### Fundamentals of Physical Layer Key Generation  
Physical layer key generation has recently emerged as a new paradigm that provides a lightweight and information-theoretic secure key sharing solution [8; 9; 10; 11; 12; 13]. It is built on the basis of channel reciprocity, which means that the channel responses of the forward and backward communication links are the same (or very similar) [14]. Besides, the dynamic, time-varying or randomly changing and complex wireless communication environment makes the channel responses change over time and hard to predict [15]. Inspired by this, legitimate users can share a pair of common randomness by estimating the channel between them. Since physical layer key generation does not require assistance from a third party, it can be deployed in parallel in massive connections. Physical layer key generation establishes secret keys between two legitimate users, named [PERSON] and [PERSON], from the common randomness of their wireless channel. As shown in Figure 1, the key generation procedure involves four stages: channel probing, quantization, information reconciliation, and privacy amplification. [PERSON] and [PERSON] probe the wireless channel alternately to obtain correlated channel measurements, which is the key step to harvest channel randomness and is the focus of this article. Key generation relies on the reciprocal channel features in the time, frequency, and spatial domains. We therefore term this technique as channel reciprocity-based key generation (CRKG). Quantization converts these analog channel measurements into digital binaries, i.e., \(Q_{BA}\) at [PERSON] and \(Q_{AB}\) at [PERSON]. It is worth noting that, when the two probes are within the same coherence time and/or coherence bandwidth, the observed channel information are self-correlated. Therefore, adjacent channel features are likely to have the same quantization value, which leads to long 0 s or 1 s and reduces key randomness. To address this problem, a preprocessing step is usually added between channel sounding and quantization. Various preprocessing approaches have been proposed to reduce the correlation and also to enhance the similarity of the channel features [16]. Next, information reconciliation enables [PERSON] and [PERSON] to agree on the same key through error detection protocols or error correction codes [17]. Finally, privacy amplification eliminates any potential information leakage to eavesdroppers. More details of the procedure can be found in [10]. Therefore, it is necessary to develop infrastructure-less, fast and lightweight secret key generation algorithms. Figure 1: An example of secure transmission exploiting the key produced by CRKG. Figure 1 also illustrates an example of safeguarding data transmission exploiting the key produced by CRKG. [PERSON] and [PERSON] share a pair of symmetric channel key \(K_{c}\) through CRKG described above. Next, these keys are fed to a stream cipher as the seed to produce session keys to encrypt the plaintext. A pair of \(K_{c}\) may be used multiple times, but the session keys are different due to the increase of the counter number. The seed can be updated when another pair of channel keys are generated. The secure transmission scheme based on CRKG is lightweight as it does not introduce additional complexity. Therefore, it is very quite suitable for IoT applications. Meanwhile, a passive eavesdropper ([PERSON]) can also observe the whole procedure of CRKG. Based on the wireless decorrelation property of wireless channels, when an eavesdropper is more than half-wavelength away from the legitimate users, it will experience statistically independent channel fading, and thus cannot infer the generated key. In this case, [PERSON] obtains the quantization of channel information from [PERSON] to [PERSON] and from [PERSON] to [PERSON] as \(Q_{AE}\) and \(Q_{BE}\). Similarly, she can generate secret key \(K_{CE}\). The secret key \(K_{CE}\) is not consistent with \(K_{c}\), and she cannot crack private messages.