Papers for

radar system developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Deep learning optimizes wireless signals for communication and sensing

Learning-Aided Short Code Design for ISAC based on MIMO-OFDM

Abstract: This paper proposes a deep learning (DL)-based coded waveform design for integrated sensing and communications (ISAC), enabling flexible trade-offs between communication reliability and ranging accuracy in short-block transmissions. The proposed scheme is built upon a practical multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) architecture, where the communication channel state information and the angles of the static targets are assumed available at the transmitter. A transformer-based transmitter encodes input information bits directly into ISAC transmit waveforms to jointly optimize the bit error rate (BER) performance and the delay modified Cramer-Rao bound (MCRB). A corresponding transformer-based receiver is adopted at the communication side to recover the transmitted information bits. We further examine the learned codewords for communication-oriented and sensing-oriented designs, revealing that a balanced ISAC waveform naturally exhibits an intermediate structure between these two extremes. Numerical results illustrate these codeword structures and demonstrate that the proposed design provides substantial trade-off gains over conventional schemes based on standard channel coding and modulation.

Wed 9 SeptInformation Theory
The gist
Communications systems often need to send data reliably while also sensing the environment around them, like detecting targets. This paper shows how deep learning can design wireless signals that balance good data transmission with accurate sensing using a common multi-antenna, multi-frequency setup. The authors build special transformer-based encoders and decoders that directly create signals tuned for both goals, and their method outperforms traditional approaches. They also analyze how the designed signals change depending on whether the focus is more on communication or sensing.
Open → 2609.09797v1

Shared-radius co-prime circular array improves 2D direction finding accuracy

Hybrid Continuous DoA Estimation with Shared-Radius Co-Prime Circular Arrays

Abstract: This paper proposes a shared-radius co-prime circular array for high-resolution, continuous 2D Direction-of-Arrival (DoA) estimation in 3D space, jointly estimating azimuth and elevation angles. The proposed architecture consists of two uniform circular sub-arrays with co-prime antenna counts sharing a common radius RR, a design that intrinsically suppresses mutual coupling leakage compared to dense uniform arrays. Unlike existing works that rely on complex phase-mode transformations to map circular structures to virtual linear arrays, we introduce a hybrid continuous-recovery framework operating directly in the physical spatial domain. By integrating a fast, discrete coarse-grid search with a swarm-intelligence continuous refinement stage, the proposed method completely bypasses discrete grid-mismatch limitations and computationally expensive eigenvalue decompositions. A rigorous theoretical analysis using Nivens Theorem establishes the spatial uniqueness of the true source direction, effectively resolving phase ambiguities. Simulation results demonstrate that this hybrid scheme achieves superior resolution and lower Root Mean Square Error (RMSE) at low Signal-to-Noise Ratios (SNR) compared to uniform configurations, while asymptotically converging to the theoretical Cramer-Rao Bound (CRB) at high SNRs.

Tue 8 SeptInformation TheoryNetworking and Internet Architecture
The gist
Finding where a signal comes from in 3D space using antennas can be tricky and sometimes inaccurate. This paper shows a new type of antenna setup that uses two circular groups of antennas with specially chosen sizes. The method helps pinpoint signals more accurately and avoids some common errors, working well even when signals are weak or noisy. The approach combines a quick rough estimate with a smart continuous way to refine the direction measurement.
Open → 2609.08827v1