arXiv subjects

Siva Aditya Gooty

Publications and source records attributed to Siva Aditya Gooty.

3 recordsLinked to original sources

arXiv2026

Precoding Design for Limited-Feedback MIMO Systems via Character-Polynomial Codes

This paper presents a precoding codebook design for limited-feedback multiple-input multiple-output (MIMO) systems under the equal-gain transmission (EGT) constraint. In particular, we demonstrate that character--polynomial (CP) codes provide a structured solution that achieves constant-envelope transmission, low storage complexity, and Grassmannian packing without dependence on array geometry or channel statistics. In contrast to geometry-dependent discrete Fourier transform (DFT) codebooks used in current 5G systems and unstructured Grassmannian codebooks with high storage complexity, CP codebooks combine practical implementation advantages with near-optimal packing performance. For multiple-input single-output (MISO) systems, we derive an upper bound on the mean squared quantization error and show that the distortion relative to the EGT baseline vanishes asymptotically as the number of transmit antennas increases and the code rate approaches one. For MIMO systems with two receive antennas, we develop an iterative method to establish the EGT baseline. Simulation results under Rayleigh, correlated, and clustered delay line (CDL) channel models show that CP codebooks approach the EGT baseline across all channel conditions while outperforming phase shift keying (PSK) and 5G DFT codebooks in several operating regimes, and, in the Rayleigh fading case, incur negligible packing loss relative to numerically optimized Grassmannian codebooks obtained via the alternating projection (AP) method.

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arXiv2026

Multi-Tag Collision Recovery in UHF-RFID Using Self-Attention Decoding

Passive ultra high frequency (UHF) radio frequency identification (RFID) enables battery-free tags to communicate with a reader through backscatter. When multiple tags respond in the same time slot, their waveforms overlap at the reader, and a conventional reader that follows framed slotted ALOHA (FSA) discards the resulting collided slot. This limits the throughput of the overall protocol even though the received signal still contains recoverable information about the responding tags. To address this limitation, we propose Self-Attention Tag Recovery (SATR), a transformer-based decoding algorithm that operates directly on the baseband in-phase and quadrature (I/Q) samples received during a standard tag response. SATR uses self-attention to model the temporal structure of the modulated waveform and learns candidate tag representations. It jointly estimates the number of responding tags and, more importantly, decodes the bit sequence of each detected tag. We numerically evaluate the decoding and throughput performance of SATR over a range of collision sizes and recovery configurations, and validate it with measurements of commercial UHF-RFID tags. The results show that, with proper design and training, SATR can reliably decode collisions of up to four tags. It achieves a throughput of approximately $0.815$ tags per slot under single acknowledgment and $1.87$ tags per slot under full recovery, corresponding to $2.2$ and $5.1$ times the conventional FSA limit of $1/e \approx 0.368$ tags per slot, while approaching optimal decoding performance and outperforming existing collision recovery methods.

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arXiv2025

Efficient Decoders for Sensing Subspace Code

Sparse antenna array sensing of source/target via direction of arrival (DoA) estimation motivates design of the sensing framework in joint communication and sensing (JCAS) systems for sixth generation (6G) communication systems. Recently, it is established by Mahdavifar, Rajamäki, and Pal that array geometry of sparse arrays has fundamental connections with the design of subspace codes in coding theory. This was then utilized to design efficient \textit{sensing subspace codes} that estimate the DoA with good resolution. Specifically, the Bose-Chowla sensing subspace code provides near optimal code design for unique DoA estimation with tight theoretical upper bound on the error performance. However, the currently known decoder for these codes, to estimate the DoA, is a traditional \textit{Maximum-a-Posterior (MAP) decoder} with complexity that is cubic with the number of antennas. In this work, we propose novel efficient decoding algorithms for sensing subspace codes, that reduce the complexity down to quadratic while providing new knobs to tune in order to tradeoff complexity with error performance. The decoders are further evaluated for their performance via Monte Carlo simulations for a range of SNRs demonstrating promising performance that smoothly approaches the MAP performance as the complexity grows from quadratic to cubic in the number of antennas.

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