Limited fronthaul capacity is a practical bottleneck in massive multiple-input multiple-output (MIMO) 5G architectures, where a base station (BS) consists of an advanced antenna system (AAS) connected to a baseband unit (BBU). Conventional downlink designs perform all precoding at the BBU and transmit a high-dimensional precoding matrix over the fronthaul, resulting in significant quantization loss and signaling overhead. This letter proposes a splitting precoding architecture that separates the design between the AAS and BBU. The AAS performs local subspace selection, after which the BBU computes a quantization-aware refinement precoding over the resulting reduced-dimensional effective channel. Numerical results show that the proposed splitting precoding strategy achieves higher sum rate than conventional one-stage precoding.

Summary:Fluorosequencing generates millions of single peptide reads, yet a principled route to quantitative protein abundances has been lacking. We present a probabilistic framework that adapts expectation-maximization (EM) to the fluorosequencing measurement process, using posterior peptide probabilities from existing classifiers to estimate relative protein abundances. The algorithm iteratively updates abundances to maximize the likelihood of observed reads. We first evaluate five-protein simulations with realistic labeling and system errors. A simple Python implementation processes one million reads in under ten seconds on a standard workstation and reduces the mean absolute error by over an order of magnitude relative to a uniform-abundance guess, indicating robust performance in small-scale settings. We also assess scalability with full human-proteome simulations (20 642 proteins). Ten million reads are processed in under four hours on an NVIDIA DGX with a single Tesla V100 GPU, confirming tractability at proteome scale. Under current fluorosequencing error rates, the method yields modest accuracy gains, but when error rates are reduced, estimation error drops markedly, indicating that chemistry improvements would translate directly into more accurate quantitative proteomics. Overall, EM-based inference provides a scalable, model-driven bridge from peptide-level classification to protein-level quantification in fluorosequencing. Furthermore, the framework can also serve as a refinement step within other inference methods. Availability and implementation:The code and data utilized to produce all the results of this paper is at https://github.com/JavierKipen/ProtInfGPU.

This paper optimizes the fronthaul bit allocation in massive multi-user multiple-input multiple-output (MU-MIMO) systems operating with limited-capacity fronthaul links. We consider an advanced antenna system (AAS) controlled by a centralized baseband unit (BBU). In the AAS, multiple antenna elements together with their radio units are integrated into a single unit. In this setup, a key challenge is allocating fronthaul bits between uplink channel state information (CSI) quantization and downlink precoding matrix quantization. We formulate the problem of maximizing the sum spectral efficiency (SE) for a given fronthaul capacity. We develop an SE expression for this scenario based on the hardening bound. We compute the expression in closed form for maximum ratio transmission, which reveals the relative impact of the two types of quantization distortion. We then formulate a bit split optimization problem and propose an algorithm that exactly solves it. Numerical results demonstrate how the relative importance of assigning bits to CSI and precoding varies depending on the signal-to-noise ratio.

This paper studies a downlink multi-user multiple-input multiple-output (MU-MIMO) system, where the precoding matrix is computed at a baseband unit (BBU) and then transmitted to the remote antenna array over a limited-capacity digital fronthaul. The limited bit resolution of the fronthaul introduces quantization effects that are explicitly modeled. We propose a novel sum rate maximization framework that directly incorporates the quantizer's constraints into the precoding design. The resulting maximization problem, a non-convex mixed-integer program, is addressed using a new iterative algorithm inspired by the weighted minimum mean square error (WMMSE) methodology. The precoding optimization subproblem is reformulated as an integer least-squares problem and solved using a novel sphere decoding (SD) algorithm. Additionally, a low-complexity expectation propagation (EP)-based method is introduced to enable the practical implementation of quantized precoding in MU-massive MIMO (MU-mMIMO) systems. Furthermore, numerical evaluations demonstrate that the proposed precoding schemes outperform conventional approaches that optimize infinite-resolution precoding followed by element-wise quantization. We also propose a heuristic quantization-aware precoding method with comparable complexity to the baseline but superior performance. In particular, the EP-based approach offers near-optimal performance with substantial complexity reduction, making it well-suited for real-time MU-mMIMO applications.

Edge intelligence enables AI inference at the network edge, co-located with or near the radio access network, rather than in centralized clouds or on mobile devices. It targets low-latency, resource-constrained applications with large data volumes, requiring tight integration of wireless access and on-site computing. Yet system performance and cost-efficiency hinge on joint pre-deployment dimensioning of radio and computational resources, especially under spatial and temporal uncertainty. Prior work largely emphasizes run-time allocation or relies on simplified models that decouple radio and computing, missing end-to-end correlations in large-scale deployments. This paper introduces a unified stochastic framework to dimension multi-cell edge-intelligent systems. We model network topology with Poisson point processes, capturing random user and base-station locations, inter-cell interference, distance-based fractional power control, and peak-power constraints. By combining this with queueing theory and empirical AI inference workload profiling, we derive tractable expressions for end-to-end offloading delay. These enable a non-convex joint optimization that minimizes deployment cost under statistical QoS guarantees, expressed through strict tail-latency and inference-accuracy constraints. We prove the problem decomposes into convex subproblems, yielding global optimality. Numerical results in noise- and interference-limited regimes identify cost-efficient design regions and configurations that cause under-utilization or user unfairness. Smaller cells reduce transmission delay but raise per-request computing cost due to weaker server multiplexing, whereas larger cells show the opposite trend. Densification reduces computational costs only when frequency reuse scales with base-station density; otherwise, sparser deployments improve fairness and efficiency in interference-limited settings.

This paper considers a wideband millimeter-wave MIMO system with fully digital transceivers at both the base station and the user equipment (UE), focusing on mobile scenarios. To reduce the baseband processing burden at the UE, we propose a two-stage digital combining architecture, where the received signals are compressed from K antennas to dimension N_c before baseband processing. The first-stage combining matrix exploits channel geometry and is updated on the beam-coherence timescale, which is longer than the channel coherence time, while the second stage is updated per channel coherence time. We develop a pilot-based channel estimation framework tailored to the proposed two-stage digital combining architecture, leveraging maximum likelihood estimation. Furthermore, we propose a time-domain method that exploits the finite delay spread to reconstruct the full channel from a reduced number of pilot subcarriers. Precoding and combining schemes are designed accordingly, and spectral efficiency expressions with imperfect channel state information are derived. Numerical results show that the proposed time-domain approach outperforms hybrid beamforming while reducing pilot overhead. We further demonstrate that the framework extends to multi-user MIMO and retains its performance advantages. These results highlight the potential of two-stage fully digital transceivers for future wideband systems.

MOTIVATION:Nanopores are highly sensitive sensors that have achieved commercial success in DNA/RNA sequencing, with potential applications in protein sequencing and biomarker identification. Solid-state nanopores, in particular, face challenges such as instability and low signal-to-noise ratios, which lead scientists to adopt data-driven methods for nanopore signal analysis, although data acquisition remains restrictive. RESULTS:We address this data scarcity by augmenting the training samples with traces that emulate Brownian motion effects, based on dynamic models in the literature. We apply this method to a publicly available dataset of a classification task containing nanopore reads of DNA with encoded barcodes. A neural network named QuipuNet was previously published for this dataset, and we demonstrate that our augmentation method produces a noticeable increase in QuipuNet's accuracy. Furthermore, we introduce a novel neural network named YupanaNet, which achieves greater accuracy (95.8%) than QuipuNet (94.6%) on the same dataset. YupanaNet benefits from both the enhanced generalization provided by Brownian motion data augmentation and the incorporation of novel architectures, including skip connections and a soft attention mask. AVAILABILITY AND IMPLEMENTATION:The source code and data are available at: https://github.com/JavierKipen/browDataAug.

Fluorosequencing produces millions of single-peptide reads, yet a principled strategy for converting these data into quantitative protein abundances has been lacking. We introduce a probabilistic framework that adapts expectation maximization to the fluorosequencing measurement process, estimating relative protein abundances with peptide inference results delivered by previously developed peptide-classification tools. The algorithm iteratively updates protein abundances, maximising the likelihood of the observed reads by obtaining more accurate protein abundance estimations. We first assess performance on simulated five-protein mixtures that reflect realistic labelling and system errors. A simple Python implementation processes one million reads in under ten seconds on a standard work-station and lowers the mean absolute error in relative abundance by more than an order of magnitude compared with a uniform-abundance guess, demonstrating robustness in protein inference for small-scale settings. Scalability is then evaluated with simulations of the complete human proteome (20 642 proteins). Ten million reads are processed in less than four hours on a NVIDIA DGX system using one Tesla V100 GPU, confirming that the method remains tractable at proteome scale. Using error rates characteristic of current fluorosequencing, the algorithm produces marginal improvements in relative abundance accuracy. However, when error rates were artificially lowered, estimation error decreased significantly. This result suggests that improvements in fluorosequencing chemistry could directly translate into substantially more accurate quantitative proteomics with this computational framework. Together, these results establish EM-based inference as a scalable model-driven bridge between peptide-level classification and protein-level quantification in fluorosequencing, laying computational groundwork for highthroughput single-molecule proteomics. Furthermore, the proposed protein inference framework can also be used as a refinement step within other inference methods, enhancing their protein abundance estimates.

This paper considers a millimeter-wave wideband point-to-point MIMO system with fully digital transceivers at the base station and the user equipment (UE), focusing on mobile UE scenarios. A main challenge when building a digital UE combining is the large volume of baseband samples to handle. To mitigate computational and hardware complexity, we propose a novel two-stage digital combining scheme at the UE. The first stage reduces the N_r received signals to N_c streams before baseband processing, leveraging channel geometry for dimension reduction and updating at the beam coherence time, which is longer than the channel coherence time of the small-scale fading. By contrast, the second-stage combining is updated per fading realization. We develop a pilot-based channel estimation framework for this hardware setup based on maximum likelihoodestimation in both uplink and downlink. Digital precoding and combining designs are proposed, and a spectral efficiency expression that incorporates imperfect channel knowledge is derived. The numerical results demonstrate that the proposed approach outperforms hybrid beamforming, showcasing the attractiveness of using two-stage fully digital transceivers in future systems.

Multi-Gbps data rates are achievable in millimeter-wave (mmWave) bands, but a prominent issue is the tiny wavelength that results in rapid fading variations and significant pilot signaling for channel estimation. In this letter, we recognize that the angles of scattering clusters seen from the UE vary slowly compared to the small-scale fading. We characterize the beam coherence time, which quantifies how frequently the UE must update its downlink receive combining matrix. The exact beam coherence time is derived in the single-cluster case, and an achievable lower bound is proposed for the multi-cluster case. These values are determined so that at least half of the received signal gain is maintained in between the combining updates. We demonstrate how the beam coherence time can be hundreds of times larger than the channel coherence time of the small-scale fading.

Inferring a state sequence from a sequence of measurements is a fundamental problem in bioinformatics and natural language processing. The Viterbi and the Beam Search (BS) algorithms are popular inference methods, but they have limitations when applied to Hierarchical Hidden Markov Models (HHMMs), where the interest lies in the outer state sequence. The Viterbi algorithm can not infer outer states without inner states, while the BS algorithm requires marginalization over prohibitively large state spaces. We propose two new algorithms to overcome these limitations: the greedy marginalized BS algorithm and the local focus BS algorithm. We show that they approximate the most likely outer state sequence with higher performance than the Viterbi algorithm, and we evaluate the performance of these algorithms on an explicit duration HMM with simulation and nanopore base calling data.

Tunnel junctions have been suggested as high-throughput electronic single molecule sensors in liquids, with several seminal experiments conducted using break junctions with reconfigurable gaps. For practical single molecule sensing applications, arrays of on-chip integrated fixed-gap tunnel junctions that can be built into compact systems are preferable. Fabricating nanogaps by electromigration is one of the most promising approaches to realize on-chip integrated tunnel junction sensors. However, the electrical behavior of fixed-gap tunnel junctions immersed in liquid media has not been systematically studied to date, and the formation of electromigrated nanogap tunnel junctions in liquid media has not yet been demonstrated. In this work, we perform a comparative study of the formation and electrical behavior of arrays of gold nanogap tunnel junctions made by feedback-controlled electromigration immersed in various liquid and gaseous media (deionized water, mesitylene, ethanol, nitrogen, and air). We demonstrate that tunnel junctions can be obtained from microfabricated gold nano-constrictions inside liquid media. Electromigration of junctions in air produces the highest yield (61 %), electromigration in deionized water and mesitylene results in a lower yield than in air (44–48 %), whereas electromigration in ethanol fails to produce viable tunnel junctions due to interfering electrochemical processes. We map out the stability of the conductance characteristics of the resulting tunnel junctions and identify medium-specific operational conditions which have an impact on the yield of forming stable junctions. Furthermore, we highlight the unique challenges associated with working with arrays of large numbers of tunnel junctions in batches. Our findings will inform future efforts to build single-molecule sensors using on-chip integrated tunnel junctions.

Recent advancements in nanopore sequencing technology, particularly the R10 nanopore from Oxford Nanopore Technology, have necessitated the development of improved data processing methods to utilize their potential for more than 9-mer resolution fully. The processing of the ion currents predominantly utilizes neural network-based methods known for their high basecalling accuracy but face developmental bottlenecks at higher resolutions. In light of this, we introduce the Helicase Hidden Markov Model (HHMM), a novel framework designed to incorporate the dynamics of the helicase motor protein alongside the nucleotide sequence during nanopore sequencing. This model supports the analysis of millions of distinct states, enhancing our understanding of raw ion currents and their alignment with nucleotide sequences. Our findings demonstrate the utility of HHMM not only as a potent visualization tool but also as an effective base for developing advanced basecalling algorithms. This approach offers a promising avenue for leveraging the full capabilities of emerging high-resolution nanopore sequencing technologies.

Hidden Markov models (HMMs) are frequently used in areas such as speech recognition and bioinformatics. However, implementing HMM algorithms correctly and efficiently is time-consuming and error-prone. Specifically, using model-specific knowledge to improve performance, such as sparsity in the transition probability matrix, ties the implementation to a particular model, making it harder to modify. Previous work has introduced high-level frameworks for defining HMMs, thus lifting the burden of efficiently implementing HMM algorithms from the user. However, existing tools are ill-suited for sparse HMMs with many states. This paper introduces Trellis, a domain-specific language for succinctly defining sparse HMMs that use GPU acceleration to achieve high performance. We show that Trellis outperforms previous work and is on par with a hand-written CUDA kernel implementation for a particular sparse HMM.

Tunnel junctions have long been used to immobilize and study the electronic transport properties of single molecules. The sensitivity of tunneling currents to entities in the tunneling gap has generated interest in developing electronic biosensors with single molecule resolution. Tunnel junctions can, for example, be used for sensing bound or unbound DNA, RNA, amino acids, and proteins in liquids. However, manufacturing technologies for on-chip integrated arrays of tunnel junction sensors are still in their infancy, and scalable measurement strategies that allow the measurement of large numbers of tunneling junctions are required to facilitate progress. Here, we describe an experimental setup to perform scalable, high bandwidth (> 10 kHz) measurements of low currents (pA–nA) in arrays of on-chip integrated tunnel junctions immersed in various liquid media. Leveraging a commercially available compact 100 kHz bandwidth low-current measurement instrument, we developed a custom two-terminal probe on which the amplifier is directly mounted to decrease parasitic probe capacitances to sub-pF levels. We also integrated a motorized 3-axis stage, which could be powered down using software control, inside the Faraday cage of the setup. This enabled automated data acquisition on arrays of tunnel junctions without worsening the noise floor despite being inside the Faraday cage. A deliberately positioned air gap in the fluidic path ensured liquid perfusion to the chip from outside the Faraday cage without coupling in additional noise. We demonstrated the performance of our setup using rapid current switching observed in electromigrated gold tunnel junctions immersed in deionized water. Our measurement setup and findings will contribute to developing the emerging field of tunnel junction sensors and can be readily extended towards higher bandwidth sensing, as well as to other types of electronic single molecule sensors.

One of the first widespread uses of multi-user multiple-input multiple-output (MU-MIMO) is in 5G networks, where each base station has an advanced antenna system (AAS) that is connected to the baseband unit (BBU) with a capacity-constrained fronthaul. In the AAS configuration, multiple passive antenna elements and radio units are integrated into a single box. This paper considers precoded downlink transmission over a single-cell MU-MIMO system. We study optimized linear precoding for AAS with a limited-capacity fronthaul, which requires the precoding matrix to be quantized. We propose a new precoding design that is aware of the fronthaul quantization and minimizes the mean-squared error at the receiver side. We compute the precoding matrix using a sphere decoding (SD) approach. We also propose a heuristic low-complexity approach to quantized precoding. This heuristic is computationally efficient enough for massive MIMO systems. The numerical results show that our proposed precoding significantly outperforms quantization-unaware precoding and other previous approaches in terms of the sum rate. The performance loss for our heuristic method compared to quantization-aware precoding is insignificant considering the complexity reduction, which makes the heuristic method feasible for real-time applications. We consider both perfect and imperfect channel state information (CSI).

Detection of bio-molecules through quantum tunneling currents could lead to the next-generation DNA sequencing methods. In order to analyze the stability of these sensitive devices, it is necessary to characterize their conductance switching statistics. This characterization can be realized by denoising the tunneling current signal and clustering the outcomes. The first step can be done with the CUSUM algorithm, which detects abrupt changes and has been used in similar devices. We found heavy-tailed non-Gaussian noise in the measurement setup of the experimental devices. This paper suggests an approximation in the likelihood ratio step of the CUSUM algorithm that is more robust than the simple Gaussian noise assumption and, at the same time, is computationally more efficient than computing the fitted true likelihoods.

The field of nanopores has garnered increased interest for nucleic acid and peptide sensing and sequencing to develop targeted therapies for various diseases, including cancer, due to versatile pore sizes, measurement conditions, signal-to-noise ratio, and integration into electronics. We utilize a SiNx solid-state nanopore platform for studying the interaction and specificity of DNA-binding proteins with DNA. These DNA-binding proteins include various transcription factors, nuclear receptors, polymerases, and nucleases, which are responsible for several essential cellular processes and intra- and intercellular signaling cascades. Some of these DNA-binding proteins are known to be diagnostic and therapeutic targets for various diseases, specifically breast, ovarian, and prostate cancers. We seek to use these DNA-binding proteins as a clinically relevant model to detect their binding sites on the DNA and identify the protein mutations that would have a detrimental effect on its functionality, as they are highly dependent on accurate post-translational modifications. Using our nanopores, we successfully observe the binding of DNA-binding proteins onto the conventional binding sites on the target DNA and correlate these with the gold-standard Electrophoretic Mobility Shift Assay (EMSA) carried out parallelly. These proteins are observed as a pronounced local spike over and above the DNA strand translocating through the nanopore, signifying their binding to the DNA. The protein binding is stable and unaffected at a relatively high salt concentration (1 M LiCl, pH 8). Furthermore, the isoelectronic point of the protein is higher than this pH, imparting a positive charge on the protein and slowing down the translocation of such nuclear receptor-DNA complex, leading to a better signal resolution. We believe studying DNA binding proteins, including nuclear receptors, using nanopores could improve understanding of these proteins on a single molecule level, furthering the development of novel targeted treatment and diagnostic strategies.

Next-generation single-molecule protein sequencing technologies have the potential to significantly accelerate biomedical research. These technologies offer sensitivity and scalability for proteomic analysis. One auspicious method is fluorosequencing, which involves: cutting naturalized proteins into peptides, attaching fluorophores to specific amino acids, and observing variations in light intensity as one amino acid is removed at a time. The original peptide is classified from the sequence of light-intensity reads, and proteins can subsequently be recognized with this information. The amino acid step removal is achieved by attaching the peptides to a wall on the C-terminal and using a process called Edman Degradation to remove an amino acid from the N-Terminal. Even though a framework (Whatprot) has been proposed for the peptide classification task, processing times remain restrictive due to the massively parallel data acquisicion system. In this paper, we propose a new beam search decoder with a novel state formulation that obtains considerably lower processing times at the expense of only a slight accuracy drop compared to Whatprot. Furthermore, we explore how our novel state formulation may lead to even faster decoders in the future.

This paper considers a multi-user multiple-input multiple-output (MU-MIMO) system where the precoding matrix is selected in a baseband unit (BBU) and then sent over a digital fronthaul to the transmitting antenna array. The fronthaul has a limited bit resolution with a known quantization behavior. We formulate a new sum rate maximization problem where the precoding matrix elements must comply with the quantizer. We solve this non-convex mixed-integer problem to local optimality by a novel iterative algorithm inspired by the classical weighted minimum mean square error (WMMSE) approach. The precoding optimization subproblem becomes an integer least-squares problem, which we solve with a new algorithm using a sphere decoding (SD) approach. We show numerically that the proposed precoding technique vastly outperforms the baseline of optimizing an infinite-resolution precoder and then quantizing it. We also develop a heuristic quantization-aware precoding that outperforms the baseline while having comparable complexity.