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

doi:10.1016/j.future.2022.11.011

Future Generation Computer Systems

Volume 141, April 2023, Pages 173-185

https://doi.org/10.1016/j.future.2022.11.011 Get rights and content

Highlights

•
A novel framework is proposed to jointly design user-centric BS clustering and resource allocation in 6G heterogeneous UDNs.
•
Two independent sub-problems are formulated, after which a user-centric BS clustering algorithm and a user-centric resource allocation algorithm are respectively developed.
•
The advantages of the user-centric BS clustering algorithm and the user-centric resource allocation algorithm are verified.

Abstract

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.

Introduction

The development of artificial intelligence and the popularization of smart devices have amplified demand for data traffic on sixth generation (6G) mobile networks [1]. Conventional heterogeneous networks consisting of macro cells and small cells cannot meet today’s urgent telecommunication service demands. Heterogeneous ultra-dense networks (UDNs) has been envisioned as one of the key technologies in 6G mobile networks [2], [3]. Generally, Heterogeneous UDNs densely deploy a large number of small cell base stations (BSs) in hotspot areas and in areas with high data traffic. This deployment accomplishes several aims: improved system capacity, seamless communication network coverage, and ultra-high data rate for users [4], [5], [6]. However, BS selection, interference management, radio resource allocation, mobility management, and other issues can become increasingly complex and unpredictable [7], [8], [9].

In a heterogeneous UDN, small cell BSs are densely deployed in an overlapping manner, and users can choose among adjacent small cell BSs for access. Thus, the network access selection for heterogeneous UDNs plays a key role in overall system performance. However, increasing the deployment density of small cell BSs complicates the network topology of heterogeneous UDNs along with intra-network interference. Many interference sources around cell-edge users have similar signal strengths, a factor which greatly affects the user experience. In addition, due to the low transmit power of small cell BSs and a coverage range of merely tens of meters, cell-edge users encounter frequent small cell BS handovers in the moving process. Mobility management in heterogeneous UDNs thus represents a major constraint to system performance.

The above circumstances have led traditional BS-centered service to no longer prevail in heterogeneous UDNs. The notion of being “user-centric” has permeated UDN research as a result. User-centric UDNs weaken the traditional service mode in which one user corresponds to one BS and alters the design concept of traditional network architecture [10]. In a user-centric heterogeneous UDN, multiple small cell BSs for users form a small cell BS cluster. The BSs within the cluster then jointly provide users with data transmission services via joint processing technology. Meanwhile, users can establish wireless links with multiple small cell BSs. Cell-edge users will hence not be disconnected from the system during BS handover. Furthermore, because small cell BSs that may have functioned as interference sources subsequently become serving BSs, system interference declines. Cell-edge users ultimately enjoy more satisfactory service experiences by aggregating data transmission from multiple small cell BSs [11].

User-centric heterogeneous UDNs can reduce the complexity of interference in traditional UDNs and enhance the user experience [12]. Yet these networks also introduce new problems. When the density of small cells and users gradually increases, the composition of small cell BS clusters exhibits greater selectivity. Inappropriate BS clustering may compromise system performance. The question of how to construct suitable small cell BS clusters for users is pertinent yet challenging. Continual rises in the distribution density of small cell BSs and users cause the coverage of small cell BS clusters of users to overlap, leading to inter-cluster interference. The limitations and orthogonality constraints of wireless resources have also highlighted resource allocation in UDNs as a salient issue. Conventional BS-centric resource allocation allows for the multiplexing and orthogonal allocation of wireless resources, whereas these tasks no longer apply in user-centric UDNs. A targeted resource allocation algorithm is thus necessary. To more effectively harness UDN-related performance gains, in-depth research is warranted on adaptive BS clustering and resource allocation in heterogeneous UDNs [13].

Against this backdrop, the present paper proposes a novel framework by jointly considering user-centric BS clustering and resource allocation for cell-edge users in heterogeneous UDNs, subject to the system capacity and limited wireless resource constraints. Theoretically, this situation represents a non-convex and nonlinear integer programming problem, which is NP-hard. It would therefore be impractical to seek optimal solutions through exhaustive searches; doing so would entail excessive computational complexity [14]. We opt to decouple the joint problem into two sub-problems: a BS clustering problem and a resource allocation problem, each of which can be solved efficiently. Specifically, the BS clustering problem is used to determine the serving small cell BS set for any user in order to fully exploit the benefits of cooperation; the resource allocation problem is designed to mitigate resultant inter-cluster interference to maximize overall spectral efficiency. We first model the BS clustering problem as a two-sided many-to-many matching problem (i.e., two-sided matching between users and small cells) and propose a user-centric BS clustering algorithm based on many-to-many matching. The algorithm is grounded in the two-sided many-to-many matching theory, considering the constraints of user access selection and system capacity. Multi-pair matching between users and small cell BSs is performed given the optimization goal of maximizing the achievable system rate. Then, based on results for the BS clustering problem, we design a novel user-centric resource allocation algorithm. This algorithm first uses spectral clustering to partition the network into several independent sub-networks according to the degree of interference between system users, aiming to maximize intra-sub-network interference while minimizing inter-sub-network interference. We then orthogonally allocate resource blocks (RBs) within each sub-network and perform spatial multiplexing of RBs between sub-networks. This algorithm accounts for resource allocation constraints under multi-cell cooperation and primarily ascribes resource allocation to network partitioning. This assignment effectively reduces the complexity of such allocation. The main contributions of this work are summarized below:

•
A novel framework is proposed to jointly design user-centric BS clustering and resource allocation in 6G heterogeneous UDNs to mitigate intra-cluster and inter-cluster interference while maximizing the benefits of BS cooperation.
•
Two independent sub-problems are formulated, after which a many-to-many matching-based user-centric BS clustering algorithm and a user-centric resource allocation algorithm are respectively developed.
•
The advantages of the user-centric BS clustering algorithm and the user-centric resource allocation algorithm are verified. The proposed solution is found to reduce co-channel interference in the system and to demonstrate superior performance in terms of the sum achievable system rate, average achievable user rate, and user spectral efficiency. compared with benchmark solutions.

The rest of this paper is organized as follows. Related work is reviewed in Section 2. We introduce the system model in Section 3. The design problem of user-centric BS clustering is formulated and a many-to-many matching-based user-centric BS clustering algorithm is proposed in Section 4. In Section 5, we formulate the resource allocation problem and propose a user-centric resource allocation algorithm for heterogeneous UDNs. Our proposed solution is evaluated through numerical experiments in Section 6. Section 7 closes the paper.

Section snippets

Related work

UDNs can realize significant gains for future wireless communication systems but feature several barriers. BS cooperation has become a key means of enhancing UDN system performance. A number of studies have addressed BS cooperation techniques. The authors in [15] investigated the BS cooperation problem in downlink heterogeneous cellular networks and proposed a power optimization scheme with a minimum spectral efficiency constraint to derive optimal received signal strength thresholds under

System model

This paper considers a two-layer heterogeneous UDN containing one central macro cell BS and multiple small cell BSs served by the macro cell BS and densely deployed in a given region. The macro cell BS connects to the core network through optical fibers, and all small cell BSs connect to the macro cell BS through backhaul links. The macro cell BS is integrated with a central controller to execute functions such as resource management, quality-of-service control, and network mobility. For

Problem formulation

We employ a user-centric clustering method; put simply, each user chooses their surrounding BSs dynamically to create a serving subset. In the user-centric clustering problem, each user $u_{i} \in U$ is assigned to a maximum of $N_{b}$ small cell BSs, and each small cell BS $b_{i} \in B$ is assigned to a maximum of $N_{a}$ users. Let $D_{i} = {b_{j} | d_{i, j} < ɛ}$ denote the candidate small cell BS set for user $u_{i}$ . Here, $d_{i, j}$ (in m) represents the distance between small cell BS $b_{j}$ and user $u_{i}$ , $ɛ$ represents the small cell BS coverage area

Problem formulation

After forming small cell BS clusters, we seek to solve the user-centric resource allocation problem in the system. Here, we develop a novel user-centric resource allocation algorithm based on the solution mentioned in [33], which comprises two stages: network partitioning and resource allocation. We first partition the system into multiple sub-networks by maximizing intra-sub-network interference and minimizing inter-sub-network interference. The set of small cell BS clusters is denoted as $C = {S C$

Parameters settings

In this section, we present the simulation results to validate our analysis and evaluate the system performance under the proposed user-centric BS clustering and resource allocation algorithms. For simplicity but without loss of generality, we consider a 250m $\times$ 250 m network plane, wherein the macro cell BS is deployed in the center, the locations of the small cell BSs and users are generated independently by independent Poisson point processes. The users preferentially access the small cell BSs.

Conclusion

This paper has demonstrated a joint user-centric BS clustering and resource allocation framework for cell-edge users in 6G heterogeneous UDNs, intended to maximize system throughput and spectral efficiency under system capacity and RB constraints. We first investigated the BS clustering problem and put forward a user-centric BS clustering algorithm using many-to-many matching. Second, we investigated the resource allocation problem by decomposing it into two steps: network partitioning and

CRediT authorship contribution statement

Yuhan Su: Conception of the study, Performed the data analyses, Wrote the manuscript. Zhibin Gao: Performed the experiment. Xiaojiang Du: Analysis, Manuscript preparation. Mohsen Guizani: Analysis with constructive discussions.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This work was supported in part by the Fundamental Research Funds for the Central Universities under Grants 20720220089 and 20720220104, and in part by the National Natural Science Foundation of China under Grants 62271424 and 61971365.

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Yuhan Su received the B.S. degree in communication engineering from Huaqiao University, Xiamen, China, and the Ph.D. degree in communication and information systems from Xiamen University, Xiamen, China, in 2015 and 2021, respectively. He is currently an assistant professor in the School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, China. Previously, he was a visiting scholar in Department of Electrical & Computer Engineering, North Carolina State University, USA, from 2020 to 2021. His research interests include wireless communications, congestion control, radio resource management, cooperative communications, underwater acoustic sensor networks and signal processing.

Zhibin Gao received the B.S. degree in communication engineering, the M.S. degree in radio physics, and the Ph.D. degree in communication engineering from Xiamen University, Xiamen, China, in 2003, 2006, and 2011, respectively. He is a Senior Engineer of Communication Engineering with Xiamen University. His research interests include wireless communication, wireless network resource management, and signal processing.

Xiaojiang Du received his B.S. and M.S. degree in Electrical Engineering (Automation Department) from Tsinghua University, Beijing, China in 1996 and 1998, respectively. He received his M.S. and Ph.D. degree in Electrical Engineering from the University of Maryland, College Park in 2002 and 2003, respectively. Dr. Du is a tenured Full Professor and the Director of the Security And Networking (SAN) Lab in the Department of Computer and Information Sciences at Temple University, Philadelphia, USA. His research interests are security, wireless networks, and systems. He has authored over 400 journal and conference papers in these areas, as well as a book published by Springer. Dr. Du has been awarded more than 6 million US Dollars research grants from the US National Science Foundation (NSF), Army Research Office, Air Force Research Lab, NASA, the State of Pennsylvania, and Amazon. He won the best paper award at IEEE GLOBECOM 2014 and the best poster runner-up award at the ACM MobiHoc 2014. He serves on the editorial boards of two international journals. Dr. Du served as the lead Chair of the Communication and Information Security Symposium of the IEEE International Communication Conference (ICC) 2015, and a Co-Chair of Mobile and Wireless Networks Track of IEEE Wireless Communications and Networking Conference (WCNC) 2015. He is (was) a Technical Program Committee (TPC) member of several premier ACM/IEEE conferences such as INFOCOM (2007–2020), IM, NOMS, ICC, GLOBECOM, WCNC, BroadNet, and IPCCC. Dr. Du is an IEEE Fellow and a Life Member of ACM.

Mohsen Guizani received the BS (with distinction), MS and PhD degrees in Electrical and Computer engineering from Syracuse University, Syracuse, NY, USA in 1985, 1987 and 1990, respectively. He is currently a Professor of Machine Learning and the Associate Provost at Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE. Previously, he worked in different institutions in the USA. His research interests include applied machine learning and artificial intelligence, Internet of Things (IoT), intelligent systems, smart city, and cybersecurity. He was elevated to IEEE Fellow in 2009 and was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2019, 2020 and 2021. Dr. Guizani has won several research awards including the “2015 IEEE Communications Society Best Survey Paper Award”, the Best ComSoc Journal Paper Award in 2021 as well five Best Paper Awards from ICC and Globecom Conferences. He is the author of ten books and more than 800 publications. He is also the recipient of the 2017 IEEE Communications Society Wireless Technical Committee (WTC) Recognition Award, the 2018 AdHoc Technical Committee Recognition Award, and the 2019 IEEE Communications and Information Security Technical Recognition (CISTC) Award. He served as the Editor in-Chief of IEEE Network and is currently serving on the Editorial Boards of many IEEE Transactions and Magazines. He was the Chair of the IEEE Communications Society Wireless Technical Committee and the Chair of the TAOS Technical Committee. He served as the IEEE Computer Society Distinguished Speaker and is currently the IEEE ComSoc Distinguished Lecturer.

View full text

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

Highlights

Abstract

Introduction

Section snippets

Related work

System model

Problem formulation

Problem formulation

Parameters settings

Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgment

Energy

Procedia Comput. Sci.

Vision, application scenarios, and key technology trends for 6G mobile communications

Sci. China Inf. Sci.