Common randomness (CR) is an important resource for future communication paradigms. Identification is one such future communication paradigm, and it has been proven that CR-assisted identification can improve identification capacity by a double exponential factor. To achieve this, the common source of observation must be perfectly correlated and contain sufficient entropy. Channel reciprocity-based CR generation (alternatively termed Physical Layer Secret Key (PLSK) generation) is one such CR generation scheme. Existing high-rate CR generation is based on the Carrier Frequency Offset (CFO); for practical implementation, CFO-based CR generation would require a re-design of the existing protocol stacks to pass the CFO from the physical layer to the higher protocol layers. In contrast, we develop high-rate CR generation based on the Received Signal Strength Indicator (RSSI), which is highly practical as the RSSI is passed to and widely used by the higher protocol layers in existing communication protocol stacks. For RSSI-based CR generation, we demonstrate that the memory resources dedicated to storing channel information play an important role in achieving a high CR generation rate. Specifically, we demonstrate, through an experimental testbed with Ettus N210 USRPs Software Defined Radios (SDRs) (from which we make datasets publicly available), how allocating higher memory resources for storing accurate RSSI channel information at the terminals (transceivers) increases the achievable CR generation rate.
Common randomness (CR) is an important resource for future communication paradigms. Identification is one such future communication paradigm, and it has been proven that CR-assisted identification can improve identification capacity by a double exponential factor. To achieve this, the common source of observation must be perfectly correlated and contain sufficient entropy. Channel reciprocity-based CR generation (alternatively termed Physical Layer Secret Key (PLSK) generation) is one such CR generation scheme. Existing high-rate CR generation is based on the Carrier Frequency Offset (CFO); for practical implementation, CFO-based CR generation would require a re-design of the existing protocol stacks to pass the CFO from the physical layer to the higher protocol layers. In contrast, we develop high-rate CR generation based on the Received Signal Strength Indicator (RSSI), which is highly practical as the RSSI is passed to and widely used by the higher protocol layers in existing communication protocol stacks. For RSSI-based CR generation, we demonstrate that the memory resources dedicated to storing channel information play an important role in achieving a high CR generation rate. Specifically, we demonstrate, through an experimental testbed with Ettus N210 USRPs Software Defined Radios (SDRs) (from which we make datasets publicly available), how allocating higher memory resources for storing accurate RSSI channel information at the terminals (transceivers) increases the achievable CR generation rate.
Common Randomness (CR) provides sequences of random variables at two physically separated locations. Ideally, the random variables at the two locations should be identical, i.e., have low probability of discrepancy, and should have high entropy. Previous CR research has focused on CR for physical layer security (mainly physical layer secret key generation), where a low CR generation rate, i.e., a low rate of CR bits per second is sufficient. However, emerging semantic communication paradigms, e.g., identification via channels, require high CR rates. We develop and evaluate a Carrier Frequency Offset (CFO) based methodology for high-rate CR generation from reciprocal observations of a common wireless channel between two distinct wireless terminals. The proposed CFO-CR methodology proceeds in several stages, including channel probing, random parameter extraction, noise reduction, quantization, information reconciliation, and randomization. Our evaluations with single-carrier software-defined radios, for which we make measurement traces publicly available, indicate that high-rate CR generation should observe (probe) the CFO and employ a Savitzky Golay low-pass filter with a low cut-off frequency for noise reduction in conjunction with multi-bit quantization, Gray code encoding, and a shuffling based randomization. We provide insights into the tradeoffs between the reconciliation cost for correcting bit discrepancies and the CR generation parameters. Our proposed CFO-CR methodology can generate 2048 bits of CR at a comparatively low reconciliation cost of 72 bytes while only making 256 channel observations and passing all common randomness tests. For generating 2048 bits of CR, other state-of-the-art approaches either require more channel observations (≥2048) or incur a higher reconciliation cost (≥450bytes).
Identification is a new paradigm introduced for goal-oriented communication that verifies the semantics of the intended message rather than just transmitting the message bits, which is traditionally done in classical communication systems. One of the key techniques to improve randomized identification capacity is by introducing Common Randomness (CR). Physical Layer Secret Key (PLSK) is one such CR generation technique, where terminals generate secret keys based on changes in channel reciprocity between the wireless terminals and are mainly used for PHY layer encryption. However, the traditional way of evaluating PLSK techniques through bit discrepancies, bit generation rate, and entropy is not sufficient for evaluating their usefulness for identification. This paper proposes three key novel information theory paradigms that can be used alongside other mentioned parameters to evaluate key PLSK techniques for their usefulness in identification, i.e., CR rate, CR capacity, and cost function, followed by a supporting implementation using Software Defined Radio (SDR)s.
Common randomness (CR), as a resource, is not commonly used in existing practical communication systems. In the common randomness framework, both sender and receiver, often described as terminals, aim to generate a common random variable observable to both, perhaps with low error probability. The knowledge of this CR allows to implement correlated random protocols that could lead to faster and more efficient algorithms. We characterize CR over Gaussian channels for their practical relevance in many communication situations by deriving the CR capacity for both Gaussian Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) cases. Furthermore, CR plays a key role in the identification scheme. In many new applications such as several machine-to-machine and human-to-machine systems and the tactile internet, which demand ultra-reliable low latency, the identification or also called post-Shannon scheme is proved to be more efficient than the classical transmission. It has been proved that through CR generation, the post-Shannon communication task allows to achieve an enormous performance gain. We consider a correlation-assisted secure identification scheme over Gaussian wiretap channels (GWC) and develop a lower bound on the corresponding secure identification capacity.
Common Randomness (CR) can be considered as a resource for future communication devices and systems that not only improve the transmission capacity of identification systems but also provide robust information theoretical security. To achieve this, CR must be generated such that the reconciliation cost must be lower than the size of the key that is being generated. This can be achieved by using filters. In this live demonstration, we prepare a CR generation mechanism using Software Defined Radio (SDR) and achieve this tradeoff using multi-bit quantization and Savitzky Golay filter.
We design and analyze a method to extract secret keys from the randomness inherent to wireless channels. We study a channel model for the IEEE 802.11a orthogonal frequency-division multiplexing channel. We analyze the fundamental limits of key generation for this model and describe an implementation based on error correcting codes. We study key extraction based both on channel state information (CSI) and on received signal strength indicators (RSSI). Due to the reduction in the degree-of-freedom when going from CSI to RSSI, the rate of key extraction based on CSI is far higher than that based on RSSI. This suggests that making CSI information available to higher layers would greatly assist in key generation. We prototype a key generation system based on low-density parity-check codes. We describe the implementation and performance of two systems: one based on binary LDPC codes and the other (useful at higher signal-to-noise ratios) based on four-ary LDPC codes.
Wireless channel state information has been widely utilized in physical layer key generation (PLKG) as shared random sources. The temporal randomness and reciprocity of the channel parameters are utilized, posing a requirement for high-precision time-division duplexing (TDD) channel measurement. However, existing real channel measurement-based PLKG studies suffer from significant channel estimation errors, which degrade the measured channel reciprocity and increase the key disagreement rate. Furthermore, PLKG in static environments has aroused extensive attention. In static environments, the channel temporal variation is very weak, and is generally considered insufficient for reliable key generation. To address these challenges, in this paper, a high-precision bidirectional wireless channel fluctuation measuring platform was designed and implemented based on generic software-defined radio (SDR) architectures. The hardware impairments of multiple SDR platforms with different architectures were systematically analyzed. With such hardware impairments, traditional channel estimation methods, such as least squares (LS) and minimum mean square error (MMSE), were found suboptimal for measurement of the received signal strength indication (RSSI), and a tailored TDD RSSI measurement algorithm was proposed for generic SDR devices. With the proposed algorithm, TDD measurement with 200∼500μs period was achieved with root mean square (RMS) RSSI measurement error of less than 0:002dB on all tested devices, significantly outperforming the state-of-the-art result in the same field. The wireless channel fluctuations in indoor office static environments were measured using the proposed system, and results have shown that RSSI fluctuations smaller than 0:5dB can be detected with high reciprocity between opposite directions, which provides fine-grained RSSI data for PLKG. Through PLKG experiment based on the measurement data, a high KGR of 80bit/s can be achieved with an acceptable KDR of 0:05∼0:2, highlighting the critical role of precise and fine-grained RSSI measurements in enabling efficient PLKG.