Cellular Base Stations Research Articles

Joint UAV channel modeling and power control for 5G IoT networks

This paper studies the communication problem between UAVs and cellular base stations in a 5G IoT scenario where multiple UAVs work together. We are dedicated to the uplink channel modeling and the performance analysis of the uplink transmission. In the channel model, we consider the impact of 3D distance and multi-UAVs reflection on wireless signal propagation. The 3D distance is used to calculate the path loss, which can better reflect the actual path loss. The power control factor is used to adjust the UAV's uplink transmit power to compensate for different propagation path losses, so as to achieve precise power control. This paper proposes a binary exponential power control algorithm suitable for 5G networked UAV transmitters and presents the entire power control process including the open-loop phase and the closed-loop phase. The effects of power control factors on coverage probability, spectrum efficiency and energy efficiency under different 3D distances are simulated and analyzed. The results show that the optimal power control factor can be found from the point of view of energy efficiency.

Evaluating the Dispatchable Capacity of Base Station Backup Batteries in Distribution Networks

Cellular base stations (BSs) are equipped with backup batteries to obtain the uninterruptible power supply (UPS) and maintain the power supply reliability. While maintaining the reliability, the backup batteries of 5G BSs have some spare capacity over time due to the traffic-sensitive characteristic of 5G BS electricity load. Therefore, the spare capacity is dispatchable and can be used as flexibility resources for power systems. This paper evaluates the dispatchable capacity of the BS backup batteries in distribution networks and illustrates how it can be utilized in power systems. The BS reliability model is first established considering potential distribution network interruptions and the effects of backup batteries. Then, the analytical formula of the BS availability index is derived with respect to batteries' backup duration. The dispatchable capacity of BS backup batteries is evaluated in different distribution networks and with differing communication load levels. Furthermore, a potential application, daily operation optimization, is illustrated. Case studies show that the proposed methodology can effectively evaluate the dispatchable capacity and that dispatching the backup batteries can reduce 5G BS electricity bills while satisfying the reliability requirement.

Secrecy-Based Energy-Efficient Mobile Edge Computing via Cooperative Non-Orthogonal Multiple Access Transmission

Mobile edge computing (MEC) has been envisioned as a promising approach for enabling the computation-intensive yet latency-sensitive mobile Internet services in future wireless networks. In this paper, we investigate the secrecy based energy-efficient MEC via cooperative Non-orthogonal Multiple Access (NOMA) transmission. We consider that an edge-computing device (ED) offloads its computation-workload to the edge-computing server (ECS) subject to the overhearing-attack of a malicious eavesdropper. To enhance the secrecy of the ED's offloading transmission, a group of conventional wireless devices (WDs) are scheduled to form a NOMA-transmission group with the ED for sending data to the cellular base station (BS) while providing cooperative jamming to the eavesdropper. We formulate a joint optimization of the ED's offloaded workload, transmit-power, NOMA-transmission duration as well as the selection of the WDs, with the objective of minimizing the total energy consumption of the ED and the selected WDs, while subject to the ED's latency-requirement and the selected WDs' required data-volumes to deliver. Despite the nature of mixed binary and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a three-layered algorithm for solving it efficiently. To further address the fairness among different WDs, we investigate a system-wise utility maximization problem that accounts for the fairness in the WDs' delivered data and the total energy consumption of the ED and WDs. By exploiting our previously designed layered-algorithm, we further propose a stochastic learning based algorithm for determining each WD's optimal data-volume delivered. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of the secrecy based computation offloading via NOMA.

Rabid Euclidean direction search algorithm for various adaptive array geometries

One of the exciting technologies used to meet the increasing demand for wireless communication services is a smart antenna. A smart antenna is basically confirmed by an array of antennas and a digital beamformer unit through which cellular base station can direct the beam toward the desired user and set nulls toward interfering users. In this paper, different array configurations (linear, circular, and planer) with the REDS algorithm are implemented in the digital beam-forming unit. The wireless system performance is investigated to check the smart antenna potentials assuming Rayleigh fading channel environment beside the AWGN channel. Results show how the REDS algorithm offers a significant improvement through antenna radiation pattern optimization, sidelobe level, and interference reduction, and also the RDES algorithm proves fast convergence with minimum MSE and better sidelobe level reduction comparing with other algorithms.

An Insight into Deployments of Green Base Stations (GBSs) for an Environmentally Sustainable World

Data traffic and the number of mobile subscribers have increased significantly prompting cellular network operators to install additional mobile cellular base stations (BSs) to meet the increasing demand. This proliferation of BSs has resulted in consequential increase in energy consumption and Green House Gases (GHGs) emission. Several techniques have been deployed to reduce the energy consumption of the base station in what is called a green base station. This paper presents an insight into these approaches and highlights key challenges and potential research directions. It also highlights what environmental sustainability activist, regulators and government can do to incentivize green BSs concept in Nigeria.

Linearization Technologies for High Efficiency Power Amplifier of Cellular Base Stations

Linearization Technologies for High Efficiency Power Amplifier of Cellular Base Stations

Dynamic User Allocation in Stochastic Mobile Edge Computing Systems

Mobile edge computing (MEC) is a new distributed computing paradigm where edge servers are deployed at, or near cellular base stations in close proximity to end-users. This offers computing resources at the edge of the network, facilitating a highly accessible platform for real-time, latency-sensitive services. A typical MEC environment is highly stochastic with random user arrivals and departures over time. Here, we address the user allocation problem from a service provider's perspective, who needs to allocate its users to the cloud or edge servers in a specific area. A user, who has a multi-dimensional resource requirement, can be allocated to either the remote cloud, which incurs a high latency, or an edge server, which results in a low latency but might require the user to wait in a queue. This article aims to achieve a controllable trade-off between performance (throughput) and several associated costs such as queuing delay and latency costs. We model this problem as a stochastic optimization problem, propose SUAC (Stochastic User AlloCation) – an online Lyapunov optimization-based algorithm, and prove its performance bounds. The experimental results demonstrate that SUAC outperforms existing approaches, effectively allocating users with a desired trade-off while keeping the system strongly stable.

Performance Analysis of Cellular-Relay Vehicle-to-Vehicle Communications

Vehicle-to-vehicle (V2V) communication-based cooperative vehicular networks are a key technology for future smarter transportation systems as they provide various applications for addressing road safety and traffic congestion. To alleviate the performance limitations of existing dedicated short-range communication (DSRC) protocols, cellular-assisted V2V communications have been proposed recently, wherein cellular base stations (BSs) relay the transmission from one vehicle to another. Such cellular-assisted communications have shown promise for more reliable V2V communications with a wider capacity and longer transmission distances. In this study, we theoretically analyze the performance of cellular-relay (CR) V2V communications, in which a vehicle first transmits a message to its nearest BS via uplink, and then, the BS forwards the message to a destination vehicle via downlink. We model the road segments through a Poisson line process and the positions of vehicles through a Poisson point process on the roads; subsequently, we derive a theoretical expression for the probability of a successful CR transmission and the mean local delay in the CR transmission. Furthermore, we propose a performance metric to evaluate the difference between the performances of CR and direct V2V communications, and it can be applied to automatic transmission mode selection (i.e., CR or direct) for V2V communications. We evaluate the analytical results using numerical examples and demonstrate the impacts of various system parameters on the performances of CR and direct V2V communications.

An Efficient Spectrum Utilization Scheme for Energy-Constrained IoT Devices in Cellular Networks

The cellular Internet of Things envisions the connection of billions of devices with the Internet through cellular networks. These Internet-of-Things Devices (IoDs) are normally energy constrained. In order to provide network services to these energy-constrained IoDs, this article proposes a cellular architecture, whereby IoDs utilize resources of the cellular network for energy harvesting and information transmission. The radio resources of the cellular network are divided by time and frequency, and each time-frequency block is called a cellular resource block (CRB). At the beginning of each time slot, IoDs select CRBs randomly. IoDs receive the control information from the base station (BS) and extract the resource allocation information about the selected CRB. When selected CRBs are busy, IoDs harvest energy from the radio-frequency signals of the BS, and then they use that stored energy to transmit their information to the cluster head (CH) when selected CRBs are idle. In order to obtain the average harvested energy, the random distance between cellular BS and randomly deployed IoD has been statistically calculated. Using the finite-state Markov chain model, the transmission probability of each IoD is calculated. Analytical expressions for the collision probability and average throughput of IoDs are also derived. To verify the derived analytical expressions, system-level simulations are carried out, and it is shown that the analytical results match the simulation results. Moreover, the overall performance of the considered architecture is studied for different system parameters.

On the Power of Microwave Communication Data to Monitor Rain for Agricultural Needs in Africa

Over the last two decades, prevalent technologies and Internet of Things (IoT) systems have been found to have potential for carrying out environmental monitoring. The data generated from these infrastructures are readily available and have the potential to provide massive spatial coverage. The costs involved in using these data are minimal since the records are already generated for the original uses of these systems. Commercial microwave links, which provide the underlying framework for data transfer between cellular network base stations, are one example of such a system and have been found useful for monitoring rainfall. Wireless infrastructure of this kind is deployed widely by communication providers across Africa and can thus be used as a rainfall monitoring device to complement the sparse proprietary resources that currently exist or to substitute for them where alternatives do not exist. Here we focus this approach’s potential to acquire valuable information required for agricultural needs across Africa using Kenya as an example.

Dual-band magneto-electric dipole antenna with high-gain for base-station applications

The paper presents the results of a novel high-gain dual-band magneto-electric dipole (MED) antenna. The antenna comprises two pairs of horizontal metal plates of different heights that are excited by a Γ-shaped feedline structure. The antenna is entirely made of metal plates. Compared to traditional MED antennas, the proposed design exhibits dual-band operation with a higher radiation gain whose frequency ratio can be modified by simply adjusting the heights of the two magneto-electric dipole segments. This feature is necessary for cellular base-station applications. The operation and characteristics of the antenna are validated by the measurements. The measured results confirm the proposed antenna achieving fractional bandwidths of 13.31% (801.9–916.2 MHz) and 19.76% (1710.7–2085.7 MHz) for S11 ≤ −10 dB. It has stable unidirectional radiation patterns and optimum radiation gains of 9.2 dBi and 7.8 dBi at the first and second operating bands, respectively.

Alarm Prediction in Cellular Base Stations Using Data-Driven Methods

The importance of cellular networks continuously increases as we assume ubiquitous connectivity in our daily lives. As a result, the underlying core telecom systems have very high reliability and availability requirements, that are sometimes hard to meet. This study presents a proactive approach that could aid satisfying these high requirements on reliability and availability by predicting future base station alarms. A data set containing 231 internal performance measures from cellular (4G) base stations is correlated with a data set containing base station alarms. Next, two experiments are used to investigate (i) the alarm prediction performance of six machine learning models, and (ii) how different predict-ahead times (ranging from 10 min to 48 hours) affect the predictive performance. A 10-fold cross validation evaluation approach and statistical analysis suggested that the Random Forest models showed best performance. Further, the results indicate the feasibility of predicting severe alarms one hour in advance with a precision of 0.812 (±0.022, 95 % CI), recall of 0.619 (±0.027) and F1-score of 0.702 (±0.022). A model interpretation package, ELI5, was used to identify the most influential features in order to gain model insight. Overall, the results are promising and indicate the potential of an early-warning system that enables a proactive means for achieving high reliability and availability requirements.

Performance Evaluation of Power-Beacon-Assisted Wireless-Powered NOMA IoT-Based Systems

In this article, we investigate power beacon (PB)-assisted wireless-powered nonorthogonal multiple access (NOMA) Internet-of-Things (IoT)-based systems, where all transmitters harvest energy from a PB to transmit their signals to a destination by employing a time-splitting mechanism. In order to utilize spectral efficiency and enhance the quality of service of an edge user (EU), a cellular base station can communicate with the EU thank to the help of one IoT node in a cellular network by employing the NOMA protocol. To characterize the performance of the proposed systems, we derive the exact closed-form expression for the outage probability, throughput, energy efficiency, and the approximate closed-form expression for the ergodic capacity. To further improving the system performance, we present two algorithms that are to minimize the outage performance of users by optimizing the time-splitting factor and to maximize sum throughput via jointly optimal power allocation and time-spitting factor. Our numerical results show that the proposed system has outstanding performance in comparison with simultaneous wireless information and power transfer NOMA systems under time switching mechanism based on the IoT relay.

Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV With Deep Reinforcement Learning

Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the full potential of UAVs in the future by reusing the cellular base stations (BSs) to enable their air-ground communications. However, how to achieve ubiquitous three-dimensional (3D) communication coverage for the UAVs in the sky is a new challenge. In this paper, we tackle this challenge by a new coverage-aware navigation approach, which exploits the UAV’s controllable mobility to design its navigation/trajectory to avoid the cellular BSs’ coverage holes while accomplishing their missions. To this end, we formulate an UAV trajectory optimization problem to minimize the weighted sum of its mission completion time and expected communication outage duration, which, however, cannot be solved by the standard optimization techniques due to the lack of an accurate and tractable end-to-end communication model in practice. To overcome this difficulty, we propose a new solution approach based on the technique of deep reinforcement learning (DRL). Specifically, by leveraging the state-of-the-art dueling double deep Q network (dueling DDQN) with multi-step learning, we first propose a UAV navigation algorithm based on direct RL, where the signal measurement at the UAV is used to directly train the action-value function of the navigation policy. To further improve the performance, we propose a new framework called simultaneous navigation and radio mapping (SNARM) , where the UAV’s signal measurement is used not only for training the DQN directly, but also to create a radio map that is able to predict the outage probabilities at all locations in the area of interest. This enables the generation of simulated UAV trajectories and predicting their expected returns, which are then used to further train the DQN via Dyna technique, thus greatly improving the learning efficiency.

A Spectral Efficient NOMA-based Two-Way Relaying Scheme for Wireless Networks with Two Relays

This paper proposes a novel two-way relaying (TWR) approach for a two-relay wireless network based on non-orthogonal multiple access (NOMA), where two terminals exchange messages with a cellular base station (BS) via two intermediate relay stations (RSs). We propose a NOMA-based TWR approach with two relaying schemes, i.e., amplify-and-forward (AF) and decode-and-forward (DF), referred to as NOMA-AF and NOMA-DF. The sum-rate performance of our proposed NOMA-AF and NOMA-DF is analyzed. A closed-form sum-rate upper bound for the NOMA-AF is obtained, and the exact ergodic sum-rate of NOMA-DF is also derived. The asymptotic sum-rate of NOMA-AF and NOMA-DF is also analyzed. Simulation results show that the proposed scheme outperforms conventional orthogonal multiple access based transmission schemes. It is also shown that increasing the transmit power budget of the relays only cannot always improve the sum-rates.

CR-LBT: Listen-Before-Talk With Collision Resolution for 5G NR-U Networks

To achieve higher throughputs in cellular networks, 3GPP proposes to use unlicensed frequency bands and develops technologies — the latest one is NR-U — allowing a cellular base station to transmit in unlicensed bands, which are already occupied by Wi-Fi networks. To enable fair channel sharing between two technologies, the base station uses a sort of CSMA/CA with binary exponential backoff similar to Wi-Fi. However, the base station can start data transmission only at strictly periodic time moments. Many papers propose sending a reservation signal between the end of the backoff procedure and such a moment to prevent nearby devices from accessing the channel. However, this approach significantly reduces Wi-Fi performance. The paper proposes a novel method CR-LBT of transmitting a reservation signal that greatly decreases channel resource waste caused by collisions and improves channel resource sharing fairness. With developed analytical models and simulations, it is shown that CR-LBT may simultaneously increase the throughput of both NR-U and Wi-Fi networks. The effect is more noticeable for the Wi-Fi network, the throughput of which may rise three times compared with the traditional method of sending the reservation signal. Finally, the influence of various factors on CR-LBT performance is studied.

Autonomous Ground Vehicle Path Planning in Urban Environments Using GNSS and Cellular Signals Reliability Maps: Models and Algorithms

In this article, autonomous ground vehicle (AGV) path planning is considered. The AGV is assumed to be equipped with receivers capable of producing pseudorange measurements to overhead global navigation satellite systems (GNSS) satellites and to cellular base stations in its environment. Parameters of the cellular pseudoranges related to the transmitter clock bias are estimated in an initialization step in an open-sky environment. The AGV fuses these pseudoranges to produce an estimate about its own states. The AGV is also equipped with a three-dimensional building map of the environment. Starting from a known starting point, the AGV desires to reach a known target point by taking the shortest distance, while minimizing the AGV's position estimation error and guaranteeing that the AGV's position estimation uncertainty is below a desired threshold. Toward this objective, a so-called signal reliability map is first generated, which provides information about regions where large errors due to poor GNSS line-of-sight or cellular signal multipath are expected. The vehicle uses the signal reliability map to calculate the position mean-squared error (MSE). An analytical expression for the AGV's state estimates is derived, which is used to find an upper bound on the position bias due to multipath. An optimal path planning generation approach, which is based on Dijkstra's algorithm, is developed to optimize the AGV's path while minimizing the path length and position MSE, subject to keeping the position estimation uncertainty and position estimation bias due to multipath below desired thresholds. The path planning approach yields the optimal path together with a list of feasible paths and reliable GNSS satellites and cellular base stations to use along these paths.

Measurement and Evaluation of Radiation Power Density Emitted from Mobile Cellular Base Stations in Abuja and its Environ, Nigeria

This paper presents measurement and evaluation of radiation power density emitted from mobile cellular base stations in Abuja and it’s environ. Forty (40) mobile base stations with ten (10) each from AIRTEL, ETISALAT, GLO and MTN located in areas with high population were randomly examined across Abuja. Broadband analysis method was employed in the measurement of the radiation power density. The results obtained shows that most of the base stations recorded lower radiation power density below the safety standard value of 4.5watt/m2 , 22.5 watt/m2 for 900MHz frequency though few base stations recorded above standard value: GLO-BS02, GLO-BS03 and MTN-BS02 recorded 59.02 mW/m2, 39.76 mW/m2 and 46.70 mW/m2 respectively. The result also revealed that as distance increases, the mean power density reduces. This is to said that human exposure to radio frequency radiation have less or no health hazard at distance far away from the mobile cellular base station.

Aerial-Ground Interference Mitigation for Cellular-Connected UAV

To support large-scale deployment of unmanned aerial vehicles (UAVs) in the future, a new wireless communication paradigm, namely, cellular-connected UAV, has recently received an upsurge of interest in both academia and industry. Specifically, cellular base stations (BSs) and spectrum are reused to serve UAVs as new aerial user equipments (UEs) for meeting their communication requirements. However, compared to traditional terrestrial UEs, the high altitude of UAVs results in more frequent line-of-sight (LoS) channels with both their associated and non-associated BSs in a much wider area, which causes stronger aeri-al-ground interference to both UAVs and terrestrial UEs. As such, conventional techniques designed for mitigating the terrestrial interference become ineffective in coping with the new and more severe UAV-terrestrial interference. To tackle this challenge, we propose in this article new interference mitigation solutions for achieving spectral efficient operation of the cellular network with co-existing UAVs and terrestrial UEs. In particular, we exploit the powerful sensing capability of UAVs and inactive BSs in the network for interference mitigation/cancellation. Numerical results are presented to verify the efficacy of the proposed solutions and show their significant spectrum efficiency gains over terrestrial interference mitigation techniques.

HOTSPOT: A UAV-Assisted Dynamic Mobility-Aware Offloading for Mobile-Edge Computing in 3-D Space

For massive access to the Internet of Things, edge computing servers are installed on cellular ground base stations (GBSs) with fixed geographical locations, which easily suffer from traffic overload of the end user (EU) with high density and mobility. To provide reliable and flexible offloading service, unmanned aerial vehicles (UAV) are explored to assist edge computing, which relieves the computation offloading pressure of both EUs and GBS. However, most existing UAV researches focus on trajectory design to reduce offloading delay, which ignoring the variability of user distribution and the energy limitation of UAV. This article proposes a novel UAV-assisted edge computing framework, named as HOTSPOT, which locates the UAV in 3-D space according to the time-varying hot spot of user distribution and provides the corresponding edge computing offloading assistance. By formulating the UAV positioning problem into a maximum clique problem, a light-weighted deterministic algorithm is proposed based on stochastic gradient descent to search the optimal location of UAV. With the elaborate UAV position, HOTSPOT further gives an opportunistic offloading balanced scheme to reach low latency. Simulation results show that when the GBS load is 75%, HOTSPOT reduces the average offloading delay by 33%. When the GBS load reaches 90%, the average delay reduction is up to 80%.