Osvaldo Simeone

King's College London

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Shlomo Shamai

Technion – Israel Institute of Technology

203

Petar Popovski

Aalborg University

Umberto Spagnolini

Politecnico di Milano

Seok-Hwan Park

Jeonbuk National University

Joonhyuk Kang

Korea Advanced Institute of Science and Technology

Y. Bar-Ness

New Jersey Institute of Technology

Sangwoo Park

Seoul National University of Science and Technology

Oren Somekh

Yahoo (United Kingdom)

Hyeryung Jang

Dongguk University

Sharu Theresa Jose

University of Birmingham

Cooperative Institutions

King's College London

New Jersey Institute of Technology

Politecnico di Milano

Korea Advanced Institute of Science and Technology

Institute of Electrical and Electronics Engineers

Signal Processing (United States)

Samsung (South Korea)

Zhejiang University

Imperial College London

King's College School

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Control-Data Separation across Edge and Cloud for Uplink Communications in C-RAN

2022 IEEE Wireless Communications and Networking Conference (WCNC) (2017)

Jinkyu Kang Osvaldo Simeone Joonhyuk Kang Shlomo Shamai

Fronthaul limitations in terms of capacity and latency motivate the growing interest in the wireless industry for the study of alternative functional splits between cloud and edge nodes in Cloud Radio Access Network (C-RAN). This work contributes to this line of work by investigating the optimal functional split of control and data plane functionalities at the edge nodes and at the Remote Cloud Center (RCC) as a function of the fronthaul latency. The model under study consists of a two-user time-varying uplink channel in which the RCC has global but delayed channel state information (CSI) due to fronthaul latency, while edge nodes have local but timely CSI. Adopting the adaptive sum-rate as the performance criterion, functional splits whereby the control functionality of rate selection and the decoding of the data-plane frames are carried out at either the edge nodes or at the RCC are compared, demonstrating the potential advantages of implementing control functions at the edge.

C-RAN

10.1109/wcnc.2017.7925636

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Citations (4)

Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning

arXiv (Cornell University) (2022)

Clément Ruah Osvaldo Simeone Bashir M. Al‐Hashimi

Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control and monitor software-based, "open", communication systems, which play the role of the physical twin (PT). In the general framework presented in this work, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT functionalities such as control via multi-agent reinforcement learning (MARL) and monitoring of the PT for anomaly detection. We specifically investigate the application of the proposed framework to a simple case-study system encompassing multiple sensing devices that report to a common receiver. The Bayesian model trained at the DT has the key advantage of capturing epistemic uncertainty regarding the communication system, e.g., regarding current traffic conditions, which arise from limited PT-to-DT data transfer. Experimental results validate the effectiveness of the proposed Bayesian framework as compared to standard frequentist model-based solutions.

Bayesian Optimization

10.48550/arxiv.2210.05582

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Xpikeformer: Hybrid Analog-Digital Hardware Acceleration for Spiking Transformers

arXiv (Cornell University) (2024)

Zihang Song Prabodh Katti Osvaldo Simeone Bipin Rajendran

This paper introduces Xpikeformer, a hybrid analog-digital hardware architecture designed to accelerate spiking neural network (SNN)-based transformer models. By combining the energy efficiency and temporal dynamics of SNNs with the powerful sequence modeling capabilities of transformers, Xpikeformer leverages mixed analog-digital computing techniques to enhance performance and energy efficiency. The architecture integrates analog in-memory computing (AIMC) for feedforward and fully connected layers, and a stochastic spiking attention (SSA) engine for efficient attention mechanisms. We detail the design, implementation, and evaluation of Xpikeformer, demonstrating significant improvements in energy consumption and computational efficiency. Through an image classification task and a wireless communication symbol detection task, we show that Xpikeformer can achieve software-comparable inference accuracy. Energy evaluations reveal that Xpikeformer achieves up to a $17.8$--$19.2\times$ reduction in energy consumption compared to state-of-the-art digital ANN transformers and up to a $5.9$--$6.8\times$ reduction compared to fully digital SNN transformers. Xpikeformer also achieves a $12.0\times$ speedup compared to the GPU implementation of spiking transformers.

Digital-to-analog converter

10.48550/arxiv.2408.08794

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Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference

arXiv (Cornell University) (2022)

Jiechen Chen Nicolas Skatchkovsky Osvaldo Simeone

Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips, when coupled with spike-based sensors, can inherently adapt to the "semantics" of the data distribution by consuming energy only when relevant events are recorded in the timing of spikes and by proving a low-latency response to changing conditions in the environment. This paper proposes an end-to-end design for a neuromorphic wireless Internet-of-Things system that integrates spike-based sensing, processing, and communication. In the proposed NeuroComm system, each sensing device is equipped with a neuromorphic sensor, a spiking neural network (SNN), and an impulse radio transmitter with multiple antennas. Transmission takes place over a shared fading channel to a receiver equipped with a multi-antenna impulse radio receiver and with an SNN. In order to enable adaptation of the receiver to the fading channel conditions, we introduce a hypernetwork to control the weights of the decoding SNN using pilots. Pilots, encoding SNNs, decoding SNN, and hypernetwork are jointly trained across multiple channel realizations. The proposed system is shown to significantly improve over conventional frame-based digital solutions, as well as over alternative non-adaptive training methods, in terms of time-to-accuracy and energy consumption metrics.

Neuromorphic engineering

10.48550/arxiv.2206.06047

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Cloud and Cache-Aided Wireless Networks: Fundamental Latency Trade-Offs.

arXiv (Cornell University) (2016)

Avik Sengupta Ravi Tandon Osvaldo Simeone

A cloud and cache-aided wireless network architecture is studied in which edge-nodes (ENs), such as base stations, are connected to a cloud processor via dedicated fronthaul links, while also being endowed with caches. Cloud processing enables the centralized implementation of cooperative transmission strategies at the ENs, albeit at the cost of an increased latency due to fronthaul transfer. In contrast, the proactive caching of popular content at the ENs allows for the low-latency delivery of the cached files, but with generally limited opportunities for cooperative transmission among the ENs. The interplay between cloud processing and edge caching is addressed from an information-theoretic viewpoint by investigating the fundamental limits of a high Signal-to-Noise-Ratio (SNR) metric, termed normalized delivery time (NDT), which captures the worst-case latency for delivering any requested content to the users. The NDT is defined under the assumption of either serial or pipelined fronthaul-edge transmissions, and is studied as a function of fronthaul and cache capacity constraints. Transmission policies that encompass the caching phase as well as the transmission phase across both fronthaul and wireless, or edge, segments are proposed, with the aim of minimizing the NDT for given fronthaul and cache capacity constraints. Informationtheoretic lower bounds on the NDT are also derived. Achievability arguments and lower bounds are leveraged to characterize the minimal NDT in a number of important special cases, including systems with no caching capability, as well as to prove that the proposed schemes achieve optimality within a constant multiplicative factor of 2 for all values of the problem parameters.

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Citations (36)

Coordinated Scheduling in C-RANs

Cambridge University Press eBooks (2016)

Tony Q. S. Quek Mugen Peng Osvaldo Simeone Wei Yu

In the wireless network literature, scheduling denotes the strategy according to which users are active at each time-frequency resource block. In this classical literature, scheduling is done per base station (BS), i.e., without coordination between the BSs. With the large demand for mobile data services that is straining wireless networks nowadays, coordination among transmitters becomes a real necessity to better manage the high levels of interference.

10.1017/9781316529669.013

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Combined linear pre-equalization and BLAST equalization with channel correlation feedback

IEEE Communications Letters (2003)

Osvaldo Simeone Umberto Spagnolini

In this letter, we study linear pre-equalization in conjunction with BLAST equalization. Linear pre-equalization is based on the assumption that the channel state information available at the transmitter is limited to the second order statistics of channel and noise (long term channel state information or LT-CSI). Through simulations, we compare different design criteria for the pre-equalizer and show the advantages of the considered structure with respect to BLAST.

Blind equalization

Channel state information

10.1109/lcomm.2003.818886

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Citations (12)

Optimizing Pilots and Analog Processing for Channel Estimation in Cell-Free Massive MIMO with One-Bit ADCs

Seok-Hwan Park Osvaldo Simeone Yonina C. Eldar Elza Erkip

In a cell-free cloud radio access network (C-RAN) architecture, uplink channel estimation is carried out by a centralized baseband processing unit (BBU) connected to distributed remote radio heads (RRHs). When the RRHs have multiple antennas and limited radio front-end resources, the design of uplink channel estimation is faced with the challenges posed by reduced radio frequency (RF) chains and one-bit analog-to-digital converters (ADCs) at the RRHs. This work tackles the problem of jointly optimizing the pilot sequences and the pre-RF chains analog combiners with the goal of minimizing the sum of mean squared errors (MSEs) of the estimated channel vectors at the BBU. The problem formulation models the impact of the ADC operation by leveraging Bussgang's theorem. An efficient solution is developed by means of an iterative alternating optimization algorithm. Numerical results validate the advantages of the proposed joint design compared to baseline schemes that randomly choose either pilots or analog combiners.

10.1109/spawc.2018.8445960

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Citations (24)

Free Energy Minimization: A Unified Framework for Modelling, Inference, Learning, and Optimization.

arXiv (Cornell University) (2020)

Sharu Theresa Jose Osvaldo Simeone

The goal of these lecture notes is to review the problem of free energy minimization as a unified framework underlying the definition of maximum entropy modelling, generalized Bayesian inference, learning with latent variables, statistical learning analysis of generalization,and local optimization. Free energy minimization is first introduced, here and historically, as a thermodynamic principle. Then, it is described mathematically in the context of Fenchel duality. Finally, the mentioned applications to modelling, inference, learning, and optimization are covered starting from basic principles.

Free Energy Principle

Energy minimization

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Distributed synchronization in wireless networks

IEEE Signal Processing Magazine (2008)

Osvaldo Simeone Umberto Spagnolini Y. Bar-Ness Steven H. Strogatz

This article has explored history, recent advances, and challenges in distributed synchronization for distributed wireless systems. It is focused on synchronization schemes based on exchange of signals at the physical layer and corresponding baseband processing, wherein analysis and design can be performed using known tools from signal processing. Emphasis has also been given on the synergy between distributed synchronization and distributed estimation/detection problems. Finally, we have touched upon synchronization of nonperiodic (chaotic) signals. Overall, we hope to have conveyed the relevance of the subject and to have provided insight on the open issues and available analytical tools that could inspire further research within the signal processing community.

Relevance

SIGNAL (programming language)

10.1109/msp.2008.926661

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Citations (271)