Base Station Cooperation Research Articles

An Aerial and Ground Base Station Cooperation Strategy for UAV and Cellular Integrated Networks

An Aerial and Ground Base Station Cooperation Strategy for UAV and Cellular Integrated Networks

Mobility Performance Analyses of Base Station Cooperation for Cooperation for Cellular-Connected UAV Networks

Mobility Performance Analyses of Base Station Cooperation for Cooperation for Cellular-Connected UAV Networks

Uplink CoMP Transmission for Cellular-Connected UAV Networks

In this letter, we study the coordinated multipoint (CoMP) scheme in the uplink cellular-connected UAV network. By considering the LoS and NLoS components in air-to-ground (A2G) transmission, we adopt the strongest average received signal strength (RSS)-based CoMP scheme. We then develop an analytical framework to evaluate the network performance in terms of coverage probability and area spectral efficiency (ASE) for the case of two cooperative base stations (BSs), followed by an extension to a generalized number of cooperative BSs. Compared to the CoMP scheme with single UAV tier and Non-CoMP baseline, the considered CoMP scheme is able to leverage favored Line-of-Sight (LoS) transmission, lower path loss and BS cooperation gain.

Energy-Efficient Base Station Switching-Off With Guaranteed Cooperative Profit Gain of Mobile Network Operators

This paper studies the energy-efficient cooperative base station (BS) switching-off in multi-input single-output (MISO) cellular networks, where the roaming-cost-based cooperation and the cooperative beamforming among multiple mobile network operators (MNOs) are jointly designed. To make the cooperation among MNOs practical, we introduce a new constraint to make sure the cooperative profit gain of MNOs is non-negative and also formulate an optimization problem to maximize the system energy efficiency (EE) by jointly considering multiple factors, including the operation modes of BSs, the connection states between users and BSs, the beamforming vectors of multi-antenna BSs under constraints of users’ quality of service, BSs’s power budget, and the non-negative profit gain of MNOs, etc. To solve the non-convex problem, we propose an energy-efficient beamforming-aware BS switching off method (EBBSOM), in which we first design a roaming-cost-based BS switching-off algorithm (EE-BSOA) to search the feasible inactive BSs and then propose a successive convex approximation (SCA) and Dinkelbach’s method-based EE maximization beamforming algorithm (EE-BFA) to optimize beamforming vectors. In order to keep the cooperative profit gain non-negative for MNOs and also to balance the load and power consumption of BS from the profit perspective, we present a profit-wise BS switching-off priority method. We theoretically prove that the proposed EBBSOM is able to resolve the inconsistency between BS switching-off and EE maximization. Simulations show that the proposed EBBSOM enhances the system EE without degrading the economic profit gain of MNOs. Also, the proposed profit-wise BS switching-off priority enables the EBBSOM to achieve higher EE than traditional load-wise one. Furthermore, it is also observed that there exists a feasible rate price region, which is able to clearly characterize network EE and the average profit gain of MNOs simultaneously.

Enabling Fully-Decoupled Radio Access With Elastic Resource Allocation

Recently, an origin fully-decoupled radio access network (FD-RAN) inspired by neurotransmission has been proposed for B5G/6G mobile communication networks, which achieves the potentials of improving the spectrum efficiency, reducing the energy consumption and meeting personalized user requirement through profound resource cooperation. To explore new radio and networking technologies, in this paper, we propose an elastic and efficient resource allocation for the FD-RAN architecture. First, we assume that the wireless spectrum is integrated for more reasonable allocation and sharing the spectrum dynamically across frequencies, location, users, services and networks. In particular, we propose a user-BS-subchannel matching algorithm to solve the dynamic uplink and downlink spectrum division, BS cooperation and subchannel assignment problems based on the many-to-many matching model. Our algorithm is proved to converge to a stable matching within finite iterations. Then, we propose a power control algorithm for uplink and downlink transmissions to further enhance the system performance based on the successive convex approximation. The obtained power control solution is proved to achieve a Karush-Kuhn-Tucker point. Extensive simulation results demonstrate that the proposed dynamic division of uplink and downlink spectrum, BS cooperation, subchannel assignment and power control can effectively improve the network performance for the FD-RAN architecture.

Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking

In the future vehicular networks with an increased number of transceiver antennas and higher vehicle speeds, more frequent beam switching is required to ensure the quality of communication, which poses challenges to beam tracking speed and resource efficiency. Integrated sensing and communication (ISAC) provide a new solution to cope with this problem since radar echo can help to predict the vehicle’s future location and beam direction. Therefore, we present a radar-assisted beam tracking algorithm based on Extended Kalman filtering (EKF) and multi-road side unit (RSU) cooperation in this article. Each RSU uses EKF and radar echo to predict and track the vehicle position and upload the prediction information to the edge server (ES). By deploying multiple RSUs, the ES uses the uploaded distributed sensing information for joint estimation and thus improves the accuracy of vehicle location prediction, which is used for the beam tracking task at the next moment. Considering the real complex road conditions, we investigate two scenarios where vehicles move linearly or curvilinearly. Simulation results show that the proposed method with multiple base station cooperation improves the spectral efficiency by 34% and 20% over non-cooperative beam tracking in linear and curvilinear mobility, respectively. In addition, compared with traditional beam tracking based on beam scanning and signaling feedback, radar-assisted beam tracking significantly reduces the communication overhead.

Simultaneous Wireless Information and Power Transfer in mmWave Networks Under User-Centric Base Station Clustering

User-centric base station (BS) deployment has been designed for the fifth-generation (5G) dense millimeter wave (mmWave) networks for alleviating the inter-cell interference and improving the cell-edge user experience. However, the system power consumption increases sharply with the network density. In this paper, we investigate a user-centric simultaneous wireless information and power transfer (SWIPT) mmWave system employing a time-switching protocol at users to allow both energy harvesting (EH) and data decoding. To enable user-centric BS cooperation, adaptive BS clustering model is used to determine the user’s serving cluster based on its channel condition. Considering both linear and non-linear EH models, we analyze the joint coverage, namely, the probability that the user harvests enough energy in a given time slot and receives the required data from its serving cluster. The random serving clusters and the correlation between the amount of harvested energy and received data rate make the joint coverage analysis more challenging. A tractable tight approximation of the joint coverage probability is thus derived for ultra-dense networks. A mathematical optimization model for the time switching coefficient is also developed to maximize the system joint rate and energy coverage performance. All mathematical expressions are validated by Monte-Carlo simulations. Our results show that the proposed analytical framework is accurate and efficient for the design and deployment of SWIPT-enabled user-centric mmWave networks.

Rate Meta Distribution of Downlink Base Station Cooperation for Cellular-Connected UAV Networks

This letter studies the downlink rate meta distribution of a typical UAV under base station (BS) cooperation in a cellular-connected UAV network. An analytical model is derived for the meta distribution of the downlink transmission rate of a typical UAV by using a standard beta distribution approximation, taking into account the LoS probability and Nakagami-m fading of a downlink. Based on the derived model, the impacts of relevant parameters on the rate meta distribution are investigated. The derived analytical model can be used to provide a reference for the setting of relevant parameters in the design of a BS cooperation strategy.

Performance Analysis of Location-based Base Station Cooperation for Cellular-Connected UAV Networks

This paper studies the downlink performance analysis of a Unmanned aerial vehicles (UAV) in a cellular-connected UAV network with a location-based base station (BS) cooperation strategy. In the network, a UAV in a non-cooperation area is provided its downlink by the closest ground BS (GBS) while a UAV in a cooperation area is provided its downlinks by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> closest GBSs. To more comprehensively measure the performance of BS cooperation, we use a refined metric, meta distribution, to reflect more fine-grained information on the transmission performance of a UAV in addition to traditional metrics, coverage probability and local delay. Correspondingly, we derive performance models for investigating the impacts of the BS cooperation level, UAV altitude, and GBS density on the coverage probability, meta distribution, and local delay of a UAV, taking account of the line-of-sight (LoS) probability of the downlink between a GBS and a UAV, and the small-scale fading of a channel following a Nakagami-m distribution. Simulation results show that the derived performance models are effective, and can be used to provide a theoretical reference for the design of a BS cooperation strategy for a cellular-connected UAV network.

Federated-Reinforcement-Learning-Enabled Joint Communication, Sensing, and Computing Resources Allocation in Connected Automated Vehicles Networks

For future connected automated vehicles (CAVs) networks, the joint optimization of communication, sensing, and computing resources is crucial to guarantee the performance of cooperative automated driving’s safety, which is attracting more and more attention. However, the existing works have not considered the low-latency requirement for the raw perception data sharing with both wireless communication link capability and computing efficiency constraints, causing a serious threat to the cooperative automated driving’s safety in CAVs networks. In this article, a vehicle–road–base station cooperation architecture is designed, and a federated reinforcement learning (FRL)-based task offloading and resource allocation algorithm in the CAVs network is proposed to reduce the task execution delay with different communication and computing constraints. The problem of execution delay minimization is theoretically formulated and analyzed under three task practical offloading modes. To adapt to the dynamic topology of the CAVs network, we design a deep reinforcement learning algorithm to achieve the optimal task offloading and resource allocation. To further reduce the data transmission overhead of the centralized reinforcement learning algorithm, the FRL-enabled algorithm is proposed to minimize the execution delay of the optimal task offloading and resource allocation among multiple CAVs. Both the simulation and hardware testbed results verify that the proposed algorithms can not only reduce the execution delay and the communication overhead but also improve the system throughput.

User-centric non-full interference cellular networks: BS cooperation and bandwidth partitioning

This paper presents a user-centric frequency reuse scheme depending on the user classification in the cell in a homogeneous Poisson point process network. Each cell is partitioned into multiple regions delimited by the signal-to-interference ratio of the user considered. A given resource block (RB) is assigned to one user in a cell and cannot be shared by another user in this cell. The typical user only uses a fraction of the RB, and the remaining part is left unused to avoid interference with the other cells. The base stations (BS) interfering set is hence determined by the coverage probability of the typical user, and this set can be approximated by a thinned version of the original PPP. Using this, coverage probability and spectral efficiency (SE) for each user types are derived, and a BS cooperation technique is proposed to improve the SE of the cell edge user. Finally, the average network spectral efficiency and fairness among users are analyzed under the developed theoretical framework. Our results show that the user-centric frequency reuse improves the coverage probability and the SE compared to the conventional frequency reuse scheme.

FEEL: Federated End-to-End Learning With Non-IID Data for Vehicular Ad Hoc Networks

Recent studies have demonstrated the potentials of federated learning (FL) in achieving cooperative and privacy-preserving data analytics. It would also be promising if FL can be employed in vehicular ad hoc networks (VANETs) for cooperative learning tasks, such as steering angle prediction, trajectory prediction, drivable road detection, etc., among integrated vehicles. However, since VANETs are characterized by ad hoc cooperating vehicles with non-independent and identically distributed (Non-IID) data, directly employing existing FL frameworks to VANETs may cause extensive communication overhead and compromised model performance. Further, most of the existing deep learning models incorporated in FL frameworks rely heavily on data with manual annotations, leading to a huge labor cost. To address these issues, in this paper we propose an efficient and effective Federated End-to-End Learning framework for cooperative learning tasks in VANETs, named FEEL. Specifically, we first formulate a distributed optimization problem for cooperative deep learning tasks with Non-IID data in multi-hop cluster VANETs. Second, two algorithms for inter-cluster learning and inner-cluster learning are respectively designed, to reduce the communication overhead and fit Non-IID data. Third, a Paillier-based communication protocol is crafted, allowing secure model parameter updates at the central server without knowing the real updates at each cooperating base station. Extensive experiments on two real-world datasets are conducted by considering various data distributions and VANET topologies, demonstrating the high efficiency and effectiveness of the proposed FEEL framework in both regression and classification tasks.

Unified Analysis of Coordinated Multipoint Transmissions in mmWave Cellular Networks

This article performs a unified analysis of three coordinated multipoint (CoMP) transmission strategies in the downlink of mmWave cellular networks, including the fixed-number base station (BS) cooperation (FNC), the fixed-region BS cooperation (FRC), and the interference-aware BS cooperation (IAC). We first develop a comprehensive framework for CoMP operation in cellular networks, and investigate the network performance under a Poisson point process (PPP) model together with mmWave spectrum. To show what fraction of users in the network achieve target reliability for a given signal to interference-plus-noise ratio (SINR)/signal-to-interference ratio (SIR), we derive the SINR/SIR meta distributions, and further obtain the coverage probability as well as mean local delay for the three cooperation strategies. A pivotal intermediate step to compute the performance metrics is the derivation of joint distributions of distances between a typical user and cooperative BSs. Our analysis demonstrates that parameters of blockage have a significant influence on the network performance for the three CoMP schemes. We find that the FRC scheme makes more users achieve the given link reliability for the scenario with a low density of BSs, while the IAC scheme provides better performance for the network with a high density of BSs. Moreover, the optimal CoMP scheme can be approximately selected by considering the nearest distance from the serving BS to user and the radius of the approximate line-of-sight (LoS) region in the cellular networks.

High-Rate Uninterrupted Internet of Vehicle Communications in Highways: Dynamic Blockage Avoidance and CSIT Acquisition

In future wireless networks, one of the use cases of interest is the Internet of Vehicles (IoV). Here, IoV refers to two different functionalities, namely, serving the in-vehicle users and supporting the connected-vehicle functionalities, where both can be well provided by the transceivers installed on top of vehicles. Such dual functionality of on-vehicle transceivers implies strict rate and reliability requirements, for which one may need to communicate at millimeter-wave (mmWave) frequencies. However, IoV communication at mmWave requires up-to-date channel state information (CSI) and blockage avoidance. In this article, we incorporate the recently proposed concept of predictor antennas (PAs) into a large-scale cooperative PA (LSCPA) setup where both temporal blockages and CSI out-dating are avoided via base stations (BSs)/vehicles cooperation. Summarizing the ongoing standardization progress enabling IoV communications, we present the potential and challenges of the LSCPA setup, and compare the effect of cooperative and non-cooperative schemes on the performance of IoV links. As we show, BS cooperation and blockage/CSI prediction can boost the performance of IoV links remarkably.

User-Centric Cluster Design and Analysis for Hybrid Sub-6GHz-mmWave-THz Dense Networks

The terahertz (THz) waves with enormous bandwidth can be used along with the existing sub-6GHz and millimeter wave (mmWave) bands to achieve the ever evolving ecosystem of applications that need to be supported by the modern wireless networks. This paper investigates a user-centric dynamic base station (BS) clustering design for a hybrid network where THz, mmWave, and sub-6GHz BSs coexist. Invoking the proposed clustering model, the BS cooperation within each band is made possible by considering long term channel variations and all the surrounding BSs within a cluster with tunable size. A typical user is associated with the best BS cluster, from either a sub-6GHz, mmWave or THz tier based on the maximum signal-to-interference-plus-noise-ratio (SINR) criterion or the maximum rate criterion. Using tools from stochastic geometry, we assess the performance of the proposed user-centric hybrid system in terms of SINR and rate coverage performances, while accounting for: band specific propagation models, directional beamfroming, and BSs random locations. The accuracy of the analytical results is validated with Monte-Carlo simulations. The obtained results recognize a clear coverage/rate trade-off where a high fraction of THz BSs improves the rate significantly but may degrade the coverage performance. Thus, with carefully planned networks, enabling user-centric BS cooperation for hybrid wireless systems can achieve ultra-high rates while maintaining sufficient coverage in sixth-generation (6G) networks.

Secure and Robust MIMO Transceiver for Multicast Mission Critical Communications

Mission-critical communications (MCC) involve all communications between people in charge of the safety of the civil society. MCC have unique requirements that include improved reliability, security and group communication support. In this paper, we propose secure and robust Multiple-Input-Multiple-Output (MIMO) transceivers, designed for multiple Base Stations (BS) supporting multicast MCC in presence of multiple eavesdroppers. We formulate minimization problems with the Sum-Mean-Square-Error (SMSE) at legitimate users as an objective function, and a lower bound for the MSE at eavesdroppers as a constraint. Security is achieved thanks to physical layer security mechanisms, namely MIMO beamforming and Artificial Noise (AN). Reliability is achieved by designing a system which is robust to two types of channel state information errors: stochastic and norm-bounded. We propose a coordinate descent-based algorithm and a worst-case iterative algorithm to solve these problems. Numerical results at physical layer and system level reveal the crucial role of robust designs for reliable MCC. We show the interest of both robust design and AN to improve the security gap. We also show that full BS cooperation in preferred for highly secured and reliable MCC but dynamic clustering allows to trade-off security and reliability against capacity.

Performance Analysis of Cache-Enabled User Association for Hybrid Heterogeneous Cellular Networks

Hybrid heterogeneous cellular networks (HCNets) with sub-6G and millimeter-wave (mmWave) base stations (BSs) achieves good performance-and-cost tradeoffs. Via noticing the negligible retrieving latency and the potential joint transmission (JT) gain of sub-6G BSs, we propose a novel cache-enabled user association scheme for hybrid HCNets with limited local storage, in which partial sub-6G local storage caches contents with relatively low popularity for higher overall content diversity. To sufficiently exploit the large bandwidth of mmWave BSs, they are endowed with higher association priority. Then, the success probability and meta distribution of achievable rate are studied for the proposed scheme, respectively. We further provide the optimization of caching parameters and the condition for superior performance over the MPC-based scheme. Analytical and numerical results show that the network performance can be improved for larger sub-6G BS cooperation size and the array size at mmWave BSs, while the mmWave BS density has two adverse effect for low-to-medium and high rate threshold. For the proposed scheme, the content diversity in combination with JT gain benefits the success probability. And its performance advantage increases for larger local storage and/or higher association probability with mmWave BSs.

Performance Modeling and Analysis of Base Station Cooperation for Cellular-Connected UAV Networks

This paper studies the performance of base station (BS) cooperation for the downlink transmission of an unmanned aerial vehicle (UAV) in a cellular-connected UAV network. Performance models are derived for investigating the downlink transmission performance of a UAV with BS cooperation in terms of coverage probability and achievable throughput. In deriving the performance models, we consider two UAV distribution cases: general case and worst case, in which the locations of UAVs follow different distributions. Meanwhile, we introduce a line-of-sight (LoS) ball model to describe the LoS probability of the downlink channel between a BS and a UAV, and show that it has a good approximation to the ITU LoS model, and can largely simplify the derivation of the performance models. Monte Carlo simulation results are shown to validate the derived performance models. The impacts of major network parameters on the downlink transmission performance are investigated through numerical results. The performance models can be used to provide theoretical guidance for the design of a BS cooperation strategy.

Exact Construction of BS-Assisted MSCR Codes With Link Constraints

It is clear that 5G network resources would be consumed by heavy data traffic owing to increased mobility, slicing, and layered/distributed storage system architecture. The problem is elevated when multiple node failures are repaired to address service quality requirements. Typical approaches include individual or cooperative data regeneration to efficiently utilize the available bandwidth. It is observed that storage systems of 5G and beyond technologies shall have a multi-layer architecture in which base stations (BS) would be present. Moreover, communication with each layer would be subject to various communication costs and link constraints. Under limited BS assistance and cooperation, the trade-off between storage per node and communication bandwidth has been established. In this trade-off, two operating points, namely minimum storage, and bandwidth regeneration are particularly important. In this study, we first identify the optimal number of BS use at the minimum storage regeneration point. An explicit code construction is provided subsequently for the exact minimum storage regeneration whereby each layer may help the repair process subject to a communication link constraint.

Base stations cooperation management algorithm based on the quality of experience and load balancing

Time is a crucial factor for decision making, especially in environments where it is necessary to provide a high quality of experience for the users and at the same time make efficient use of their resources; functions that in some cases may conflict. That is one of the challenges that mobile operators have now; the unceasing growth in terms of users and service requirements that generate not only technical challenges. In an initial approach to this problem, we proposed a multi-objective model for selecting the cooperation scheme between base stations and an evolutionary algorithm that provides a set of optimal solutions. However, to find the optimal set of solutions, the convergence time of the proposed algorithm varies between a few seconds to hours, which makes it feasible only for network planning exercises. In this paper, we present an algorithm that allows us to find an efficient solution to the multi-objective problem in a reasonable time for operational decision-making.