arXiv subjects

Malcolm Egan

Publications and source records attributed to Malcolm Egan.

At least 19 recordsLinked to original sources

arXiv2025

Adversary-Aware Private Inference over Wireless Channels

AI-based sensing at wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for vision and perception tasks such as in autonomous driving and environmental monitoring. AI systems rely both on efficient model learning and inference. In the inference phase, features extracted from sensing data are utilized for prediction tasks (e.g., classification or regression). In edge networks, sensors and model servers are often not co-located, which requires communication of features. As sensitive personal data can be reconstructed by an adversary, transformation of the features are required to reduce the risk of privacy violations. While differential privacy mechanisms provide a means of protecting finite datasets, protection of individual features has not been addressed. In this paper, we propose a novel framework for privacy-preserving AI-based sensing, where devices apply transformations of extracted features before transmission to a model server.

cs.IT↗

arXiv2025

Streaming Federated Learning with Markovian Data

Federated learning (FL) is now recognized as a key framework for communication-efficient collaborative learning. Most theoretical and empirical studies, however, rely on the assumption that clients have access to pre-collected data sets, with limited investigation into scenarios where clients continuously collect data. In many real-world applications, particularly when data is generated by physical or biological processes, client data streams are often modeled by non-stationary Markov processes. Unlike standard i.i.d. sampling, the performance of FL with Markovian data streams remains poorly understood due to the statistical dependencies between client samples over time. In this paper, we investigate whether FL can still support collaborative learning with Markovian data streams. Specifically, we analyze the performance of Minibatch SGD, Local SGD, and a variant of Local SGD with momentum. We answer affirmatively under standard assumptions and smooth non-convex client objectives: the sample complexity is proportional to the inverse of the number of clients with a communication complexity comparable to the i.i.d. scenario. However, the sample complexity for Markovian data streams remains higher than for i.i.d. sampling.

cs.LG↗

arXiv2024

Joint Slot and Power Optimization for Grant Free Random Access with Unknown and Heterogeneous Device Activity

Grant Free Random Access (GFRA) is a popular protocol in the Internet of Things (IoT) to reduce the control signaling. GFRA is a framed protocol where each frame is split into two parts: device identification; and data transmission part which can be viewed as a form of Frame Slotted ALOHA (FSA). A common assumption in FSA is device homogeneity; that is the probability that a device seeks to transmit data in a particular frame is common for all devices and independent of the other devices. Recent work has investigated the possibility of tuning the FSA protocol to the statistics of the network by changing the probability for a particular device to access a particular slot. However, power control with a successive interference cancellation (SIC) receiver has not yet been considered to further increase the performance of the tuned FSA protocols. In this paper, we propose algorithms to jointly optimize both the slot selection and the transmit power of the devices to minimize the outage of the devices in the network. We show via a simulation study that our algorithms can outperform baselines (including slotted ALOHA) in terms of expected number of devices transmitting without outage and in term of transmit power.

cs.IT↗

arXiv2024

Exploiting Device Heterogeneity in Grant-Free Random Access: A Data-Driven Approach

Grant-free random access (GFRA) is now a popular protocol for large-scale wireless multiple access systems in order to reduce control signaling. Resource allocation in GFRA can be viewed as a form of frame slotted ALOHA, where a ubiquitous design assumption is device homogeneity. In particular, the probability that a device seeks to transmit data is common to all devices. Recently, there has been an interest in designing frame slotted ALOHA algorithms for networks with heterogeneous activity probabilities. These works have established that the throughput can be significantly improved over the standard uniform allocation. However, the algorithms for optimizing the probability a device accesses each slot require perfect knowledge of the active devices within each frame. In practice, this assumption is limiting as device identification algorithms in GFRA rarely provide activity estimates with zero errors. In this paper, we develop a new algorithm based on stochastic gradient descent for optimizing slot allocation probabilities in the presence of activity estimation errors. Our algorithm exploits importance weighted bias mitigation for stochastic gradient estimates, which is shown to provably converge to a stationary point of the throughput optimization problem. In moderate size systems, our simulations show that the performance of our algorithm depends on the type of error distribution. We study symmetric bit flipping, asymmetric bit flipping and errors resulting from a generalized approximate message passing (GAMP) algorithm. In these scenarios, we observe gains up to 40\%, 66\%, and 19\%, respectively.

cs.IT↗

arXiv2024

Closing the Implementation Gap in MC: Fully Chemical Synchronization and Detection for Cellular Receivers

In the context of the Internet of Bio-Nano Things (IoBNT), nano-devices are envisioned to perform complex tasks collaboratively, i.e., by communicating with each other. One candidate for the implementation of such devices are engineered cells due to their inherent biocompatibility. However, because each engineered cell has only little computational capabilities, transmitter and receiver (RX) functionalities can afford only limited complexity. In this paper, we propose a simple, yet modular, architecture for a cellular RX that is capable of processing a stream of observed symbols using chemical reaction networks. Furthermore, we propose two specific detector implementations for the RX. The first detector is based on a machine learning model that is trained offline, i.e., before the cellular RX is deployed. The second detector utilizes pilot symbol-based training and is therefore able to continuously adapt to changing channel conditions online, i.e., after deployment. To coordinate the different chemical processing steps involved in symbol detection, the proposed cellular RX leverages an internal chemical timer. Furthermore, the RX is synchronized with the transmitter via external, i.e., extracellular, signals. Finally, the proposed architecture is validated using theoretical analysis and stochastic simulations. The presented results confirm the feasibility of both proposed implementations and reveal that the proposed online learning-based RX is able to perform reliable detection even in initially unknown or slowly changing channels. By its modular design and exclusively chemical implementation, the proposed RX contributes towards the realization of versatile and biocompatible nano-scale communication networks for IoBNT applications narrowing the existing implementation gap in cellular molecular communication (MC).

cs.ET↗

arXiv2023

Broadcast Channels with Heterogeneous Arrival and Decoding Deadlines: Second-Order Achievability

A standard assumption in the design of ultra-reliable low-latency communication systems is that the duration between message arrivals is larger than the number of channel uses before the decoding deadline. Nevertheless, this assumption fails when messages arrive rapidly and reliability constraints require that the number of channel uses exceed the time between arrivals. In this paper, we consider a broadcast setting in which a transmitter wishes to send two different messages to two receivers over Gaussian channels. Messages have different arrival times and decoding deadlines such that their transmission windows overlap. For this setting, we propose a coding scheme that exploits Marton's coding strategy. We derive rigorous bounds on the achievable rate regions. Those bounds can be easily employed in point-to-point settings with one or multiple parallel channels. In the point-to-point setting with one or multiple parallel channels, the proposed achievability scheme is consistent with the normal approximation. In the broadcast setting, our scheme agrees with Marton's strategy for sufficiently large numbers of channel uses and shows significant performance improvements over standard approaches based on time sharing for transmission of short packets.

cs.IT↗

arXiv2023

Macroscopic limit for stochastic chemical reactions involving diffusion and spatial heterogeneity

To model bio-chemical reaction systems with diffusion one can either use stochastic, microscopic reaction-diffusion master equations or deterministic, macroscopic reaction-diffusion system. The connection between these two models is not only theoretically important but also plays an essential role in applications. This paper considers the macroscopic limits of the chemical reaction-diffusion master equation for first-order chemical reaction systems in highly heterogeneous environments. More precisely, the diffusion coefficients as well as the reaction rates are spatially inhomogeneous and the reaction rates may also be discontinuous. By carefully discretizing these heterogeneities within a reaction-diffusion master equation model, we show that in the limit we recover the macroscopic reaction-diffusion system with inhomogeneous diffusion and reaction rates.

math.AP↗

arXiv2023

Stochastic Chemical Reaction Networks for MAP Detection in Cellular Receivers

In order to fully exploit the potential of molecular communication (MC) for intra-body communication, practically implementable cellular receivers are an important long-term goal. A variety of receiver architectures based on chemical reaction networks (CRNs) and gene-regulatory networks (GRNs) has been introduced in the literature, because cells use these concepts to perform computations in nature. However, practical feasibility is still limited by stochastic fluctuations of chemical reactions and long computation times in GRNs. Therefore, in this paper, we propose two receiver designs based on stochastic CRNs, i.e., CRNs that perform computations by exploiting the intrinsic fluctuations of chemical reactions with very low molecule counts. The first CRN builds on a recent result from chemistry that showed how Boltzmann machines (BMs), a commonly used machine learning model, can be implemented with CRNs. We show that BMs with optimal parameter values and their CRN implementations can act as maximum-a-posteriori (MAP) detectors. Furthermore, we show that BMs can be efficiently trained from simulation data to achieve close-to-MAP performance. While this approach yields a fixed CRN once deployed, our second approach based on a manually designed CRN can be trained with pilot symbols even within the cell and thus adapt to changing channel conditions. We extend the literature by showing that practical robust detectors can achieve close-to-MAP performance even without explicit channel knowledge.

cs.ET↗

arXiv2022

An Information-Theoretic View of Mixed-Delay Traffic in 5G and 6G

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 the 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 well captures 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 on the two communication regimes.

cs.IT↗

arXiv2020

Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees

Asynchronous distributed algorithms are a popular way to reduce synchronization costs in large-scale optimization, and in particular for neural network training. However, for nonsmooth and nonconvex objectives, few convergence guarantees exist beyond cases where closed-form proximal operator solutions are available. As most popular contemporary deep neural networks lead to nonsmooth and nonconvex objectives, there is now a pressing need for such convergence guarantees. In this paper, we analyze for the first time the convergence of stochastic asynchronous optimization for this general class of objectives. In particular, we focus on stochastic subgradient methods allowing for block variable partitioning, where the shared-memory-based model is asynchronously updated by concurrent processes. To this end, we first introduce a probabilistic model which captures key features of real asynchronous scheduling between concurrent processes; under this model, we establish convergence with probability one to an invariant set for stochastic subgradient methods with momentum. From the practical perspective, one issue with the family of methods we consider is that it is not efficiently supported by machine learning frameworks, as they mostly focus on distributed data-parallel strategies. To address this, we propose a new implementation strategy for shared-memory based training of deep neural networks, whereby concurrent parameter servers are utilized to train a partitioned but shared model in single- and multi-GPU settings. Based on this implementation, we achieve on average 1.2x speed-up in comparison to state-of-the-art training methods for popular image classification tasks without compromising accuracy.

math.OC↗

arXiv2017

Modeling and Design of Millimeter-Wave Networks for Highway Vehicular Communication

Connected and autonomous vehicles will play a pivotal role in future Intelligent Transportation Systems (ITSs) and smart cities, in general. High-speed and low-latency wireless communication links will allow municipalities to warn vehicles against safety hazards, as well as support cloud-driving solutions to drastically reduce traffic jams and air pollution. To achieve these goals, vehicles need to be equipped with a wide range of sensors generating and exchanging high rate data streams. Recently, millimeter wave (mmWave) techniques have been introduced as a means of fulfilling such high data rate requirements. In this paper, we model a highway communication network and characterize its fundamental link budget metrics. In particular, we specifically consider a network where vehicles are served by mmWave Base Stations (BSs) deployed alongside the road. To evaluate our highway network, we develop a new theoretical model that accounts for a typical scenario where heavy vehicles (such as buses and lorries) in slow lanes obstruct Line-of-Sight (LOS) paths of vehicles in fast lanes and, hence, act as blockages. Using tools from stochastic geometry, we derive approximations for the Signal-to-Interference-plus-Noise Ratio (SINR) outage probability, as well as the probability that a user achieves a target communication rate (rate coverage probability). Our analysis provides new design insights for mmWave highway communication networks. In considered highway scenarios, we show that reducing the horizontal beamwidth from $90^\circ$ to $30^\circ$ determines a minimal reduction in the SINR outage probability (namely, $4 \cdot 10^{-2}$ at maximum). Also, unlike bi-dimensional mmWave cellular networks, for small BS densities (namely, one BS every $500$ m) it is still possible to achieve an SINR outage probability smaller than $0.2$.

cs.IT↗

arXiv2016

Wireless Vehicular Networks in Emergencies: A Single Frequency Network Approach

Obtaining high quality sensor information is critical in vehicular emergencies. However, existing standards such as IEEE 802.11p/DSRC and LTE-A cannot support either the required data rates or the latency requirements. One solution to this problem is for municipalities to invest in dedicated base stations to ensure that drivers have the information they need to make safe decisions in or near accidents. In this paper we further propose that these municipality-owned base stations form a Single Frequency Network (SFN). In order to ensure that transmissions are reliable, we derive tight bounds on the outage probability when the SFN is overlaid on an existing cellular network. Using our bounds, we propose a transmission power allocation algorithm. We show that our power allocation model can reduce the total instantaneous SFN transmission power up to $20$ times compared to a static uniform power allocation solution, for the considered scenarios. The result is particularly important when base stations rely on an off-grid power source (i.e., batteries).

cs.IT↗

arXiv2016

Liftago On-Demand Transport Dataset and Market Formation Algorithm Based on Machine Learning

This document serves as a technical report for the analysis of on-demand transport dataset. Moreover we show how the dataset can be used to develop a market formation algorithm based on machine learning. Data used in this work comes from Liftago, a Prague based company which connects taxi drivers and customers through a smartphone app. The dataset is analysed from the machine-learning perspective: we give an overview of features available as well as results of feature ranking. Later we propose the SImple Data-driven MArket Formation (SIDMAF) algorithm which aims to improve a relevance while connecting customers with relevant drivers. We compare the heuristics currently used by Liftago with SIDMAF using two key performance indicators.

cs.AI↗

arXiv2015

A Ruin Theoretic Design Approach for Wireless Cellular Network Sharing with Facilities

With the rise of cheap small-cells in wireless cellular networks, there are new opportunities for third party providers to service local regions via sharing arrangements with traditional operators. In fact, such arrangements are highly desirable for large facilities---such as stadiums, universities, and mines---as they already need to cover property costs, and often have fibre backhaul and efficient power infrastructure. In this paper, we propose a new network sharing arrangement between large facilities and traditional operators. Our facility network sharing arrangement consists of two aspects: leasing of core network access and spectrum from traditional operators; and service agreements with users. Importantly, our incorporation of a user service agreement into the arrangement means that resource allocation must account for financial as well as physical resource constraints. This introduces a new non-trivial dimension into wireless network resource allocation, which requires a new evaluation framework---the data rate is no longer the only main performance metric. Moreover, despite clear economic incentives to adopt network sharing for facilities, a business case is lacking. As such, we develop a general socio-technical evaluation framework based on ruin-theory, where the key metric for the sharing arrangement is the probability that the facility has less than zero revenue surplus. We then use our framework to evaluate our facility network sharing arrangement, which offers guidance for leasing and service agreement negotiations, as well as design of the wireless network architecture, taking into account network revenue streams.

cs.CY↗

arXiv2015

Market Mechanism Design for Profitable On-Demand Transport Services

On-demand transport services in the form of dial-a-ride and taxis are crucial parts of the transport infrastructure in all major cities. However, not all on-demand transport services are equal. In particular, not-for-profit dial-a-ride services with coordinated drivers significantly differ from profit-motivated taxi services with uncoordinated drivers. As such, there are two key threads of research for efficient scheduling, routing, and pricing for passengers: dial-a-ride services (first thread); and taxi services (second thread). Unfortunately, there has been only limited development of algorithms for joint optimization of scheduling, routing, and pricing; largely due to the widespread assumption of fixed pricing. In this paper, we introduce another thread: profit-motivated on-demand transport services with coordinated drivers. To maximize provider profits and the efficiency of the service, we propose a new market mechanism for this new thread of on-demand transport services, where passengers negotiate with the service provider. In contrast to previous work, our mechanism jointly optimizes scheduling, routing, and pricing. Ultimately, we demonstrate that our approach can lead to higher profits, compared with standard fixed price approaches, while maintaining comparable efficiency.

cs.CY↗

arXiv2014

Variance-Constrained Capacity of the Molecular Timing Channel with Synchronization Error

Molecular communication is set to play an important role in the design of complex biological and chemical systems. An important class of molecular communication systems is based on the timing channel, where information is encoded in the delay of the transmitted molecule---a synchronous approach. At present, a widely used modeling assumption is the perfect synchronization between the transmitter and the receiver. Unfortunately, this assumption is unlikely to hold in most practical molecular systems. To remedy this, we introduce a clock into the model---leading to the molecular timing channel with synchronization error. To quantify the behavior of this new system, we derive upper and lower bounds on the variance-constrained capacity, which we view as the step between the mean-delay and the peak-delay constrained capacity. By numerically evaluating our bounds, we obtain a key practical insight: the drift velocity of the clock links does not need to be significantly larger than the drift velocity of the information link, in order to achieve the variance-constrained capacity with perfect synchronization.

cs.IT↗

arXiv2014

The Unreasonable Effectiveness of Blood Pressure Measurement: Molecular Communication in Biological Systems

Arterial blood pressure is a key vital sign for the health of the human body. As such, accurate and reproducible measurement techniques are necessary for successful diagnosis. Blood pressure measurement is an example of molecular communication in regulated biological systems. In general, communication in regulated biological systems is difficult because the act of encoding information about the state of the system can corrupt the message itself. In this paper, we propose three strategies to cope with this problem to facilitate reliable molecular communication links: communicate from the outskirts; build it in; and leave a small footprint. Our strategies---inspired by communication in natural biological systems---provide a classification to guide the design of molecular communication mechanisms in synthetic biological systems. We illustrate our classification using examples of the first two strategies in natural systems. We then consider a molecular link within a model based on the Michaelis-Menten kinetics. In particular, we compute the capacity of the link, which reveals the potential of communicating using our leave a small footprint strategy. This provides a way of identifying whether the molecular link can be improved without affecting the function, and a guide to the design of synthetic biological systems.

cs.IT↗

arXiv2014

Low-High SNR Transition in Multiuser MIMO

Multiuser MIMO (MU-MIMO) plays a key role in the widely adopted 3GPP LTE standard for wireless cellular networks. While exact and asymptotic sum-rate results are well known, the problem of obtaining intuitive analytical results for medium signal-to-noise ratios (SNRs) is still not solved. In this paper, we propose the bend point, which quantifies the transition between low and high SNR; i.e., the beginning of the high SNR region. We derive the bend point for MU-MIMO with zero-forcing precoding and show that it is intimately related to the intercept of the high SNR asymptote with the zero sum-rate line. Using this result, we obtain a new approximation of the sum-rate at the bend point---providing a useful rule of thumb for the effect of increasing the number of antennas at medium SNR.

cs.IT↗