User Association for Load Balancing in Heterogeneous Cellular Networks

By Qiaoyang Ye, B. Rong, Yudong Chen, M. Al-Shalash, Constantine Caramanis, Jeffrey G. Andrews. IEEE Transactions on Wireless Communications, 2012

1,221 citations recorded by the source corpus.

For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7df5f7ceecc3322255aab8a9e5366ba983131d16

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Citations in the corpus

Joint Resource Partitioning and Offloading in Heterogeneous Cellular Networks (2013, methodology): “not based on rate distribution. In this paper, we use the metric of rate coverage, which captures the effect of both SINR and load distribution across the network. Semi-analytical approaches in [17], [18] showed, through simulations, that there exists an optimal association bias for ﬁfth percentile ”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, background): “One is max-SINR based association, which means each user chooses the BS with the maximal SINR to associate, and the other one is based on optimization theory, which adopts gradient descent and dual decomposition [144].”
Wireless Communication Using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization (2017, background): “The sum-log utility is a commonly used performance metric that accounts for both total system performance and individual fairness [26], [35].”
User Association in 5G Networks: A Survey and an Outlook (2015, methodology): “It is particularly suitable for large networks, especially for HetNets associated with many autonomous femtocells [85, 86].”
A Primer on Cellular Network Analysis Using Stochastic Geometry (2016, background): “The selection bias is a simple and crude but remarkably effective tool for pushing mobiles onto the small cells, and has been observed to achieve near-optimum results when the bias is chosen correctly [37], [39].”
What Will 5G Be? (2014, background): “ 500% [39]. The joint problem of user association and resource allocation in two-tier heterogeneous networks (HetNets), with adaptive tuning of the biasing and blanking in each cell, is considered in [35], [36], [40]–[45]. An interesting model of hotspot trafﬁc is considered in [41]–[43] where it is”
Collaborative Cloud and Edge Computing for Latency Minimization (2019, methodology): “In this paper, we assume that each user has been already associated with a BS, which can be determined by some user association strategies [30]–[32].”
Initial Access in 5G mmWave Cellular Networks (2016, background): “This issue already emerged in HetNets, as introduced in [7].”
Energy-Efficient Design of MIMO Heterogeneous Networks With Wireless Backhaul (2015, background): “To this end, heterogeneous netw orks (HetNets) can provide higher coverage and throughput by overlaying macro cells with a large number of small cells and access points, thus offloading traffic and reducing the distance between transmitter and receiver [13], [14].”
An overview of load balancing in hetnets: old myths and open problems (2013, background): “Constantine Caramanis was also instrumental in formulating our initial optimization approach in [3], which started our group’s work on load balancing.”
Analysis on Cache-Enabled Wireless Heterogeneous Networks (2015, background): “Such a HetNet decreases the distance between BSs/relays and users, and thus increases the area spectral efficiency, yielding the increase of network capacity [6]–[8].”
Modeling and Analyzing Millimeter Wave Cellular Systems (2016, background): “The network can then be optimized following a network optimization framework [148]–[150] or a simpler but often effective biasing approach whereby the minimum path loss rule used in this paper is modified to have an affinity for lower power or more lightly weighted cells, as in [151]–[155].”
Energy Efficient User Association and Power Allocation in Millimeter-Wave-Based Ultra Dense Networks With Energy Harvesting Base Stations (2017, background): “Load awareness based user association was proposed in [10] and [11].”
Convergence Time Optimization for Federated Learning Over Wireless Networks (2020, methodology): “Similarly, the downlink data rate of the BS when transmitting the global FL model parameters to each user i is given by [22]”
Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks (2017, background): “Similarly to the system that performs centralized joint transmission for interference management [16] and/or joint user scheduling [18], our proposed scheme comes with the cost of increased demand on backhaul due to additional signaling overhead.”