Base Station Research Articles

An efficient parallel self-attention transformer for CSI feedback

In massive multi-input multi-output (MIMO) systems, it is necessary for user equipment (UE) to transmit downlink channel state information (CSI) back to the base station (BS). As the number of antennas increases, the feedback overhead of CSI consumes a significant amount of uplink bandwidth resources. To minimize the bandwidth overhead, we propose an efficient parallel attention transformer, called EPAformer, a lightweight network that utilizes the transformer architecture and efficient parallel self-attention (EPSA) for CSI feedback tasks. The EPSA expands the attention area of each token within the transformer block effectively by dividing multiple heads into parallel groups and conducting self-attention in horizontal and vertical stripes. The proposed EPSA achieves better feature compression and reconstruction. The simulation results display that the EPAformer surpasses previous deep learning-based approaches in terms of reconstruction performance and complexity.

Cooperative edge-caching based transmission with minimum effective delay in heterogeneous cellular networks

In heterogeneous cellular networks (HCNs), neighboring users often request similar contents asynchronously. Based on the content popularity, base stations (BSs) can download and cache contents when the network is idle, and transmit them locally when the network is busy, which can effectively reduce the backhaul burden and the transmission delay. We consider a two-tier HCN, where macro base stations (MBSs) and small base stations (SBSs) can cooperatively and probabilistically cache contents. Each user is associated to the BS with the maximum average received signal power in any tier. With the cooperative content transfer between MBS tier and SBS tier, users can adaptively obtain contents from BSs or remote content servers. We properly model both wired and wireless delays when a user requests an arbitrary content, and propose the concept of effective delay. Content caching probabilities are optimized using the Marine Predators Algorithm via minimizing the average effective delay. Numerical results show that our proposed cooperative caching scheme achieves much shorter delays than the benchmark caching schemes.

Analysis of 5G Capacity in Line-Of-Sight Condition of Urban Macro (UMa) Using 3.5 GHz Frequency

This study is projected to be the initial design for the implementation of 5G technology, particularly in the Urban Macro (UMa) scenario, with a frequency of 3.5GHz and a bandwidth of 100 MHz. 3GPP (3rd Generation Partnership Project) TR 38.901 recommends performing performance analysis with line-of-sight (LOS) situations using the propagation model urban macro (uMa). In this study, 100 users are distributed at random distances to examine system capacity using the frequency reuse factors K = 1/3 and K = 1/7. According to the calculation results, this study demonstrates that system capacity is dependent on user distribution arrangement and the frequency reuse factor approach used. A high frequency reuse factor increases bandwidth efficiency and yields more optimum capacity. Furthermore, distributing users near the base station will boost capacity.

A double-layer optimization strategy for distribution networks considering 5G base station clusters

The reliability of the power supply for 5G base stations (BSs) is increasing. A large amount of BS backup energy storage (BES) remains underutilized. This study establishes a double-layer optimization distribution network (DN) considering BS clusters. An energy consumption characteristics and scheduling ability model of the BSs was established to address the differences in the characteristics of different traffic flows. A double-tier planning model for BS-joining grid market ancillary services is proposed. The upper-layer model addresses optimal tidal flow problems in DNs to minimize integrated operating costs, while the lower-layer model focuses on BES economic optimization. The double-layer model changes into a single-layer linear model using the Karush–Kuhn–Tucker (KKT) condition and the Big M method. Simulation validation using the IEEE-33 node DN proves that this approach can reduce DN operating costs, regulate voltage fluctuations, and guarantee economical and safe DN operation.

Wormhole attack detection and mitigation model for Internet of Things and WSN using machine learning.

The Internet of Things (IoT) is revolutionizing diverse sectors like business, healthcare, and the military, but its widespread adoption has also led to significant security challenges. IoT networks, in particular, face increasing vulnerabilities due to the rapid proliferation of connected devices within smart infrastructures. Wireless sensor networks (WSNs) comprise software, gateways, and small sensors that wirelessly transmit and receive data. WSNs consist of two types of nodes: generic nodes with sensing capabilities and gateway nodes that manage data routing. These sensor nodes operate under constraints of limited battery power, storage capacity, and processing capabilities, exposing them to various threats, including wormhole attacks. This study focuses on detecting wormhole attacks by analyzing the connectivity details of network nodes. Machine learning (ML) techniques are proposed as effective solutions to address these modern challenges in wormhole attack detection within sensor networks. The base station employs two ML models, a support vector machine (SVM) and a deep neural network (DNN), to classify traffic data and identify malicious nodes in the network. The effectiveness of these algorithms is validated using traffic generated by the NS3.37 simulator and tested against real-world scenarios. Evaluation metrics such as average recall, false positive rates, latency, end-to-end delay, response time, throughput, energy consumption, and CPU utilization are used to assess the performance of the proposed models. Results indicate that the proposed model outperforms existing methods in terms of efficacy and efficiency.

Deep learning‐based spectrum sharing in next generation multi‐operator cellular networks

SummaryOwing to the exponential increase in wireless network services and bandwidth requirements, sharing the radio spectrum among multiple network operators seems inevitable. In wireless networks, enabling efficient spectrum sharing for resource allocation is quite challenging due to several random factors, especially in multi‐operator spectrum sharing. While spectrum sensing can be useful in spectrum‐sharing networks, the chance of collision exists due to the inherent unreliability of wireless networks, making operators reluctant to use sensing‐based mechanisms for spectrum sharing. To circumvent these issues, we utilize an alternative approach, whereby we propose an efficient spectrum‐sharing mechanism leveraging a spectrum coordinator (SC) in a multi‐operator spectrum‐sharing scenario assisted by deep learning (DL). In our proposed scheme, before the beginning of each timeslot, the base station of each operator transmits the number of required resources based on the number of packets in the base station's queue to SC. In addition, base stations also transmit the list of available channels to SC. After gathering information from all base stations, SC distributes this collected information to all the base stations. Each base station then utilizes the DL‐based spectrum‐sharing algorithm and computes the number of resources it can use based on the number of packets in its queue and the number of packets in the queues of other operators. Furthermore, by leveraging DL, each operator also computes the cost it must pay to other operators for using their resources. We evaluate the performance of the proposed network through extensive simulations. It is shown that the proposed DL‐based spectrum‐sharing mechanism outperforms the conventional spectrum allocation scheme, thus paving the way for more dynamic and efficient multi‐operator spectrum sharing.

Low-complexity cooperative active and passive beamforming multi-RIS-assisted communication networks

Reconfigurable intelligent surface (RIS) is a groundbreaking technology that has a significant potential for sixth generation (6G) networks. Its unique capability to control wireless environments makes it an attractive option. However, the spatial diversity increased by assisting users with all deployed RISs, this investigation has two drawbacks the high complexity design, and the received signals by the far RISs are severely attenuated. Therefore, we propose a RIS selection strategy to select the proper RISs as a pre-stage before the joint beamforming between the base station (BS) and RISs to reduce the high complexity of joint beamforming optimization. Furthermore, the joint active and passive beamforming problem based on the selection is formulated. Hence, achieving spatial diversity by examining cooperation between passive beamforming of multi-hop RIS, leads to a challenging problem. To tackle this issue, we design an algorithm for the RIS selection scheme. Also, to relax the non-convexity of the proposed problem, we decouple the problem into solvable subproblems by utilizing the fractional programming (FP) and quadratic transform (QT) optimization methods. Simulation results have demonstrated through different user locations the effectiveness of the selection strategy in performance enhancement by 30% in the sum rate, besides an obvious reduction in the complexity cost than other techniques.

Bayesian 3D User Localization and Channel Reconstruction with Planar Extremely Large-Scale Antenna Array

An extremely large-scale antenna array (ELAA) can potentially provide significantly increased spatial multiplexing and beamforming gains, as well as enhanced localization capability. While presenting new potential, its near-field propagation and spatial non-stationary properties also impose a great challenge on the receiver design. This paper focuses on the receiver design in an uplink orthogonal frequency-division multiplexing system with a planar ELAA deployed at the base station. To solve the challenging problem of 3D user localization and channel estimation with the planar ELAA, a space-frequency user localization and channel reconstruction (SF-ULCR) receiver is proposed. Under the Bayesian framework, an extended probability model is first established, to capture the channel structural information comprehensively, based on which an iterative receiver consisting of three modules is derived: element-wise line spectrum estimation (ELSE), distance parameter estimation (DPE), and near-field localization (NFL). In particular, the ELSE module handles the line spectrum relationships among multiple subcarriers in the frequency domain, the DPE module extracts and integrates the distance information from the line spectrum parameters, and the NFL module utilizes the messages of distances for user localization based on near-field spatial characteristics. Our numerical results demonstrate that the proposed SF-ULCR algorithm outperforms existing baselines in terms of channel estimation and localization performance, and that it approaches the Cramèr–Rao bound.

Research on Mobile Network Parameters Using Unmanned Aerial Vehicles.

The study of phenomena related to the propagation of electromagnetic waves is a necessity with the development of mobile telephony networks. This paper deals with the problem of the quality of mobile telephony signals. The study uses a BTS scanner, dedicated to scanning the frequency spectrum and decoding information from base stations. The device is capable of scanning mobile networks from the second, third, fourth, and fifth generation (2G/3G/4G/5G) using a passive sensor. The article presents preliminary results of monitoring cellular network signals using a scanner mounted on an unmanned aircraft. The collected data call for the need to evaluate the signal parameters of the cellular network depending on the altitude of signal reception. This property emerges as very important in the case of areas with a high level of urbanization.

Combining the spatiotemporal mobility patterns and MMC for next location prediction of fake base stations

The spatiotemporal mobility patterns and next location prediction of fake base stations (FBS) provide important technical support for the police to prevent spam messages from FBS. However, due to the difficulty in locating their real-time locations, our understanding of the mobility patterns and predictability of FBS is still limited. Based on the crowdsourced spam data, we extract the time and potential locations of FBS and propose a Tucker-MMC method that combines Tucker decomposition with a Mobility Markov Chain (MMC) model to investigate the mobility patterns and predictability of FBS sending spam messages. First, we utilize Tucker decomposition to reflect the spatial and temporal preferences during the movement of the corresponding FBS. Then the mobility regularity and the theoretical maximum predictability of the FBS trajectories with similar mobility preferences are analyzed by entropy and Fano's inequality. A Tucker-MMC is also established for the next location prediction. The results using the spam dataset in Beijing show that the accuracy of Tucker-MMC is more than double that of the MMC. The accuracy of the actual location prediction model is more likely to approach the theoretical maximum predictability when FBS send spam messages in a shorter time, shorter transfer distance, and smaller access range.

Sustainable Resource Allocation and Base Station Optimization Using Hybrid Deep Learning Models in 6G Wireless Networks

Researchers are currently exploring the anticipated sixth-generation (6G) wireless communication network, poised to deliver minimal latency, reduced power consumption, extensive coverage, high-level security, cost-effectiveness, and sustainability. Quality of Service (QoS) improvements can be attained through effective resource management facilitated by Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper proposes two models for enhancing QoS through efficient and sustainable resource allocation and optimization of base stations. The first model, a Hybrid Quantum Deep Learning approach, incorporates Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). CNNs handle resource allocation, network reconfiguration, and slice aggregation tasks, while RNNs are employed for functions like load balancing and error detection. The second model introduces a novel neural network named the Base Station Optimizer net. This network includes various parameters as input and output information about the condition of the base station within the network. Node coverage, number of users, node count and user locations, operating frequency, etc., are different parametric inputs considered for evaluation, providing a binary decision (ON or SLEEP) for each base station. A dynamic allocation strategy aims for network lifetime maximization, ensuring sustainable operations and power consumption are minimized across the network by 2 dB. The QoS performance of the Hybrid Quantum Deep Learning model is evaluated for many devices based on slice characteristics and congestion scenarios to attain an impressive overall accuracy of 98%.

Fuzzy Similarity Fusion based Feature Selection and Optimization Enabled Deep Learning for Intrusion Detection in WSN

An essential component of data protection is intrusion detection. With the widespread use of wireless sensor networks in military and environmental monitoring, security concerns have grown. The lack of physical defence devices makes data transferred via wireless sensor networks susceptible to attackers. To protect against such threats, appropriate intrusion detection techniques are essential. In this paper, we will construct and explore the suggested fuzzy similarity fusion approach, as well as student psychology Earthworm Optimization Algorithm (SPEWA)-based Deep Q network for intrusion detection. The WSN network will be simulated first, followed by data packet transmission in WSN. The intrusion detection process will then be carried out at the base station. In the base station, the input data will be transmitted to the feature selection module, which will use a newly invented fuzzy similarity fusion approach that combines wrapper, Canberra distance, and fuzzy logic. Once the relevant features have been chosen, intrusion detection will be performed by the Deep Q network, which will be trained using the Student Psychology Earthworm Optimization Algorithm (SPEWA).The SPEWA will be redesigned to incorporate the Student Psychology Based Optimization Algorithm (SPBO) and the Earthworm Optimization Algorithm (EWA). The experiment will be conducted out using the Python programmed BoT-IoT and the MQTT-IoT-IDS2020: MQTT Internet of Things Intrusion Detection Dataset. Furthermore, the performance analysis of the created fuzzy similarity fusion approach will be assessed using four metrics: energy, accuracy, recall, and f-measure.

Performance analysis of a versatile bulk-service queue with group-arrival, batch-size-dependent service time and queue-length-dependent vacation

ABSTRACT Queueing systems with bulk-service and vacation policy have become one of the pivotal interest for the researchers due to their widespread application in food processing technology, manufacturing systems, power consumption in small cell base stations etc. This article analyzes a single server versatile bulk-service queueing system wherein the customers arrive according to a compound Poisson process and the service time is dependent on the batch size of undergoing service. Moreover, single and multiple vacation policies have been incorporated along with queue-length-dependent vacation. After providing the steady-state system equations, the bivariate probability generating function of queue and server content distribution together has been derived at departure epoch. After the evaluation of the unknown probabilities, complete joint distributions have been extracted in terms of roots of the denominator of the bivariate probability generating function. The discussed procedure and the reported results have been depicted through some numerical examples for different service and vacation time distributions. Some significant observation about the model has been sketched graphically.

Energy efficient multi-user task offloading through active RIS with hybrid TDMA-NOMA transmission

In this paper, we address the challenge of minimizing system energy consumption for task offloading within non-line-of-sight (NLoS) mobile edge computing (MEC) environments. Our approach integrates an active reconfigurable intelligent surface (RIS) and employs a hybrid transmission scheme combining time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) to enhance the quality of service (QoS) for user task offloading. The formulation of this problem as a non-convex optimization issue presents significant challenges due to its inherent complexity. To overcome this, we introduce an innovative method termed element refinement-based differential evolution (ERBDE). Initially, through rigorous theoretical analysis, we optimally determine the allocation of local computation resources, computation resources at the base station (BS), and transmit power of users, while maintaining fixed values for the offloading ratio, amplification factor, phase of the reflecting element, and the transmission period. Subsequently, we employ the differential evolution (DE) algorithm to iteratively fine-tune the offloading ratio, amplification factor, phase of the reflecting element, and transmission period towards near-optimal configurations. Our simulation results demonstrate that the implementation of active RIS-supported task offloading utilizing the hybrid TDMA-NOMA scheme results in an average system energy consumption reduction of 80.3%.

A robust OFDM IM-QAM NOMA scheme for URLLC and eMBB downlink service coexistence

This work introduces a non-orthogonal multiple access (NOMA) scheme suitable for low-rate ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services multiplexing in downlink communication. Tailored to minimize mutual interference, URLLC information is conveyed via a modified index modulation (IM) scheme on top of quadrature amplitude modulation (QAM) transferring eMBB traffic. Aiming at providing a proof of concept of the proposed scheme, we calculate the bit error rate of both services over Rayleigh fading with diversity, as well as the eMBB service achievable rate with typical multi-input multi-output (MIMO) configuration when the URLLC service utilizes space–time coding or diversity. The proposed scheme achieves a low bit error rate for the URLLC IM signal, at the cost of a lower information rate, while affecting the performance of the eMBB user mainly due to power sharing among the IM and QAM signals. To further investigate the feasibility of the proposed scheme, we calculate the transmission energy required by the base station to support both services over a typical cellular channel model, for various service requirements and user distances, in comparison to an orthogonal multiple access (OMA) puncturing scheme utilizing time-multiplexing of QAM for the eMBB traffic and BPSK for the URLLC traffic. Overall, our results show that the proposed scheme attains better performance compared to the puncturing scheme and offers a robust solution with easy user pairing for low-rate URLLC and typical eMBB downlink service multiplexing for 5G communications and beyond.

Joint resource scheduling and flight path planning of UAV-assisted IoTs in response to emergencies

In unmanned aerial vehicles (UAV)-assisted Internet of Things (IoT), emergencies can lead to changes in the status of ground sensor networks. This necessitates UAV-assisted IoT systems to possess the capability to dynamically respond to changes in the status of the ground sensor network. Therefore, this paper proposes a UAV scheduling scheme based on regional coordination (USRC). In this scheme, we divide the ground sensor network into task sub-regions according to the deployment of base stations. Then, we introduce a scheduling relationship pairing algorithm to determine the scheduling relationships between task sub-regions and UAV resources that need to be scheduled for each sub-region. Based on this, a dynamic path planning algorithm is designed to synchronize the planning of cross-region flight paths and intra-region flight paths for UAVs. Experimental results have demonstrated that the proposed scheme can efficiently respond to changes in the status of the ground sensor network by scheduling UAVs from sub-regions with abundant resources to those with scarce resources. Compared to other schemes, our scheme exhibits superior performance in reducing the age of information (AoI) and packet loss rate.

Modeling and Performance Analysis of LBT-Based RF-Powered NR-U Network for IoT.

Energy harvesting combined with spectrum sharing offers a promising solution to the growing demand for spectrum while keeping energy costs low. New Radio Unlicensed (NR-U) technology enables telecom operators to utilize unlicensed spectrum in addition to the licensed spectrum already in use. Along with this, the energy demands for the Internet of Things (IoT) can be met through energy harvesting. In this regard, the ubiquity and ease of implementation make the RF-powered NR-U network a sustainable solution for cellular IoT. Using a Markov chain, we model the NR-U network with nodes powered by the base station (BS). We derive closed-form expressions for the normalized saturated throughput of nodes and the BS, along with the mean packet delay at the node. Additionally, we compute the transmit outage probability of the node. These quality of service (QoS) parameters are analyzed for different values of congestion window size, TXOP parameter, maximum energy level, and energy threshold of the node. Additionally, the effect of network density on collision, transmission, and energy harvesting probabilities is observed. We validate our model through simulations.

Wireless sensor network coverage of improved sea lion algorithm

SummaryThe base station receives the environmental data from a predetermined field that is collected and transferred by the sensors for processing and analysis. However, coverage maximization is the major issue that requires the deployment of varied sensor nodes (SNs), in such a way that optimizes network coverage while enduring practical limitations. This is pointed out to be a significant challenge in constructing WSNs. Since this is considered to be a well‐known NP‐hard issue, metaheuristic methods must be used for solving the realistic problem sizes. Hence, our work considers the problem of finding the best placement to ensure good network coverage in WSN. Accordingly, the solution to the above‐mentioned problem is modeled by covering a new 2‐D distance evaluation based on weighted Minkowski. Further, we deploy the Self Improved Sealion with Opposition Behavior (SI‐SLOB) algorithm for determining the optimal placement of given sensor nodes. In the end, we perform varied evaluations on distance and coverage area to ensure the enhancement of the SI‐SLOB scheme over the other state‐of‐the‐art algorithms. The proposed method achieves minimum distance mean value in target node 25, which is 4.1%, 4.0%, 2.3%, 5.1%, 3.5%, 3.0%, and 4.1% better than the other methods such as SLO, GWO, PSO, BMO, BOA, RHSO, and WOA, respectively. Thus the proposed WSN node coverage models have diverse applications across various domains, contributing to improved efficiency, safety, and resource management.

A dual-base station constraint method to improve deformation monitoring precision consistency in strip regions

The precision of deformation monitoring with Global Navigation Satellite System (GNSS) relative positioning is significantly influenced by the distance between the monitoring and base stations. In long strip regions, the considerable differences in station spacing lead to inconsistent monitoring precision among multiple stations. This presents a challenge to accurately model and predict the deformation pattern. To tackle this issue, this paper introduces a novel dual-base station constraint method. This method integrates the baseline length constraint between two base stations into the conventional relative positioning model. The formulae of the proposed method are first derived in detail. Then the data collected at eight monitoring stations in two strip regions of 6 km and 8 km over a 28-day period are used to validate the effectiveness of the proposed method. The quantitative analysis of monitoring precision consistency indicators and hypothesis testing on the correlation between monitoring precision and station spacing are conducted. The results show that: (1) median values of the East, North, and Up consistency indicators are reduced from 2.14, 1.41, and 1.83 to 0.91, 0.67, and 0.55 and from 1.85, 1.85, and 2.32 to 0.69, 1.00, and 0.87, respectively, indicating monitoring precision consistency improvement for two case studies; (2) the absolute values of the correlation coefficients between monitoring precision and station spacing decrease from 0.99, 0.94, and 0.98 to 0.09, 0.36, and 0.32. Using the t-test with a significant level of 0.01, it is demonstrated that there is no significant correlation between monitoring precision and station spacing when employing the proposed method.

A novel health monitoring system for vital signs using IoT

The current research looked at how to use the Internet of Things (IoT) to create a vital sign health monitoring system. Eight indications are employed to get critical patient information. Therefore, the number of nodes of the IoT embedded in the human body is 8, which have been worked on in different places of the body. Among the 8 nodes, node number 1 is located in the center of the grid (the center of the human body). The number of rounds is 9000 and the nodes are adopted with the initial energy of the nodes of 0.5 J and the radio range of 10 m. MATLAB software was used to simulate the WBAN network, which consists of IoT sensors embedded in the human body. The eight-item health assessment tool takes the following into account: pulse rate, blood pressure (mm Hg), serum cholesterol (mg/dl), temperature (°C), exercise-induced angina, and exercise-induced ST-wave depression, major blood vessels are counted using a medical procedure called endoscopy that involves examining the alveoli, which are small air sacs in the lungs where gas exchange occurs. We compared the number of major vessels at rest with the maximal heart rate during activity. The sensors were responsible for sending this data to the health center (base station). The data collected from the installation of these 8 sensors on 303 patients were collected and evaluated by machine learning method using MLP neural network method. Finally, it can be claimed that the present study has provided an automated method of determining the health of people using the IoT in a way that provides a state of health with an accuracy of over 99% and can be used in medical centers.