Publications
346 results found
Shao Y, Gunduz D, 2023, Semantic Communications With Discrete-Time Analog Transmission: A PAPR Perspective, IEEE Wireless Communications Letters, Vol: 12, Pages: 510-514, ISSN: 2162-2337
Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications. Two salient features of DeepJSCC-based semantic communications are the exploitation of semantic-aware features directly from the source signal, and the discrete-time analog transmission (DTAT) of these features. Compared with traditional digital communications, semantic communications with DeepJSCC provide superior reconstruction performance at the receiver and graceful degradation with diminishing channel quality, but also exhibit a large peak-to-average power ratio (PAPR) in the transmitted signal. An open question has been whether the gains of DeepJSCC come at the expense of high-PAPR continuous-amplitude signal, which can limit its adoption in practice. In this letter, we first show that conventional DeepJSCC does suffer from high PAPR. Then, we explore three PAPR reduction techniques and confirm that the superior image reconstruction performance of DeepJSCC can be retained while the PAPR is suppressed to an acceptable level. This is an important step towards the implementation of DeepJSCC in practical semantic communication systems.
Shao Y, Gunduz D, Liew SC, 2023, Bayesian Over-the-Air Computation, IEEE Journal on Selected Areas in Communications, Vol: 41, Pages: 589-606, ISSN: 0733-8716
As an important piece of the multi-tier computing architecture for future wireless networks, over-the-air computation (OAC) enables efficient function computation in multiple-access edge computing, where a fusion center aims to compute a function of the data distributed at edge devices. Existing OAC relies exclusively on the maximum likelihood (ML) estimation at the fusion center to recover the arithmetic sum of the transmitted signals from different devices. ML estimation, however, is much susceptible to noise. In particular, in the misaligned OAC where there are channel misalignments among received signals, ML estimation suffers from severe error propagation and noise enhancement. To address these challenges, this paper puts forth a Bayesian approach by letting each edge device transmit two pieces of statistical information to the fusion center such that Bayesian estimators can be devised to tackle the misalignments. Numerical and simulation results verify that, 1) For the aligned and synchronous OAC, our linear minimum mean squared error (LMMSE) estimator significantly outperforms the ML estimator. In the low signal-to-noise ratio (SNR) regime, the LMMSE estimator reduces the mean squared error (MSE) by at least 6 dB; in the high SNR regime, the LMMSE estimator lowers the error floor of MSE by 86.4%; 2) For the asynchronous OAC, our LMMSE and sum-product maximum a posteriori (SP-MAP) estimators are on an equal footing in terms of the MSE performance, and are significantly better than the ML estimator. Moreover, the SP-MAP estimator is computationally efficient, the complexity of which grows linearly with the packet length.
Sreekumar S, Gündüz D, 2023, Distributed hypothesis testing over a noisy channel: error-exponents trade-off, Entropy: international and interdisciplinary journal of entropy and information studies, Vol: 25, Pages: 1-33, ISSN: 1099-4300
A two-terminal distributed binary hypothesis testing problem over a noisy channel is studied. The two terminals, called the observer and the decision maker, each has access to n independent and identically distributed samples, denoted by U and V, respectively. The observer communicates to the decision maker over a discrete memoryless channel, and the decision maker performs a binary hypothesis test on the joint probability distribution of (U,V) based on V and the noisy information received from the observer. The trade-off between the exponents of the type I and type II error probabilities is investigated. Two inner bounds are obtained, one using a separation-based scheme that involves type-based compression and unequal error-protection channel coding, and the other using a joint scheme that incorporates type-based hybrid coding. The separation-based scheme is shown to recover the inner bound obtained by Han and Kobayashi for the special case of a rate-limited noiseless channel, and also the one obtained by the authors previously for a corner point of the trade-off. Finally, we show via an example that the joint scheme achieves a strictly tighter bound than the separation-based scheme for some points of the error-exponents trade-off.
Gunduz D, Qin Z, Aguerri IE, et al., 2023, Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 41, Pages: 5-41, ISSN: 0733-8716
Shiri I, Sadr AV, Akhavan A, et al., 2022, Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning, European Journal of Nuclear Medicine and Molecular Imaging, Vol: 50, Pages: 1034-1050, ISSN: 0340-6997
Purpose:Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images.Methods:Non-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) 18F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center).Results:In terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21–14.81%) and FL-PL (CI:11.82–13.84%) models demo
Lan Q, Zeng Q, Popovski P, et al., 2022, Progressive feature transmission for split classification at the wireless edge, IEEE Transactions on Wireless Communications, ISSN: 1536-1276
We consider the scenario of inference at the wire-less edge , in which devices are connected to an edge server and ask the server to carry out remote classification, that is, classify data samples available at edge devices. This requires the edge devices to upload high-dimensional features of samples over resource-constrained wireless channels, which creates a communication bottleneck. The conventional feature pruning solution would require the device to have access to the inference model, which is not available in the current split inference scenario. To address this issue, we propose the progressive feature transmission (ProgressFTX) protocol, which minimizes the overhead by progressively transmitting features until a target confidence level is reached. A control policy is proposed to accelerate inference, comprising two key operations: importance-aware feature selection at the server and transmission-termination control . For the former, it is shown that selecting the most important features, characterized by the largest discriminant gains of the corresponding feature dimensions, achieves a sub-optimal performance. For the latter, the proposed policy is shown to exhibit a threshold structure. Specifically, the transmission is stopped when the incremental uncertainty reduction by further feature transmission is outweighed by its communication cost. The indices of the selected features and transmission decision are fed back to the device in each slot. The control policy is first derived for the tractable case of linear classification, and then extended to the more complex case of classification using a convolutional neural network . Both Gaussian and fading channels are considered. Experimental results are obtained for both a statistical data model and a real dataset. It is shown that ProgressFTX can substantially reduce the communication latency compared to conventional feature pruning and random feature transmission strategies.
Wu H, Shao Y, Mikolajczyk K, et al., 2022, Channel-adaptive wireless image transmission with OFDM, IEEE Wireless Communications Letters, Vol: 11, Pages: 2400-2404, 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.
Yang Z, Xia J-Y, Luo J, et al., 2022, A learning-aided flexible gradient descent approach to MISO beamforming, IEEE Wireless Communications Letters, Vol: 11, Pages: 1895-1899, ISSN: 2162-2337
This letter proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming. The proposed LAGD algorithm directly optimizes the transmit precoder through implicit gradient descent based iterations, at each of which the optimization strategy is determined by a neural network, and thus, is dynamic and adaptive. At each instance of the problem, this network is initialized randomly, and updated throughout the iterative solution process. Therefore, the LAGD algorithm can be implemented at any signal-to-noise ratio (SNR) and for arbitrary antenna/user numbers, does not require labelled data or training prior to deployment. Numerical results show that the LAGD algorithm can outperform of the well-known WMMSE algorithm as well as other learning-based solutions with a modest computational complexity. Our code is available at https://github.com/XiaGroup/LAGD .
Li GY, Saad W, Ozgur A, et al., 2022, Series Editorial The Sixth Issue of the Series on Machine Learning in Communications and Networks, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 40, Pages: 2507-2509, ISSN: 0733-8716
Tung T-Y, Gunduz 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.
Yilmaz SF, Hasircioglu B, Gunduz D, 2022, Over-the-air ensemble inference with model privacy, 2022 IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 1265-1270
We consider distributed inference at the wireless edge, where multiple clients with an ensemble of models, each trained independently on a local dataset, are queried in parallel to make an accurate decision on a new sample. In addition to maximizing inference accuracy, we also want to maximize the privacy of local models. We exploit the superposition property of the air to implement bandwidth-efficient ensemble inference methods. We introduce different over-the-air ensemble methods and show that these schemes perform significantly better than their orthogonal counterparts, while using less resources and providing privacy guarantees. We also provide experimental results verifying the benefits of the proposed over-the-air inference approach, whose source code is shared publicly on Github.
Pase F, Gunduz D, Zorzi M, 2022, Remote contextual bandits, 2022 IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 1665-1670
We consider a remote contextual multi-armed bandit (CMAB) problem, in which the decision-maker observes the context and the reward, but must communicate the actions to be taken by the agents over a rate-limited communication channel. This can model, for example, a personalized ad placement application, where the content owner observes the individual visitors to its website, and hence has the context information, but must convey the ads that must be shown to each visitor to a separate entity that manages the marketing content. In this remote CMAB (R-CMAB) problem, the constraint on the communication rate between the decision-maker and the agents imposes a trade-off between the number of bits sent per agent and the acquired average reward. We are particularly interested in characterizing the rate required to achieve sub-linear regret. Consequently, this can be considered as a policy compression problem, where the distortion metric is induced by the learning objectives. We first study the fundamental information theoretic limits of this problem by letting the number of agents go to infinity, and study the regret achieved when Thompson sampling strategy is adopted. In particular, we identify two distinct rate regions resulting in linear and sub-linear regret behavior, respectively. Then, we provide upper bounds for the achievable regret when the decision-maker can reliably transmit the policy without distortion.
Egger M, Bitar R, Wachter-Zeh A, et al., 2022, Efficient distributed machine learning via combinatorial multi-armed bandits, 2022 IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 1653-1658
We consider the distributed stochastic gradient descent problem, where a main node distributes gradient calculations among n workers from which at most b ≤ n can be utilized in parallel. By assigning tasks to all the workers and waiting only for the k fastest ones, the main node can trade-off the error of the algorithm with its runtime by gradually increasing k as the algorithm evolves. However, this strategy, referred to as adaptive k-sync, can incur additional costs since it ignores the computational efforts of slow workers. We propose a cost-efficient scheme that assigns tasks only to k workers and gradually increases k. As the response times of the available workers are unknown to the main node a priori, we utilize a combinatorial multi-armed bandit model to learn which workers are the fastest while assigning gradient calculations, and to minimize the effect of slow workers. Assuming that the mean response times of the workers are independent and exponentially distributed with different means, we give empirical and theoretical guarantees on the regret of our strategy, i.e., the extra time spent to learn the mean response times of the workers. Compared to adaptive k-sync, our scheme achieves significantly lower errors with the same computational efforts while being inferior in terms of speed.
Li GY, Saad W, Ozgur A, et 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
ul Haque S, Chandak S, Chiariotti F, et 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
The rapid development of Internet of Things (IoT) technologies has not only enabled new applications, but also presented new challenges for reliable communication with limited resources. In this work, we define a novel problem that can arise in these scenarios, in which a set of sensors need to communicate a joint observation. This observation is shared by a random subset of the nodes, which need to propagate it to the rest of the network, but coordination is complex: as signaling constraints require the use of random access schemes over shared channels, sensors need to implicitly coordinate, so that at least one transmission gets through without collisions. Unlike the majority of existing medium access schemes, the goal is to make sure that the shared message gets through, regardless of the sender. We analyze this coordination problem theoretically and provide low-complexity solutions. While a clustering-based approach is near-optimal if the sensors have prior knowledge, we provide a distributed multi-armed bandit (MAB) solution for the more general case and validate it by simulation.
Shiri I, Sadr AV, Amini M, et 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
- Author Web Link
- Cite
- Citations: 5
Sun Y, Zhang F, Zhao J, et al., 2022, Coded Computation Across Shared Heterogeneous Workers With Communication Delay, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 70, Pages: 3371-3385, ISSN: 1053-587X
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.
Teng F, Chhachhi SAURAB, Ge PUDONG, et 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.
Ozfatura E, Gunduz D, 2022, Uncoded caching and cross-level coded delivery for non-uniform file popularity, IEEE Transactions on Information Theory, Vol: 68, Pages: 6842-6859, ISSN: 0018-9448
Proactive content caching at user devices and coded delivery is studied for a non-uniform file popularity distribution. A novel centralized uncoded caching and coded delivery scheme, called cross-level coded delivery (CLCD) , is proposed, which can be applied to large file libraries under non-uniform demands. In the CLCD scheme, the same sub-packetization is used for all the files in the library in order to prevent additional zero-padding in the delivery phase, and unlike the existing schemes in the literature, users requesting files from different popularity groups can still be served by the same multicast message in order to reduce the delivery rate. Simulation results indicate more than 10% reduction in the average delivery rate for typical Zipf distribution parameter values.
Xia J-Y, Li S, Huang J-J, et 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.
Buyukates B, Ozfatura E, Ulukus S, et 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.
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
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
We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has been shown to be an effective solution in distributed matrix multiplication, both providing privacy against workers and boosting the computation speed by efficiently mitigating stragglers. In this work, we present a non-direct secure extension of the recently introduced bivariate polynomial codes. Bivariate polynomial codes have been shown to be able to further speed up distributed matrix multiplication by exploiting the partial work done by the stragglers rather than completely ignoring them while reducing the upload communication cost and/or the workers’ storage’s capacity needs. We show that, especially for upload communication or storage constrained settings, the proposed approach reduces the average computation time of SDMM compared to its competitors in the literature.
Zecchin M, Mashhadi MB, Jankowski M, et 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
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility. To reduce the search overhead of iterative beam discovery procedures, contextual information from light detection and ranging (LIDAR) sensors mounted on vehicles has been leveraged by data-driven methods to produce useful side information. In this paper, we propose a lightweight neural network (NN) architecture along with the corresponding LIDAR preprocessing, which significantly outperforms previous works. Our solution comprises multiple novelties that improve both the convergence speed and the final accuracy of the model. In particular, we define a novel loss function inspired by the knowledge distillation idea, introduce a curriculum training approach exploiting line-of-sight (LOS)/non-line-of-sight (NLOS) information, and we propose a non-local attention module to improve the performance for the more challenging NLOS cases. Simulation results on benchmark datasets show that, utilizing solely LIDAR data and the receiver position, our NN-based beam selection scheme can achieve 79.9% throughput of an exhaustive beam sweeping approach without any beam search overhead and 95% by searching among as few as 6 beams. In a typical mmWave V2I scenario, our proposed method considerably reduces the beam search time required to achieve a desired throughput, in comparison with the inverse fingerprinting and hierarchical beam selection schemes.
Amiri MM, Gunduz D, Kulkarni SR, et 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
Chen M, Gunduz D, Huang K, et 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
Elbir AM, Soner B, Coleri S, et al., 2022, Federated Learning in Vehicular Networks, Pages: 72-77
Machine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response. However, most of these ML applications employ centralized learning (CL), which brings significant overhead for data trans-mission between the parameter server and vehicular edge devices. Federated learning (FL) framework has been recently introduced as an efficient tool with the goal of reducing transmission overhead while achieving privacy through the transmission of model updates instead of the whole dataset. In this paper, we investigate the usage of FL over CL in vehicular network applications to develop intelligent transportation systems. We provide a comprehensive analysis on the feasibility of FL for the ML based vehicular applications, as well as investigating object detection by utilizing image-based datasets as a case study. Then, we identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead, privacy and resource management. Finally, we highlight related future research directions for FL in vehicular networks.
Li GY, Saad W, Ozgur A, et 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
Hamidi SM, Mehrabi M, Khandani AK, et al., 2022, Over-the-Air Federated Learning Exploiting Channel Perturbation
Federated learning (FL) is a promising technology which trains a machine learning model on edge devices in a distributed manner orchestrated by a parameter server (PS). To realize fast model aggregation, the uplink phase of FL could be carried out by over-the-air computation (OAC). On the one hand, engaging more devices in FL yields a model with higher prediction accuracy. On the other hand, the edge devices in OAC need to perform appropriate magnitude alignment to compensate for underlying channel coefficients. However, due to the limited power budget, this is not possible for devices experiencing deep fade. Consequently, these devices are excluded from the FL algorithm. In this paper, we propose a channel perturbation method so that no edge device is excluded due to experiencing deep fade. To this end, OAC is performed in multiple phases. In each phase, the radio frequency (RF) vicinity of PS's antenna is intentionally perturbed by means of RF mirror structure coined in [1]. This yields independent realizations of channels between PS and devices in each phase. By using proper transmit scalars, all devices concurrently transmit their local model updates in each phase subject to a total power constraint. Then, the PS estimates the arithmetic sum of the local updates by properly combining the aggregated models obtained across all phases. The devices' transmit scalars and PS's de-noising factors can be efficiently found by solving a tractable optimization problem. Index Terms - Federated learning, over-the-air computation, edge machine learning, wireless communications.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.