A scalable framework is developed to allocate radio resources across a large
number of densely deployed small cells with given traffic statistics on a slow
timescale. Joint user association and spectrum allocation is first formulated
as a convex optimization problem by dividing the spectrum among all possible
transmission patterns of active access points (APs). To improve scalability
with the number of APs, the problem is reformulated using local patterns of
interfering APs. To maintain global consistency among local patterns,
inter-cluster interaction is characterized as hyper-edges in a hyper-graph with
nodes corresponding to neighborhoods of APs. A scalable solution is obtained by
iteratively solving a convex optimization problem for bandwidth allocation with
reduced complexity and constructing a global spectrum allocation using
hyper-graph coloring. Numerical results demonstrate the proposed solution for a
network with 100 APs and several hundred user equipments. For a given quality
of service (QoS), the proposed scheme can increase the network capacity several
fold compared to assigning each user to the strongest AP with full-spectrum
reuse.

通过对运行速度缓慢的密集部署的小型基站进行交互用户关联和频谱分配来开发可扩展的框架，以满足给定的流量统计需求。通过将频谱划分给所有可能的主动访问点 (AP) 的传输模式来首先将联合用户关联和频谱分配表述为凸优化问题。

大规模小区网络中的可扩展频谱分配和用户关联

Scalable Spectrum Allocation and User Association in Networks with Many  Small Cells

We study the problem of joint load balancing and interference mitigation in
heterogeneous networks (HetNets) in which massive multiple-input
multiple-output (MIMO) macro cell base station (BS) equipped with a large
number of antennas, overlaid with wireless self-backhauled small cells (SCs)
are assumed. Self-backhauled SC BSs with full-duplex communication employing
regular antenna arrays serve both macro users and SC users by using the
wireless backhaul from macro BS in the same frequency band. We formulate the
joint load balancing and interference mitigation problem as a network utility
maximization subject to wireless backhaul constraints. Subsequently, leveraging
the framework of stochastic optimization, the problem is decoupled into dynamic
scheduling of macro cell users, backhaul provisioning of SCs, and offloading
macro cell users to SCs as a function of interference and backhaul links. Via
numerical results, we show the performance gains of our proposed framework
under the impact of small cells density, number of base station antennas, and
transmit power levels at low and high frequency bands. We further provide
insights into the performance analysis and convergence of the proposed
framework. The numerical results show that the proposed user association
algorithm outperforms other baselines. Interestingly, we find that even at
lower frequency band the performance of open access small cell is close to that
of closed access at some operating points, the open access full- duplex small
cell still yields higher gain as compared to the closed access at higher
frequency bands. With increasing the small cell density or the wireless
backhaul quality, the open access full- duplex small cells outperform and
achieve a 5.6x gain in terms of cell-edge performance as compared to the closed
access ones in ultra-dense networks with 350 small cell base stations per km2 .

本文研究了异质网络中负载均衡和干扰抑制问题，提出了一种基于随机优化的框架，动态调度宏基站用户、提供小基站的回传以及按照干扰和回传链路把宏基站用户卸载到小基站上。数值结果表明，所提出的用户关联算法优于其他基线，开放式全双工小单元的性能优于封闭式，超密集网络中，开放式全双工小型单元的表现是封闭式的 5.6 倍。

5G HETNETS 中的联合负载平衡和干扰抑制

Joint load balancing and interference mitigation in 5G HETNETS

Due to the increasing demand of capacity in wireless cellular networks, the
small cells such as pico and femto cells are becoming more popular to enjoy a
spatial reuse gain, and thus cells with different sizes are expected to coexist
in a complex manner. In such a heterogeneous environment, the role of
interference management (IM) becomes of more importance, but technical
challenges also increase, since the number of cell-edge users, suffering from
severe interference from the neighboring cells, will naturally grow. In order
to overcome low performance and/or high complexity of existing static and other
dynamic IM algorithms, we propose a novel low-complex and fully distributed IM
scheme, called REFIM, in the downlink of heterogeneous multi-cell networks. We
first formulate a general optimization problem that turns out to require
intractable computation complexity for global optimality. To have a practical
solution with low computational and signaling overhead, which is crucial for
low-cost small-cell solutions, e.g., femto cells, in REFIM, we decompose it
into per-BS problems based on the notion of reference user and reduce feedback
overhead over backhauls both temporally and spatially. We evaluate REFIM
through extensive simulations under various configurations, including the
scenarios from a real deployment of BSs. We show that, compared to the schemes
without IM, REFIM can yield more than 40% throughput improvement of cell-edge
users while increasing the overall performance by 10~107%. This is equal to
about 95% performance of the existing centralized IM algorithm that is known to
be near-optimal but hard to implement in practice due to prohibitive
complexity. We also present that as long as interference is managed well, the
spectrum sharing policy can outperform the best spectrum splitting policy where
the number of subchannels is optimally divided between macro and femto cells.

研究了在异构网络中小型细胞相互干扰的问题，提出了一种低复杂度的分布式干扰管理算法 REFIM，可使细胞边缘用户的吞吐量提高超过 40%，整体性能提高 10~107%。