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　　所有 Massive MIMO 解决方案都包含硬件（一个或多个 Massive MIMO 无线电）和软件（Massive MIMO 功能）。大规模 MIMO 功能可以用三个因素来描述 [3]：

　　大规模 MIMO 功能旨在满足的网络要求

　　可用的渠道知识

　　可以使用#2 中收集的信道知识来应用多天线技术（或技术组合）以满足#1 中的要求。

　　这三个因素的不同排列将产生独特的大规模 MIMO 特性——可能具有不同的权衡和对不同条件的适用性。

　　首先，必须明确该功能旨在满足的要求——它应该提高覆盖范围、提高容量还是提高吞吐量？在某些情况下，一个功能可以解决多个问题，而在其他情况下，可能需要进行权衡。例如，提高能源效率的功能可能会对容量产生负面影响。因此，必须在特定时间对于特定小区哪些性能要求最重要。例如，在非高峰时段，电池中的容量需求可能较低，因此可以接受甚至希望应用牺牲容量来提高能源效率的功能。

　　所有 Massive MIMO 特性都源于将三种基本多天线技术（波束成形、零点成形和空间复用）组合应用到物理信道或信号，并使用可用的信道知识来解决特定问题。这听起来很简单，但有几个方面需要考虑，从而产生各种各样的潜在功能。一个中心问题是如何获得执行波束成形、零点成形或空间复用所需的信道知识。这可以通过多种方式实现，但重要的是要了解获取信道状态信息 (CSI) 始终存在相关成本。增加的开销只是一个例子。

　　还有一个CSI可用性的问题。3GPP标准中有不同的探测和反馈方法，不同的用户设备(UE)可能有不同的能力，支持不同的CSI反馈和探测格式。因此，网络必须同时支持多个大规模 MIMO 功能。即使 UE 支持某种 CSI 反馈和探测格式，该 CSI 也可能在某个时间点上不可用。例如，当 UE 连接到小区时，通常没有可用的信道信息，并且需要设置测量或探测配置，这意味着在此类 CSI 可用于网络之前存在提前时间。

　　与 CSI 可用时相比，当 CSI 有限/没有可用时，需要不同的 MIMO 特性集。大规模 MIMO 功能可以在较高级别分类为使用基于反馈或探测的信道信息以及使用 SU-MIMO 或 MU-MIMO。实际上，从 3GPP 标准中可用的内容和专有算法的角度来看，如何实现功能的这些方面有很多选择。

　　通过比较大规模 MIMO 功能与感兴趣的网络关键性能指标（覆盖范围、容量和用户吞吐量），它们表现出不同的优势和劣势。基于反馈的波束赋形在覆盖范围上优于基于探测的波束赋形。同样，SU-MIMO 比 MU-MIMO 具有覆盖优势。这是因为 MU-MIMO 需要更详细的 CSI，并且因为 MU-MIMO 需要在多个用户之间分配可用的发射功率。为了充分发挥Massive MIMO解决方案的潜力，需要动态调整/切换算法，使覆盖、容量和峰值速率共同化，这就是Massive MIMO解决方案的典型设计方式。

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　　All Massive MIMO solutions consist of both hardware (one or more Massive MIMO radios) and software (Massive MIMO features). A Massive MIMO feature can be described in terms of three factors [3]:

　　The network requirement(s) that the Massive MIMO feature is intended to meet

　　The available channel knowledge

　　The multi-antenna technique (or combination of techniques) that can be applied using the channel knowledge gathered in #2 to meet the requirement(s) in #1.

　　Different permutations of these three factors will yield unique Massive MIMO features – potentially with varying trade-offs and applicability to different conditions.

　　Firstly, it is essential to be clear about the requirement(s) that the feature is intended to meet – should it improve coverage, boost capacity or increase throughput? In some cases, one feature can solve multiple problems, while in others, trade-offs may be necessary. A feature that improves energy efficiency may have a negative effect on capacity, for example. It is therefore essential to assess which performance requirements are most important for a certain cell at a certain time. For instance, during off-peak hours, the capacity demand in a cell may be low, making it acceptable or even desirable to apply a feature that sacrifices capacity to improve energy efficiency.

　　All Massive MIMO features result from applying a combination of the three basic multi-antenna techniques – beamforming, null forming and spatial multiplexing – to a physical channel or signal, using available channel knowledge to solve a certain problem. This may sound simple, but there are several aspects to consider, resulting in a wide variety of potential features. A central question is how to acquire the channel knowledge required to perform beamforming, null forming or spatial multiplexing. This can be achieved in several ways, but it is important to understand that there is always a cost associated with acquiring channel-state information (CSI). Increased overhead is just one example.

　　There is also a problem of CSI availability. Different sounding and feedback methods are available in the 3GPP standard, and different user equipment (UE) may have different capabilities and support different CSI feedback and sounding formats. The network must therefore support several Massive MIMO features in parallel. Even if a UE supports a certain CSI feedback and sounding formats, that CSI may not be available at a certain instance in time. For example, when a UE first connects to a cell, no channel information is generally available and measurement or sounding configurations will need to be set up, implying that there is a lead time before such CSI is available to the network.

　　Different sets of MIMO features are needed when limited/no CSI is available, compared with when CSI is available. Massive MIMO features can be classified at a high level as using either feedback- or sounding-based channel information and employing either SU-MIMO or MU-MIMO. In practice, there are many options for how to implement these aspects of a feature, both from what is available in the 3GPP standard and from a proprietary algorithm perspective.

　　By comparing the Massive MIMO features with respect to the network key performance indicators of interest (coverage, capacity and user throughput) they exhibit different strengths and weaknesses. Feedback-based beamforming has an advantage in coverage over sounding-based beamforming. Similarly, SU-MIMO has a coverage advantage over MU-MIMO. This is because MU-MIMO requires more detailed CSI and because MU-MIMO needs to split the available transmit power between multiple users. To fully utilize the potential of a Massive MIMO solution, it is necessary to dynamically adapt/switch the algorithm so that coverage, capacity and peak rate can be maximized jointly, which is how Massive MIMO solutions are typically designed.