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 rates 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 A_j>=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 optimal 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 A_j 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 max received power association.
The existing work on cell association can be broadly classified into two groups: Strategies based on from lightly-loaded cells, such as hybrid channel assignment (HCA) @cite_21 , channel borrowing without locking (CBWL) @cite_16 , load balancing with selective borrowing (LBSB) @cite_15 @cite_24 , etc; Strategies based on to lightly-loaded cells, such as directed retry @cite_8 , mobile-assisted call admission algorithms (MACA) @cite_14 , hierarchical macrocell overlay systems @cite_17 @cite_25 , cell breathing techniques @cite_2 @cite_28 , and biasing methods in HetNets @cite_11 . The approach in this paper is based on traffic transfer. There have been many efforts in the literature toward traffic transfer strategies in macro-only cellular networks. The so-called cell breathing'' technique @cite_2 @cite_28 dynamically changes (contracts or expands) the coverage area depending on the load situation (over-loaded or under-loaded) of the cells by adjusting the transmit power. Sang @cite_9 proposed an integrated framework consisting of MAC-layer cell breathing and load-aware handover cell-site selection. Cell breathing aims to balance the load among neighboring macrocells, while in HetNets we additionally need to balance the load among different tiers.
@cite_14 @cite_11 @cite_8 @cite_28 @cite_9 @cite_21 @cite_24 @cite_2 @cite_15 @cite_16 @cite_25 @cite_17
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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 propose a dynamic load balancing scheme for the channel assignment problem in a cellular mobile environment. As an underlying approach, we start with a fixed assignment scheme where each cell is initially allocated a set of channels, each to be assigned on demand to a user in the cell. A cell is classified as ‘hot’, if the degree of coldness of a cell (defined as the ratio of the number of available channels to the total number of channels for that cell), is less than or equal to some threshold value. Otherwise the cell is cold'. Our load balancing scheme proposes to migrate unused channels from underloaded cells to an overloaded one. This is achieved through borrowing a fixed number of channels from cold cells to a hot one according to a channel borrowing algorithm. A channel assignment strategy is also proposed based on dividing the users in a cell into three broad types—‘new’, ‘departing’, ‘others’—and forming different priority classes of channel demands from these three types of users. Assignment of the local and borrowed channels are performed according to the priority classes. Next, a Markov model for an individual cell is developed, where the state is determined by the number of occupied channels in the cell. The probability for a cell being hot and the call blocking probability in a hot cell are derived, and a method to estimate the value of the threshold is also given. Detailed simulation experiments are carried out in order to evaluate our proposed methodology. The performance of our load balancing scheme is compared with the fixed channel assignment, simple borrowing, and two existing strategies with load balancing (e.g., directed retry and CBWL), and a significant improvement of the system behavior is noted in all cases. In heterogeneous cellular networks (HCNs), it is desirable to offload mobile users to small cells, which are typically significantly less congested than the macrocells. To achieve sufficient load balancing, the offloaded users often have much lower SINR than they would on the macrocell. This SINR degradation can be partially alleviated through interference avoidance, for example time or frequency resource partitioning, whereby the macrocell turns off in some fraction of such resources. Naturally, the optimal offloading strategy is tightly coupled with resource partitioning; the optimal amount of which in turn depends on how many users have been offloaded. In this paper, we propose a general and tractable framework for modeling and analyzing joint resource partitioning and offloading in a two-tier cellular network. With it, we are able to derive the downlink rate distribution over the entire network, and an optimal strategy for joint resource partitioning and offloading. We show that load balancing, by itself, is insufficient, and resource partitioning is required in conjunction with offloading to improve the rate of cell edge users in co-channel heterogeneous networks. In a cellular network, a fixed number of channels is normally assigned to each cell. However, under this scheme, the channel usage may not be efficient because of the variability in the offered traffic. Different approaches such as channel borrowing (CB) and dynamic channel allocation (DCA) have been proposed to accommodate variable traffic. In this paper, we expand on the CB scheme and propose a new channel allocation scheme-mobile-assisted connection-admission (MACA) algorithm-to achieve load balancing in a cellular network. In this scheme, some special channels are used to connect mobile units from different cells; thus, a mobile unit, which is unable to connect to its own base station because it is in a heavily-loaded "hot" cell, may be able to get connected to its neighboring cold cell's base station through a two-hop link. We find that MACA can greatly improve the performance of a cellular network.