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On 25 July 2023, a workshop entitled “Molecular Communication Approaches for Wetware Artificial Life” took place as a satellite event at the international conference ALIFE 2023 (The 2023 Conference on Artificial Life). In this report, we comment on the workshop by focusing on the main theme and the motivations that led us to develop this initiative. In particular, we highlight how recent progress in synthetic biology and in the study of molecular communication from an engineering perspective can be fruitfully joined to provide a powerful platform to develop frontier research lines in “wetware” Artificial Life. The talks presented at the workshop are briefly summarized. This report is, ultimately, an opportunity to promote an emerging field that calls for collaborative efforts of scholars from multiple disciplines, from chemistry to molecular biology, from communication engineering to nanotechnology, and up to those interested in more theoretical aspects about complex artificial systems that mimic natural ones.

Abstract Effective symbol detection, channel estimation and decoding of channel codes require an accurate characterization of the noise probability distribution. In many systems, notably the internet of things, noise is largely in the form of interference, arising from a massive number of simultaneous transmissions from uncoordinated devices. Obtaining the probability distribution of the interference is a challenging problem due to the use of non-orthogonal multiple access schemes over several subcarriers (leading to multivariate statistical models) and the heavy-tailed nature of the interference due to the random locations of devices. In this paper, we derive a novel tractable characterization of the interference probability distribution based on an application of Sklar’s theorem to develop a combination of $$\alpha$$ α -stable and t-copula dependence models. We demonstrate that this formulation produces an accurate statistical modeling framework that admits efficient parameter estimation methods. As an illustration of the utility of our models, we develop a simple-to-implement nonlinear receiver when a binary signal is transmitted over all subcarriers by the desired transmitter, which is effective in a range of scenarios and can significantly outperform existing approaches.

As industrial plants increase the number of wirelessly connected sensors for fault detection, a key problem is to identify and obtain data from the sensors. Due to the large number of sensors, random access protocols exploiting non-orthogonal multiple access (NOMA) are a natural approach. In this paper, we develop new algorithms based on approximate message passing for sensor identification and channel estimation accounting for correlation in the activity probability of each sensor and observations of physical variables (e.g., temperature) by the access point. These algorithms form the basis for data decoding, while also identifying faulty machines and estimating local values of the temperature, which can lead to a reduction in the amount of data required to be transmitted. Numerical results show that for an expected activity probability of 0.35, our algorithms improve the normalized mean-square error of the channel estimate by up to 5dB and reduce the rate of sensor identification errors by a factor of four.

Within many chemical and biological systems, both synthetic and natural, communication via chemical messengers is widely viewed as a key feature. Often known as molecular communication, such communication has been a concern in the fields of synthetic biologists, nanotechnologists, communications engineers, and philosophers of science. However, interactions between these fields are currently limited. Nevertheless, the fact that the same basic phenomenon is studied by all of these fields raises the question of whether there are unexploited interdisciplinary synergies. In this paper, we summarize the perspectives of each field on molecular communications, highlight potential synergies, discuss ongoing challenges to exploit these synergies, and present future perspectives for interdisciplinary efforts in this area.

A wide range of communication systems are corrupted by non-Gaussian noise, ranging from wireless to power line. In some cases, including interference in uncoordinated OFDM-based wireless networks, the noise is both impulsive and multivariate. At present, little is known about the information capacity and corresponding optimal input distributions. In this paper, we derive upper and lower bounds of the information capacity by exploiting non-isotropic inputs. For the special case of sub-Gaussian α-stable noise models, a numerical study reveals that isotropic Gaussian inputs can remain a viable choice, although the performance depends heavily on the dependence structure of the noise.

A key justification for molecular communications is low energy consumption and limited complexity. However, this is only the case if effective architectures for transmitting and receiving devices exist, which is not the case for most modulation and coding schemes at present. One approach to implementing these devices is to use biological circuits, based on chemical reaction networks and DNA transcription processes. In this paper, we develop a biological circuit to demodulate a class of molecular shift keying modulation schemes. A feature of our scheme is that only a single kind of receptor is required to which either of the signaling molecules can bind. We analyze our scheme to tune parameters and compare it to an optimal demodulation scheme. This reveals tradeoffs between performance and complexity in biological circuit implementations.

Fifth generation mobile communication systems (5G) have to accommodate both Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile Broadband (eMBB) services. While eMBB applications support high data rates, URLLC services aim at guaranteeing low-latencies and high-reliabilities. eMBB and URLLC services are scheduled on the same frequency band, where the different latency requirements of the communications render their coexistence challenging. In this survey, we review, from an information theoretic perspective, coding schemes that simultaneously accommodate URLLC and eMBB transmissions and show that they outperform traditional scheduling approaches. Various communication scenarios are considered, including point-to-point channels, broadcast channels, interference networks, cellular models, and cloud radio access networks (C-RANs). The main focus is on the set of rate pairs that can simultaneously be achieved for URLLC and eMBB messages, which captures well the tension between the two types of communications. We also discuss finite-blocklength results where the measure of interest is the set of error probability pairs that can simultaneously be achieved in the two communication regimes.

Abstract Fast varying active transmitter sets are a key feature of wireless communication networks with very short length transmissions arising in communications for the Internet of Things. As a consequence, the interference is dynamic, leading to non-Gaussian statistics. At the same time, the very high density of devices is motivating non-orthogonal multiple access (NOMA) techniques, such as sparse code multiple access (SCMA). In this paper, we study the statistics of the dynamic interference from devices using SCMA. In particular, we show that the interference is α-stable with non-trivial dependence structure for large-scale networks modeled via Poisson point processes. Moreover, the interference on each frequency band is shown to be sub-Gaussian α-stable in the special case of disjoint SCMA codebooks. We investigate the impact of the α-stable interference on achievable rates and on the optimal density of devices. Our analysis suggests that ultra dense networks are desirable even with α-stable interference.

On-demand transport has been disrupted by Uber and other providers, which are challenging the traditional approach adopted by taxi services. Instead of using fixed passenger pricing and driver payments, there is now the possibility of adaptation to changes in demand and supply. Properly designed, this new approach can lead to desirable tradeos between passenger prices, individual driver profits and provider revenue. However, pricing and allocations—known as mechanisms—are challenging problems falling in the intersection of economics and computer science. In this paper, we develop a general framework to classify mechanisms in on-demand transport. Moreover, we show that data is key to optimizing each mechanism and analyze a dataset provided by a real-world on-demand transport provider. This analysis provides valuable new insights into eÿcient pricing and allocation in on-demand transport.