Unlike the classical encryption schemes,keys are dispensable in certain PLS technigues, known as the keyless secure strat egy. Sophisticated signal processing techniques such as arti- ficial noise, beamforming,and diversitycan be developed to ensure the secrecy of the MC networks.In the Alice-Bob-Eve model, Alice is the legitimate transmitter, whose intended target is the legitimate receiver Bob,while Eve is the eavesdropper that intercepts the information from Alice to Bob.The secrecy performance is quantified via information leakagei.ethe dif ference of the mutual information between the Alice-Bob and Alice-Eve links. The upper bound of the information leakage is called secrecy capacity realized by a specific distribution of the input symbols, namely,capacity-achieving distribution.The secrecy performance of the diffusion-based MC system with concentration shift keying(CSK)is analyzed from an informa- tion-theoretical point of view,providing two paramount secrecy metrics, i.e., secrecy capacity and secure distance[13].How ever, only the estimation of lower bound secrecy capacity is derived as both links attain their channel capacity.The secrecy capacity highly depends on the system parameters such as the average signal energy,diffusion coefficientand reception duration. Moreover, the distance between the transmitter and the eavesdropper is also an important aspect of secrecy per- formance. For both amplitude and energy detection schemes secure distance is proposed as a secret metricover which the eavesdropper is incapable of signal recovery. Despite the case with CSK,the results of the secure metrics vary with the modulation type(e.g.pulse position,spacetype) and reception mechanism(e.g.passive,partially absorbingper fectly absorbing).For ease of understanding,Figure 3 depicts the modulation types and the corresponding CIRs with different reception mechanisms. Novel signa processing techniques and the biochemical channel properties can further assist the secrecy enhancement in the MC system.The molecular beam forming that avoids information disclosure can be realized via the flow generated in the channel.Besidesnew dimensions of diversity, such as the aforementioned molecular diversity of ionic compounds, can beexploited. Note that the feasibility of these methods can be validated by the derived secrecy metrics.

该论文主要介绍了在某些物理层安全技术中，与传统加密技术不同的是，密钥并不是必须的，这种技术被称为“无密钥安全策略”。通过使用先进的信号处理技术，如人工噪声、波束成形和多样性等，可以确保分子通信网络的安全性。文章还介绍了Alice-Bob-Eve模型，其中Alice是合法的发射器，其目标是合法的接收器Bob，而Eve是窃听者，从Alice到Bob的信息进行拦截。通过信息泄漏的度量，即Alice-Bob和Alice-Eve之间的互信息差异，来量化保密性能。信息泄漏的上界称为特定输入符号分布实现的保密容量。从信息论的角度分析了基于扩散的浓度偏移键控分子通信系统的保密性能，提供了两个重要的保密度量，即保密容量和安全距离。然而，只得到了保密容量的下界估计，因为两条链路都达到了其信道容量。保密容量高度依赖于系统参数，如平均信号能量、扩散系数和接收持续时间。此外，发射器与窃听者之间的距离也是保密性能的重要方面。对于幅度和能量检测方案，提出了安全距离作为一种保密度量，超过这个距离窃听者无法恢复信号。尽管使用CSK的情况，保密度量的结果会随着调制类型（如脉冲位置、空间类型）和接收机制（如被动、部分吸收、完全吸收）而有所不同。为了方便理解，图3描述了不同接收机制下的调制类型及其相应的通道冲激响应。此外，新的信号处理技术和生物化学通道特性可以进一步提高分子通信系统的保密性。通过通道中产生的流可以实现分子波束成形，避免信息泄露。此外，还可以利用分子离子化合物等新的多样性维度。需要注意的是，这些方法的可行性可以通过推导的保密度量进行验证。

相关问题

分析《Unlike the classical encryption schemes,keys are dispensable in certain PLS technigues, known as the keyless secure strat egy. Sophisticated signal processing techniques such as arti- ficial noise, beamforming,and diversitycan be developed to ensure t》

该论文主要讲了在某些物理层安全技术中，与传统加密技术不同的是，密钥并不是必须的，这种技术被称为“无密钥安全策略”。通过使用先进的信号处理技术，如人工噪声、波束成形和多样性等，可以确保通信的安全性。文章还提到，这种无密钥安全策略可以应用于无线通信、光通信、分子通信等不同的通信领域，并且可以提高通信的效率和安全性。此外，文章还对这些技术的实现进行了探讨，包括如何选择合适的信号处理技术、如何进行系统设计和实现等方面。综上所述，该论文主要探讨了一种新型的物理层安全技术，即无密钥安全策略，并介绍了该技术的优点和实现方法。

The starting configuration of this puzzle is a row of cells, with disks located on cells through . The goal is to move the disks to the end of the row using a constrained set of actions. At each step, a disk can only be moved to an adjacent empty cell, or to an empty cell two spaces away if another disk is located on the intervening square. Given these restrictions, it can be seen that in many cases, no movements will be possible for the majority of the disks. For example, from the starting position, the only two options are to move the last disk from cell to cell , or to move the second-to-last disk from cell to cell . 1. [15 points] Write a function solve_identical_disks(length, n) that returns an optimal solution to the above problem as a list of moves, where length is the number of cells in the row and n is the number of disks. Each move in the solution should be a twoelement tuple of the form (from, to) indicating a disk movement from the cell from to the cell to. As suggested by its name, this function should treat all disks as being identical. Your solver for this problem should be implemented using a breadth-first graph search. The exact solution produced is not important, as long as it is of minimal length. Unlike in the previous two sections, no requirement is made with regards to the manner in which puzzle configurations are represented. Before you begin, think carefully about which data structures might be best suited for the problem, as this choice may affect the efficiency of your search

Here's a possible implementation of the `solve_identical_disks` function using breadth-first graph search:

from collections import deque

def solve_identical_disks(length, n):
    # Initialize the starting configuration
    start = [True] * n + [False] * (length - n)

    # Define a function to generate all possible successor configurations
    def successors(config):
        for i in range(length):
            if not config[i]:
                if i + 1 < length and config[i + 1]:
                    # Move disk to adjacent empty cell
                    yield config[:i] + [True] + [False] + config[i + 2:]
                elif i + 2 < length and config[i + 2]:
                    # Move disk to empty cell two spaces away
                    yield config[:i] + [True] + config[i + 2:i + 3] + [False] + config[i + 3:]

    # Perform breadth-first graph search to find the goal configuration
    queue = deque([(start, [])])
    visited = set([tuple(start)])
    while queue:
        config, moves = queue.popleft()
        if config == [False] * (length - n) + [True] * n:
            return moves
        for successor in successors(config):
            if tuple(successor) not in visited:
                queue.append((successor, moves + [(config.index(True), successor.index(True))]))
                visited.add(tuple(successor))

    # If the goal configuration is not reachable, return None
    return None

The `start` variable represents the starting configuration as a list of `True` values for the cells occupied by disks and `False` values for the empty cells. The `successors` function generates all possible successor configurations for a given configuration by moving a disk to an adjacent empty cell or to an empty cell two spaces away. The `queue` variable is used to store the configurations to be explored, along with the list of moves required to reach them. The `visited` set is used to keep track of the configurations that have already been explored, in order to avoid revisiting them.

The function returns the list of moves required to reach the goal configuration, which is represented as a list of `False` values for the cells before the disks and `True` values for the cells occupied by the disks. If the goal configuration is not reachable, the function returns `None`.