at 28 GHz: Considering Water Surface Reflection and Building Diffraction

This letter proposes a path loss model in urban water–land environments for line-of-sight (LOS) and non-LOS (NLOS) propagation at millimeter-wave (mm-wave) band, in which water surface reflection and building diffraction are considered simultaneously. An extensive measurement campaign in a campus pond environment at 28 GHz is conducted. Other channel characteristics, such as power delay profile and root-mean-square delay spread are also analyzed. Results indicate that the proposed model can predict the path loss in urban water–land environments accurately. This work can be applied to the deployment of wireless networks in urban water–land environments.

O-RAN architecture, interfaces, and standardization: Study and application to user intelligent admission control

Open radio access network (O-RAN), driven by O-RAN Alliance is based on the disaggregation of the traditional RAN systems into radio unit (RU), distributed unit (DU) and central unit (CU) components. It provides a unique opportunity to reduce the cost of wireless network deployment by using open-source software, serving as a foundation for O-RAN compliant functions, and by utilizing low-cost, generic white-box hardware for radio components. Relying on the two core pillars of openness and intelligence, there has been a coordinated global effort from operators and equipment providers to enhance the RAN architecture and improve its performance through virtualized network elements and open interfaces that incorporate intelligence in RAN. With the increased complexity of 5G networks and the demand to fulfill requirements, intelligence is becoming a key factor for automated deployment, operation, and optimization of open wireless networks. The first thrust of this paper surveys the AI/ML architecture in O-RAN specifications, key discussion points and future standardization directions, respectively. In the second part, we introduce a proof-of-concept use case on AI-driven network optimization within the near real-time RAN intelligent controller (near-RT RIC) and non-real time RIC (non-RT RIC). In particular, we investigate the user admission control problem, led by a deep learning-based algorithm, implemented as an xApp for network performance enhancement. Extensive system-level simulations are performed with NS-3 LTE to assess the proposed admission control algorithm. Accordingly, the proposed dynamic algorithm shows a significant admission control performance improvement and flexibility, compared to existing admission control static techniques, while satisfying the stringent quality of service targets of admitted devices. Finally, the paper offers insightful conclusions and findings on the AI-based modeling, model inference performance, key performance challenges and future research directions, respectively.

Proactive Anti-Eavesdropping With Trap Deployment in Wireless Networks

Due to the open nature of the wireless medium, wireless communications are especially vulnerable to eavesdropping attacks. This article designs a new wireless communication system to deal with eavesdropping attacks. The proposed system can enable a legitimate receiver to get desired messages and meanwhile an eavesdropper to hear “fake” but meaningful messages by combining confidentiality and deception, thereby confusing the eavesdropper and achieving additional concealment that further protects exchanged messages. Towards this goal, we propose techniques that can conceal exchanged messages by utilizing wireless channel characteristics between the transmitter and the receiver, as well as techniques that can attract an eavesdropper to gradually approach a trap region, where the eavesdropper can get fake messages. We also provide both theoretical and empirical analysis of the established secure channel between the transmitter and the receiver. We develop a prototype system using Universal Software Defined Radio Peripherals (USRPs). Experimental results show that an eavesdropper at a trap location can receive fake information with a bit error rate (BER) close to 0, and the transmitter with multiple antennas can successfully deploy a trap area.