Two key traits of 5G cellular networks are much higher base station (BS) densities - especially in the case of low-power BSs - and the use of massive MIMO at these BSs. This paper explores how massive MIMO can be used to jointly maximize the offloading gains and minimize the interference challenges arising from adding small cells. We consider two interference management approaches: joint transmission (JT) with local precoding, where users are served simultaneously by multiple BSs without requiring channel state information exchanges among cooperating BSs, and resource blanking, where some macro BS resources are left blank to reduce the interference in the small cell downlink. A key advantage offered by massive MIMO is channel hardening, which enables to predict instantaneous rates a priori. This allows us to develop a unified framework, where resource allocation is cast as a network utility maximization (NUM) problem, and to demonstrate large gains in cell-edge rates based on the NUM solution. We propose an efficient dual subgradient based algorithm, which converges towards the NUM solution. A scheduling scheme is also proposed to approach the NUM solution. Simulations illustrate more than 2x rate gain for 10th percentile users vs. an optimal association without interference management.
Several approaches have been used to study load balancing in HetNets, including stochastic geometry @cite_20 @cite_32 , game theory @cite_16 and system-level simulations @cite_15 @cite_43 . Meanwhile, in industry, proactive load balancing is accomplished by UE association towards the small cells @cite_15 @cite_43 . Biasing refers to artificially adding a bias value (e.g., @math dB) to received signal power from small cell layer at UEs. Our initial study on load balancing @cite_44 formulated a network utility maximization (NUM) problem for user association in HetNets with single-antenna BSs, where resources are equally allocated among users in the same cell. The equal resource allocation can be suboptimal if the user associations happen on a much slower time scale than the channel variations. In general, the user association and scheduling (resource allocation) problems are coupled, and it is quite difficult to jointly optimize them.
@cite_32 @cite_44 @cite_43 @cite_15 @cite_16 @cite_20
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The rate distribution in heterogeneous networks (HetNets) greatly benefits from load balancing, by which mobile users are pushed onto lightly-loaded small cells despite the resulting loss in SINR. This offloading can be made more aggressive and robust if the macrocells leave a fraction of time frequency resource blank, which reduces the interference to the offloaded users. We investigate the joint optimization of this technique - referred to in 3GPP as enhanced intercell interference coordination (eICIC) via almost blank subframes (ABSs) - with offloading in this paper. Although the joint cell association and blank resource (BR) problem is nominally combinatorial, by allowing users to associate with multiple base stations (BSs), the problem becomes convex, and upper bounds the performance versus a binary association. We show both theoretically and through simulation that the optimal solution of the relaxed problem still results in an association that is mostly binary. The optimal association differs significantly when the macrocell is on or off; in particular the offloading can be much more aggressive when the resource is left blank by macro BSs. Further, we observe that jointly optimizing the offloading with BR is important. The rate gain for cell edge users (the worst 3-10 ) is very large - on the order of 5-10x - versus a naive association strategy without macrocell blanking. We present a novel approach for distributed load balancing in heterogeneous networks that use cell range expansion (CRE) for user association and almost blank subframe (ABS) for interference management. First, we formulate the problem as a minimization of an @math -fairness objective function with load and outage constraints. Depending on @math , different objectives in terms of network performance or fairness can be achieved. Next, we model the interactions among the base stations for load balancing as a near-potential game, in which the potential function is the @math -fairness function. The optimal pure Nash equilibrium (PNE) of the game is found by using distributed learning algorithms. We propose log-linear and binary log-linear learning algorithms for complete and partial information settings, respectively. We give a detailed proof of convergence of learning algorithms for a near-potential game. We provide sufficient conditions under which the learning algorithms converge to the optimal PNE. By running extensive simulations, we show that the proposed algorithms converge within few hundreds of iterations. The convergence speed in the case of partial information setting is comparable to that of the complete information setting. Finally, we show that outage can be controlled and a better load balancing can be achieved by introducing ABS. Matching the demand for resources (?load?) with the supply of resources (?capacity?) is a basic problem occurring across many fields of engineering, logistics, and economics, and has been considered extensively in both the Internet and wireless networks. The ongoing evolution of cellular communication networks into dense, organic, and irregular heterogeneous networks (HetNets) has elevated load awareness to a central problem, and introduces many new subtleties. This article explains how several long-standing assumptions about cellular networks need to be rethought in the context of a load-balanced HetNet: we highlight these as three deeply entrenched myths that we then dispel. We survey and compare the primary technical approaches to HetNet load balancing: (centralized) optimization, game theory, Markov decision processes, and the newly popular cell range expansion (a.k.a. biasing), and draw design lessons for OFDMA-based cellular systems. We also identify several open areas for future exploration. In this paper, we investigate the load balancing problem with the consideration of different qualityof- service (QoS) and channel state in 3GPP long term evolution (LTE) multi-cell networks. Load imbalance among nearby cells often causes inefficient utilization of the system resources. Then in overloaded cells, users cannot get satisfied services for short resources, while in idle cells, there is plenty of unoccupied resources. However, switching users only for load balancing with no consideration of channel state often results in aggressive handover of users, which may occupy too many resources in their target cells and deteriorate the network efficiency. Thus, how to efficiently perform load balancing with the consideration of channel state becomes an important issue. Meanwhile, LTE network aims to serve users with different QoS requirements, which should be taken into account when performing load balancing. Thus we propose a multi-objective optimization problem, whose objectives are load balancing index and network average load for users with QoS requirements and a unified utility function for users without QoS requirements. The constraints are physical resource limits and users’ QoS demands. Then we analyze its complexity and propose a practical algorithm, load balancing, with the consideration of different QoS requirements and channel state services (LBQC). Extensive simulations are conducted. The results show that the proposed algorithm can approximate the optimal algorithm efficiently, and leads to significantly better performances than load balancing with no consideration of channel state, e.g. a lower new call blocking rate with a fewer system resources occupation for users with QoS requirements, a higher cell edge throughput and total throughput for users without QoS requirements.