Spectrum sensing technology is a crucial aspect of modern communication
technology, serving as one of the essential techniques for efficiently
utilizing scarce information resources in tight frequency bands. This paper
first introduces three common logical circuit decision criteria in hard
decisions and analyzes their decision rigor. Building upon hard decisions, the
paper further introduces a method for multi-user spectrum sensing based on soft
decisions. Then the paper simulates the false alarm probability and detection
probability curves corresponding to the three criteria. The simulated results
of multi-user collaborative sensing indicate that the simulation process
significantly reduces false alarm probability and enhances detection
probability. This approach effectively detects spectrum resources unoccupied
during idle periods, leveraging the concept of time-division multiplexing and
rationalizing the redistribution of information resources. The entire
computation process relies on the calculation principles of power spectral
density in communication theory, involving threshold decision detection for
noise power and the sum of noise and signal power. It provides a secondary
decision detection, reflecting the perceptual decision performance of logical
detection methods with relative accuracy.

利用频谱感知技术进行多用户频谱感知，提高虚警概率和检测概率，从而有效地探测闲置时段的频谱资源，优化信息资源的重新分配。

多个认知用户的频谱感知联合检测算法

Joint Detection Algorithm for Multiple Cognitive Users in Spectrum  Sensing

The concept of attention, numerical weights that emphasize the importance of
particular data, has proven to be very relevant in artificial intelligence.
Relative entropy (RE, aka Kullback-Leibler divergence) plays a central role in
communication theory. Here we combine these concepts, attention and RE. RE
guides optimal encoding of messages in bandwidth-limited communication as well
as optimal message decoding via the maximum entropy principle (MEP). In the
coding scenario, RE can be derived from four requirements, namely being
analytical, local, proper, and calibrated. Weighted RE, used for attention
steering in communications, turns out to be improper. To see how proper
attention communication can emerge, we analyze a scenario of a message sender
who wants to ensure that the receiver of the message can perform well-informed
actions. If the receiver decodes the message using the MEP, the sender only
needs to know the receiver's utility function to inform optimally, but not the
receiver's initial knowledge state. In case only the curvature of the utility
function maxima are known, it becomes desirable to accurately communicate an
attention function, in this case a by this curvature weighted and re-normalized
probability function. Entropic attention communication is here proposed as the
desired generalization of entropic communication that permits weighting while
being proper, thereby aiding the design of optimal communication protocols in
technical applications and helping to understand human communication. For
example, our analysis shows how to derive the level of cooperation expected
under misaligned interests of otherwise honest communication partners.

提出了一种新型的基于关注度和相对熵的通信模型，旨在设计和优化技术应用中的最佳通信协议，以及理解人类通信的方式。

熵通信注意力

Attention to Entropic Communication

Semantic communication is viewed as a revolutionary paradigm that can
potentially transform how we design and operate wireless communication systems.
However, despite a recent surge of research activities in this area, the
research landscape remains limited. In this tutorial, we present the first
rigorous vision of a scalable end-to-end semantic communication network that is
founded on novel concepts from artificial intelligence (AI), causal reasoning,
and communication theory. We first discuss how the design of semantic
communication networks requires a move from data-driven networks towards
knowledge-driven ones. Subsequently, we highlight the necessity of creating
semantic representations of data that satisfy the key properties of minimalism,
generalizability, and efficiency so as to do more with less. We then explain
how those representations can form the basis a so-called semantic language. By
using semantic representation and languages, we show that the traditional
transmitter and receiver now become a teacher and apprentice. Then, we define
the concept of reasoning by investigating the fundamentals of causal
representation learning and their role in designing semantic communication
networks. We demonstrate that reasoning faculties are majorly characterized by
the ability to capture causal and associational relationships in datastreams.
For such reasoning-driven networks, we propose novel and essential semantic
communication metrics that include new "reasoning capacity" measures that could
go beyond Shannon's bound to capture the convergence of computing and
communication. Finally, we explain how semantic communications can be scaled to
large-scale networks (6G and beyond). In a nutshell, we expect this tutorial to
provide a comprehensive reference on how to properly build, analyze, and deploy
future semantic communication networks.

介绍了一个建立在人工智能、因果推理和通信理论新概念之上的可扩展端到端语义通信网络，它要求将数据驱动网络转向为知识驱动的网络，并使用叫做语义语言的语言与语义表示方式，提出了新的 “推理能力” 度量方法，为未来的语义通信网络的建立、分析和应用提供了一个全面的参考。

少数据，更多知识：构建下一代语义通信网络

Less Data, More Knowledge: Building Next Generation Semantic Communication Networks

The emerging massive/large-scale MIMO (LS-MIMO) systems relying on very large
antenna arrays have become a hot topic of wireless communications. Compared to
the LTE based 4G mobile communication system that allows for up to 8 antenna
elements at the base station (BS), the LS-MIMO system entails an unprecedented
number of antennas, say 100 or more, at the BS. The huge leap in the number of
BS antennas opens the door to a new research field in communication theory,
propagation and electronics, where random matrix theory begins to play a
dominant role.
In this paper, we provide a recital on the historic heritages and novel
challenges facing LS-MIMOs from a detection perspective. Firstly, we highlight
the fundamentals of MIMO detection, including the nature of co-channel
interference, the generality of the MIMO detection problem, the received signal
models of both linear memoryless MIMO channels and dispersive MIMO channels
exhibiting memory, as well as the complex-valued versus real-valued MIMO system
models. Then, an extensive review of the representative MIMO detection methods
conceived during the past 50 years (1965-2015) is presented, and relevant
insights as well as lessons are inferred for designing complexity-scalable MIMO
detection algorithms that are potentially applicable to LS-MIMO systems.
Furthermore, we divide the LS-MIMO systems into two types, and elaborate on the
distinct detection strategies suitable for each of them. The type-I LS-MIMO
corresponds to the case where the number of active users is much smaller than
the number of BS antennas, which is currently the mainstream definition of
LS-MIMO. The type-II LS-MIMO corresponds to the case where the number of active
users is comparable to the number of BS antennas. Finally, we discuss the
applicability of existing MIMO detection algorithms in LS-MIMO systems, and
review some of the recent advances in LS-MIMO detection.

该论文提供了有关 LS-MIMO 的历史遗产和挑战以及基础 MIMO 检测的重要性的回顾，包括线性无记忆 MIMO 通道和展示记忆的离散 MIMO 通道的接收信号模型，以及复值与实值 MIMO 系统模型，介绍了代表性 MIMO 检测算法、区分两种不同类型的 LS-MIMO 系统并讨论了现有 MIMO 检测算法在 LS-MIMO 系统中的适用性和最新进展。