arXiv subjects

Marina Petrova

Publications and source records attributed to Marina Petrova.

At least 19 recordsLinked to original sources

arXiv2026

Cell-Free Massive MIMO Under Mobility: A Fairness-Differentiated Handover Scheme

While cell-free massive MIMO (CF-mMIMO) offers high and uniform network-wide throughput in static networks, its performance in mobile networks is not yet fully addressed. In this paper, we evaluate the throughput performance of urban mobile CF-mMIMO networks under a comprehensive throughput model and show that it suffers from large performance degradation due to the combined effect of channel aging and handover overheads. To restore the uniformly good performance of CF-mMIMO under mobility, we formulate a novel optimization problem to maximize the nett throughput that considers both channel aging and handover cost. We derive a near-optimal solution nearOpt for our transformed and relaxed optimization problem with Newton's method. We then design a heuristic handover algorithm, FairDiff, to differentiate prioritized and optional handovers using a policy threshold based on Jain's fairness index, in order to achieve uniform throughput over the network. Our extensive evaluation of the mobile throughput performance of our handover schemes in realistic urban mobile networks shows that, unlike the existing literature benchmarks that obtain very low throughput under mobility, our FairDiff scheme consistently achieves the near-optimal throughput comparable to nearOpt and highest network-wide throughput with the lowest computational complexity among all considered schemes. We thus for the first time propose a handover scheme that delivers the promise of uniformly good throughput for mobile CF-mMIMO, making it a feasible architecture for practical mobile networks.

cs.NI↗

arXiv2026

SARAMS: Split-Aware Joint Resource Allocation for Multi-Monostatic Sensing

Cooperative multi-monostatic sensing enables sub-meter passive localization in integrated sensing and communication~(ISAC) networks by fusing base station~(BS) observations at a central unit~(CU). Existing studies, however, treat the fused data as ideally available at the CU, overlooking how the per-BS 3GPP functional split jointly constrains fronthaul bitrate, computational load, and whether coherent or non-coherent fusion is feasible at the CU. We propose the \textit{Split-Aware Joint Resource Allocation for Multi-Monostatic Sensing}~(SARAMS) algorithm, which minimizes the worst-case multi-target squared position error bound~(SPEB) by jointly optimizing per-BS power, bandwidth, and observation time under fronthaul, computational, and power constraints. A split-dependent coefficient embeds the feasible fusion type into the Fisher information matrix~(FIM), casting SPEB minimization as a mixed-integer nonlinear program (MINLP) solved via a semidefinite program for power allocation and block coordinate descent (BCD) over a dominance-pruned configuration set. Simulation results demonstrate that SARAMS reduces the 90th-percentile worst-case SPEB by $65.7\%$ over equal power allocation while attaining an $8.7\%$ optimality gap relative to exhaustive search.

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arXiv2026

The Price of the Golden 6G Band: Evaluation of Beam Management Effort in FR3

Frequency Range 3 (FR3), 7.125-24.25 GHz, regarded as the "golden band" for 6G networks, has less challenging propagation characteristics than FR2 while offering much wider bandwidth for high data rate applications than FR1. Reusing existing FR1 infrastructure for FR3 network deployments requires gNodeBs (gNBs) to employ antenna arrays and perform beam management, which has proven challenging at FR2. In this paper, we extensively study and characterize the beam management effort in an FR3 urban network, in terms of: beam alignment sensitivity, number of directional link opportunities, gNB handover and beam switch rates, and beam steering distance. Our results show that achieving a high and stable mobile throughput requires significant beam management effort across FR3 bands. While the beam tracking requirements are less stringent at the lower frequencies due to wider beams, the beam switching rate to a non-adjacent beam is relatively comparable at FR3 and FR2.

cs.NI↗

arXiv2026

UnifSrv: AP Selection for Achieving Uniformly Good Performance of CF-mMIMO in Realistic Urban Networks

Under the ideal assumption of uniform propagation, cell-free massive MIMO (CF-mMIMO) provides uniformly high throughput over the network by effectively surrounding each user with its serving access point (AP) set. However, in realistic non-uniform urban propagation environments, it is difficult to consistently select good limited serving AP sets, resulting in significantly degraded throughput, especially for the worst-served (formerly "cell-edge") users. To restore the uniformly good performance of scalable CF-mMIMO in realistic urban networks, we formulate a novel multi-objective optimization problem to jointly achieve high throughput by maximizing the sum data rate, uniform throughput by maximizing Jain's fairness index of the throughput per user, and scalability by minimizing the serving AP set size. We then propose the UnifSrv AP selection algorithms to solve this optimization problem, consisting of a deep reinforcement learning (DRL)-based algorithm UnifSrv-DRL and a heuristic algorithm UnifSrv-heu. We conduct a comprehensive performance evaluation of scalable CF-mMIMO under realistic urban network distributions, propagation, and mobility patterns. Our results show that UnifSrv significantly outperforms the prior benchmark AP selection schemes, and for the first time achieves uniformly high throughput of CF-mMIMO under non-uniform urban propagation. Importantly, our heuristic algorithm achieves equivalent throughput to our DRL one, but with orders of magnitude lower complexity. We thus for the first time propose a practical AP selection algorithm that makes CF-mMIMO viable in realistic urban networks.

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arXiv2026

Outlier-Resistant Fusion for Multi-static Positioning using 5G NR Signals

Indoor positioning faces ongoing challenges due to complex propagation conditions, such as multipath propagation, signal blockages, and intrinsic target characteristics that substantially impact measurement reliability and positioning accuracy. Existing methods, in particular Least Squares (LS), frequently struggle to maintain robustness when confronted with unreliable observations caused by multipath interactions and extended targets. In this work, we propose an outlier-resistant algorithm designed to mitigate the impact of outlier measurements and accurately estimate the position of an extended target in multipath-rich environments. We develop a two-step algorithm in which an initial coarse position estimate is obtained using the angle-of-arrival (AoA) and subsequently refined using the Cauchy loss function to suppress outliers. The numerical results confirm that the proposed algorithm improves robustness and accuracy, outperforming existing benchmark methods, such as Iterative Reweighted Least Squares (IRLS), LS, and Huber loss function, and achieving a positioning error of less than $70$ cm in $90\%$ of cases. Its effectiveness in mitigating multipath effects is further assessed by comparing tracking performance in cluttered and empty room scenarios.

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arXiv2026

Dynamic Interference Management for TN-NTN Coexistence in the Upper Mid-Band

The coexistence of terrestrial networks (TN) and non-terrestrial networks (NTN) in the frequency range 3 (FR3) upper mid-band presents considerable interference concerns, as dense TN deployments can severely degrade NTN downlink performance. Existing studies rely on interference-nulling beamforming, precoding, or exclusion zones that require accurate channel state information (CSI) and static coordination, making them unsuitable for dynamic NTN scenarios. To overcome these limitations, we develop an optimization framework that jointly controls TN downlink power, uplink power, and antenna downtilt to protect NTN links while preserving terrestrial performance. The resultant non-convex coupling between TN and NTN parameters is addressed by a Proximal Policy Optimization (PPO)-based reinforcement learning method that develops adaptive power and tilt control strategies. Simulation results demonstrate a reduction up to 8 dB in the median interference-to-noise ratio (INR) while maintaining over 87% TN basestation activity, outperforming conventional baseline methods and validating the feasibility of the proposed strategy for FR3 coexistence.

cs.IT↗

arXiv2026

Joint Communication Scheduling and Resource Allocation for Distributed Edge Learning: Seamless Integration in Next-Generation Wireless Networks

Distributed edge learning (DL) is considered a cornerstone of intelligence enablers, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires a coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round-wise designs that assume a rigid resource allocation throughout each communication round (CR). However, rigid resource allocation within a CR is a highly inefficient and inaccurate representation of the system's realistic behavior, especially when CR duration far exceeds the channel coherence time due to large model size or limited resources. This is due to the heterogeneous nature of the system, as clients inherently may need to access the network at different time instants. This work zooms into one arbitrary CR, and demonstrates the importance of considering a time-dependent design for sharing the resource pool with HB traffic. We first formulate a timeslot-wise optimization problem to minimize the consumed time by DL within the CR while constrained by a DL energy budget. Due to its intractability, a session-based optimization problem is formulated assuming a CR lasts less than a large-scale coherence time. Some scheduling properties of such multi-server joint communication scheduling and resource allocation framework have been established. An iterative algorithm has been designed to solve such non-convex and non-block-separable-constrained problems. Simulation results confirm the importance of the efficient and accurate integration design proposed in this work.

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arXiv2025

Unleashing Sensor-Aided Environment Awareness for Beam Management in Beyond-5G Networks: An OpenAirInterface Experimental Platform

Large antenna arrays and beamforming techniques are key components for exploiting the spectrum-rich FR2 bands in next-generation mobile communication networks. Given the site-specific spatio-temporal variations of the mm-wave channel, non-RF sensor inputs and environment awareness can be leveraged to greatly enhance beam management decisions, e.g. via machine learning (ML) techniques. However, the current literature lacks open platforms to gather datasets for the training of such ML techniques and to evaluate novel beam management approaches in real-time, real-world scenarios and full-stack endto-end networks. In this work, we present our SDR-based experimental platform based on OpenAirInterface and are the first to integrate popular low-cost antenna array transceivers, beam sweeping capabilities, and a highly-modular sensor framework and associated interfaces into such a full-stack experimental platform. This enables beam management experimentation in real-world, real-time scenarios and facilitates gathering datasets necessary for developing ML-based beam management protocols that incorporate environment awareness via sensor modalities.

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arXiv2025

Cell-Free Beamforming Design for Physical Layer Multigroup Multicasting

In many wireless communication applications, it is desirable to transmit the same data to multiple user equipments (UEs). Physical layer multicasting presents an efficient transmission topology to exploit the beamforming capabilities at the transmitting nodes and broadcast nature of the wireless channel to satisfy the demand for the same content from several UEs. An advantage of multicasting is to avoid unnecessary co-channel interference between UEs requesting the same data. The difficulty is to find the suitable beamforming configuration that guarantees an acceptable minimum data rate, among the receiving UE group, to the multicast transmission. This paper addresses the max-min fair multigroup multicast optimization problem and proposes a novel iterative elimination procedure coupled with semidefinite relaxation (SDR) to find the near-optimal rank-1 beamforming vectors in a cell-free massive MIMO (multiple-input multiple-output) network. The proposed optimization procedure significantly improves computational complexity and spectral efficiency compared to common methods that use SDR followed by some randomization procedure and the state-of-the-art difference-of-convex approximation algorithm. The importance of the proposed procedure is that it is applicable to any SDR problem where a low-rank solution is desirable. Further, we propose a low-complexity algorithm that achieves 87 % of the optimal rank-1 solution at orders-of-magnitude lower computational time.

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arXiv2025

Coexistence analysis of Wi-Fi 6E and 5G NR-U in the 6 GHz band

The ever-increasing demand for broadband and IoT wireless connectivity has recently urged the regulators around the world to start opening the 6 GHz spectrum for unlicensed use. These bands will, for example, permit the use of additional 1.2 GHz in the US and 500 MHz in Europe for unlicensed radio access technologies (RATs) such as Wi-Fi and 5G New Radio Unlicensed (5G NR-U). To support QoS-sensitive applications with both technologies, fair and efficient coexistence approaches between the two RATs, as well as with incumbents already operating in the 6 GHz band, are crucial. In this paper, we study through extensive simulations the achievable mean downlink throughput of both Wi-Fi 6E APs and 5G NR-U gNBs when they are co-deployed in a dense residential scenario under high-interference conditions. We also explore how different parameter settings e.g., MAC frame aggregation, energy detection threshold and maximum channel occupancy time (MCOT) affect the coexistence. Our findings give important insights into how to tune the key parameters to design fair coexistence policies.

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arXiv2025

Cross-Environment Transfer Learning for Location-Aided Beam Prediction in 5G and Beyond Millimeter-Wave Networks

Millimeter-wave (mm-wave) communications requirebeamforming and consequent precise beam alignmentbetween the gNodeB (gNB) and the user equipment (UE) toovercome high propagation losses. This beam alignment needs tobe constantly updated for different UE locations based on beamsweepingradio frequency measurements, leading to significantbeam management overhead. One potential solution involvesusing machine learning (ML) beam prediction algorithms thatleverage UE position information to select the serving beamwithout the overhead of beam sweeping. However, the highlysite-specific nature of mm-wave propagation means that MLmodels require training from scratch for each scenario, whichis inefficient in practice. In this paper, we propose a robustcross-environment transfer learning solution for location-aidedbeam prediction, whereby the ML model trained on a referencegNB is transferred to a target gNB by fine-tuning with a limiteddataset. Extensive simulation results based on ray-tracing in twourban environments show the effectiveness of our solution forboth inter- and intra-city model transfer. Our results show thatby training the model on a reference gNB and transferring themodel by fine-tuning with only 5% of the target gNB dataset,we can achieve 80% accuracy in predicting the best beamfor the target gNB. Importantly, our approach improves thepoor generalization accuracy of transferring the model to newenvironments without fine-tuning by around 75 percentage points.This demonstrates that transfer learning enables high predictionaccuracy while reducing the computational and training datasetcollection burden of ML-based beam prediction, making itpractical for 5G-and-beyond deployments.

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arXiv2025

Efficient Integration of Distributed Learning Services in Next-Generation Wireless Networks

Distributed learning (DL) is considered a cornerstone of intelligence enabler, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round (CR)-wise designs that assume a fixed resource allocation during each CR. However, fixed resource allocation within a CR is a highly inefficient and inaccurate representation of the system's realistic behavior. This is due to the heterogeneous nature of the system, where clients inherently need to access the network at different times. This work zooms into one arbitrary communication round and demonstrates the importance of considering a time-dependent resource-sharing design with HB traffic. We propose a time-dependent optimization problem for minimizing the consumed time and energy by DL within the CR. Due to its intractability, a session-based optimization problem has been proposed assuming a large-scale coherence time. An iterative algorithm has been designed to solve such problems and simulation results confirm the importance of such efficient and accurate integration design.

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arXiv2025

BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selection

Recent advances in distributed intelligence have driven impressive progress across a diverse range of applications, from industrial automation to autonomous transportation. Nevertheless, deploying distributed learning services over wireless networks poses numerous challenges. These arise from inherent uncertainties in wireless environments (e.g., random channel fluctuations), limited resources (e.g., bandwidth and transmit power), and the presence of coexisting services on the network. In this paper, we investigate a mixed service scenario wherein high-priority ultra-reliable low latency communication (URLLC) and low-priority distributed learning services run concurrently over a network. Utilizing device selection, we aim to minimize the convergence time of distributed learning while simultaneously fulfilling the requirements of the URLLC service. We formulate this problem as a Markov decision process and address it via BSAC-CoEx, a framework based on the branching soft actor-critic (BSAC) algorithm that determines each device's participation decision through distinct branches in the actor's neural network. We evaluate our solution with a realistic simulator that is compliant with 3GPP standards for factory automation use cases. Our simulation results confirm that our solution can significantly decrease the training delays of the distributed learning service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all wireless resources.

cs.NI↗

arXiv2025

Environment-Aware Scheduling of URLLC and Sensing Services for Smart Industries

In this paper, we address the problem of scheduling sensing and communication functionality in an integrated sensing and communication (ISAC) enabled base station (BS) operating in an indoor factory (InF) environment. The BS is performing the task of detecting an AGV while managing downlink transmission of ultra-reliable low-latency communication (URLLC) data in a time-sharing manner. Scheduling fixed time slots for both sensing and communication is inefficient for the InF environment, as the instantaneous environmental changes necessitate a higher frequency of sensing operations to accurately detect the AGV. To address this issue, we propose an environment-aware scheduling scheme, in which we first formulate an optimization problem to maximize the probability of detection of AGV while considering the survival time constraint of URLLC data. Subsequently, utilizing the Nash bargaining theory, we propose an adaptive time-sharing scheme that assigns sensing duration in accordance with the environmental clutter density and distributes time to URLLC depending on the incoming traffic rate. Using our own Python-based discrete-event link-level simulator, we demonstrate the effectiveness of our proposed scheme over the baseline scheme in terms of probability of detection and downlink latency.

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arXiv2025

When Next-Gen Sensing Meets Legacy Wi-Fi: Performance Analyses of IEEE 802.11bf and IEEE 802.11ax Coexistence

Sensing is emerging as a vital future service in next-generation wireless networks, enabling applications such as object localization and activity recognition. The IEEE 802.11bf standard extends Wi-Fi capabilities to incorporate these sensing functionalities. However, coexistence with legacy Wi-Fi in densely populated networks poses challenges, as contention for channels can impair both sensing and communication quality. This paper develops an analytical framework and a system-level simulation in ns-3 to evaluate the coexistence of IEEE 802.11bf and legacy 802.11ax in terms of sensing delay and communication throughput. Forthis purpose, we have developed a dedicated ns-3 module forIEEE 802.11bf, which is made publicly available as open-source. We provide the first coexistence analysis between IEEE 802.11bfand IEEE 802.11ax, supported by link-level simulation in ns-3to assess the impact on sensing delay and network performance. Key parameters, including sensing intervals, access categories, network densities, and antenna configurations, are systematically analyzed to understand their influence on the sensing delay and aggregated network throughput. The evaluation is further extended to a realistic indoor office environment modeled after the 3GPP TR 38.901 standard. Our findings reveal key trade-offs between sensing intervals and throughput and the need for balanced sensing parameters to ensure effective coexistence in Wi-Fi networks.

cs.NI↗

arXiv2024

Demo: Testing AI-driven MAC Learning in Autonomic Networks

6G networks will be highly dynamic, re-configurable, and resilient. To enable and support such features, employing AI has been suggested. Integrating AIin networks will likely require distributed AI deployments with resilient connectivity, e.g., for communication between RL agents and environment. Such approaches need to be validated in realistic network environments. In this demo, we use ContainerNet to emulate AI-capable and autonomic networks that employ the routing protocol KIRA to provide resilient connectivity and service discovery. As an example AI application, we train and infer deep RL agents learning medium access control (MAC) policies for a wireless network environment in the emulated network.

cs.NI↗

arXiv2024

Optimal Weight Scheme for Fusion-Assisted Cooperative Multi-Monostatic Object Localization in 6G Networks

Cooperative multi-monostatic sensing enables accurate positioning of passive targets by combining the sensed environment of multiple base stations (BS). In this work, we propose a novel fusion algorithm that optimally finds the weight to combine the time-of-arrival (ToA) and angle-of-arrival (AoA) likelihood probability density function (PDF) of multiple BSs. In particular, we employ a log-linear pooling function that fuses all BSs' PDFs using a weighted geometric average. We formulated an optimization problem that minimizes the Reverse Kullback Leibler Divergence (RKLD) and proposed an iterative algorithm based on the Monte Carlo importance sampling (MCIS) approach to obtain the optimal fusion weights. Numerical results verify that our proposed fusion scheme with optimal weights outperforms the existing benchmark in terms of positioning accuracy in both unbiased (line-of-sight only) and biased (multipath-rich environment) scenarios.

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arXiv2024

Wireless MAC Protocol Synthesis and Optimization with Multi-Agent Distributed Reinforcement Learning

In this letter, we propose a novel Multi-Agent Deep Reinforcement Learning (MADRL) framework for Medium Access Control (MAC) protocol design. Unlike centralized approaches, which rely on a single entity for decision-making, MADRL empowers individual network nodes to autonomously learn and optimize their MAC based on local observations. Leveraging ns3-ai and RLlib, as far as we are aware of, our framework is the first of a kind that enables distributed multi-agent learning within the ns-3 environment, facilitating the design and synthesis of adaptive MAC protocols tailored to specific environmental conditions. We demonstrate the effectiveness of the MADRL MAC framework through extensive simulations, showcasing superior performance compared to legacy protocols across diverse scenarios. Our findings highlight the potential of MADRL-based MAC protocols to significantly enhance Quality of Service (QoS) requirements for future wireless applications.

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