Wireless Cellular Networks Research Articles

A stacked broad learning system with multitask learning method for cellular wireless network traffic prediction

A stacked broad learning system with multitask learning method for cellular wireless network traffic prediction

Trustability for Resilient Internet of Things Services on 5G Multiple Access Edge Cloud Computing

Billions of Internet of Things (IoT) devices and sensors are expected to be supported by fifth-generation (5G) wireless cellular networks. This highly connected structure is predicted to attract different and unseen types of attacks on devices, sensors, and networks that require advanced mitigation strategies and the active monitoring of the system components. Therefore, a paradigm shift is needed, from traditional prevention and detection approaches toward resilience. This study proposes a trust-based defense framework to ensure resilient IoT services on 5G multi-access edge computing (MEC) systems. This defense framework is based on the trustability metric, which is an extension of the concept of reliability and measures how much a system can be trusted to keep a given level of performance under a specific successful attack vector. Furthermore, trustability is used as a trade-off with system cost to measure the net utility of the system. Systems using multiple sensors with different levels of redundancy were tested, and the framework was shown to measure the trustability of the entire system. Furthermore, different types of attacks were simulated on an edge cloud with multiple nodes, and the trustability was compared to the capabilities of dynamic node addition for the redundancy and removal of untrusted nodes. Finally, the defense framework measured the net utility of the service, comparing the two types of edge clouds with and without the node deactivation capability. Overall, the proposed defense framework based on trustability ensures a satisfactory level of resilience for IoT on 5G MEC systems, which serves as a trade-off with an accepted cost of redundant resources under various attacks.

Exploiting in-Slot Micro-Synchronism for S-ALOHA

Proliferation of the urban Internet-of-Things (IoTs) for smart cities has fuelled massive amounts of data over wireless cellular networks. Random access (RA) system of wireless cellular networks, e.g., 5G New Radio (NR), based on S-ALOHA system should cope with ever-growing IoT traffic. This work proposes S-ALOHA system with time offsets (TOs), where one slot consists of K TOs and one packet transmission time. The length of the overall TOs is a fraction of a packet transmission time. In the system users (re)transmit to the boundary of a TO randomly selected. This enables the base station (BS) to inform the users of who transmits the first and the last packets in the slot with collision so that the two users can retransmit successfully in the following two slots respectively. Our throughput analysis compared to simulations shows that adopting even with three and four TOs surpasses the throughput limit of S-ALOHA system without TOs. Additionally, we propose two Bayesian-optimized backoff algorithms for S-ALOHA system with TOs, with which users can apply throughput-optimal (re)transmission probability or uniform backoff window even in unsaturated traffic scenarios. Numerical results demonstrate that the proposed backoff algorithms can achieve the throughput close to an ideal system and drastically reduce the access delay compared to S-ALOHA system.

A Power Efficiency Metric for Comparing Energy Consumption in Future Wireless Networks in the Millimeter-Wave and Terahertz Bands

Future wireless cellular networks will utilize millimeter-wave and sub-THz frequencies and deploy small-cell base stations to achieve data rates on the order of hundreds of gigabits per second per user. The move to sub-THz frequencies will require attention to sustainability and reduction of power whenever possible to reduce the carbon footprint while maintaining adequate battery life for the massive number of resource-constrained devices to be deployed. This article analyzes power consumption of future wireless networks using a new metric, a figure of merit called the power waste factor ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W$</tex> ), which shows promise for the study and development of “green G” - green technology for future wireless networks. Using <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W$</tex> , power efficiency can be considered by quantifying the power wasted by all devices on a signal path in a cascade. We then show that the consumption efficiency factor (CEF), defined as the ratio of the maximum data rate achieved to the total power consumed, is a novel and powerful measure of power efficiency which shows that less energy per bit is expended as the cell size shrinks and carrier frequency and channel bandwidth increase. Our findings offer a standard approach to calculating and comparing power consumption and energy efficiency.

Deep learning‐based scalable and robust channel estimator for wireless cellular networks

Deep learning‐based scalable and robust channel estimator for wireless cellular networks

Optimal and Suboptimal Dynamic Cache Update Algorithms for Wireless Cellular Networks

In this letter, we study the dynamic cache update problem for wireless content caching networks. The cost minimization perspective is considered, wherein the system cost contains three components, including the cache cost, the retrieval cost, and the update cost. Particularly, the update cost lies at the root of the coded caching strategy. To address the non-convex optimization problem, we propose both suboptimal and optimal algorithms. More specifically, we prove that with previous caching state, the cost minimization problem for the current time interval is a 0–1 Knapsack problem, which can be optimally solved by a dynamic programming with pseudo polynomial time complexity. On this basis, the original optimization problem is solved by an iterative manner, resulting in an suboptimal solution. Thereafter, we design the optimal dynamic caching policy, which is derived from the recursive algorithm. Simulation result shows that the suboptimal solution achieves near-optimal performance. It also demonstrates the superiority of our developed strategies compared to various baselines in terms of cost saving. Moreover, counterintuitive insight is judiciously acquired, such as that the high cache capacity does not always reveal positive effect on system’s performance when update cost is taken into consideration.

NeuroRAN Rethinking Virtualization for AI‐native Radio Access Networks in 6G

ABSTRACTNetwork softwarization has revolutionized the architecture of cellular wireless networks. State‐of‐the‐art container based virtual radio access networks (vRAN) provide enormous flexibility and reduced life‐cycle management costs, but they also come with prohibitive energy consumption. We argue that for future AI‐native wireless networks to be flexible and energy efficient, there is a need for a new abstraction in network softwarization that caters for neural network type of workloads and allows a large degree of service composability. In this paper we present the NeuroRAN architecture, which leverages stateful function as a user facing execution model, and is complemented with virtualized resources and decentralized resource management. We show that neural network based implementations of common transceiver functional blocks fit the proposed architecture, and we discuss key research challenges related to compilation and code generation, resource management, reliability and security.

Random access sequence set design in wireless cellular communication networks

In modern wireless cellular communication systems, Zadoff–Chu (ZC) sequences have been chosen as random access sequences due to their perfect autocorrelation and cross-correlation properties. However, the orthogonality between these sequences is no longer true when there is a frequency offset between the access device and access point. Thereby, the overall performance of ZC sequence-based random access signals will be limited. In this paper, we give a comprehensive analysis on the effect of frequency offset. We show how frequency offset affects the orthogonal property between sequences, and hence incurs the “energy attenuation” and “energy leakage” problems. Consequently, the detection probabilities of the transmitted random access sequences and the false alarm probabilities of the unsent sequences are affected. A frequency offset resistant detection metric is proposed in this paper to improve the detection probability. In order to resist the frequency offset effect as well as apply to the proposed detection metric, mathematical properties of the optimized roots and cyclic shift set are exploited. Finally, we illustrate how to construct a practical random access signal set under the given timing and frequency offset uncertainties. This analytical framework provides a useful insight into ZC sequence-based random access signal design and performance analyses in wireless cellular communication systems.

H-HOME: A learning framework of federated FANETs to provide edge computing to future delay-constrained IoT systems

In 6G systems, it will be mandatory that the network is able to support edge computing powered by Artificial Intelligence (AI) to provide mobile devices with the opportunity of job offloading for computation, so implementing the new paradigm of Intelligent Internet of Intelligent Things (IIoIT). In areas that are very difficult to be covered by the structured networks, Flying Ad-Hoc Networks (FANET) can be considered one of the most promising technologies to enhance coverage, capacity, reliability, and energy efficiency of wireless cellular networks, also providing edge-computing services. The goal of this paper is to propose a two-layer Hierarchical Horizontal-Offload ManagEment (H-HOME) framework for horizontal offload among the Unmanned Aerial Vehicles (UAV) of the same FANET, in order to minimize processing delay and jitter. The framework exploits Federated Reinforcement Learning in order to take advantage of knowledge sharing without incurring into problems of privacy and network overloading. A Markov Decision Process (MDP) is also defined to optimize decisions of the FANET Orchestrator (FO). Simulation results, obtained from two different analyses, demonstrate that H-HOME outperforms the traditional local training approach, based on simple recursive learning, and it can be effectively used to reduce power consumption and increase FANETs flight autonomy.

An Investigation of Factors Influencing Call-Drop among Wireless Cellular Network Users in Ghana

The aim of the study was to examine the factors influencing call-drops among wireless cellular network users in Ghana. A total of 1109 wireless cellular network users participated in the study. Exploratory and confirmatory factor analyses were used to find out the latent factors influencing the call-drops among wireless cellular network users in Ghana. The results of the exploratory and confirmatory factor analyses revealed eight-factor constructs. Among these factors, limited cell towers, cell tower overload, and general power problems were found to be the highest contributory factors. The combined three factors accounted for almost 57% of the total variance. The study's results reveal that network factors play a major role among the factors influencing call-drops in Ghana. This implies that in dealing with call-drop problems in Ghana, policymakers, regulators, and stakeholders should pay attention to the network factors of wireless cellular networks.

Anti-interference and Handoff in Wireless Cellular Network

Anti-interference and Handoff in Wireless Cellular Network

Interference Challenges and Management in B5G Network Design: A Comprehensive Review

Beyond Fifth Generation (B5G) networks are expected to be the most efficient cellular wireless networks with greater capacity, lower latency, and higher speed than the current networks. Key enabling technologies, such as millimeter-wave (mm-wave), beamforming, Massive Multiple-Input Multiple-Output (M-MIMO), Device-to-Device (D2D), Relay Node (RN), and Heterogeneous Networks (HetNets) are essential to enable the new network to keep growing. In the forthcoming wireless networks with massive random deployment, frequency re-use strategies and multiple low power nodes, severe interference issues will impact the system. Consequently, interference management represents the main challenge for future wireless networks, commonly referred to as B5G. This paper provides an overview of the interference issues relating to the B5G networks from the perspective of HetNets, D2D, Ultra-Dense Networks (UDNs), and Unmanned Aerial Vehicles (UAVs). Furthermore, the existing interference mitigation techniques are discussed by reviewing the latest relevant studies with a focus on their methods, advantages, limitations, and future directions. Moreover, the open issues and future directions to reduce the effects of interference are also presented. The findings of this work can act as a guide to better understand the current and developing methodologies to mitigate the interference issues in B5G networks.

Building trust via blockchain in UAV‐assisted ultra‐dense 6G cellular networks

AbstractThe ultra‐dense deployment of unmanned aerial vehicles (UAVs) as mobile small cell base stations (SBS) is expected to support ultra‐high‐speed, ultra‐reliable, and ultra‐low‐latency wireless connections in sixth generation (6G) wireless cellular networks. Inevitably, the explosively increasing number of SBSs each with ever‐shrinking cell size will result in the spectrum scarcity and pose critical challenges to the spectrum management. Towards the centralized spectrum management system, a third‐party authority is employed to coordinate the spectrum sensing, sharing, and allocation among participants. However, a centralized authority is vulnerable to numerous security threats, such as single point of failure, denial of service attacks, and privacy disclosure, and cannot guarantee fair and efficient spectrum management without mutual trusts among participants. To address these problems, a novel framework of blockchain‐aided spectrum management is designed to securely record the spectrum auction data and spectrum allocation data in the decentralized spectrum sharing. Moreover, a trust management scheme is proposed to evaluate the trusts of all UAVs participating in the decentralized spectrum sharing, where the trust increase depends on legal use of spectrum allocation and honest report of spectrum sensing. In particular, it is shown that the proposed trust management can not only incentivize the UAVs to comply with the legal rule of spectrum sharing in blockchain, but also punish the malicious behaviour that either violates the spectrum sharing rule or provides misleading spectrum sensing results.

Buffer transference strategy for power control in B5G-ultra-dense wireless cellular networks

Buffer transference strategy for power control in B5G-ultra-dense wireless cellular networks

Performance Comparison of Wireless Aerial 3D Cellular Network Models

In this letter, stochastic geometry tools are utilized to compare the downlink performance of two wireless cellular network models with Unmanned Aerial Vehicles (UAVs), in terms of coverage probability and average achievable rate. The considered models are: i) a truncated octahedron-based cellular network, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">regular</i> in the following, and ii) a binomial-Voronoi cellular network. The locations of all UAV-user equipments (UAV-UEs) are modeled as a three-dimensional (3D) Poisson Point Process (PPP) in both cellular models. Analytical expressions on coverage probability and achievable average rate are derived, and numerical results are presented. The binomial-Voronoi network is also compared via Monte-Carlo simulations to a regular network with uniformly random UAV-UE receiver selection policy.

Spatial–Temporal Energy Management of Base Stations in Cellular Networks

The operations of base stations (BSs) contribute most of the energy consumption in the cellular wireless networks. Powering BSs by distributed energy resources (DERs), such as photovoltaic (PV) and energy storage, is an effective way to reduce on-grid power consumption and build green wireless networks. Optimal energy management of BSs helps to reduce electricity bills for the wireless network and provides flexibility to the power networks. This article proposes the concept of spatial–temporal energy management (ST-EM) for the energy management of BSs. On the one hand, the BSs manage their power consumption according to the real-time prices; on the other hand, the BSs adjust the user associations and change their power consumption according to the price differences among different BSs. The ST-EM of BSs is formulated as a large-scale mixed-integer nonlinear programming (MINLP) problem, which is proven to be NP-hard. We propose a heuristic approach to search for one suboptimal solution by decomposing the original problem into an energy network optimization subproblem and a communication network optimization subproblem. The two subproblems are solved alternatively until convergence. Numerical experiments are conducted to verify the effectiveness of our proposed method.

Knowledge-defined networking: Applications, challenges and future work

Future 6G wireless communication systems are expected to feature intelligence and automation. Knowledge-defined networking (KDN) is an evolutionary step toward autonomous and self-driving networks. The building blocks of the KDN paradigm in achieving self-driving networks are software-defined networking (SDN), packet-level network telemetry, and machine learning (ML). The KDN paradigm intends to integrate intelligence to manage and control networks automatically. In this study, we first introduce the disadvantages of current network technologies. Then, the KDN and associated technologies are explored with three possible KDN architectures for heterogeneous wireless networks. Furthermore, a thorough investigation of recent survey studies on different wireless network applications was conducted. The aim is to identify and review suitable ML-based studies for KDN-based wireless cellular networks. These applications are categorized as resource management, network management, mobility management, and localization. Resource management applications can be further classified as spectrum allocation, power management, quality-of-service (QoS), base station (BS) switching, cache, and backhaul management. Within network management configurations, routing strategies, clustering, user/BS association, traffic classification, and data aggregation were investigated. Applications in mobility management include user mobility prediction and handover management. To improve the accuracy of positioning in indoor environments, localization techniques were discussed. We classify existing research into the respective KDN architecture and identify how the knowledge obtained will enhance future networks; as a result, researchers can extend their work to empower intelligence and self-organization in the network using the KDN paradigm. Finally, the requirements, motivations, applications, challenges, and open issues are presented.

A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks.

As one of the most critical elements in the hydrological cycle, real-time and accurate rainfall measurement is of great significance to flood and drought disaster risk assessment and early warning. Using commercial microwave links (CMLs) to conduct rainfall measure is a promising solution due to the advantages of high spatial resolution, low implementation cost, near-surface measurement, and so on. However, because of the temporal and spatial dynamics of rainfall and the atmospheric influence, it is necessary to go through complicated signal processing steps from signal attenuation analysis of a CML to rainfall map. This article first introduces the basic principle and the revolution of CML-based rainfall measurement. Then, the article illustrates different steps of signal process in CML-based rainfall measurement, reviewing the state of the art solutions in each step. In addition, uncertainties and errors involved in each step of signal process as well as their impacts on the accuracy of rainfall measurement are analyzed. Moreover, the article also discusses how machine learning technologies facilitate CML-based rainfall measurement. Additionally, the applications of CML in monitoring phenomena other than rain and the hydrological simulation are summarized. Finally, the challenges and future directions are discussed.

Development of a Multilayer Perceptron Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments

Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths of a base transmitter and the mobile station receiver must be appropriately estimated. Although many log-distance-based linear models for path loss prediction in wireless cellular networks exist, radio frequency planning requires advanced non-linear models for more accurate predictive path loss estimation, particularly for complex microcellular environments. The precision of the conventional models on path loss prediction has been reported in several works, generally ranging from 8–12 dB in terms of Root Mean Square Error (RMSE), which is too high compared to the acceptable error limit between 0 and 6 dB. Toward this end, the need for near-precise machine learning-based path loss prediction models becomes imperative. This work develops a distinctive multi-layer perception (MLP) neural network-based path loss model with well-structured implementation network architecture, empowered with the grid search-based hyperparameter tuning method. The proposed model is designed for optimal path loss approximation between mobile station and base station. The hyperparameters examined include the neuron number, learning rate and hidden layers number. In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss experimental datasets. The experimental path loss data is acquired via a field drive test conducted over an operational 4G LTE network in an urban microcellular environment. The results were assessed using several first-order statistical performance indicators. The results show that prediction errors of the proposed MLP model compared favourably with measured data and were better than those obtained using conventional log-distance-based path loss models.

Fair and Energy-Efficient Coverage Optimization for UAV Placement Problem in the Cellular Network

Unmanned Aerial Vehicle (UAV) Base Station (BS) placement optimization is an essential operational task to improve the Quality of Service (QoS) in UAV-aided wireless cellular networks. The existing approaches are almost zeroth order methods, and the few first order methods mainly ignore the allocation fairness, computational efficiency, and backhaul constraints. In this paper, we formulate the UAV placement problem as a constrained optimization problem, with the objective of maximizing the fair coverage versus energy consumption while satisfying the backhaul constraints at different time nodes. To guarantee fair QoS allocation, we introduce a novel fairness index to ensure fair communication opportunity and the novel region coverage ratio to avoid excess QoS on covered spots. An accurate and efficient proximal stochastic gradient descent based alternating algorithm that iteratively executes two optimization steps is proposed to optimize the UAV locations, which enables the fast single point-based first order methods to solve the complex problems with constraints. Experiment results manifest that the proposed algorithm performs well both in synthetic data scenario and in real city scenario. Furthermore, the proposed first order algorithm is more efficient than the existing zeroth order algorithm, typically referring to the meta-heuristic method.