Coded caching provides significant gains over conventional uncoded caching by
creating multicasting opportunities among distinct requests.
Massive multiple-input multiple-output (MIMO) systems require downlink
channel state information (CSI) at the base station (BS) to better utilize the
available spatial diversity and multiplexing gains. However, in a frequency
division duplex (FDD) massive MIMO system, the huge CSI feedback overhead
becomes restrictive and degrades the overall spectral efficiency. In this
paper, we propose a deep learning based channel state matrix compression
scheme, called DeepCMC, composed of convolutional layers followed by
quantization and entropy coding blocks. In comparison with previous works, the
main contributions of DeepCMC are two-fold: i) DeepCMC is fully convolutional,
and it can be used in a wide range of scenarios with various numbers of
sub-channels and transmit antennas; ii) DeepCMC includes quantization and
entropy coding blocks and minimizes a cost function that accounts for both the
rate of compression and the reconstruction quality of the channel matrix at the
BS. Simulation results demonstrate that DeepCMC significantly outperforms the
state of the art compression schemes in terms of the reconstruction quality of
the channel state matrix for the same compression rate, measured in bits per
channel dimension.

本文提出基于深度学习的 CSI 压缩方案，DeepCMC，它包括卷积层和量化熵编码块，能够显著提高频道状态矩阵的重构质量，并且在相同的压缩率下优于现有的压缩方案。

大规模 MIMO CSI 反馈的深度卷积压缩

Deep Convolutional Compression for Massive MIMO CSI Feedback

We consider a basic content distribution scenario consisting of a single
origin server connected through a shared bottleneck link to a number of users
each equipped with a cache of finite memory. The users issue a sequence of
content requests from a set of popular files, and the goal is to operate the
caches as well as the server such that these requests are satisfied with the
minimum number of bits sent over the shared link. Assuming a basic Markov model
for renewing the set of popular files, we characterize approximately the
optimal long-term average rate of the shared link. We further prove that the
optimal online scheme has approximately the same performance as the optimal
offline scheme, in which the cache contents can be updated based on the entire
set of popular files before each new request. To support these theoretical
results, we propose an online coded caching scheme termed coded least-recently
sent (LRS) and simulate it for a demand time series derived from the dataset
made available by Netflix for the Netflix Prize. For this time series, we show
that the proposed coded LRS algorithm significantly outperforms the popular
least-recently used (LRU) caching algorithm.

通过 Markov 模型，提出了在线编码 Least-recently sent (LRS) 缓存方案以减少共享网络连接中位传输的字节数，并在 Netflix Prize 数据集上进行了模拟，表明该方案比 Least-recently used (LRU) 缓存算法更优

在线编码缓存

Online Coded Caching

We consider a wireless Device-to-Device (D2D) network where communication is
restricted to be single-hop, users make arbitrary requests from a finite
library of possible files and user devices cache information in the form of
linear combinations of packets from the files in the library (coded caching).
We consider the combined effect of coding in the caching and delivery phases,
achieving "coded multicast gain", and of spatial reuse due to local short-range
D2D communication. Somewhat counterintuitively, we show that the coded
multicast gain and the spatial reuse gain do not cumulate, in terms of the
throughput scaling laws. In particular, the spatial reuse gain shown in our
previous work on uncoded random caching and the coded multicast gain shown in
this paper yield the same scaling laws behavior, but no further scaling law
gain can be achieved by using both coded caching and D2D spatial reuse.

本文介绍了一个将编码缓存和 Device-to-Device（D2D）网络结合起来的方法，通过使用线性组合的包来缓存信息（coded caching），并分别分析了编码缓存和 D2D 空间重用对于网络性能的影响，结果显示，使用编码缓存和 D2D 空间重用不能进一步提高吞吐量的比例尺度。

D2D 无线网络分布式缓存的基本限制

Fundamental Limits of Distributed Caching in D2D Wireless Networks

Caching is a technique to reduce peak traffic rates by prefetching popular
content into memories at the end users. Conventionally, these memories are used
to deliver requested content in part from a locally cached copy rather than
through the network. The gain offered by this approach, which we term local
caching gain, depends on the local cache size (i.e, the memory available at
each individual user). In this paper, we introduce and exploit a second,
global, caching gain not utilized by conventional caching schemes. This gain
depends on the aggregate global cache size (i.e., the cumulative memory
available at all users), even though there is no cooperation among the users.
To evaluate and isolate these two gains, we introduce an
information-theoretic formulation of the caching problem focusing on its basic
structure. For this setting, we propose a novel coded caching scheme that
exploits both local and global caching gains, leading to a multiplicative
improvement in the peak rate compared to previously known schemes. In
particular, the improvement can be on the order of the number of users in the
network. Moreover, we argue that the performance of the proposed scheme is
within a constant factor of the information-theoretic optimum for all values of
the problem parameters.

本文介绍了一种利用局部缓存增益和全局缓存增益的编码缓存设计方法，并对其性能进行分析和比较。