arXiv subjects

H. Vincent Poor

Publications and source records attributed to H. Vincent Poor.

At least 163 records · Page 9Linked to original sources

arXiv2024

Wireless Federated Learning over Resource-Constrained Networks: Digital versus Analog Transmissions

To enable wireless federated learning (FL) in communication resource-constrained networks, two communication schemes, i.e., digital and analog ones, are effective solutions. In this paper, we quantitatively compare these two techniques, highlighting their essential differences as well as respectively suitable scenarios. We first examine both digital and analog transmission schemes, together with a unified and fair comparison framework under imbalanced device sampling, strict latency targets, and transmit power constraints. A universal convergence analysis under various imperfections is established for evaluating the performance of FL over wireless networks. These analytical results reveal that the fundamental difference between the digital and analog communications lies in whether communication and computation are jointly designed or not. The digital scheme decouples the communication design from FL computing tasks, making it difficult to support uplink transmission from massive devices with limited bandwidth and hence the performance is mainly communication-limited. In contrast, the analog communication allows over-the-air computation (AirComp) and achieves better spectrum utilization. However, the computation-oriented analog transmission reduces power efficiency, and its performance is sensitive to computation errors from imperfect channel state information (CSI). Furthermore, device sampling for both schemes are optimized and differences in sampling optimization are analyzed. Numerical results verify the theoretical analysis and affirm the superior performance of the sampling optimization.

cs.IT↗

arXiv2024

On the Rate-Distortion Function for Sampled Cyclostationary Gaussian Processes with Memory: Extended Version with Proofs

In this work we study the rate-distortion function (RDF) for lossy compression of asynchronously-sampled continuous-time (CT) wide-sense cyclostationary (WSCS) Gaussian processes with memory. As the case of synchronous sampling, i.e., when the sampling interval is commensurate with the period of the cyclostationary statistics, has already been studied, we focus on discrete-time (DT) processes obtained by asynchronous sampling, i.e., when the sampling interval is incommensurate with the period of the cyclostationary statistics of the CT WSCS source process. It is further assumed that the sampling interval is smaller than the maximal autocorrelation length of the CT source process, which implies that the DT process possesses memory. Thus, the sampled process is a DT wide-sense almost cyclostationary (WSACS) processes with memory. This problem is motivated by the fact that man-made communications signals are modelled as CT WSCS processes; hence, applications of such sampling include, e.g., compress-and-forward relaying and recording systems. The main challenge follows because, with asynchronous sampling, the DT sampled process is not information-stable, and hence the characterization of its RDF should be carried out within the information-spectrum framework instead of using conventional information-theoretic arguments. This work expands upon our previous work which addressed the special case in which the DT process is independent across time. The existence of dependence between the samples requires new tools to obtain the characterization of the RDF.

cs.IT↗

arXiv2024

Decentralized Federated Learning Over Imperfect Communication Channels

This paper analyzes the impact of imperfect communication channels on decentralized federated learning (D-FL) and subsequently determines the optimal number of local aggregations per training round, adapting to the network topology and imperfect channels. We start by deriving the bias of locally aggregated D-FL models under imperfect channels from the ideal global models requiring perfect channels and aggregations. The bias reveals that excessive local aggregations can accumulate communication errors and degrade convergence. Another important aspect is that we analyze a convergence upper bound of D-FL based on the bias. By minimizing the bound, the optimal number of local aggregations is identified to balance a trade-off with accumulation of communication errors in the absence of knowledge of the channels. With this knowledge, the impact of communication errors can be alleviated, allowing the convergence upper bound to decrease throughout aggregations. Experiments validate our convergence analysis and also identify the optimal number of local aggregations on two widely considered image classification tasks. It is seen that D-FL, with an optimal number of local aggregations, can outperform its potential alternatives by over 10% in training accuracy.

cs.DC↗

arXiv2024

Performance Analysis of Finite Blocklength Transmissions Over Wiretap Fading Channels: An Average Information Leakage Perspective

Physical-layer security (PLS) is a promising technique to complement more traditional means of communication security in beyond-5G wireless networks. However, studies of PLS are often based on ideal assumptions such as infinite coding blocklengths or perfect knowledge of the wiretap link's channel state information (CSI). In this work, we study the performance of finite blocklength (FBL) transmissions using a new secrecy metric $\unicode{x2013}$ the average information leakage (AIL). We evaluate the exact and approximate AIL with Gaussian signaling and arbitrary fading channels, assuming that the eavesdropper's instantaneous CSI is unknown. We then conduct case studies that use artificial noise (AN) beamforming to analyze the AIL in both Rayleigh and Rician fading channels. The accuracy of the analytical expressions is verified through extensive simulations, and various insights regarding the impact of key system parameters on the AIL are obtained. Particularly, our results reveal that allowing a small level of AIL can potentially lead to significant reliability enhancements. To improve the system performance, we formulate and solve an average secrecy throughput (AST) optimization problem via both non-adaptive and adaptive design strategies. Our findings highlight the significance of blocklength design and AN power allocation, as well as the impact of their trade-off on the AST.

cs.IT↗

arXiv2024

Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning

Split Federated Learning (SFL) has recently emerged as a promising distributed learning technology, leveraging the strengths of both federated and split learning. It emphasizes the advantages of rapid convergence while addressing privacy concerns. As a result, this innovation has received significant attention from both industry and academia. However, since the model is split at a specific layer, known as a cut layer, into both client-side and server-side models for the SFL, the choice of the cut layer in SFL can have a substantial impact on the energy consumption of clients and their privacy, as it influences the training burden and the output of the client-side models. In this article, we provide a comprehensive overview of the SFL process and thoroughly analyze energy consumption and privacy. This analysis considers the influence of various system parameters on the cut layer selection strategy. Additionally, we provide an illustrative example of the cut layer selection, aiming to minimize clients' risk of reconstructing the raw data at the server while sustaining energy consumption within the required energy budget, which involves trade-offs. Finally, we address open challenges in this field. These directions represent promising avenues for future research and development.

cs.LG↗

arXiv2024

On the Capacity Region of Reconfigurable Intelligent Surface Assisted Symbiotic Radios

In this paper, we are interested in a reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) system, where an RIS assists a primary transmission by passive beamforming and simultaneously acts as an information transmitter by periodically adjusting its reflection coefficients. The above RIS functions innately enable a new multiplicative multiple access channel (M-MAC), where the primary and secondary signals are superposed in a multiplicative manner. To pursue the fundamental performance limits of M-MAC, we focus on the characterization of the capacity region for such a system when the direct link is blocked. Due to the reflection nature of RIS, the signal transmitted from the RIS should satisfy a passive reflection constraint. We consider two types of passive reflection constraints, one for the case that only the phases of the RIS can be adjusted, while the other for the case that both the amplitude and the phase can be adjusted. Under the passive reflection constraints at the RIS as well as the average power constraint at the primary transmitter (PTx), we characterize the capacity region of RIS-assisted SR. It is observed that: 1) the number of sum-rate-optimal points on the boundary of the capacity region is infinite; 2) for the rate pairs with the maximum sum rate, the optimal distribution of the amplitude of the primary signal is a continuous Rayleigh distribution, while for the remaining rate pairs on the capacity region boundary, the optimal amplitude of the primary signal is discrete; 3) the adjustment of the amplitude for the RIS can enlarge the capacity region as compared to the phase-adjusted-only case.

cs.IT↗

arXiv2024

A Reconfigurable Subarray Architecture and Hybrid Beamforming for Millimeter-Wave Dual-Function-Radar-Communication Systems

Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks. By integrating hybrid analog-digital (HAD) beamforming into a multi-user millimeter-wave (mmWave) DFRC system, we design a new reconfigurable subarray (RS) architecture and jointly optimize the HAD beamforming to maximize the communication sum-rate and ensure a prescribed signal-to-clutter-plus-noise ratio for radar sensing. Considering the non-convexity of this problem arising from multiplicative coupling of the analog and digital beamforming, we convert the sum-rate maximization into an equivalent weighted mean-square error minimization and apply penalty dual decomposition to decouple the analog and digital beamforming. Specifically, a second-order cone program is first constructed to optimize the fully digital counterpart of the HAD beamforming. Then, the sparsity of the RS architecture is exploited to obtain a low-complexity solution for the HAD beamforming. The convergence and complexity analyses of our algorithm are carried out under the RS architecture. Simulations corroborate that, with the RS architecture, DFRC offers effective communication and sensing and improves energy efficiency by 83.4% and 114.2% with a moderate number of radio frequency chains and phase shifters, compared to the persistently- and fullyconnected architectures, respectively.

eess.SP↗

arXiv2024

PAC Learnability for Reliable Communication over Discrete Memoryless Channels

In practical communication systems, knowledge of channel models is often absent, and consequently, transceivers need be designed based on empirical data. In this work, we study data-driven approaches to reliably choosing decoding metrics and code rates that facilitate reliable communication over unknown discrete memoryless channels (DMCs). Our analysis is inspired by the PAC (probably approximately correct) learning theory and does not rely on any assumptions on the statistical characteristics of DMCs. We show that a naive plug-in algorithm for choosing decoding metrics is likely to fail for finite training sets. We propose an alternative algorithm called the virtual sample algorithm and establish a non-asymptotic lower bound on its performance. The virtual sample algorithm is then used as a building block for constructing a learning algorithm that chooses a decoding metric and a code rate using which a transmitter and a receiver can reliably communicate at a rate arbitrarily close to the channel mutual information. Therefore, we conclude that DMCs are PAC learnable.

cs.IT↗

arXiv2024

Integrated Sensing and Communication for Edge Inference with End-to-End Multi-View Fusion

Integrated sensing and communication (ISAC) is a promising solution to accelerate edge inference via the dual use of wireless signals. However, this paradigm needs to minimize the inference error and latency under ISAC co-functionality interference, for which the existing ISAC or edge resource allocation algorithms become inefficient, as they ignore the inter-dependency between low-level ISAC designs and high-level inference services. This letter proposes an inference-oriented ISAC (IO-ISAC) scheme, which minimizes upper bounds on end-to-end inference error and latency using multi-objective optimization. The key to our approach is to derive a multi-view inference model that accounts for both the number of observations and the angles of observations, by integrating a half-voting fusion rule and an angle-aware sensing model. Simulation results show that the proposed IO-ISAC outperforms other benchmarks in terms of both accuracy and latency.

eess.SP↗

arXiv2024

Modeling and Analysis of Near-Field ISAC

As the technical trends for the next-generation wireless network significantly extend the near-field region, a performance reevaluation of integrated sensing and communications (ISAC) with an appropriate channel model to account for the effects introduced by the near field becomes essential. In this paper, a near-field ISAC framework is proposed for both downlink and uplink scenarios based on an accurate channel model. A uniform planar array is equipped at a base station, where the impacts of the effective aperture and polarization of antennas are considered. For the downlink case, three distinct designs are studied: a communications-centric (C-C) design, a sensing-centric (S-C) design, and a Pareto optimal design. Regarding the uplink case, the C-C design, the S-C design and a time-sharing strategy are considered. Within each design, sensing rates (SRs) and communication rates (CRs) are derived. To gain further insights, high signal-to-noise ratio slopes and rate scaling laws concerning the number of antennas are examined. The attainable near-field SR-CR regions of ISAC and the baseline frequency-division S&C are also characterized. Numerical results reveal that, as the number of antennas in the array grows, the SRs and CRs under our accurate model converge to finite values, while those under conventional far- and near-field models exhibit unbounded growth, highlighting the importance of precisely modeling the channels for near-field ISAC.

cs.IT↗

arXiv2024

Next Generation Multiple Access for IMT Towards 2030 and Beyond

Multiple access techniques are fundamental to the design of wireless communication systems, since many crucial components of such systems depend on the choice of the multiple access technique. Because of the importance of multiple access, there has been an ongoing quest during the past decade to develop next generation multiple access (NGMA). Among those potential candidates for NGMA, non-orthogonal multiple access (NOMA) has received significant attention from both the industrial and academic research communities, and has been highlighted in the recently published International Mobile Telecommunications (IMT)-2030 Framework. However, there is still no consensus in the research community about how exactly NOMA assisted NGMA should be designed. This perspective is to outline three important features of NOMA assisted NGMA, namely multi-domain utilization, multi-mode compatibility, and multi-dimensional optimality, where important directions for future research into the design of NOMA assisted NGMA are also discussed.

cs.IT↗

arXiv2024

Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm

In some applications, edge learning is experiencing a shift in focusing from conventional learning from scratch to new two-stage learning unifying pre-training and task-specific fine-tuning. This paper considers the problem of joint communication and computation resource management in a two-stage edge learning system. In this system, model pre-training is first conducted at an edge server via centralized learning on local pre-stored general data, and then task-specific fine-tuning is performed at edge devices based on the pre-trained model via federated edge learning. For the two-stage learning model, we first analyze the convergence behavior (in terms of the average squared gradient norm bound), which characterizes the impacts of various system parameters such as the number of learning rounds and batch sizes in the two stages on the convergence rate. Based on our analytical results, we then propose a joint communication and computation resource management design to minimize an average squared gradient norm bound, subject to constraints on the transmit power, overall system energy consumption, and training delay. The decision variables include the number of learning rounds, batch sizes, clock frequencies, and transmit power control for both pre-training and fine-tuning stages. Finally, numerical results are provided to evaluate the effectiveness of our proposed design. It is shown that the proposed joint resource management over the pre-training and fine-tuning stages well balances the system performance trade-off among the training accuracy, delay, and energy consumption. The proposed design is also shown to effectively leverage the inherent trade-off between pre-training and fine-tuning, which arises from the differences in data distribution between pre-stored general data versus real-time task-specific data, thus efficiently optimizing overall system performance.

cs.IT↗

arXiv2024

Prescribed Robustness in Optimal Power Flow

For a timely decarbonization of our economy, power systems need to accommodate increasing numbers of clean but stochastic resources. This requires new operational methods that internalize this stochasticity to ensure safety and efficiency. This paper proposes a novel approach to compute adaptive safety intervals for each stochastic resource that internalize power flow physics and optimize the expected cost of system operations, making them ``prescriptive''. The resulting intervals are interpretable and can be used in a tractable robust optimal power flow problem as uncertainty sets. We use stochastic gradient descent with differentiable optimization layers to compute a mapping that obtains these intervals from a given vector of context parameters that captures the expected system state. We demonstrate and discuss the proposed approach on two case studies.

math.OC↗

arXiv2024

Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling

Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling. While the effect of delays has been extensively studied for optimization, the manner in which they interact with the underlying Markov process to shape the finite-time performance of SA remains poorly understood. In this context, our first main contribution is to show that under time-varying bounded delays, the delayed SA update rule guarantees exponentially fast convergence of the \emph{last iterate} to a ball around the SA operator's fixed point. Notably, our bound is \emph{tight} in its dependence on both the maximum delay $τ_{max}$, and the mixing time $τ_{mix}$. To achieve this tight bound, we develop a novel inductive proof technique that, unlike various existing delayed-optimization analyses, relies on establishing uniform boundedness of the iterates. As such, our proof may be of independent interest. Next, to mitigate the impact of the maximum delay on the convergence rate, we provide the first finite-time analysis of a delay-adaptive SA scheme under Markovian sampling. In particular, we show that the exponent of convergence of this scheme gets scaled down by $τ_{avg}$, as opposed to $τ_{max}$ for the vanilla delayed SA rule; here, $τ_{avg}$ denotes the average delay across all iterations. Moreover, the adaptive scheme requires no prior knowledge of the delay sequence for step-size tuning. Our theoretical findings shed light on the finite-time effects of delays for a broad class of algorithms, including TD learning, Q-learning, and stochastic gradient descent under Markovian sampling.

cs.LG↗

arXiv2024

Estimation of Complex Valued Laplacian Matrices for Topology Identification in Power Systems

In this paper, we investigate the problem of estimating a complex-valued Laplacian matrix with a focus on its application in the estimation of admittance matrices in power systems. The proposed approach is based on a constrained maximum likelihood estimator (CMLE) of the complex-valued Laplacian, which is formulated as an optimization problem with Laplacian and sparsity constraints. The complex-valued Laplacian is a symmetric, non-Hermitian matrix that exhibits a joint sparsity pattern between its real and imaginary parts. Thus, we present a group-sparse-based penalized log-likelihood approach for the Laplacian estimation. Leveraging the mixed \ell 2,1 norm relaxation of the joint sparsity constraint, we develop a new alternating direction method of multipliers (ADMM) estimation algorithm for the implementation of the CMLE of the Laplacian matrix under a linear Gaussian model. Next, we apply the proposed ADMM algorithms for the problem of estimating the admittance matrix under three commonly used measurement models that stem from Kirchhoff and Ohm laws, each with different assumptions and simplifications: 1) the nonlinear alternating current (AC) model; 2) the decoupled linear power flow (DLPF) model; and 3) the direct current (DC) model. The performance of the ADMM algorithm is evaluated using data from the IEEE 33-bus power system data under different settings. The numerical experiments demonstrate that the proposed algorithm outperforms existing methods in terms of mean-squared-error (MSE) and F-score, thus providing a more accurate recovery of the admittance matrix.

eess.SP↗

arXiv2024

SignSGD with Federated Voting

Distributed learning is commonly used for accelerating model training by harnessing the computational capabilities of multiple-edge devices. However, in practical applications, the communication delay emerges as a bottleneck due to the substantial information exchange required between workers and a central parameter server. SignSGD with majority voting (signSGD-MV) is an effective distributed learning algorithm that can significantly reduce communication costs by one-bit quantization. However, due to heterogeneous computational capabilities, it fails to converge when the mini-batch sizes differ among workers. To overcome this, we propose a novel signSGD optimizer with \textit{federated voting} (signSGD-FV). The idea of federated voting is to exploit learnable weights to perform weighted majority voting. The server learns the weights assigned to the edge devices in an online fashion based on their computational capabilities. Subsequently, these weights are employed to decode the signs of the aggregated local gradients in such a way to minimize the sign decoding error probability. We provide a unified convergence rate analysis framework applicable to scenarios where the estimated weights are known to the parameter server either perfectly or imperfectly. We demonstrate that the proposed signSGD-FV algorithm has a theoretical convergence guarantee even when edge devices use heterogeneous mini-batch sizes. Experimental results show that signSGD-FV outperforms signSGD-MV, exhibiting a faster convergence rate, especially in heterogeneous mini-batch sizes.

cs.LG↗

arXiv2024

A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids

The integration of various power sources, including renewables and electric vehicles, into smart grids is expanding, introducing uncertainties that can result in issues like voltage imbalances, load fluctuations, and power losses. These challenges negatively impact the reliability and stability of online scheduling in smart grids. Existing research often addresses uncertainties affecting current states but overlooks those that impact future states, such as the unpredictable charging patterns of electric vehicles. To distinguish between these, we term them static uncertainties and dynamic uncertainties, respectively. This paper introduces WDR-MPC, a novel approach that stands for two-stage Wasserstein-based Distributionally Robust (WDR) optimization within a Model Predictive Control (MPC) framework, aimed at effectively managing both types of uncertainties in smart grids. The dynamic uncertainties are first reformulated into ambiguity tubes and then the distributionally robust bounds of both dynamic and static uncertainties can be established using WDR optimization. By employing ambiguity tubes and WDR optimization, the stochastic MPC system is converted into a nominal one. Moreover, we develop a convex reformulation method to speed up WDR computation during the two-stage optimization. The distinctive contribution of this paper lies in its holistic approach to both static and dynamic uncertainties in smart grids. Comprehensive experiment results on IEEE 38-bus and 94-bus systems reveal the method's superior performance and the potential to enhance grid stability and reliability.

eess.SY↗

arXiv2024

Knowledge and Data Dual-Driven Channel Estimation and Feedback for Ultra-Massive MIMO Systems under Hybrid Field Beam Squint Effect

Acquiring accurate channel state information (CSI) at an access point (AP) is challenging for wideband millimeter wave (mmWave) ultra-massive multiple-input and multiple-output (UMMIMO) systems, due to the high-dimensional channel matrices, hybrid near- and far- field channel feature, beam squint effects, and imperfect hardware constraints, such as low-resolution analog-to-digital converters, and in-phase and quadrature imbalance. To overcome these challenges, this paper proposes an efficient downlink channel estimation (CE) and CSI feedback approach based on knowledge and data dual-driven deep learning (DL) networks. Specifically, we first propose a data-driven residual neural network de-quantizer (ResNet-DQ) to pre-process the received pilot signals at user equipment (UEs), where the noise and distortion brought by imperfect hardware can be mitigated. A knowledge-driven generalized multiple measurement vector learned approximate message passing (GMMV-LAMP) network is then developed to jointly estimate the channels by exploiting the approximately same physical angle shared by different subcarriers. In particular, two wideband redundant dictionaries (WRDs) are proposed such that the measurement matrices of the GMMV-LAMP network can accommodate the far-field and near-field beam squint effect, respectively. Finally, we propose an encoder at the UEs and a decoder at the AP by a data-driven CSI residual network (CSI-ResNet) to compress the CSI matrix into a low-dimensional quantized bit vector for feedback, thereby reducing the feedback overhead substantially. Simulation results show that the proposed knowledge and data dual-driven approach outperforms conventional downlink CE and CSI feedback methods, especially in the case of low signal-to-noise ratios.

cs.IT↗