Cluster Base Station Research Articles

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

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

Joint Optimization of Base Station Clustering and Service Caching in User-Centric MEC

Joint Optimization of Base Station Clustering and Service Caching in User-Centric MEC

Enhancing VoIP quality in mobile networks through optimised MOS prediction and base station clustering with SDN

Enhancing VoIP quality in mobile networks through optimised MOS prediction and base station clustering with SDN

Enhancing VoIP Quality in Mobile Networks through Optimized MOS Prediction and Base Station Clustering with SDN

Enhancing VoIP Quality in Mobile Networks through Optimized MOS Prediction and Base Station Clustering with SDN

QoS-driven resource allocation in fog radio access network: A VR service perspective.

While immersive media services represented by virtual reality (VR) are booming, They are facing fundamental challenges, i.e., soaring multimedia applications, large operation costs and scarce spectrum resources. It is difficult to simultaneously address these service challenges in a conventional radio access network (RAN) system. These problems motivated us to explore a quality-of-service (QoS)-driven resource allocation framework from VR service perspective based on the fog radio access network (F-RAN) architecture. We elaborated details of deployment on the caching allocation, dynamic base station (BS) clustering, statistical beamforming and cost strategy under the QoS constraints in the F-RAN architecture. The key solutions aimed to break through the bottleneck of the network design and to deep integrate the network-computing resources from different perspectives of cloud, network, edge, terminal and use of collaboration and integration. Accordingly, we provided a tailored algorithm to solve the corresponding formulation problem. This is the first design of VR services based on caching and statistical beamforming under the F-RAN. A case study provided to demonstrate the advantage of our proposed framework compared with existing schemes. Finally, we concluded the article and discussed possible open research problems.

Joint User Scheduling, Base Station Clustering, and Beamforming Design Based on Deep Unfolding Technique

Joint User Scheduling, Base Station Clustering, and Beamforming Design Based on Deep Unfolding Technique

User Scheduling at Base Station Cluster Boundary for Massive MIMO Downlink Transmission

User Scheduling at Base Station Cluster Boundary for Massive MIMO Downlink Transmission

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.

User-centric base station clustering and resource allocation for cell-edge users in 6G ultra-dense networks

Ultra-Dense Networks (UDNs) have been proposed to meet the ultra-high system capacity and ultra-high user experience rate requirements of sixth generation (6G) mobile networks. However, ultra-dense base stations (BSs) deployment poses challenges for cell-edge users such as BS selection, severe interference, resource allocation, and inter-cell handover. To tackle these obstacles, this paper formulates a joint BS clustering and resource allocation problem for cell-edge users in a 6G heterogeneous UDN system. For efficient resolution, this problem is decoupled into two sub-problems to be solved independently. We first propose a user-centric BS clustering algorithm based on many-to-many matching for the BS clustering problem in the heterogeneous UDN system; This algorithm considers the constraint of the system capacity. A many-to-many stable matching between users and small cell BSs is constructed under the optimization objective of maximizing the achievable user rate. Furthermore, we analyze the effectiveness, stability, convergence, and complexity of the BS clustering algorithm. Given established small cell BS clusters, we also propose a user-centric resource allocation algorithm based on network partitioning. This algorithm accounts for the interference of all users in the heterogeneous UDN system and maximizes intra-sub-network interference while minimizing inter-sub-network interference via a spectral clustering-based sub-network partitioning algorithm. Next, orthogonal allocation of resource blocks (RBs) is implemented within each sub-network, and RBs are spatially multiplexed between sub-networks. Numerical results confirm the benefits of the proposed methods: our algorithms outperform benchmark solutions in terms of the sum achievable system rate, average achievable user rate, and user spectral efficiency.

Total Delay Optimization in Cache-Enabled C-RANs With Hierarchical Rate Splitting

This paper proposes a Hierarchical Rate Splitting (HRS)-based and Macro Base Station (MBS)-assisted cooperative content delivery scheme for Cache-enabled Cloud Radio Access Networks (C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -RANs). In this scheme, a user is served by a cluster of Small Base Stations (SBSs) that cache contents. If the user-requested content is missing in its cache, the SBS retrieves it from the central processor (backhaul link) and the MBS (wireless broadcast). We minimize the total delay of this system. We thus optimize user grouping, decoding order, RS variables, BS clustering, and beamforming subject to the transmit power and backhaul capacity constraints. This process formulates a non-convex problem, which we decouple into three subproblems. First, we must pair users to reduce intra-group interference. We thus develop a Low-Complexity User Pairing (LCUP) algorithm. Second, we optimize the decoding order to minimize the broadcast link delay. We, therefore, propose a Dynamic Delay-based Decoding Order Update (D <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> OU) algorithm. Third, we jointly design the RS variables, beamforming, and BS clustering. We use surrogate optimization via the Quadratic Transform (QT), Woodbury matrix identity, and Taylor expansion. We then recast this problem to a Two-Tier Alternating (TTA) problem, which we solve with closed-form (outer tier) and convex (inner tier) formulations. We integrate these algorithms to propose an overall algorithm. It achieves significant performance gains and a 3-fold reduction in time-complexity over the standard Semi-Definite Relaxation (SDR)-based algorithm and several benchmark schemes.

Coverage and Capacity of Terahertz Cellular Networks With Joint Transmission

Beamforming with high dimensional antenna arrays provides the gain needed to enable high bandwidth communication in the sub-terahertz (THz) band. The resulting narrow beams, however, come at the cost of increased sensitivity to beam alignment errors. A potential remedy to this problem is to introduce a form a macrodiversity through non-coherent joint transmission (NC-JT). We employ a stochastic geometry framework to analyze the performance of a THz network employing user-centric base station clustering and NC-JT. We derive semi-closed form lower bounds for the coverage probability and ergodic capacity experienced by a typical user in the network. Our model includes THz networks without joint transmission as a special case. Overall, our analysis indicates that joint transmission from a few base stations improves coverage and capacity by mitigating the impact of beam misalignment. Moreover, in certain settings, NC-JT is a more efficient usage a network access points compared to the non-cooperative case, and reduces the sensitivity of coverage and capacity to the choice of user beamwidth.

Energy Efficient Resource Allocation for IRS Assisted CoMP Systems

A novel intelligent reconfigurable surface (IRS) assisted coordinated multi-point (CoMP) system is proposed. Our objective is to maximize the energy efficiency (EE) of this system by jointly optimizing base station (BS) clustering, user association, sub-carrier assignment, power allocation, and optimal design of the IRS, while satisfying the users’ quality of service requirements. Considering the amplitude and phase shift characteristics, both ideal and non-ideal IRS are investigated. The formulated problem is proved to be NP-hard. By analyzing its structure, we decouple it into the power allocation sub-problem, the BS clustering, UE association, and sub-carrier assignment sub-problem, and the reflection coefficients design sub-problem. For the power allocation sub-problem, we invoke the fractional programming to find the optimal solution. For the reflection coefficients design sub-problem of ideal IRS, the optimal solution is derived with the Lagrangian dual method. Whereas quantization-based method is employed to find the discrete phase shifts for non-ideal IRS. We finally propose a genetic algorithm (GA) to represent the potential solutions of sub-carrier assignment, and combine the other two optimization algorithms as fitness estimator in GA. Numerical results validate the feasibility, fast convergence, and the flexibility of the proposed algorithm. It shows that the proposed scheme outperform the system without IRS and that with a random initialized IRS.

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.

A Hierarchical Distributed Operational Framework for Renewables-Assisted 5G Base Station Clusters and Smart Grid Interaction

A Hierarchical Distributed Operational Framework for Renewables-Assisted 5G Base Station Clusters and Smart Grid Interaction

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

The clustering algorithms play a crucial role for energy saving solutions in mobile opportunistic networks. If the selection of cluster head is made appropriately, then the energy can be consumed optimally. The existing clustering algorithms do not consider the optimal selection of the cluster head resulting in low survival rates and high energy consumption rates in nodes. The adaptive clustering is required in Industrial Internet of Things (IIoT) based sophisticated networks where seamless connectivity is imperative for rapid communication. In order to meet this research gap, an adaptive clustering algorithm for mobile opportunistic networks is proposed within military bases that uses a heuristic algorithm (GA) for adaptive clustering. An analysis of the opportunity network at the connected military base is carried out and the mobile opportunity model is constructed using the adaptive clustering for the similar traffic. In a mobile machine network, the next hop node is determined by the node clustering principle. A LEACH clustering protocol enables communication between cluster heads and base stations based on single-hop and multihop cluster nodes. In order to perform adaptive clustering of mobile network nodes based on network partitioning and scheduling of clusters, genetic algorithms are used. The proposed approach can be applied to the IIoT systems in places where adaptive clustering is required to optimize energy consumption, to reduce latency rates, and to enhance the throughput of mobile networks. The experimental findings suggest that the proposed adaptive strategy is capable of optimizing energy consumption rates, reducing network latency, and boosting efficiency by increasing throughput in mobile opportunistic networks.

A Multi-Zone Based Hierarchical Protocol for Routing in Wireless Sensor Networks

Wireless sensor networks (WSNs) are used in many applications due to their small size and low cost. However, due to the limited battery constraint in WSNs, it faces several challenges of node failure, data drop, etc. There are many routing algorithms in the literature that ensures the energy efficiency routing by enhancing the network lifetime. But still the challenges of data aggregation, dynamic path selection needs much attention to further maximize the nodes lifetime. In this paper, a multi-hop routing is proposed with an additionally deployed master node to minimize the load on cluster head (CH) and base station (BS) node. The master node is deployed in the middle of the network, and it has been assumed that a master node is static node with power connection. The proposed network is divided into multiple zones according to nodes energy. The proposed network has shown better throughput and network lifetime. Also, the proposed scheme has shown lesser data drop and failure rate.

Joint Cache Placement and Cooperative Multicast Beamforming in Integrated Satellite-Terrestrial Networks

This paper studies joint cache placement and cooperative multicast beamforming to provide content-centric data services for mobile users in the integrated satellite-terrestrial network (ISTN). Specifically, in the ISTN, users requesting the same content are arranged into a multicast group and served by the cache-enabled base stations (BSs) and low earth orbit (LEO) satellite via cooperative beamforming. To maximize the network utility that takes network throughput and backhaul traffic into consideration, the cache placement, LEO satellite and BS clustering, and multicast beamforming are jointly designed and formulated as a two-timescale optimization problem. However, the original problem is anti-causal since the cache placement strategy and content delivery policy are coupled in different timescales. By utilizing historical information, we propose a two-step scheme to decompose the problem into a short-term content delivery subproblem and a long-term cache placement subproblem. As the former subproblem is nonconvex with mixed-integer variables and coupling constraints, we transform it into an equivalent problem and propose a penalty concave-convex procedure based algorithm to solve it. To address the latter subproblem, a centralized iterative algorithm and a distributed alternating algorithm with low complexity are developed, respectively. Simulation results validate that the proposed schemes can effectively enhance the network throughput and reduce the backhaul traffic compared with benchmark scheme.

Energy Efficient Resource Allocation for MSCA Enabled CoMP in HetNets

A novel multi-stream carrier aggregation (MSCA) enabled coordinated multi-point (CoMP) framework in heterogeneous cellular networks (HetNets) is proposed. Our objective is to maximize the energy efficiency (EE) of the proposed framework by jointly optimizing base station (BS) clustering, user association, sub-carrier assignment and power allocation. The formulated problem is proved to be NP-hard. By analyzing the structure of the original problem, we decouple it into power allocation sub-problem and sub-carrier assignment sub-problem. For the power allocation sub-problem, we propose sequential minimization technique and Zoutendijk feasible direction method to find the optimal power allocation with fixed sub-carrier assignment. For the sub-carrier assignment sub-problem, we propose genetic algorithm (GA) to represent the BS clustering and sub-carrier assignment, and design two GA based joint optimization frameworks. One is using the power allocation algorithm for fitness evaluation and guiding the evolution of GA. Another is optimizing these two sub-problems in an iterative manner, whereas the optimal sub-carrier assignment is updated timely as any change of the power allocation. Numerical results validate the feasibility, fast convergence, and flexibility of the proposed algorithm, and is superior to conventional MSCA or CoMP enabled HetNets.

An Interference-Managed Hybrid Clustering Algorithm to Improve System Throughput.

In the current smart era of 5G, cellular devices and mobile data have increased exponentially. The conventional network deployment and protocols do not fulfill the ever-increasing demand for mobile data traffic. Therefore, ultra-dense networks have widely been suggested in the recent literature. However, deploying an ultra-dense network (UDN) under macro cells leads to severe interference management challenges. Although various centralized and distributed clustering methods have been used in most research work, the issue of increased interference persists. This paper proposes a joint small cell power control algorithm (SPC) and interference-managed hybrid clustering (IMHC) scheme, to resolve the issue of co-tier and cross-tier interference in the small cell base station cluster tiers. The small cell base stations (SBSs) are categorized based on their respective transmitting power, as high-power SBSs (HSBSs) and low-power SBSs (LSBSs). The simulation results show that by implementing the IMHC algorithm for SBSs in a three-tier heterogeneous network, the system throughput is improved with reduced interference.

Double-Layer K-Means++ Clustering Method for Evaluation of Dispatchable Potential of Massive Regional 5G Base Stations

5G base stations (BSs), which are the essential parts of the 5G network, are important user-side flexible resources in demand response (DR) for electric power system. However, a 5G BS has little and difference dispatchable potential, how to make massive 5G BSs participate in DR conveniently is an urgent problem to be solved. Clustering is an effective solution. Aiming at the special requirements of big data analysis and dispatching difficulties brought by the massive and ultra-dense distribution of 5G BSs, this paper proposes a double-layer K-means++ clustering method for regional 5G BSs considering the main characteristics of 5G BSs. The proposed clustering method considers both the geographical location and power consumption characteristics of the regional 5G BSs to partition the 5G BSs into appropriate number of clusters. Besides, the comprehensive charging and discharging dispatchable potential evaluation indices are established based on the clustering results to evaluate the dispatchable potential of each cluster. The effectiveness of the proposed method and the dispatchable potential evaluation indices are verified through a case of 5000 5G BSs in Jiaxing, China. The simulation findings reveal that the performance of the proposed clustering method is better than classical clustering algorithm and the dispatchable potential of the 5G BSs clusters can be accurately analyzed by the proposed comprehensive indices, which lay the foundation for friendly and fine interaction between 5G BSs and the power grid.