arXiv subjects

Erik G. Larsson

Publications and source records attributed to Erik G. Larsson.

At least 19 recordsLinked to original sources

arXiv2026

Secure Over-the-Air Computation Against Multiple Eavesdroppers using Correlated Artificial Noise

Over-the-air (OtA) computation enables scalable analog aggregation by exploiting the superposition property of wireless channels, making it an attractive joint communication and computation paradigm for distributed sensing and learning. However, the uncoded nature of analog transmission exposes the computation result to eavesdropping, and the fundamental security limits of OtA computation against multiple cooperating adversaries remain poorly understood. In this paper, we develop an estimation-theoretic framework for analyzing the security of analog OtA computation in the presence of multiple distributed eavesdroppers that may jointly process their observations. We first derive the optimal estimator for cooperating eavesdroppers and bounds on the achievable estimation accuracy of both the legitimate receiver and the adversaries. Our analysis reveals a key insight: while random channel phase misalignment provides significant inherent MSE-security against individual eavesdroppers, this protection largely disappears once multiple eavesdroppers cooperate. Motivated by this observation, we propose a correlated artificial noise design based on zero-forcing that preserves the aggregation accuracy at the legitimate receiver while maximizing the estimation error at the cooperative eavesdroppers. Numerical results demonstrate that the proposed design substantially reduces the security advantage gained through eavesdropper cooperation and achieves security close to uncorrelated artificial-noise schemes without sacrificing computation accuracy. These results provide both a theoretical characterization of the security limits of analog OtA computation and a practical design guideline for its secure deployment in real-world wireless systems.

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arXiv2026

Topology Design for Distributed Consensus with Relay-Assisted Communication

This paper focuses on relay-assisted topology optimization to accelerate the convergence of distributed consensus algorithms over weakly connected networks. Instead of permanently adding a fixed set of relay links, we introduce a time-sharing framework where multiple relay configurations are activated in a probabilistic manner. An ActiveSet-based algorithm incrementally constructs candidate relay edge sets and jointly optimizes the mixing matrices and the associated relay set activation probabilities, allowing adaptive relay selection in the optimization process. Simulation results demonstrate a substantially improved performance-cost trade-off compared with fixed-cardinality relay selection strategies.

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arXiv2026

Budget-Constrained Multi-Consensus Decentralized Gradient Descent

We investigate decentralized gradient descent (DGD) with emphasis on efficient communication and computation resource utilization under budget constraints. As a first step toward the broader communication-computation allocation problem, we consider and analyze a \textit{multi-consensus decentralized gradient descent} (mcDGD) scheme, where the number of consensus rounds and the stepsize are allowed to vary across iterations. Building on a unified analytical framework for DGD, we derive finite-time convergence bounds that explicitly characterize the interaction between consensus quality and optimization dynamics. Our analysis requires only convexity of the local objective functions while assuming smoothness and strong convexity of the global objective. The resulting bounds enable a principled consensus-allocation strategy under resource constraints, for which we show that equal allocation of consensus rounds across iterations is optimal under our stepsize rule, up to integer rounding. Numerical experiments corroborate the theoretical findings and demonstrate favorable communication-computation tradeoffs compared with existing multi-consensus decentralized optimization baselines.

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arXiv2026

Physical-Layer Aspects of Repeater-Assisted MIMO

Network-controlled repeaters (NCRs) are emerg- ing as low-cost, band-selective active scatterers that can re- shape the wireless propagation environment without backhaul or tight phase synchronization. We study physical-layer design for repeater-assisted multiple-input multiple-output (RA-MIMO) networks, such as hardware impairments, gain and activation control, duplexing, channel-state information acquisition, and wideband delay effects. We then discuss integrated sensing and communication (ISAC), where repeaters can extend non- line-of-sight (NLoS) visibility, improve target observability, and enhance weak-echo detection. These gains, however, are limited by amplified noise, clutter, feedback, and calibration errors, motivating joint optimization and gain control frameworks for swarm repeater deployments within ISAC.

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arXiv2026

Exact Average Consensus under Noisy Communication Links: A Decentralized Gradient Perspective

We study the distributed average consensus problem under persistent link-level disturbances modeled as a martingale difference sequence with uniformly bounded conditional second moments. Under such disturbances, the standard stochastic-approximation-based linear iteration with diminishing stepsizes drives the network to consensus on an unbiased random variable with non-vanishing variance instead of the exact initial average. To understand and resolve this limitation, we develop an anchoring-based mechanism derived from a decentralized gradient descent formulation and study the effect of incorporating a decaying anchoring term that continuously pulls each agent state toward its initial value. This perspective provides an intuitive interpretation of how state anchoring counteracts disturbance accumulation. Under standard summability conditions, we prove that the resulting algorithm achieves exact average consensus almost surely. Furthermore, this decentralized gradient perspective offers a unifying framework for several related methods and an interpretable design principle for exact average consensus under persistent disturbances.

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arXiv2026

Leveraging Slowly Time-Varying AP-AP Channels for Interference Mitigation in Dynamic TDD

We address the challenge of cross-link interference in dynamic time-division duplexing (TDD) systems. Specifically, we focus on mitigating the interference caused by access points (APs) operating in downlink to APs operating in uplink. To this end, we exploit that channels between APs typically vary much more slowly over time than channels between users and APs. This observation allows us to jointly estimate the uplink user data and the AP-AP channels using a least-squares formulation over multiple coherence intervals, during which the AP-AP channels stay constant. We derive conditions for unique solvability of this least-squares problem by analyzing the rank of the regression matrix. For cases where a unique solution does not exist, we propose to transform the problem into a uniquely solvable one by sacrificing a subset of the uplink data samples. Numerical results demonstrate that our proposed methods achieve substantial gains over baseline algorithms. Further, we observe that one of our proposed algorithms achieves almost perfect AP-AP interference mitigation when the AP-AP channels vary very slowly over time.

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arXiv2026

Estimating Timing Advance for Sub-THz Distributed Systems from Sub-10 GHz Channel State Information

Dual-band wireless architectures transmit the control information over the sub-10 GHz while reserving sub-THz for high data rate links, offering notable capacity gains. However, a critical bottleneck in such systems is timing synchronization. Due to the narrow beams of the sub-THz radio units (RUs), when the dual-band user equipment (UE) rotates or moves, it becomes necessary to switch the transmission between the sub-THz RUs. This switching requires recalibrating the timing of uplink (UL) and downlink (DL) transmissions to prevent communication disruptions. Moreover, for sub-THz RUs, the method introduces significant overhead and latency, especially when switches are frequent. Leveraging the reliable sub-10 GHz band offers greater resilience to UE mobility, making it suitable for control signalling. Thus, in this paper, we propose a deep learning-based algorithm that infers the propagation delay from the sub-THz RUs to the UE using sub-10 GHz channel characteristics. The inferred delay is used for calculating the timing advance for UL transmissions without the need for two-way synchronization. Simulation results show that the RU switch can be made seamless at the physical layer, without incurring any synchronization-related latency.

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arXiv2026

Distributed Optimization with Streaming Data: A Temporal Weighting Perspective

Optimization theory is a widely used tool for intelligent decision-making. While classical optimization deals with fixed, time-invariant objective functions, many modern applications operate in dynamic environments where data arrive sequentially, and the learning objective evolves over time, often under decentralized data and communication constraints. Motivated by these trends, we study decentralized optimization from streaming data through a structured time-varying formulation in which the global objective is a temporally weighted average of losses observed across the network. We analyze multi-iteration decentralized first-order methods, including decentralized gradient descent. For strongly convex and smooth losses, we develop guarantees for the Euclidean-norm \emph{tracking error} through a contraction-mapping viewpoint. The resulting bounds decompose the tracking error into a fixed-point tracking component and a bias term induced by decentralization and data heterogeneity. We specialize our analysis to uniform and exponentially discounted weights, as well as their finite-memory \emph{windowed} counterparts. The bounds explicitly characterize the roles of the temporal weighting rule, per-step iteration budget, step size, and network connectivity. Uniform weighting yields a vanishing fixed-point tracking contribution of order $\mathcal O(1/t)$, whereas discounted and windowed strategies generally induce non-vanishing tracking floors governed by the discount factor and effective memory, respectively. In all cases, decentralization induces an additional non-zero bias floor under a constant step size. Numerical experiments illustrate the predicted trends.

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arXiv2026

Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning

In standard federated learning systems, the parameter server broadcasts the global model to the participating devices in every iteration. Motivated by the temporal correlation between consecutive global models, differential coding can be applied to global model dissemination to reduce the information magnitude, thereby enabling communication with fewer quantization bits. However, due to wireless link failures, devices may occasionally miss differential updates and consequently fail to reconstruct the global model. As a result, they either continue local training based on an outdated model or remain idle until the next full-model broadcast becomes available. To address this challenge, we propose a mixed-timescale differential coding (MTDC) scheme that performs differential coding at two different levels by adjusting the reference model. With MTDC, a device can reconstruct the latest global model between two full-model broadcasts even if it misses a differential update. We provide a convergence analysis that motivates the design of an age-aware variant of MTDC, along with a device scheduling policy to further improve communication efficiency. Simulation results demonstrate that the proposed MTDC schemes achieve superior learning performance compared to baseline methods under similar communication resource budgets in the presence of downlink transmission failures.

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arXiv2026

Physically Large Apertures for Wireless Power Transfer: Performance and Regulatory Aspects

Wireless power transfer (WPT) is a promising service for the Internet of Things, providing a cost-effective and sustainable solution to deploy so-called energy-neutral devices on a massive scale. The power received at the device side from a conventional transmit antenna with a physically small aperture decays rapidly with the distance. New opportunities arise from the transition from conventional far-field beamforming to near-field beam focusing. We argue that a physically large aperture, i.e., large with respect to the distance to the receiver, enables a power budget that remains practically independent of distance. Distance-dependent array gain patterns allow focusing the power density maximum precisely at the device location, while reducing the power density near the infrastructure. Physical aperture size is a key resource in enabling efficient yet regulatory-compliant WPT. We use real-world measurements to demonstrate that a regulatory-compliant system operating at sub-10GHz frequencies can increase the power received at the device into the milliwatt range. Our empirical demonstration shows that power-optimal near-field beam focusing inherently exploits multipath propagation, yielding both increased WPT efficiency and improved human exposure safety.

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arXiv2026

Decentralized Power Control for Over-the-Air Computation with Phase Noise

Estimation of uplink channels is required for coherent over-the-air computation (OAC). When channel estimation is done using calibrated reciprocity, the estimates are only available locally to the devices. This poses a challenge for precoding and decoding, which cannot be coordinated centrally. To this end we use truncated channel inversion (TCI) and propose an approximate closed form solution and an exact numerical solver to optimize the TCI parameters. Importantly, we prove that the proposed TCI scheme is independent of the number of receiver antennas in terms of mean-square-error (MSE). Furthermore, our analysis reveals a clear connection between the MSE and expected aggregate phase error across devices which gives insight to the scalability of OAC. Finally, simulations with comparisons to reference methods from prior work with globally available error-free channel estimates show that proposed is close, even outperforming these references in MSE under some conditions.

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arXiv2026

Repeater-Assisted Massive MIMO Downlink Performance with Calibration Errors

Reciprocity-based downlink beamforming is imperative for a scalable time-division duplex massive multiple-input multiple-output~(MIMO) deployment. Specifically, for a dual-antenna repeater-assisted massive MIMO system, a mismatch between forward and reverse path gains at the repeater can exacerbate the overall calibration error between the user equipments (UEs) and the base station (BS), which potentially also contains calibration errors of their individual radio-frequency chains. This paper models the effects of such calibration errors, underpins the relations between the uplink and downlink channels for repeater-assisted systems with calibration errors clubbed with the over-the-air channel estimation errors, and derives analytical expressions of the downlink spectral efficiency. The presented results can then be simplified to several special cases, underscoring situations wherein such errors can become pronounced.

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arXiv2026

On Optimal Strategies for Joint Reciprocity Calibration in Distributed MIMO

This paper investigates the impact of reciprocity calibration errors on the downlink spectral efficiency (SE) of multi-user large antenna systems. Specifically, we consider two calibration approaches: (a) global calibration, in which all antennas (can be distributed access-points (APs)) in the system cooperatively perform calibration, and (b) local calibration, wherein only a subset of antennas involved in downlink beamforming performs calibration. We derive the downlink SE considering the use-and-then-forget bound and side-information bound, and then demonstrate that, when downlink pilots are employed (in the case of side-information bound), the global calibration outperforms local calibration for arbitrary calibration topologies.

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arXiv2026

DeMuon: A Decentralized Muon for Matrix Optimization over Graphs

In this paper, we propose DeMuon, a method for decentralized matrix optimization over a given communication topology. DeMuon incorporates matrix orthogonalization via Newton-Schulz iterations-a technique inherited from its centralized predecessor, Muon-and employs gradient tracking to mitigate heterogeneity among local functions. Under heavy-tailed noise conditions and additional mild assumptions, we establish the iteration complexity of DeMuon for reaching an approximate stochastic stationary point. This complexity result matches the best-known complexity bounds of centralized algorithms in terms of dependence on the target tolerance. To the best of our knowledge, DeMuon is the first direct extension of Muon to decentralized optimization over graphs with provable complexity guarantees. We conduct preliminary numerical experiments on decentralized transformer pretraining over graphs with varying degrees of connectivity. Our numerical results demonstrate a clear margin of improvement of DeMuon over other popular decentralized algorithms across different network topologies.

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arXiv2026

A Unified Framework for Unbiased Non-Coherent Over-the-Air Computation

Over-the-Air Computation (OAC) enables efficient data aggregation in large-scale distributed systems by exploiting the superposition property of wireless multiple-access channels. In contrast to most existing studies on OAC assuming exact channel state information, we consider non-coherent OAC (NC-OAC) where the channel phase is unknown at the transmitters. A three-step framework for NC-OAC with a mapping between source data and codewords is proposed: 1) Devices encode their data to non-negative codewords; 2) Devices transmit a sequence of symbols with amplitude proportional to their codewords, such that the receiver can estimate the codeword sum. Estimation of the codeword sum is studied under two scenarios of global channel amplitude knowledge: statistical or instantaneous; 3) The estimated codeword sum is decoded to the desired source data sum at the receiver. With the proposed framework, we first study prior work on NC-OAC and map these to the framework. Next, we define and compare the two most commonly (often implicitly) used mappings for NC-OAC: the Affine and the Augmented Affine mappings. Under the constraint of unbiased estimation, we show that with uniformly distributed data and standard channel assumptions, the Augmented Affine mapping exhibits an order of magnitude lower estimation variance than the Affine mapping with both statistical and instantaneous channel knowledge. This result is validated by extensive simulations. Finally, we propose and analyze a new mapping, which demonstrates superior performance over the previous two affine mappings.

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arXiv2026

Distributed MIMO With Over-the-Air Phase Calibration Integrated Into the TDD Flow

Reciprocity-based, joint coherent downlink beamforming from multiple access points (APs) in distributed multiple-input multiple-output (MIMO) with independent local oscillators (LOs) requires the APs to be periodically phase-calibrated (a.k.a. phase-synchronized or phase-aligned). Such phase calibration can be accomplished by bidirectional over-the-air measurements between the APs. In this paper, we show how such over-the-air measurements can be integrated into the time-division duplexing (TDD) flow by appropriately shifting the uplink/downlink switching points of the TDD slot structure, creating short time segments during which APs can measure on one another. We also show how this technique scales to large networks. Furthermore, we analytically characterize the tradeoff between the amount of resources spent on calibration measurements and the resulting spectral efficiency of the system, when conjugate beamforming or zero-forcing beamforming is used. The results demonstrate the feasibility of distributed MIMO with phase-calibration through over-the-air inter-AP measurements integrated into the TDD flow, and the advantage of this design over schemes with dedicated calibration slots.

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arXiv2026

Decentralized Time-Varying Optimization for Streaming Data via Temporal Weighting

Classical optimization theory largely focuses on fixed objective functions, whereas many modern learning systems operate in dynamic environments where data arrive sequentially and decisions must be updated continuously. In this work, we study optimization with streaming data over a distributed network of agents. We adopt a structured, weight-based formulation that explicitly captures the streaming-data origin of the time-varying objective: at each time step, every agent receives a new sample, and the network seeks to track the minimizer of a temporally weighted objective formed from all samples observed across the network so far. We focus on decentralized gradient descent (DGD) with a limited communication/computation budget, where at each time step, only a limited number of DGD iterations can be performed before the objective changes again. For strongly convex and smooth losses, we analyze the tracking error with respect to the time-varying minimizer through a fixed-point theory lens. Our analysis reveals that the tracking error decomposes into a fixed-point tracking term and a bias term induced by data heterogeneity across agents. We specialize the analysis to two natural weighting strategies: uniform weights, which treat all samples equally, and exponentially discounted weights, which geometrically decay the influence of older data. Under uniform weighting, DGD tracks the fixed-point at a rate $\mathcal{O}(1/t)$, whereas discounted weighting yields a non-vanishing fixed-point tracking floor controlled by the discount factor. In both cases, decentralization induces an additional non-zero bias floor under a constant step size. We validate our theoretical findings through numerical simulations.

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arXiv2026

Is Repeater-Assisted Massive MIMO Compatible with Dynamic TDD?

We present a framework for joint amplification and phase shift optimization of the repeater gain in dynamic time-division duplex (TDD) repeater-assisted massive MIMO networks. Repeaters, being active scatterers with amplification and phase shift, enhance the received signal strengths for users. However, they inevitably also amplify undesired noise and interference signals, which become particularly prominent in dynamic TDD systems due to the concurrent downlink (DL) and uplink (UL) transmissions, introducing cross-link interference among access points and users operating in opposite transmit directions. This causes a non-trivial trade-off between amplification of desired and undesired signals. To underpin the conditions under which such a trade-off can improve performance, we first derive DL and UL spectral efficiencies (SEs), and then develop a repeater gain optimization algorithm for SE maximization. Numerically, we show that our proposed algorithm successfully calibrates the repeater gain to amplify the desired signal while limiting the interference.

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