arXiv subjects

Giuseppe Durisi

Publications and source records attributed to Giuseppe Durisi.

At least 19 recordsLinked to original sources

arXiv2026

Type-Based Unsourced Multiple Access Over Fading Channels in Distributed MIMO With Application to Multi-Target Localization

We consider the problem of type estimation over unsourced multiple access fading channels in distributed multiple-input multiple-output (D-MIMO) systems. Unlike classical unsourced multiple access, type-based unsourced multiple access (TUMA) aims to estimate the type, i.e., the empirical distribution of transmitted messages. We extend our prior work on TUMA over additive white Gaussian channels to fading scenarios in which neither the transmitters nor the receiver have channel state information. To mitigate the impact of path-loss variability, we employ location-based codebook partitioning: users with similar large-scale fading coefficients use the same codebook. The decoder is built on the multisource approximate message passing algorithm proposed by Cakmak et al. (2025), and supports both centralized and distributed implementations. As an application, we demonstrate how TUMA enables efficient communication in a multi-target localization setting, where distributed sensors report to a D-MIMO receiver quantized target positions. We propose a performance cost function that combines localization errors with a misdetection penalty, and use it to characterize how performance depends on the fraction of resources assigned to sensing vs. communication, as well as on the number of bits used to quantize the positions of the targets.

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arXiv2026

Distributed Massive MIMO with 1-Bit Radio-over-Fiber Fronthaul: Uplink Spectral Efficiency and Power Control

We analyze the uplink spectral efficiency achievable in a distributed multiple-input multiple-output (D-MIMO) architecture employing a 1-bit radio-over-fiber fronthaul. This architecture eliminates the need for local oscillators at the access points, hence enabling coherent-phase transmission without costly over-the-air synchronization. With this fronthaul architecture, the uplink signal at the central processing unit is a dithered, oversampled, and 1-bit quantized version of the passband signal received at the access points. This makes some of the conventional spectral-efficiency expressions used in the D-MIMO literature not directly applicable for two key reasons: the nonlinearity of the input-output relation and the practical unavailability of minimum mean square error (MMSE) channel estimates. To address this issue, we propose novel achievable-rate expressions that do not require MMSE channel estimates and rely on the Bussgang decomposition to linearize the input-output relation. We use these expressions to determine the optimal signal-to-dither ratio (SDR) that maximizes the achievable rates in both single- and multiuser scenarios and to assess the impact of oversampling. We then use one of the proposed achievable-rate expressions to investigate the max-min fairness problem when the access points cannot maintain the optimal SDR because of limitations in their dynamic range.

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arXiv2026

Minimum Energy per Bit of Unsourced Multiple Access with Location-Based Codebook Partitioning

We derive finite-blocklength bounds on the minimum achievable energy per bit over a Gaussian unsourced multiple access (UMA) channel in the presence of heterogeneous path-loss conditions. We consider a setting in which the path loss is known to the users, which enables the use of location-based codebook partitioning [Çakmak et al., 2025]. Through numerical simulations and a large-system analysis based on the replica method, we quantify the performance gain of this strategy relative to the conventional UMA approach in which all users employ a common codebook.

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arXiv2026

Type-Based Unsourced Federated Learning With Client Self-Selection

We address the client-selection problem in federated learning over wireless networks under data heterogeneity. Existing client-selection methods often rely on server-side knowledge of client-specific information, thus compromising privacy. To overcome this issue, we propose a client self-selection strategy based solely on the comparison between locally computed training losses and a centrally updated selection threshold. Furthermore, to support robust aggregation of clients' updates over wireless channels, we integrate this client self-selection strategy into the recently proposed type-based unsourced multiple-access framework over distributed multiple-input multiple-output (D-MIMO) networks. The resulting scheme is completely unsourced: the server does not need to know the identity of the clients. Moreover, no channel state information is required, neither at the clients nor at the server side. Simulation results conducted over a D-MIMO wireless network show that the proposed self-selection strategy matches the performance of a comparable state-of-the-art server-side selection method and consistently outperforms random client selection.

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arXiv2025

Insights on the Uplink Operation of a 1-bit Radio-Over-Fiber Architecture in Multi-User D-MIMO Communication

We consider a distributed multiple-input multiple-output (D-MIMO) testbed in which, to enable coherent-phase transmission without over-the-air synchronization, the remote radio heads (RRHs) are connected to a central unit via a 1-bit radio-over-fiber fronthaul. Specifically, 1-bit samples of the radio-frequency signal are exchanged over the fronthaul. We investigate via both measurements and simulations based on an accurate model of the testbed hardware, the capability of the proposed architecture to provide uniform quality of services over the coverage area--one of the promises of D-MIMO. Our results are encouraging: for the case in which two user equipments (UEs) communicate over the same 75MHz signal bandwidth, the measured error-vector magnitude meets the 3GPP New Radio specification of 12.5\% for 16QAM across all tested DMIMO scenarios. We also determine that uplink transmission is a potential bottleneck, due to the limited dynamic range of the automatic gain controller, which prevents the 1-bit quantizer to benefit from dithering. We show that this issue can be mitigated via UE power control.

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arXiv2025

Prediction-Powered Communication with Distortion Guarantees

The development of 6G wireless systems is taking place alongside the development of increasingly intelligent wireless devices and network nodes. The changing technological landscape is motivating a rethinking of classical Shannon information theory that emphasizes semantic and task-oriented paradigms. In this paper, we study a prediction-powered communication setting, in which devices, equipped with artificial intelligence (AI)-based predictors, communicate under zero-delay constraints with strict distortion guarantees. Two classes of distortion measures are considered: (i) outage-based metrics, suitable for tasks tolerating occasional packet losses, such as real-time control or monitoring; and (ii) bounded distortion metrics, relevant to semantic-rich tasks like text or video transmission. We propose two zero-delay compression algorithms leveraging online conformal prediction to provide per-sequence guarantees on the distortion of reconstructed sequences over error-free and packet-erasure channels with feedback. For erasure channels, we introduce a doubly-adaptive conformal update to compensate for channel-induced errors and derive sufficient conditions on erasure statistics to ensure distortion constraints. Experiments on semantic text compression validate the approach, showing significant bit rate reductions while strictly meeting distortion guarantees compared to state-of-the-art prediction-powered compression methods.

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arXiv2025

Information Age and Correctness for Energy Harvesting Devices with Random Access

We investigate accuracy and freshness of status updates from a large number of energy-harvesting devices that monitor two-state Markov processes and access the medium using the slotted ALOHA protocol without feedback. Using a Markovian framework, we analyze the average value of a generic state-dependent penalty function that grows whenever there is a state estimation error. The age of incorrect information (AoII) is an example of such penalty function. We propose an accurate and easy-to-compute approximation for the average penalty. Numerical results demonstrate the benefits of optimizing the transmission probabilities according to the process state transitions and current battery levels to minimize the average penalty. Minimizing a state-independent penalty function can be highly suboptimal in terms of average penalty when one of the process states is critical, i.e., entails a high penalty if wrongly estimated. Furthermore, minimizing the average penalty does not guarantee a low probability of misdetecting a critical state period.

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arXiv2025

Theoretical Performance Guarantees for Partial Domain Adaptation via Partial Optimal Transport

In many scenarios of practical interest, labeled data from a target distribution are scarce while labeled data from a related source distribution are abundant. One particular setting of interest arises when the target label space is a subset of the source label space, leading to the framework of partial domain adaptation (PDA). Typical approaches to PDA involve minimizing a domain alignment term and a weighted empirical loss on the source data, with the aim of transferring knowledge between domains. However, a theoretical basis for this procedure is lacking, and in particular, most existing weighting schemes are heuristic. In this work, we derive generalization bounds for the PDA problem based on partial optimal transport. These bounds corroborate the use of the partial Wasserstein distance as a domain alignment term, and lead to theoretically motivated explicit expressions for the empirical source loss weights. Inspired by these bounds, we devise a practical algorithm for PDA, termed WARMPOT. Through extensive numerical experiments, we show that WARMPOT is competitive with recent approaches, and that our proposed weights improve on existing schemes.

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arXiv2025

An Achievability Bound for Type-Based Unsourced Multiple Access

We derive an achievability bound to quantify the performance of a type-based unsourced multiple access system -- an information-theoretic model for grant-free multiple access with correlated messages. The bound extends available achievability results for the per-user error probability in the unsourced multiple access framework, where, different from our setup, message collisions are treated as errors. Specifically, we provide an upper bound on the total variation distance between the type (i.e., the empirical probability mass function) of the transmitted messages and its estimate over a Gaussian multiple access channel. Through numerical simulations, we illustrate that our bound can be used to determine the message type that is less efficient to transmit, because more difficult to detect. We finally show that a practical scheme for type estimation, based on coded compressed sensing with approximate message passing, operates approximately 3 dB away from the bound, for the parameters considered in the paper.

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arXiv2025

Type-Based Unsourced Multiple Access over Fading Channels with Cell-Free Massive MIMO

Type-based unsourced multiple access (TUMA) is a recently proposed framework for type-based estimation in massive uncoordinated access networks. We extend the existing design of TUMA, developed for an additive white Gaussian channel, to a more realistic environment with fading and multiple antennas. Specifically, we consider a cell-free massive multiple-input multiple-output system and exploit spatial diversity to estimate the set of transmitted messages and the number of users transmitting each message. Our solution relies on a location-based codeword partition and on the use at the receiver of a multisource approximate message passing algorithm in both centralized and distributed implementations. The proposed TUMA framework results in a robust and scalable architecture for massive machine-type communications.

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arXiv2025

Online Conformal Compression for Zero-Delay Communication with Distortion Guarantees

We investigate a lossy source compression problem in which both the encoder and decoder are equipped with a pre-trained sequence predictor. We propose an online lossy compression scheme that, under a 0-1 loss distortion function, ensures a deterministic, per-sequence upper bound on the distortion (outage) level for any time instant. The outage guarantees apply irrespective of any assumption on the distribution of the sequences to be encoded or on the quality of the predictor at the encoder and decoder. The proposed method, referred to as online conformal compression (OCC), is built upon online conformal prediction--a novel method for constructing confidence intervals for arbitrary predictors. Numerical results show that OCC achieves a compression rate comparable to that of an idealized scheme in which the encoder, with hindsight, selects the optimal subset of symbols to describe to the decoder, while satisfying the overall outage constraint.

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arXiv2025

Undetected Error Probability in the Short Blocklength Regime: Approaching Finite-Blocklength Bounds with Polar Codes

We analyze the trade-off between the undetected error probability (i.e., the probability that the channel decoder outputs an erroneous message without detecting the error) and the total error probability in the short blocklength regime. We address the problem by developing two new finite blocklength achievability bounds, which we use to benchmark the performance of two coding schemes based on polar codes with outer cyclic redundancy check (CRC) codes -- also referred to as CRC-aided (CA) polar codes. The first bound is obtained by considering an outer detection code, whereas the second bound relies on a threshold test applied to the generalized information density. Similarly, in the first CA polar code scheme, we reserve a fraction of the outer CRC parity bits for error detection, whereas in the second scheme, we apply a threshold test (specifically, Forney's optimal rule) to the output of the successive cancellation list decoder. Numerical simulations performed on the binary-input AWGN channel reveal that, in the short-blocklength regime, the threshold-based approach is superior to the CRC-based approach, both in terms of bounds and performance of CA polar code schemes. We also consider the case of decoding with noisy channel-state information, which leads to a mismatched decoding setting. Our results illustrate that, differently from the previous case, in this scenario, the CRC-based approach outperforms the threshold-based approach, which is more sensitive to the mismatch.

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arXiv2025

Timely Status Updates in Slotted ALOHA Networks With Energy Harvesting

We investigate the age of information (AoI) in a scenario where energy-harvesting devices send status updates to a gateway following the slotted ALOHA protocol and receive no feedback. We let the devices adjust the transmission probabilities based on their current battery level. Using a Markovian analysis, we derive analytically the average AoI. We further provide an approximate analysis for accurate and easy-to-compute approximations of both the average AoI and the age-violation probability (AVP), i.e., the probability that the AoI exceeds a given threshold. We also analyze the average throughput. Via numerical results, we investigate two baseline strategies: transmit a new update whenever possible to exploit every opportunity to reduce the AoI, and transmit only when sufficient energy is available to increase the chance of successful decoding. The two strategies are beneficial for low and high update-generation rates, respectively. We show that an optimized policy that balances the two strategies outperforms them significantly in terms of both AoI metrics and throughput. Finally, we show the benefit of decoding multiple packets in a slot using successive interference cancellation and adapting the transmission probability based on both the current battery level and the time elapsed since the last transmission.

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arXiv2024

Wireless 6G Connectivity for Massive Number of Devices and Critical Services

Compared to the generations up to 4G, whose main focus was on broadband and coverage aspects, 5G has expanded the scope of wireless cellular systems towards embracing two new types of connectivity: massive machine-type communication (mMTC) and ultra-reliable low-latency communications (URLLC). This paper discusses the possible evolution of these two types of connectivity within the umbrella of 6G wireless systems. The paper consists of three parts. The first part deals with the connectivity for a massive number of devices. While mMTC research in 5G predominantly focuses on the problem of uncoordinated access in the uplink for a large number of devices, the traffic patterns in 6G may become more symmetric, leading to closed-loop massive connectivity. One of the drivers for this is distributed learning/inference. The second part of the paper discusses the evolution of wireless connectivity for critical services. While latency and reliability are tightly coupled in 5G, 6G will support a variety of safety critical control applications with different types of timing requirements, as evidenced by the emergence of metrics related to information freshness and information value. Additionally, ensuring ultra-high reliability for safety critical control applications requires modeling and estimation of the tail statistics of the wireless channel, queue length, and delay. The fulfillment of these stringent requirements calls for the development of novel AI-based techniques, incorporating optimization theory, explainable AI, generative AI and digital twins. The third part analyzes the coexistence of massive connectivity and critical services. We will consider scenarios in which a massive number of devices need to support traffic patterns of mixed criticality. This is followed by a discussion about the management of wireless resources shared by services with different criticality.

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arXiv2024

Is Synchronization a Bottleneck for Pilot-Assisted URLLC Links?

We propose a framework to evaluate the so-called random-coding union bound with parameter $s$ (RCUs) on the achievable error probability in the finite-blocklength regime for a pilot-assisted transmission scheme operating over an imperfectly synchronized and memoryless block-fading waveform channel. Unlike previous results, which disregard the effects of imperfect synchronization, our framework utilizes pilots for both synchronization and channel estimation. Specifically, we provide an algorithm to perform joint synchronization and channel estimation, and verify its accuracy by observing its tightness in comparison with the Cramer-Rao bound. Then, we develop an RCUs bound on the error probability, which applies for a receiver that treats the estimates provided by the algorithm as accurate. Additionally, we utilize the saddlepoint approximation to provide a numerically efficient method for evaluating the RCUs bound in this scenario. Our numerical experiments verify the accuracy of the proposed approximation. Moreover, when the delays are modeled as fully dependent across fading blocks, numerical results indicate that the number of pilot symbols needed to estimate the fading channel gains to the level of accuracy required in ultra-reliable low-latency communication is also sufficient to acquire sufficiently good synchronization. However, when the delays are modeled as independent across blocks, synchronization becomes the bottleneck for the system performance.

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arXiv2024

Secure Aggregation is Not Private Against Membership Inference Attacks

Secure aggregation (SecAgg) is a commonly-used privacy-enhancing mechanism in federated learning, affording the server access only to the aggregate of model updates while safeguarding the confidentiality of individual updates. Despite widespread claims regarding SecAgg's privacy-preserving capabilities, a formal analysis of its privacy is lacking, making such presumptions unjustified. In this paper, we delve into the privacy implications of SecAgg by treating it as a local differential privacy (LDP) mechanism for each local update. We design a simple attack wherein an adversarial server seeks to discern which update vector a client submitted, out of two possible ones, in a single training round of federated learning under SecAgg. By conducting privacy auditing, we assess the success probability of this attack and quantify the LDP guarantees provided by SecAgg. Our numerical results unveil that, contrary to prevailing claims, SecAgg offers weak privacy against membership inference attacks even in a single training round. Indeed, it is difficult to hide a local update by adding other independent local updates when the updates are of high dimension. Our findings underscore the imperative for additional privacy-enhancing mechanisms, such as noise injection, in federated learning.

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arXiv2024

Type-Based Unsourced Multiple Access

We generalize the type-based multiple access framework proposed by Mergen and Tong (2006) to the case of unsourced multiple access. In the proposed framework, each device tracks the state of a physical/digital process, quantizes this state, and communicates it to a common receiver through a shared channel in an uncoordinated manner. The receiver aims to estimate the type of the states, i.e., the set of states and their multiplicity in the sequence of states reported by all devices. We measure the type estimation error using the Wasserstein distance. Considering an example of multi-target position tracking, we show that type estimation can be performed effectively via approximate message passing. Furthermore, we determine the quantization resolution that minimizes the type estimation error by balancing quantization distortion and communication error.

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arXiv2024

Deep-Learning-Based Channel Estimation for Distributed MIMO with 1-bit Radio-Over-Fiber Fronthaul

We consider the problem of pilot-aided, uplink channel estimation in a distributed massive multiple-input multiple-output (MIMO) architecture, in which the access points are connected to a central processing unit via fiber-optical fronthaul links, carrying a two-level-quantized version of the received analog radio-frequency signal. We adapt to this architecture the deep-learning-based channel-estimation algorithm recently proposed by Nguyen et al. (2023), and explore its robustness to the additional signal distortions (beyond 1-bit quantization) introduced in the considered architecture by the automatic gain controllers (AGCs) and by the comparators. These components are used at the access points to generate the two-level analog waveform from the received signal. Via simulation results, we illustrate that the proposed channel-estimation method outperforms significantly the Bussgang linear minimum mean-square error channel estimator, and it is robust against the additional impairments introduced by the AGCs and the comparators.

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