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

Multi-tier networks with large-array base stations (BSs) that are able to
operate in the "massive MIMO" regime are envisioned to play a key role in
meeting the exploding wireless traffic demands. Operated over small cells with
reciprocity-based training, massive MIMO promises large spectral efficiencies
per unit area with low overheads. Also, near-optimal user-BS association and
resource allocation are possible in cellular massive MIMO HetNets using simple
admission control mechanisms and rudimentary BS schedulers, since scheduled
user rates can be predicted a priori with massive MIMO.
Reciprocity-based training naturally enables coordinated multi-point
transmission (CoMP), as each uplink pilot inherently trains antenna arrays at
all nearby BSs. In this paper we consider a distributed-MIMO form of CoMP,
which improves cell-edge performance without requiring channel state
information exchanges among cooperating BSs. We present methods for harmonized
operation of distributed and cellular massive MIMO in the downlink that
optimize resource allocation at a coarser time scale across the network. We
also present scheduling policies at the resource block level which target
approaching the optimal allocations. Simulations reveal that the proposed
methods can significantly outperform the network-optimized cellular-only
massive MIMO operation (i.e., operation without CoMP), especially at the cell
edge.

本文研究了在大规模 MIMO 通信系统中采用 CoMP 和分布式 MIMO 技术的资源分配优化问题，发现该方法显著提高了网络性能并优于仅采用局部协作的大规模 MIMO 系统。

融合的小区级和分布式大规模 MIMO：负载均衡和调度

Harmonized Cellular and Distributed Massive MIMO: Load Balancing and  Scheduling

Next generation cellular networks will be heterogeneous with dense deployment
of small cells in order to deliver high data rate per unit area. Traffic
variations are more pronounced in a small cell, which in turn lead to more
dynamic interference to other cells. It is crucial to adapt radio resource
management to traffic conditions in such a heterogeneous network (HetNet). This
paper studies the optimization of spectrum allocation in HetNets on a
relatively slow timescale based on average traffic and channel conditions
(typically over seconds or minutes). Specifically, in a cluster with $n$ base
transceiver stations (BTSs), the optimal partition of the spectrum into $2^n$
segments is determined, corresponding to all possible spectrum reuse patterns
in the downlink. Each BTS's traffic is modeled using a queue with Poisson
arrivals, the service rate of which is a linear function of the combined
bandwidth of all assigned spectrum segments. With the system average packet
sojourn time as the objective, a convex optimization problem is first
formulated, where it is shown that the optimal allocation divides the spectrum
into at most $n$ segments. A second, refined model is then proposed to address
queue interactions due to interference, where the corresponding optimal
allocation problem admits an efficient suboptimal solution. Both allocation
schemes attain the entire throughput region of a given network. Simulation
results show the two schemes perform similarly in the heavy-traffic regime, in
which case they significantly outperform both the orthogonal allocation and the
full-frequency-reuse allocation. The refined allocation shows the best
performance under all traffic conditions.

本文旨在研究如何针对下行中的所有可能频谱复用模式来确定如何将频谱最优地分配给基础收发信台，以便在繁忙的交通条件下获得整个吞吐量区域，同时考虑了干扰和流量条件。