Static Base Station Research Articles

Positioning and Association Rules for Transparent Flying Relay Stations

Transparent flying relay stations (FlyRSs), represented by transparent relays mounted on unmanned aerial vehicles (UAVs), have the potential to improve cellular network's capacity and coverage at little extra complexity and energy cost, especially when compared with non-transparent relays. As the transparent relays do not transmit reference signals, they do not lend themselves easily to channel estimation. This makes solving the problems of user association and positioning of transparent FlyRSs much harder. We propose a solution enabling an efficient association of users to the FlyRSs and determining suitable positions of the FlyRSs. Surprisingly, this can be done knowing neither the qualities of the channels linking the FlyRSs and the users nor the users' location information. Our approach involves the users being grouped into clusters based on the channels to nearby static base stations via agglomerative hierarchical clustering. Then, 3D positions of one FlyRS per cluster are determined by deep neural networks. The proposal improves the users' sum capacity with respect to existing solutions that rely on the knowledge of users' positions.

A Novelistic GSA and CSA Based Optimization for Energy-Efficient Routing Using Multiple Sinks in HWSNs Under Critical Scenarios

In this paper, GSA-based Exigency Routing (GSA-ER) and CSA-based Near Exigency Relaying (CSA-NER) models have been proposed that support Multiple SS-based Optimized Cross-layer Routing (MB-OCR) framework. The GSA-ER model uses channel statistics, delivery estimates, cluster population, and energy parameters to shortlist an optimized multi-hop path to the static Base-Station (SS) held responsible for critical data-delivery within WSN. The CSA-NER model defines an optimized route for the mobile-sink (MS) to gather near-exigency support data within the network. With GSA-ER and CSA-NER models employed, the MB-OCR-configured WSN can adhere to the critical data deadlines while also achieve reliability under any unanticipated situation. MB-OCR framework further alleviates the hot-spot problem. As evident from the simulation analysis, MB-OCR outperforms the MS sojourn path based existing frameworks. Furthermore, MB-OCR performance has been investigated for varying critical scenarios to limelight the essence of employing MS and SS in WSN.

On the Capacity of Full‐Duplex AF/DF Relay System with Energy Harvesting for Vehicle‐to‐Vehicle Communications

This paper studies the ergodic capacity (EC) of full‐duplex (FD) amplify‐and‐forward (AF) and decode‐and‐forward (DF) relay system with energy harvesting (EH) for vehicle‐to‐vehicle (V2V) communications. Unlike previous works on FD‐EH systems, we consider the case that both relay and destination are mobile vehicles while the source is a static base station. We mathematically derive the exact closed‐form expressions of ECs of both AF and DF protocols of the considered FD‐EH‐V2V relay system over cascade (double) Rayleigh fading. Our numerical results show that the ECs in the case of the V2V communication system are reduced compared to those in the case of stationary nodes. Also, with a specific value of residual self‐interference (RSI), the ECs of the considered FD‐EH‐V2V relay system can be higher or lower than those of half‐duplex‐ (HD‐) EH‐V2V system, depending on the average transmission power of the source. Furthermore, when the transmission power of the source and RSI are fixed, the ECs of the considered system can achieve peak values by using optimal EH time duration. On the other hand, the ECs of both AF and DF protocols reach the capacity floors in the high signal‐to‐noise ratio (SNR) regime due to the RSI impact. Also, the effect of RSI dominates the impact of cascade Rayleigh fading in the high SNR regime. Finally, we validate our analysis approach through Monte‐Carlo simulations.

Predictive Trajectory‐Based Mobile Data Gathering Scheme for Wireless Sensor Networks

Tradition wireless sensor networks (WSNs) transmit data by single or multiple hops. However, some sensor nodes (SNs) close to a static base station forward data more frequently than others, which results in the problem of energy holes and makes networks fragile. One promising solution is to use a mobile node as a mobile sink (MS), which is especially useful in energy‐constrained networks. In these applications, the tour planning of MS is a key to guarantee the network performance. In this paper, a novel strategy is proposed to reduce the latency of mobile data gathering in a WSN while the routing strategies and tour planning of MS are jointly optimized. First, the issue of network coverage is discussed before the appropriate number of clusters being calculated. A dynamic clustering scheme is then developed where a virtual cluster center is defined as the MS sojourn for data collection. Afterwards, a tour planning of MS based on prediction is proposed subject to minimizing the traveling distance to collect data. The proposed method is simulated in a MATLAB platform to show the overall performance of the developed system. Furthermore, the physical tests on a test rig are also carried out where a small WSN based on an unmanned aerial vehicle (UAV) is developed in our laboratory. The test results validate the feasibility and effectiveness of the method proposed.

Angular-Domain Selective Channel Tracking and Doppler Compensation for High-Mobility mmWave Massive MIMO

In this paper, we consider a mmWave massive multiple-input multiple-output (MIMO) communication system with one static base station (BS) serving a fast-moving user, both equipped with a very large array. The transmitted signal arrives at the user through multiple paths, each with a different angle-of-arrival (AoA) and hence Doppler frequency offset (DFO), thus resulting in a fast time-varying multipath fading MIMO channel. In order to mitigate the Doppler-induced channel aging for reduced pilot overhead, we propose a new angular-domain selective channel tracking and Doppler compensation scheme at the user side. Specifically, we formulate the joint estimation of partial angular–domain channel and DFO parameters as a dynamic compressive sensing (CS) problem. Then we propose a Doppler-aware-dynamic variational Bayesian inference (DD-VBI) algorithm to solve this problem efficiently. Finally, we propose a practical DFO compensation scheme which selects the dominant paths of the fast time-varying channel for DFO compensation and thereby converts it into a slow time-varying effective channel. Compared with the existing methods, the proposed scheme can enjoy the huge array gain provided by the massive MIMO and also balance the tradeoff between the CSI signaling overhead and spatial multiplexing gain. Simulation results verify the advantages of the proposed scheme over various baseline schemes.

A Beamforming Scheme With Doppler Suppression for High-Mobility Wireless Communications

In this letter, we consider the high-mobility wireless communications between a high speed train (HST) and a base station (BS) and propose a beamforming scheme with Doppler suppression for the stationary node BS. The typical Rician channel model is assumed, which occupies most of the HST communication scenarios. Based on the fact that the channel time-variation induces interference to the standard time-invariant signal model, the beamforming is then designed to maximize the signal-to-interference-plus-noise ratio (SINR) for both uplink and downlink transmissions. Simulation results are provided to verify that the channel time-variation can be effectively suppressed by the stationary BS through the proposed beamforming scheme.

Extended RTS/CTS Control based on Transmission Request Distribution in Wireless Ad-Hoc Networks

In a wireless ad-hoc network where wireless nodes exchange data messages without help of stationary base stations, collisions of control and data messages are reduced and/or avoided by CSMA/CA and RTS/CTS control of wireless LAN protocols. Random backoff timers for avoidance of collisions among RTS control messages provides equally opportunities to transmit data messages to neighbor wireless nodes since the value of the backoff timer monotonically decreases. In usual wireless ad-hoc networks, wireless nodes are not equally distributed and frequency of transmission requests in wireless nodes is also not the same. Thus, especially in a region with high density of transmissions and receipts requests for data messages, it is not always possible to receive a response CTS control message even though a wireless node has an opportunity to transmit an RTS control message. Hence, the equal opportunities to transmit an RTS control message is not enough to realize the equal opportunities to transmit a data message. In order to solve this problem, this paper proposes a novel RTS/CTS control to equally provide opportunities to transmit data messages whose receiver node is hard to transmit a CTS control message on response to an RTS control message. Here, a transmission of a CTS control message precedes a transmission of an RTS control message in cases that transmissions of a CTS control message fail repeatedly.

A re-organizing biosurveillance framework based on fog and mobile edge computing

Biological threats are becoming a serious security issue for many countries across the world. Effective biosurveillance systems can primarily support appropriate responses to biological threats and consequently save human lives. Nevertheless, biosurveillance systems are costly to implement and hard to operate. Furthermore, they rely on static infrastructures that might not cope with the evolving dynamics of the monitored environment. In this paper, we present a reorganizing biosurveillance framework for the detection and localization of biological threats with fog and mobile edge computing support. In the proposed framework, a hierarchy of fog nodes are responsible for aggregating monitoring data within their regions and detecting potential threats. Although fog nodes are deployed on a fixed base station infrastructure, the framework provides an innovative technique for reorganizing the monitored environment structure to adapt to the evolving environmental conditions and to overcome the limitations of the static base station infrastructure. Evaluation results illustrate the ability of the framework to localize biological threats and detect infected areas. Moreover, the results show the effectiveness of the reorganization mechanisms in adjusting the environment structure to cope with the highly dynamic environment.

Resource allocation of simultaneous wireless information and power transmission of multi-beam solar power satellites in space–terrestrial integrated networks for 6G wireless systems

The technique of simultaneous wireless information and power transmission (SWIPT) has been applied to wireless sensor networks, which employ static or mobile base stations (BSs) such as drones and ships to charge passively powered devices. SWIPT can be strongly expanded by solar power satellites (SPSs), which collect solar energy and transmit it to the earth through microwaves to alleviate the power shortage problem. Furthermore, multi-beam SPSs can serve a broader range than terrestrial BSs for information transmission.In 6G networks, satellites are core devices in space-terrestrial integrated networks (STINs) supporting super Internet-of-Things. However, when discussing 6G wireless systems, previous works did not consider SWIPT applied in STINs through multi-beam SPSs. Therefore, this work proposes a novel resource allocation problem for SWIPT performed by multi-beam SPSs in the STIN while optimizing the following two objectives: minimizing deficit or excess of information transmission rate and maximizing power transmission based on two receiving architectures of terrestrial devices for information decoding and energy harvesting. Different from previous works, this problem considers not only assigning power to one of multiple satellite beams but also further allocating power in each beam into two parts for information and power transmission. This problem is NP-hard as it includes an NP-hard problem. Artificial intelligence (AI) algorithms can be used to optimize the network resource management. Hence, this problem with continuous decision variables is further solved by a classical and two recent AI algorithms specially designed for continuous variables, i.e., particle swarm optimization, improved harmony search algorithm, and monkey algorithm. Through simulation, the most appropriate AI algorithms to the concerned problem are analyzed, and the results show that for the two special designed receiving architectures of the terrestrial devices, the power splitting architecture generally outperforms the time switching architecture.

The Performance Analysis of Downlink NOMA in LEO Satellite Communication System

Non-orthogonal multiple access (NOMA) has the potential to provide higher throughput than conventional orthogonal multiple access (OMA), which has been considered as a key technology for 5G. NOMA in satellite communication system can provide anytime, anywhere access with improved spectral efficiency and system capacity because of its ubiquitous coverage. However, the characteristics of the satellite channels are different from that of the terrestrial network, i.e., huge time delay and Doppler shift. In this paper, different from the existing works, which mainly focus on the performance of NOMA in static terrestrial base stations and Geostationary orbit (GEO) scenarios, we investigate the performance analysis of downlink NOMA in dynamic low earth orbit (LEO) satellite communication system with Doppler shift considered. We combine NOMA and orthogonal frequency division multiplexing (OFDM) for better spectral efficiency. Our channel model includes both small scale model and large scale model. For simplify, we only consider two users in one spot beam. Besides, we express the performance analysis of downlink NOMA in LEO satellite communication system, like ergodic capacity, outage probability (OP) and mutual information. However, in traditional NOMA scheme, the proceeding error decision of the high power user will cause the deterioration of the subsequent detection performance. Therefore, a symmetrical coding (SC) scheme for different modulation mode is proposed for low power users to get better performance. Finally, simulation results validate the performance of the NOMA scheme is better than that of the OMA scheme. The proposed SC scheme can achieve a prominent increase performance contrasted to the traditional NOMA scheme.

Hierarchical Game-Theoretic and Reinforcement Learning Framework for Computational Offloading in UAV-Enabled Mobile Edge Computing Networks With Multiple Service Providers

We present a novel game-theoretic (GT) and reinforcement learning (RL) framework for computational offloading in the mobile edge computing (MEC) network operated by multiple service providers (SPs). The network is formed by MEC servers installed at stationary base stations (BSs) and unmanned aerial vehicles (UAVs) deployed as quasi-stationary BSs. Since computing powers of MEC servers are limited, the BSs in proximity can form coalitions with shared data processing resources to serve their users more efficiently. However, as BSs can be privately owned or controlled by different SPs, in any coalition, the BSs: 1) take only the actions that maximize their long-term payoffs and 2) do not coordinate their actions with other BSs in the coalition. That is, inside each coalition, BSs act in an independent and self-interested manner. Therefore, the interactions among BSs cannot be described by conventional coalitional games. Instead, the network operation is modeled by a two-level hierarchical model. The upper level is a cooperative game that defines the process of coalition formation. The lower level comprises the set of noncooperative subgames to represent a self-interested and independent behavior of BSs in coalitions. To enable each BS to select a coalition and decide on its action maximizing its long-term payoff, we propose two algorithms that combine coalition formation with RL and prove that these algorithms converge to the states where the coalitional structure is strongly stable and the strategies of BSs are in the mixed-strategy Nash equilibrium (NE).

Two-Dimensional Drone Base Station Placement in Cellular Networks Using MINLP Model

Utilization of drones is going to become predominated in cellular networks as aerial base stations in order to temporary cover areas where stationary base stations cannot serve the users. Detecting optimal location and efficient number of drone-Base Stations (DBSs) are the targets we tackle in this paper. Toward this goal, we first model the problem using mixed integer non-linear programming. The output of the proposed method is the number and the optimal location of DBSs in a two-dimension area, and the object is to maximize the number of covered users. In the second step, since the proposed method is not solvable using conventional methods, we use a proposed method to solve the optimization problem. Simulation results illustrate that the proposed method has achieved its goals.

A load balancing virtual level routing (LBVLR) using mobile mule for large sensor networks

In a large sensor network, the data are usually routed back to the static base station (BS) using multi-hop communication. The sensor nodes near the BS suffer from an energy hole problem owing to the heavier data traffic load on them. Hence, the nodes near to the BS die early and thereby minimize the network lifetime. The mobile carriers have been considered as a better approach to balance the energy consumption by minimizing the number of transmissions. In spite of several benefits, the mobile carriers have a lot of challenges in data collection from sensor nodes. This paper presents a load balancing virtual level-based routing (LBVLR) using a data mule as a mobile carrier to deal with energy holes and ensure reliable data transfer. Our scheme has attempted to palliate the energy hole problem by applying horizontal level routing in the left and right directions. Fixed virtual rectangular grids of equal size are formed with the help of a mobile data mule and the BS that minimizes the network overheads, which saves a significant amount of energy. This approach uses three different data collection schemes based on the mule speed during trajectory, and we name them uniform speed of mule, variable speed of mule and sojourn point of mule. To compare the performance with the other works, LBVLR is evaluated mathematically and extensive simulations are conducted. The simulation results show a significant improvement in terms of average energy usage, data delivery delay and data delivery ratio compared with the other two similar kinds of well-known routing protocols.

Energy-efficient genetic algorithm variants of PEGASIS for 3D Wireless Sensor Networks

Due to its boundless potential applications, Wireless Sensor Networks have been subject to much research in the last two decades. WSNs are often deployed in remote environments making replacement of batteries not feasible. Low energy consumption being of prime requisite led to the development of energy-efficient routing protocols. The proposed routing algorithms seek to prolong the lifetime of sensor nodes in the relatively unexplored area of 3D WSNs. The schemes use chain-based routing technique PEGASIS as basis and employ genetic algorithm to build the chain instead of the greedy algorithm. Proposed schemes will incorporate an energy and distance aware CH selection technique to improve load balancing. Clustering of the network is also implemented to reduce number of nodes in a chain and hence reduce delay. Simulation of our proposed protocols is carried out for homogeneous networks considering separately cases for a static base-station inside and outside the network. Results indicate considerable improvement in lifetime over PEGASIS of 817% and 420% for base station inside and outside the network respectively. Residual energy and delay performance are also considered.

Optimized deployment of drone base station to improve user experience in cellular networks

Modern wireless traffic demand pushes Internet Service Providers to develop effective strategies to improve user experience. Since deploying dense stationary base stations (SBSs) is not cost efficient, an alternative is to deploy drone base stations (drone-BSs) to supplement existing SBSs. We focus on the 3D deployment problem of drone-BSs to serve ground users in a given area. We formulate an optimization problem to find the optimal 3D positions for drone-BSs with the objective of maximizing the number of covered users, subject to the constraints that drone-BSs should be deployed at safe positions and the covered users receive acceptable Quality of Service. We analyze the difficulty of such a problem and show that it is NP-hard. A greedy algorithm is developed with computational complexity analysis. Extensive computer simulations are conducted to illustrative the effectiveness of the proposed algorithm and comparison with a baseline algorithm is provided to assess the performance gains.

High-Mobility Wideband Massive MIMO Communications: Doppler Compensation, Analysis and Scaling Laws

In this paper, we apply angle-domain Doppler compensation for high-mobility wideband massive multi-input multi-output (MIMO) uplink communications. The time-varying multipath channel is considered between high-speed terminal and static base station (BS), where multiple Doppler frequency offsets (DFOs) are associated with distinct angle of departures (AoDs). With the aid of large-scale uniform linear array (ULA) at the transmitter, we design a beamforming network to generate multiple parallel beamforming branches, each transmitting signal pointing to one particular angle. Then, the transmitted signal in each branch will experience only one dominant DFO when passing over the time-varying channel, which can be easily compensated before transmission starts. We theoretically analyze the Doppler spread of the equivalent uplink channel after angle-domain Doppler compensation, which takes into account both the mainlobe and sidelobes of the transmit beam in each branch. It is seen that the channel time-variation can be effectively suppressed if the number of transmit antennas is sufficiently large. Interestingly, the asymptotic scaling law of channel variation is obtained, which shows that the Doppler spread is proportional to the maximum DFO and decreases approximately as $1/\sqrt {M}$ ( $M$ is the number of transmit antennas) when $M$ is sufficiently large. The numerical results are provided to corroborate the proposed scheme.

On the Location-Dependent SIR Gain in Cellular Networks

In wireless networks, the distances from a user to its desired transmitters and undesired interferers play a critical role in its channel quality. In this letter, we study this location-dependence in a cellular network, where a user is always served by its nearest base station. For any stationary and ergodic base station process, we partition its associated Voronoi cells into the cell centers and the cell boundaries. We show that in Poisson networks, the top fraction ${x}$ of users enjoy a signal-to-interference ratio (SIR) gain of ${-}{5}{\alpha }\text {log}_{{10}}{x}$ dB relative to the typical user for Rayleigh fading and the power-law path loss with the exponent $\alpha $ . For the cell boundary users, we give both the exact and asymptotic form of the SIR distribution. As such, this letter permits the grouping of users and the analysis of different groups of users.

The Stochastic Geometry Analyses of Cellular Networks With <inline-formula> <tex-math notation="LaTeX">$\alpha$ </tex-math> </inline-formula>-Stable Self-Similarity

To understand the spatial deployment of base stations (BSs) is the first step to analyze the performance of cellular networks and further design efficient networking protocols. Poisson point process (PPP), which has been widely adopted to characterize the deployment of BSs and established the reputation to give tractable results in the stochastic geometry analyses, usually assumes a static BS deployment density in homogeneous PPP (HPPP) models or delicately designed location-dependent density functions in in-homogeneous PPP models. However, the simultaneous existence of attractiveness and repulsiveness among BSs practically deployed in a large-scale area defies such an assumption, and the $\alpha $ -stable distribution, one kind of heavy-tailed distributions, has recently demonstrated superior accuracy to statistically model the varying BS density in different areas. In this paper, we start with these new findings and investigate the intrinsic feature (i.e., the spatial self-similarity) embedded in the BSs. Afterwards, we refer to a generalized PPP setup with $\alpha $ -stable distributed density and theoretically derive the related coverage probability. In particular, we give an upper bound of the derived coverage probability for high signal-to-interference-plus-noise ratio thresholds and show the monotonically decreasing property of this bound with respect to the variance of BS density. Besides, we prove that our model could reduce to the single-tier HPPP for some special cases and demonstrate the superior accuracy of the $\alpha $ -stable model to approach the real environment.

Fundamental Trade-offs in Communication and Trajectory Design for UAV-Enabled Wireless Network

The use of unmanned aerial vehicles (UAVs) as aerial communication platforms is of high practical value to future wireless systems such as 5G, especially for swift and on-demand deployment in temporary events and emergency situations. Compared to the static terrestrial base stations (BSs) in cellular networks, UAV-mounted aerial BSs possess stronger line-of-sight links with the ground users due to their high altitude as well as high and flexible mobility in 3D space, which can be exploited to enhance the communication performance. On the other hand, unlike terrestrial BSs that have reliable power supply, aerial BSs in practice have limited onboard energy, but require high propulsion power to stay airborne and support high mobility. Motivated by the above new considerations, this article aims to revisit some fundamental trade-offs in UAV-enabled communication and trajectory design. Specifically, it is shown that the communication throughput, delay, and (propulsion) energy consumption can be traded off among each other by adopting different UAV trajectory designs, which sheds new light on their existing trade-offs in terrestrial communication. The main design challenges and promising directions for future research are also discussed.

Deployment of Unmanned Aerial Vehicle Base Stations for Optimal Quality of Coverage

This letter studies the network performance improvement by deploying flying base stations mounted on unmanned aerial vehicles (UAV-BSs) during some occasional events. Specifically, we consider the problem to minimize the average UAV-user distance while keeping the UAV-BSs connected to the stationary base stations. We propose a distributed algorithm which requires only local information and is scalable to a large number of UAV-BSs. We prove its convergence and conduct simulations with a real data set to evaluate its performance.