A multiantenna multiuser broadcast channel with transmitter beamforming and
user selection is considered. Different from the conventional works, we
consider imperfect channel state information (CSI) which is a practical
scenario for multiuser broadcast channels. We propose a robust regularized
zero-forcing (RRZF) beamforming at the base station. Then we show that the RRZF
outperforms zero-forcing (ZF) and regularized ZF (RZF) beamforming even as the
number of users grows to infinity. Simulation results validate the advantage of
the proposed robust RZF beamforming.

研究了一种多天线多用户广播信道，考虑了不完美的信道状态信息，提出了基站上的稳健正则化零强制波束成形，使用 RRZF 的方案优于 ZF 和 RZF，仿真结果验证了提出的稳健 RZF 波束成形的优势。

具用户选择的多天线广播信道的正则化零强制技术

Regularized Zero-Forcing for Multiantenna Broadcast Channels with User  Selection

In this paper we consider energy efficient scheduling in a multiuser setting
where each user has a finite sized queue and there is a cost associated with
holding packets (jobs) in each queue (modeling the delay constraints). The
packets of each user need to be sent over a common channel. The channel
qualities seen by the users are time-varying and differ across users; also, the
cost incurred, i.e., energy consumed, in packet transmission is a function of
the channel quality. We pose the problem as an average cost Markov Decision
Problem, and prove that this problem is Whittle Indexable. Based on this
result, we propose an algorithm in which the Whittle index of each user is
computed and the user who has the lowest value is selected for transmission. We
evaluate the performance of this algorithm via simulations and show that it
achieves a lower average cost than the Maximum Weight Scheduling and Weighted
Fair Scheduling strategies.

本文研究了多用户情况下的能源效率调度问题，考虑了有限队列、延迟和传输能量耗费对传输的影响，采用 Whittle index 方法计算用户优先级，结果表明该算法优于最大权重调度和加权公平调度策略。

以不安静强盗策略为特点的机会调度

Opportunistic Scheduling as Restless Bandits

Performing link adaptation in a multiantenna and multiuser system is
challenging because of the coupling between precoding, user selection, spatial
mode selection and use of limited feedback about the channel. The problem is
exacerbated by the difficulty of selecting the proper modulation and coding
scheme when using orthogonal frequency division multiplexing (OFDM). This paper
presents a data-driven approach to link adaptation for multiuser multiple input
mulitple output (MIMO) OFDM systems. A machine learning classifier is used to
select the modulation and coding scheme, taking as input the SNR values in the
different subcarriers and spatial streams. A new approximation is developed to
estimate the unknown interuser interference due to the use of limited feedback.
This approximation allows to obtain SNR information at the transmitter with a
minimum communication overhead. A greedy algorithm is used to perform spatial
mode and user selection with affordable complexity, without resorting to an
exhaustive search. The proposed adaptation is studied in the context of the
IEEE 802.11ac standard, and is shown to schedule users and adjust the
transmission parameters to the channel conditions as well as to the rate of the
feedback channel.

本文针对多天线、多用户系统中链路自适应的问题，提出了一种数据驱动的多用户 MIMO OFDM 系统的链路自适应方法，其中引入了机器学习分类器来选择调制和编码方案，并使用新的估计方法来估计由有限反馈引起的未知用户间干扰，通过贪心算法使用可负担的复杂性进行空间模式和用户选择。在 IEEE 802.11ac 标准中的应用研究表明，该方法可以调度用户和根据信道出路和反馈速率自适应地调整传输参数。