Imperial College London

ProfessorDenizGunduz

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor in Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 6218d.gunduz Website

 
 
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Assistant

 

Ms Joan O'Brien +44 (0)20 7594 6316

 
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Location

 

1016Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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331 results found

Wu H, Shao Y, Mikolajczyk K, Gunduz Det al., 2022, Channel-adaptive wireless image transmission with OFDM, IEEE Wireless Communications Letters, Pages: 1-1, ISSN: 2162-2337

We present a learning-based channel-adaptive joint source and channel coding (CA-JSCC) scheme for wireless image transmission over multipath fading channels. The proposed method is an end-to-end autoencoder architecture with a dual-attention mechanism employing orthogonal frequency division multiplexing (OFDM) transmission. Unlike the previous works, our approach is adaptive to channel-gain and noise-power variations by exploiting the estimated channel state information (CSI). Specifically, with the proposed dual-attention mechanism, our model can learn to map the features and allocate transmission-power resources judiciously to the available subchannels based on the estimated CSI. Extensive numerical experiments verify that CA-JSCC achieves state-of-the-art performance among existing JSCC schemes. In addition, CA-JSCC is robust to varying channel conditions and can better exploit the limited channel resources by transmitting critical features over better subchannels.

Journal article

Tung TY, Gündüz D, 2022, DeepWiVe: Deep-Learning-Aided Wireless Video Transmission, IEEE Journal on Selected Areas in Communications, Vol: 40, Pages: 2570-2583, ISSN: 0733-8716

We present DeepWiVe, the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform. Our DNN decoder predicts residuals without distortion feedback, which improves the video quality by accounting for occlusion/disocclusion and camera movements. We simultaneously train different bandwidth allocation networks for the frames to allow variable bandwidth transmission. Then, we train a bandwidth allocation network using reinforcement learning (RL) that optimizes the allocation of limited available channel bandwidth among video frames to maximize the overall visual quality. Our results show that DeepWiVe can overcome the cliff-effect, which is prevalent in conventional separation-based digital communication schemes, and achieve graceful degradation with the mismatch between the estimated and actual channel qualities. DeepWiVe outperforms H.264 video compression followed by low-density parity check (LDPC) codes in all channel conditions by up to 0.0485 in terms of the multi-scale structural similarity index measure (MS-SSIM), and H.265+ LDPC by up to 0.0069 on average. We also illustrate the importance of optimizing bandwidth allocation in JSCC video transmission by showing that our optimal bandwidth allocation policy is superior to uniform allocation as well as a heuristic policy benchmark.

Journal article

Yang Z, Xia J-Y, Luo J, Zhang S, Gunduz Det al., 2022, A Learning-Aided Flexible Gradient Descent Approach to MISO Beamforming, IEEE WIRELESS COMMUNICATIONS LETTERS, Vol: 11, Pages: 1895-1899, ISSN: 2162-2337

Journal article

Li GY, Saad W, Ozgur A, Kairouz P, Qin Z, Hoydis J, Han Z, Gunduz D, Elmirghani Jet al., 2022, Series Editorial The Fifth Issue of the Series on Machine Learning in Communications and Networks, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 40, Pages: 2251-2253, ISSN: 0733-8716

Journal article

ul Haque S, Chandak S, Chiariotti F, Gunduz D, Popovski Pet al., 2022, Learning to Speak on Behalf of a Group: Medium Access Control for Sending a Shared Message, IEEE COMMUNICATIONS LETTERS, Vol: 26, Pages: 1843-1847, ISSN: 1089-7798

Journal article

Shiri I, Sadr AV, Amini M, Salimi Y, Sanaat A, Akhavanallaf A, Razeghi B, Ferdowsi S, Saberi A, Arabi H, Becker M, Voloshynovskiy S, Gunduz D, Rahmim A, Zaidi Het al., 2022, Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework, CLINICAL NUCLEAR MEDICINE, Vol: 47, Pages: 606-617, ISSN: 0363-9762

Journal article

Sun Y, Zhang F, Zhao J, Zhou S, Niu Z, Gunduz Det al., 2022, Coded Computation Across Shared Heterogeneous Workers With Communication Delay, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 70, Pages: 3371-3385, ISSN: 1053-587X

Journal article

Shao Y, Gunduz D, Liew SC, 2022, Federated edge learning with misaligned over-the-air computation, IEEE Transactions on Wireless Communications, Vol: 21, Pages: 1-1, ISSN: 1536-1276

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel-gain precoding and strict synchronization among edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for FEEL and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent samples from the misaligned and overlapped signals. Given the whitened samples, a sum-product ML (SP-ML) estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. In particular, the computational complexity of our SP-ML estimator is linear in the packet length, and hence is significantly lower than the conventional ML estimator. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is not severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement in the estimation process. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment.

Journal article

Teng F, Chhachhi SAURAB, Ge PUDONG, Graham J, Gunduz Det al., 2022, Balancing privacy and access to smart meter data: an Energy Futures Lab briefing paper

Digitalising the energy system is expected to be a vital component of achieving the UK’s climate change targets. Smart meter data, in particular, is seen a key enabler of the transition to more dynamic, cost-effective, cost-reflective, and decarbonised electricity. However, access to this data faces a challenge due to consumer privacy concerns. This Briefing Paper investigates four key elements of smart meter data privacy: existing data protection regulations; the personal information embedded within smart meter data; consumer privacy concerns; and privacy-preserving techniques that could be incorporated alongside existing mechanisms to minimise or eliminate potential privacy infringements.

Report

Ozfatura E, Gunduz D, 2022, Uncoded caching and cross-level coded delivery for non-uniform file popularity, IEEE Transactions on Information Theory, Pages: 1-1, ISSN: 0018-9448

Journal article

Xia J-Y, Li S, Huang J-J, Yang Z, Jaimoukha IM, Gunduz Det al., 2022, Metalearning-based alternating minimization algorithm for nonconvex optimization, IEEE Transactions on Neural Networks and Learning Systems, ISSN: 1045-9227

In this article, we propose a novel solution for nonconvex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of subproblems corresponding to each variable and then iteratively optimizes each subproblem using a fixed updating rule. However, due to the intrinsic nonconvexity of the original optimization problem, the optimization can be trapped into a spurious local minimum even when each subproblem can be optimally solved at each iteration. Meanwhile, learning-based approaches, such as deep unfolding algorithms, have gained popularity for nonconvex optimization; however, they are highly limited by the availability of labeled data and insufficient explainability. To tackle these issues, we propose a meta-learning based alternating minimization (MLAM) method that aims to minimize a part of the global losses over iterations instead of carrying minimization on each subproblem, and it tends to learn an adaptive strategy to replace the handcrafted counterpart resulting in advance on superior performance. The proposed MLAM maintains the original algorithmic principle, providing certain interpretability. We evaluate the proposed method on two representative problems, namely, bilinear inverse problem: matrix completion and nonlinear problem: Gaussian mixture models. The experimental results validate the proposed approach outperforms AM-based methods.

Journal article

Buyukates B, Ozfatura E, Ulukus S, Gunduz Det al., 2022, Gradient coding with dynamic clustering for straggler-tolerant distributed learning, IEEE Transactions on Communications, Pages: 1-1, ISSN: 0090-6778

Distributed implementations are crucial in speeding up large scale machine learning applications. Distributed gradient descent (GD) is widely employed to parallelize the learning task by distributing the dataset across multiple workers. A significant performance bottleneck for the per-iteration completion time in distributed synchronous GD is straggling workers. Coded distributed computation techniques have been introduced recently to mitigate stragglers and to speed up GD iterations by assigning redundant computations to workers. In this paper, we introduce a novel paradigm of dynamic coded computation, which assigns redundant data to workers to acquire the flexibility to dynamically choose from among a set of possible codes depending on the past straggling behavior. In particular, we propose gradient coding (GC) with dynamic clustering, called GC-DC, and regulate the number of stragglers in each cluster by dynamically forming the clusters at each iteration. With time-correlated straggling behavior, GC-DC adapts to the straggling behavior over time; in particular, at each iteration, GC-DC aims at distributing the stragglers across clusters as uniformly as possible based on the past straggler behavior. For both homogeneous and heterogeneous worker models, we numerically show that GC-DC provides significant improvements in the average per-iteration completion time without an increase in the communication load compared to the original GC scheme.

Journal article

Mital N, Ling C, Gunduz D, 2022, Secure distributed matrix computation with discrete fourier transform, IEEE Transactions on Information Theory, Vol: 68, Pages: 1-1, ISSN: 0018-9448

We consider the problem of secure distributed matrix computation (SDMC), where a user queries a function of data matrices generated at distributed source nodes. We assume the availability of N honest but curious computation servers, which are connected to the sources, the user, and each other through orthogonal and reliable communication links. Our goal is to minimize the amount of data that must be transmitted from the sources to the servers, called the upload cost, while guaranteeing that no T colluding servers can learn any information about the source matrices, and the user cannot learn any information beyond the computation result. We first focus on secure distributed matrix multiplication (SDMM), considering two matrices, and propose a novel polynomial coding scheme using the properties of finite field discrete Fourier transform, which achieves an upload cost significantly lower than the existing results in the literature. We then generalize the proposed scheme to include straggler mitigation, and to the multiplication of multiple matrices while keeping the input matrices, the intermediate computation results, as well as the final result secure against any T colluding servers. We also consider a special case, called computation with own data, where the data matrices used for computation belong to the user. In this case, we drop the security requirement against the user, and show that the proposed scheme achieves the minimal upload cost. We then propose methods for performing other common matrix computations securely on distributed servers, including changing the parameters of secret sharing, matrix transpose, matrix exponentiation, solving a linear system, and matrix inversion, which are then used to show how arbitrary matrix polynomials can be computed securely on distributed servers using the proposed procedure

Journal article

Zecchin M, Mashhadi MB, Jankowski M, Gunduz D, Kountouris M, Gesbert Det al., 2022, LIDAR and Position-Aided mmWave Beam Selection With Non-Local CNNs and Curriculum Training, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, Vol: 71, Pages: 2979-2990, ISSN: 0018-9545

Journal article

Hasircioglu B, Gomez-Vilardebo J, Gunduz D, 2022, Bivariate Polynomial Codes for Secure Distributed Matrix Multiplication, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 40, Pages: 955-967, ISSN: 0733-8716

Journal article

Amiri MM, Gunduz D, Kulkarni SR, Vincent Poor Het al., 2022, Convergence of federated learning over a noisy downlink, IEEE Transactions on Wireless Communications, Vol: 21, Pages: 1422-1437, ISSN: 1536-1276

We study federated learning (FL), where power-limited wireless devices utilize their local datasets to collaboratively train a global model with the help of a remote parameter server (PS). The PS has access to the global model and shares it with the devices for local training using their datasets, and the devices return the result of their local updates to the PS to update the global model. The algorithm continues until the convergence of the global model. This framework requires downlink transmission from the PS to the devices and uplink transmission from the devices to the PS. The goal of this study is to investigate the impact of the bandwidth-limited shared wireless medium on the performance of FL with a focus on the downlink. To this end, the downlink and uplink channels are modeled as fading broadcast and multiple access channels, respectively, both with limited bandwidth. For downlink transmission, we first introduce a digital approach, where a quantization technique is employed at the PS followed by a capacity-achieving channel code to transmit the global model update over the wireless broadcast channel at a common rate such that all the devices can decode it. Next, we propose analog downlink transmission, where the global model is broadcast by the PS in an uncoded manner. We consider analog transmission over the uplink in both cases, since its superiority over digital transmission for uplink has been well studied in the literature. We further analyze the convergence behavior of the proposed analog transmission approach over the downlink assuming that the uplink transmission is error-free. Numerical experiments show that the analog downlink approach provides significant improvement over the digital one with a more notable improvement when the data distribution across the devices is not independent and identically distributed. The experimental results corroborate the convergence analysis, and show that a smaller number of local iterations should be used when

Journal article

Chen M, Gunduz D, Huang K, Saad W, Bennis M, Feljan AV, Poor HVet al., 2022, Guest Editorial Special Issue on Distributed Learning Over Wireless Edge Networks-Part II, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 40, Pages: 445-448, ISSN: 0733-8716

Journal article

Li GY, Saad W, Ozgur A, Kairouz P, Qin Z, Hoydis J, Han Z, Gunduz D, Elmirghani Jet al., 2022, Series Editorial The Fourth Issue of the Series on Machine Learning in Communications and Networks, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 40, Pages: 1-4, ISSN: 0733-8716

Journal article

Ozfatura E, Ulukus S, Gunduz D, 2021, Coded distributed computing with partial recovery, IEEE Transactions on Information Theory, Vol: 68, Pages: 1945-1959, ISSN: 0018-9448

Coded computation techniques provide robustness against straggling workers in distributed computing. However, most of the existing schemes require exact provisioning of the straggling behavior and ignore the computations carried out by straggling workers. Moreover, these schemes are typically designed to recover the desired computation results accurately, while in many machine learning and iterative optimization algorithms, faster approximate solutions are known to result in an improvement in the overall convergence time. In this paper, we first introduce a novel coded matrix-vector multiplication scheme, called coded computation with partial recovery (CCPR), which benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and the decoding complexity by allowing a trade-off between the accuracy and the speed of computation. We then extend this approach to distributed implementation of more general computation tasks by proposing a coded communication scheme with partial recovery, where the results of subtasks computed by the workers are coded before being communicated. Numerical simulations on a large linear regression task confirm the benefits of the proposed scheme in terms of the trade-off between the computation accuracy and latency.

Journal article

Chen M, Gunduz D, Huang K, Saad W, Bennis M, Feljan AV, Poor HVet al., 2021, Guest Editorial Special Issue on Distributed Learning Over Wireless Edge Networks-Part I, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 39, Pages: 3575-3578, ISSN: 0733-8716

Journal article

Chen M, Gunduz D, Huang K, Saad W, Bennis M, Feljan AV, Poor HVet al., 2021, Distributed Learning in Wireless Networks: Recent Progress and Future Challenges, IEEE Journal on Selected Areas in Communications, Vol: 39, Pages: 3579-3605, ISSN: 0733-8716

The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment (e.g., dynamic channel and interference), limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources (e.g., computational power). This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks. We present a detailed overview of several emerging distributed learning paradigms, including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning. For each learning framework, we first introduce the motivation for deploying it over wireless networks. Then, we present a detailed literature review on the use of communication techniques for its efficient deployment. We then introduce an illustrative example to show how to optimize wireless networks to improve its performance. Finally, we introduce future research opportunities. In a nutshell, this paper provides a holistic set of guidelines on how to deploy a broad range of distributed learning frameworks over real-world wireless communication networks.

Journal article

Mital N, Kralevska K, Ling C, Gunduz Det al., 2021, Functional Broadcast Repair of Multiple Partial Failures in Wireless Distributed Storage Systems, IEEE Journal on Selected Areas in Information Theory, Vol: 2, Pages: 1093-1107

Journal article

Erkip E, Gunduz D, Ioannidis S, Kliewer J, Malak D, Medard M, Srikant Ret al., 2021, JSAIT Editorial for the Special Issue on “Beyond Errors and Erasures: Coding for Data Management and Delivery in Networks”, IEEE Journal on Selected Areas in Information Theory, Vol: 2, Pages: 1075-1077

Journal article

Sun Y, Zhou S, Niu Z, Gunduz Det al., 2021, Dynamic scheduling for over-the-air federated edge learning with energy constraints, IEEE Journal on Selected Areas in Communications, Vol: 40, Pages: 227-242, ISSN: 0733-8716

Machine learning and wireless communication technologies are jointly facilitating an intelligent edge, where federated edge learning (FEEL) is emerging as a promising training framework. As wireless devices involved in FEEL are resource limited in terms of communication bandwidth, computing power and battery capacity, it is important to carefully schedule them to optimize the training performance. In this work, we consider an over-the-air FEEL system with analog gradient aggregation, and propose an energy-aware dynamic device scheduling algorithm to optimize the training performance within the energy constraints of devices, where both communication energy for gradient aggregation and computation energy for local training are considered. The consideration of computation energy makes dynamic scheduling challenging, as devices are scheduled before local training, but the communication energy for over-the-air aggregation depends on the l2-norm of local gradient, which is known only after local training. We thus incorporate estimation methods into scheduling to predict the gradient norm. Taking the estimation error into account, we characterize the performance gap between the proposed algorithm and its offline counterpart. Experimental results show that, under a highly unbalanced local data distribution, the proposed algorithm can increase the accuracy by 4.9% on CIFAR-10 dataset compared with the myopic benchmark, while satisfying the energy constraints.

Journal article

Malekzadeh M, Borovykh A, Gunduz D, 2021, Honest-but-curious nets: sensitive attributes of private inputs can be secretly coded into the classifiers' outputs, ACM CCS 2021, Publisher: ACM, Pages: 825-844

It is known that deep neural networks, trained for the classification of non-sensitive target attributes, can reveal sensitive attributes of their input data through internal representations extracted by the classifier. We take a step forward and show that deep classifiers can be trained to secretly encode a sensitive attribute of their input data into the classifier's outputs for the target attribute, at inference time. Our proposed attack works even if users have a full white-box view of the classifier, can keep all internal representations hidden, and only release the classifier's estimations for the target attribute. We introduce an information-theoretical formulation for such attacks and present efficient empirical implementations for training honest-but-curious (HBC) classifiers: classifiers that can be accurate in predicting their target attribute, but can also exploit their outputs to secretly encode a sensitive attribute. Our work highlights a vulnerability that can be exploited by malicious machine learning service providers to attack their user's privacy in several seemingly safe scenarios; such as encrypted inferences, computations at the edge, or private knowledge distillation. Experimental results on several attributes in two face-image datasets show that a semi-trusted server can train classifiers that are not only perfectly honest but also accurately curious. We conclude by showing the difficulties in distinguishing between standard and HBC classifiers, discussing challenges in defending against this vulnerability of deep classifiers, and enumerating related open directions for future studies.

Conference paper

Temesgene DA, Miozzo M, Gunduz D, Dini Pet al., 2021, Distributed deep reinforcement learning for functional split control in energy harvesting virtualized small cells, IEEE Transactions on Sustainable Computing, Vol: 6, Pages: 626-640, ISSN: 2377-3782

To meet the growing quest for enhanced network capacity, mobile network operators (MNOs) are deploying dense infrastructures of small cells. This, in turn, increases the power consumption of mobile networks, thus impacting the environment. As a result, we have seen a recent trend of powering mobile networks with harvested ambient energy to achieve both environmental and cost benefits. In this paper, we consider a network of virtualized small cells (vSCs) powered by energy harvesters and equipped with rechargeable batteries, which can opportunistically offload baseband (BB) functions to a grid-connected edge server depending on their energy availability. We formulate the corresponding grid energy and traffic drop rate minimization problem, and propose a distributed deep reinforcement learning (DDRL) solution. Coordination among vSCs is enabled via the exchange of battery state information. The evaluation of the network performance in terms of grid energy consumption and traffic drop rate confirms that enabling coordination among the vSCs via knowledge exchange achieves a performance close to the optimal. Numerical results also confirm that the proposed DDRL solution provides higher network performance, better adaptation to the changing environment, and higher cost savings with respect to a tabular multi-agent reinforcement learning (MRL) solution used as a benchmark.

Journal article

Mashhadi MB, Gunduz D, 2021, Pruning the pilots: deep learning-based pilot design and channel estimation for MIMO-OFDM systems, IEEE Transactions on Wireless Communications, Vol: 20, Pages: 6315-6328, ISSN: 1536-1276

With the large number of antennas and subcarriers the overhead due to pilot transmission for channel estimation can be prohibitive in wideband massive multiple-input multiple-output (MIMO) systems. This can degrade the overall spectral efficiency significantly, and as a result, curtail the potential benefits of massive MIMO. In this paper, we propose a neural network (NN)-based joint pilot design and downlink channel estimation scheme for frequency division duplex (FDD) MIMO orthogonal frequency division multiplex (OFDM) systems. The proposed NN architecture uses fully connected layers for frequency-aware pilot design, and outperforms linear minimum mean square error (LMMSE) estimation by exploiting inherent correlations in MIMO channel matrices utilizing convolutional NN layers. Our proposed NN architecture uses a non-local attention module to learn longer range correlations in the channel matrix to further improve the channel estimation performance.We also propose an effective pilot reduction technique by gradually pruning less significant neurons from the dense NN layers during training. This constitutes a novel application of NN pruning to reduce the pilot transmission overhead. Our pruning-based pilot reduction technique reduces the overhead by allocating pilots across subcarriers non-uniformly and exploiting the inter-frequency and inter-antenna correlations in the channel matrix efficiently through convolutional layers and attention module.

Journal article

Mashhadi MB, Jankowski M, Tung T-Y, Kobus S, Gunduz Det al., 2021, Federated mmWave beam selection utilizing LIDAR data, IEEE Wireless Communications Letters, Vol: 10, Pages: 2269-2273, ISSN: 2162-2337

Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection. For vehicle-to-infrastructure (V2I) networks, side information from LIDAR sensors mounted on the vehicles has been leveraged to reduce the beam search overhead. In this letter, we propose a federated LIDAR aided beam selection method for V2I mmWave communication systems. In the proposed scheme, connected vehicles collaborate to train a shared neural network (NN) on their locally available LIDAR data during normal operation of the system. We also propose a reduced-complexity convolutional NN (CNN) classifier architecture and LIDAR preprocessing, which significantly outperforms previous works in terms of both the performance and the complexity.

Journal article

Faqir OJ, Kerrigan EC, Gunduz D, 2021, Accuracy-awareness: A pessimistic approach to optimal control of triggered mobile communication networks, 7th IFAC Conference on Nonlinear Model Predictive Control (NMPC), Publisher: ELSEVIER, Pages: 296-301, ISSN: 2405-8963

We use nonlinear model predictive control to procure a joint control of mobility and transmission to minimize total network communication energy use. The nonlinear optimization problem is solved numerically in a self-triggered framework, where the next control update time depends on the predicted state trajectory and the accuracy of the numerical solution. Solution accuracy must be accounted for in any circumstance where systems are run in open-loop for long stretches of time based on potentially inaccurate predictions. These triggering conditions allow us to place wireless nodes in low energy ‘idle’ states for extended periods, saving over 70% of energy compared to a periodic policy where nodes consistently use energy to receive control updates.

Conference paper

Gindullina E, Badia L, Gunduz D, 2021, Age-of-information with information source diversity in an energy harvesting system, IEEE Transactions on Green Communications and Networking, Vol: 5, Pages: 1529-1540, ISSN: 2473-2400

Age of information (AoI) is a key performance metric for the Internet of things (IoT). Timely status updates are essential for many IoT applications; however, they often suffer from harsh energy constraints and the unreliability of underlying information sources. To overcome these unpredictabilities, one can employ multiple sources that track the same process of interest, but with different energy costs and reliabilities. We consider an energy-harvesting (EH) monitoring node equipped with a finite-size battery and collecting status updates from multiple heterogeneous information sources. We investigate the policies that minimize the average AoI, formulating a Markov decision process (MDP) to choose the optimal actions of either updating from one of the sources or remaining idle, based on the current energy level and the AoI at the monitoring node. We analyze the structure of the optimal solution for different cost/AoI distribution combinations, and compare its performance with an aggressive policy that transmits whenever possible.

Journal article

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