arXiv subjects

Markku Juntti

Publications and source records attributed to Markku Juntti.

At least 19 recordsLinked to original sources

arXiv2026

6G ISAC Drone Detection: Signal Processing and Experimental Validation at FR1

Integrated sensing and communications (ISAC) enables mobile networks to detect passive targets such as drones at minimal additional cost. However, in ISAC, a small drone must be detected against strong ground clutter with the limited radio resources left by communications. In this paper, we propose two ISAC target detectors: a coherent detector, which combines all frames and beam dwells of a revisit, and a non-coherent detector, robust to inter-frame clock phase drift. The algorithms leverage per-dwell clutter whitening and the adaptive coherence estimator (ACE) detection statistic to improve performance. Both detectors estimate the range, radial velocity, and azimuth of targets in real time on a laptop graphics processing unit (GPU). Measurements with an FR1 ISAC proof-of-concept system based on a commercial base station show that the coherent detector outperforms the non-coherent one and recovers the full track.

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arXiv2026

Temporal vs Spectral Resolution in CSI Reporting for Wi-Fi ISAC

CSI feedback is fundamental to proposed uses of Wi-Fi sensing such as human activity recognition (HAR); but it also constitutes significant overhead as resources must be allocated away from data communications. Wi-Fi CSI extraction can be explored along two dimensions in IEEE 802.11bf: reporting intensity, i.e., temporal resolution, and number of reported subcarriers, i.e., spectral resolution. We explore periodic and IEEE 802.11bf threshold-based reporting at matched rates, and uniform decimation and block-mean grouping at matched report sizes. We further propose reporting the mean CSI accumulated since the last report. Using the 802.11bf report size as the metric, we evaluate the accuracy-cost tradeoffs on three public datasets with our proposed model, a convolutional neural network combined with a gated recurrent unit and temporal pyramid pooling. The proposed periodic reporting with mean payload and block-mean grouping improves accuracy by up to 53 percentage points at equal CSI overhead cost. Temporal resolution is generally more cost-effective than spectral resolution. With only at most 7.7% of full-CSI feedback cost, the proposed scheme achieves 96-98% of full-CSI accuracy.

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arXiv2026

Time-Varying Parametric Channel Estimation With CP Decomposition Tensor Processing

Integrated sensing and communications (ISAC) is a key use case for sixth-generation (6G) wireless systems, where parametric channel estimation (PCE) plays a central role in enabling sensing, localization, and channel equalization in high-mobility scenarios. However, PCE is typically more computationally demanding than conventional channel estimation, which motivates the development of lower-complexity solutions. In this letter, we propose a fast PCE algorithm for time-varying and frequency-selective (TVFS) channels based on canonical polyadic (CP) decomposition and tensor processing, combined with ESPRIT-based initialization, component refinement, and exact line-search alternating coordinate descent. Two variants are presented: one for fully digital and another for hybrid receiver architectures. Numerical results show that the proposed method clearly outperforms a related CP-based baseline while achieving estimation performance close to a multiple-start SAGE benchmark at a substantially lower computational cost, with about one order of magnitude shorter execution time.

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arXiv2026

Energy-Efficient Spiking Neural Networks for Sensing-Aided Beam Prediction

Sensing-aided beam prediction can reduce the beam-training overhead of millimeter-wave (mmWave) systems by leveraging environmental information from complementary modalities such as vision and position. However, the resulting sensing and inference pipeline can be energy-intensive, motivating the use of energy-efficient spiking neural networks (SNNs) as an alternative to conventional artificial neural networks (ANNs). In this work, we develop SNN architectures for beam prediction and systematically compare them with ANNs using position-only, vision-only, and multimodal inputs. Our results show that compact ANNs consistently outperform SNNs for position-only sensing. In contrast, for vision and multimodal sensing, SNNs achieve higher accuracy than ANNs at similar energy costs and require up to an order of magnitude less energy at a comparable accuracy. For example, the multimodal SNN achieves 58.08% top-1 accuracy, outperforming the closest-energy ANN by 18.11 percentage points. We further show that SNN accuracy is limited by the binary communication between layers rather than by model size. These results demonstrate the potential of SNNs for energy-efficient sensing-aided beam prediction while highlighting their accuracy limitation.

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arXiv2026

Resource-Efficient Wi-Fi CSI-Based Sensing via Exploiting the Age of Samples

Wi-Fi channel state information (CSI)-based sensing must coexist with data communications, limiting the availability of temporally-dense CSI measurements. We formulate CSI-based human activity and identity recognition under an average sensing budget that limits the fraction of CSI measurement and reporting opportunities within a sensing session. The budget captures sensing-communication resource sharing, packet loss, and traffic-induced irregularity, which we model using deterministic (accumulated) and stochastic (Bernoulli) sampling policies. We propose a low-cost, age-aware WiFi sensing framework that encodes the age of each retained CSI sample and multiplicatively fuses it with the CSI embedding. On the NTU-Fi human activity recognition and person identification datasets, the proposed model outperforms both a CSI-only baseline and the time-aware attention model of the UniFi benchmark across most operating regimes. For person identification, it improves over UniFi by more than 10 percentage points, with the largest gains under strict sensing budgets.

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arXiv2026

Parametric Channel Estimation with Hardware Impaired Hybrid Beamformers: Sensing, Communications, and Power Efficiency Tradeoffs

Due to high power consumption and hardware costs of fully digital arrays, hybrid beamformers are often considered as a more economic alternative. Furthermore, using high resolution analog to digital converters (ADCs) can also have prohibitive power consumption, which leads to lower resolution converters being considered for radio frequency (RF) front end design. The finite quantization resolution as well as the nonlinearities caused by the power amplifiers (PAs) and low noise amplifiers (LNAs) can have a substantial impact on system performance. While widely studied for communications, the impact of hardware impairments on sensing performance is considerably less explored. In this work, we study the interplay between hybrid beamforming architectures, hardware impairments, and sensing and communications performance. Additionally, we define the concept of double-isotropy for pilot-combiner pairs, formalizing the notion of a perfectly energy-fair beam sweep. The multiple start (MS) space alternating generalized expectation maximization algorithm (SAGE) is also introduced, aimed at addressing the optimization issues arising from parametric channel estimation (PCE) in hybrid beamformed systems. We then provide a set of numerical results assessing the impacts of beamformer architecture and ADC resolution on PCE, sensing, and communications performance. The results show that medium resolution ADCs lead to the most power efficient configurations, with the best tradeoff between power consumption and performance for the majority of beamforming architectures. Additionally, fully digital beamforming architectures with high resolution converters can often be substituted for a hybrid beamformer setup with medium resolution converters without significant performance loss at a lower power consumption and overall hardware cost.

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arXiv2026

Tri-Hybrid Beamforming Design for Large-Scale MIMO ISAC Systems

Tri-hybrid multiple-input multiple-output (MIMO) architectures have been proposed as a promising solution for enabling energy-efficient communications systems in large-scale antenna arrays by replacing conventional antenna arrays in hybrid beamforming (HBF) systems with low-cost dynamic metasurface antennas (DMAs). In this work, we investigate beamforming design for integrated sensing and communications (ISAC) systems based on tri-HBF architectures, with the objective of jointly enhancing the communications sum rate and sensing sum mutual information. Specifically, we formulate a weighted multi-objective optimization problem that balances communications throughput and sensing mutual information, subject to a total transmit power consumption constraint and the physical limitations inherent to tri-HBF architectures. By exploiting the problem structure, we develop an efficient iterative algorithm with closed-form updates to solve the non-convex optimization problem with low complexity. Numerical results are provided to evaluate the performance of the proposed tri-HBF architecture in various setups. The results demonstrate that the proposed tri-HBF architecture can achieve significant improvement in energy efficiency (EE) compared to the conventional hybrid MIMO and DMA-only configurations, at the cost of minor degradations in sum rate and sensing mutual information.

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arXiv2026

Massive MIMO ISAC Under Target-Angle Uncertainty: CRLB Outage Analysis and Robust Resource Allocation

In integrated sensing and communications (ISAC), the same spectral and hardware resources are shared for two functionalities. Most ISAC designs assume perfect target-angle information neglecting angle estimation errors, which introduce steering-vector mismatches, degrade sensing accuracy, and may invalidate deterministic sensing guarantees. This paper investigates monostatic massive multiple-input-multiple-output (MIMO) ISAC systems under imperfect target-angle estimates. We derive closed-form expressions for the Cramér-Rao lower bounds (CRLBs) of target azimuth and elevation estimates in the presence of angle uncertainty. We characterize the cumulative distribution functions and outage probabilities of the CRLBs under Gaussian, generalized uniform, and von Mises angle-error models. Our analysis reveals that, in the small-error regime, the CRLBs increase quadratically with the angle errors due to transmit steering-vector mismatch. To ensure reliable sensing, we propose a robust power allocation framework that jointly optimizes pilot training and communications/sensing transmission powers to maximize the communications sum rate while satisfying CRLB outage constraints. The resulting nonconvex problem is solved using an alternating-optimization algorithm based on successive convex approximation. Numerical results validate the developed analysis and show that the proposed robust design reduces azimuth and elevation CRLB outage probabilities by up to $60\%$ compared with conventional non-robust schemes. It attains up to $45\%$ higher sum rates than the non-robust design under strict CRLB thresholds.

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arXiv2026

Freshness-Aware Constrained Sensing-Aided Beam Prediction with Knowledge Distillation

Beam prediction leveraging environmental data reduces over-the-air beam training overhead. Existing frameworks, however, assume continuous access to fresh sensory data, an assumption that breaks down under sensing budget constraints or sensor failures. To make this more practical, this paper proposes a sensing-aided beam prediction framework that operates under an average sensing rate constraint. We incorporate the age of information (AoI) directly into the beam prediction pipeline as a synthetic input modality: the age of the most recently captured data is encoded and fused with visual features through a gating mechanism. This provides the predictor with explicit context information about input sensory data reliability. We formalize and examine three fixed sampling policies, accumulated, uniform, and randomized, under the average sensing budget. We further develop a knowledge distillation (KD) framework that operates as a robustness regularizer rather than a pure model compression method. In particular, the high-capacity teacher is trained unconstrained on fully sampled data, and its representational knowledge is transferred to a compact student deployed under the sensing budget. We conduct numerical experiments on the DeepSense 6G data set. The results show that AoI fusion nearly doubles top-1 accuracy at strict sensing budgets, and age-aware models achieve near-optimal top-3 accuracy with only 20% of the data. Furthermore, we find that the teacher training regime is a more consequential design choice than the distillation loss function.

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arXiv2026

Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI

Beamforming methods in millimeter-wave (mmWave) cell-free massive multiple-input multiple-output (CFmMIMO) systems require accurate channel state information (CSI), whose acquisition entails significant training overhead. This paper shows that fully digital cell-free mmWave beamforming can be effectively learned from sub-6 GHz CSI using a graph neural network (GNN). Specifically, we represent a CFmMIMO system as a wireless graph, and the GNN is trained to approximate beamformers that maximize the downlink sum-rate based on the available sub-6 GHz CSI. A message-passing mechanism is proposed to capture inter-user interference and inter-base-station cooperation across different network topologies. Simulation results demonstrate that the proposed sub-6 GHz-assisted GNN-based beamformer achieves competitive and often superior sum-rate performance compared to classical baselines that rely on full mmWave CSI.

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arXiv2026

Beamforming and Filter Design for Bistatic ISAC under Known and Unknown Transmit Symbols

This paper investigates the joint design of beamforming and radar receive filters in a multiuser bistatic integrated sensing and communications (ISAC) system, aiming to maximize the minimum radar signal-to-interference-plus-noise ratio (SINR) under communications SINR and transmit power constraints. We consider two scenarios: transmitted signals are either known or unknown at the radar receiver. We develop tractable solutions to the resulting non-convex optimization problems in both cases. For the known-signal case, we derive closed-form radar receive filters and iteratively design beamforming using fractional programming (FP) and successive convex approximation (SCA). For the unknown case, we adopt an alternating optimization (AO) approach to jointly design the beamforming and receive filters. Numerical results demonstrate that, while both approaches achieve comparable performance under per-slot optimization, knowledge of the transmitted symbols provides significant gains in multi-slot processing via coherent integration. Moreover, the proposed ISAC designs perform close to the radar-only benchmark under moderate communication requirements.

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arXiv2026

Deep-Unfolded Accelerated Projected Gradient for Energy-Efficient Cell-Free Massive MIMO

This paper investigates energy efficiency (EE) maximization for the downlink of cell-free massive multiple-input multiple-output systems under quality-of-service and per-access point power constraints. We first derive closed-form gradient expressions of the objective function with respect to the power allocation coefficients, and then propose an accelerated projected gradient (APG) approach to solve this problem. To reduce the computational complexity and runtime of APG, we propose a deep-unfolded APG framework that maps iterative APG updates onto a finite number of neural network layers, where parameters such as step sizes and penalty coefficients are learned from data. The proposed approach produces power allocation solutions through a fixed number of gradient-based updates without the need for line search or manual parameter tuning. Numerical results show that the method achieves EE performance comparable to the iterative APG approach while requiring significantly lower computational cost, with up to a 30-fold reduction in floating-point operations under the considered system settings.

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arXiv2026

Performance Analysis and Joint Beamforming for Hybrid RIS-Aided Massive MIMO ISAC

In integrated sensing and communication (ISAC) systems, stringent sensing performance constraints can severely limit the power available for communication. Hybrid reconfigurable intelligent surfaces (HRISs) with capabilities of both passive reflection and active signal amplification can significantly improve communication performance in the power-limited regime. This motivates us to analyze and optimize the performance of an HRIS-aided multiple-input-multiple-output (mMIMO) ISAC system. We first estimate the effective uplink/downlink channels using the minimum mean square error method. We then derive closed-form expressions for the communication sum-rate and sensing Cramér-Rao lower bound (CRLB). It is shown that under the equal power allocation strategy, the CRLB remains independent of the HRIS coefficients. Then, we formulate a joint optimization problem of power allocation and HRIS beamforming to maximize the communication sum-rate while ensuring specified sensing CRLB constraints. To solve the formulated non-convex problem, we propose an alternating optimization algorithm based on fractional programming and successive convex approximation. Extensive simulations validate our analysis and proposed algorithm, showing significant improvements in both communication and sensing performances enabled by the HRIS. For example, an HRIS with only $4$ active elements offers $97.30\%$ improvement in the communication sum-rate, while ensuring a sensing CRLB constraint of $-30$ dB.

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arXiv2026

Energy Efficiency Maximization for Hybrid RIS-Aided Communications via Deep Unfolding

We address energy-efficiency (EE) maximization in a multiuser (MU) multiple-input single-output (MISO) downlink system assisted by a hybrid reconfigurable intelligent surface(RIS), where each element can be dynamically configured to operate in either active or passive mode depending on whether its power amplifier is engaged. Practical hardware effects are explicitly incorporated, including base station (BS) and RIS power budgets, active-element amplifier gain limits, amplification noise, and binary phase control. To solve the problem, we develop an alternating-optimization framework in which the BS beamforming subproblem is handled via zero-forcing with closed-form power allocation, while the RIS subproblem is addressed using a model-driven deep unfolding approach. Numerical results show that the proposed method achieves faster convergence and higher EE than the considered benchmark schemes. In particular, it attains about 30% higher EE than the procedure without deep unfolding. Furthermore, our simulations demonstrate at least 10% EE improvement over the fully active RIS configuration and up to threefold EE gains compared with the fully passive RIS design. The results also show that most of the achievable EE gain can be captured by activating only a small fraction of RIS elements and allocating only a small portion of the dynamic power budget to the RIS.

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arXiv2026

Lightweight Vision-Aided Beam Tracking for Cross-Environment mmWave Communications

Sensing-aided beam tracking is a promising approach to reduce the overhead for millimeter-wave beam management. However, real-world application remains challenging due to rapid channel variations and substantial environmental differences across deployment scenarios. Developing low-complexity sensing assisted approaches that generalize to diverse environments can alleviate the problem. With this motivation, this paper proposes a lightweight vision-aided model for cross-environment beam tracking. The task is formulated as a sequence-to-sequence classification problem, where the model jointly predicts the current and future optimal beams from past visual observations. We develop a low-complexity model based on depthwise separable convolutions and introduce hierarchical data augmentation and beam power-based label smoothing to improve robustness and generalization. Experimental results on real-world images from two geometrically distinct DeepSense 6G scenarios show that the proposed strategies consistently improve cross-environment beam prediction accuracy up to 84% across the current and three future time slots, outperforming the state-of-the-art solution. Notably, this performance is achieved while reducing the number of model parameters and computational complexity by factors of approximately 52 and 79, respectively, compared with the high-capacity ResNet baseline.

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arXiv2026

Data-Free Knowledge Distillation for LiDAR-Aided Beam Tracking in MmWave Systems

We propose a data-free knowledge distillation (DF- KD) framework for LiDAR-aided mmWave beam tracking, where the objective is to predict the optimal current and future beams from a sequence of past LiDAR measurements. Specifically, we propose a knowledge inversion approach where a generator synthesizes LiDAR-like sequences from random noise, using a metadata loss to align the teachers internal feature statistics of synthetic and real data, without access to raw LiDAR samples. The student model is then trained exclusively on the synthetic data using either the Kullback- Leibler (KL) divergence loss or a proposed mean squared error (MSE) loss between the teachers and students raw output logits. Simulation results on the DeepSense dataset demonstrate the effectiveness of the proposed approach. In particular, the proposed convolutional neural network-gated recurrent unit (CNN-GRU) teacher architecture yields superior DF-KD student performance compared to GRU-only alternatives, and the MSE loss achieves performance comparable to the standard KD loss while requiring fewer hyperparamete

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arXiv2026

Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications

Infrastructure-mounted sensors can capture rich environmental information to enhance communications and facilitate beamforming in millimeter-wave systems. This work presents an efficient sensing-assisted long-term beam tracking framework that selects optimal beams from a codebook for current and multiple future time slots. We first design a large attention-enhanced neural network (NN) to fully exploit past visual observations for beam tracking. A convolutional NN extracts compact image features, while gated recurrent units with attention capture the temporal dependencies within sequences. The large NN then acts as the teacher to guide the training of a lightweight student NN via knowledge distillation. The student requires shorter input sequences yet preserves long-term beam prediction ability. Numerical results demonstrate that the teacher achieves Top-5 accuracies exceeding 93% for current and six future time slots, approaching state-of-the-art performance with a 90% reduction of model parameters. The student closely matches the teacher's performance while reducing the number of model parameters by over 1670% and cutting complexity by over 450%, despite operating with 60% shorter input sequences. This improvement significantly enhances data efficiency, reduces latency, and reduces power consumption in sensing and processing.

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arXiv2026

Resource Allocation and AoI-Aware Detection for ISAC with Stacked Intelligent Metasurfaces

Stacked intelligent metasurfaces (SIMs) provide wave-domain degrees of freedom that can empower integrated sensing and communication (ISAC) through flexible beampattern synthesis and interference management, while reducing hardware cost. In this paper, we investigate energy-efficient resource allocation for a downlink SIM-aided multi-user ISAC system that supports the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) via puncturing, while simultaneously illuminating sensing targets. We formulate an energy efficiency (EE) maximization problem that jointly optimizes resource block (RB) allocation, transmit power control, and SIM phase shifts. The formulated problem is highly challenging due to the large number of variables optimized on different time scales. To overcome this, we leverage the intrinsic two-timescale structure induced by the puncturing approach to decompose the original problem into two tractable subproblems: EE maximization for eMBB users in each time slot and EE maximization for URLLC users and sensing targets in each mini-slot. To address each subproblem, we develop an iterative algorithm that transforms the original non-convex formulation into a sequence of tractable subproblems, yielding convex updates for RB allocation and power control, along with low-complexity updates for SIM phase shifts. Simulation results show that the proposed design achieves up to 230% improvement in EE over a No-SIM baseline. In addition, it requires significantly fewer transmit antennas than conventional BS architectures, while preserving the EE achieved and satisfying the communication and sensing quality of service (QoS) requirements. Moreover, the results reveal fundamental trade-offs between EE and heterogeneous QoS requirements across communication and sensing functionalities.

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