Scalable Attention for 5G NR Channel Estimation

Attention-based neural estimators achieve strong channel-estimation accuracy, but the computational cost of global attention over the time-frequency resource grid grows quadratically with the number of subcarriers, and these estimators are typically tied to a single resource allocation. This paper proposes Channel Estimation Attention (CHEA), a low-complexity channel estimator for 5G New Radio (5G NR) multi-user multiple-input multiple-output (MU-MIMO). CHEA replaces global attention with a multi-resolution windowed design: a high-resolution encoder preserves local pilot detail, a low-resolution encoder captures wider frequency-domain context, and a local cross-attention decoder transfers this coarse context back to the high-resolution pilot tokens. A per-Physical Resource Block (PRB) upsampling module then reconstructs the channel over the full slot. Because every attention operation is confined to a fixed-size window and reconstruction is performed per PRB, the cost of CHEA scales linearly with the number of subcarriers, and a single trained model supports different PRB allocations without retraining. On a standard-compliant Physical Uplink Shared Channel (PUSCH), CHEA achieves the lowest Mean Squared Error (MSE) among conventional and state-of-the-art neural estimators, while requiring 2.8\(\times\) to 22.0\(\times\) lower operations than existing attention-based estimators.

On-board AI-based Channel Estimation for LEO NTNs

Artificial Intelligence(AI) methods have shown strong channel estimation performance in terrestrial networks, but they typically rely on substantial computational resources. As 6G moves toward a unified architecture that will include Non-Terrestrial Networks (NTN) from day 0, availability of large and power hungry computational resources shall not be taken for granted. At the same time, NTN propagation often exhibits high predictability, limited multipath richness and significant Doppler shifts, representing a specific channel estimation problem. In this work, we propose a lightweight convolution-based channel estimator designed specifically for NTN operation and real-time onboard inference. We evaluate its channel estimation accuracy under stringent NGSO power budgets and quantify the resulting end-to-end impact on link performance. We show the improvement in terms of Mean Squared Error (MSE) achieved by the proposed approach compared with established algorithms, demonstrating that efficient AI models can deliver robust performance even on power-constrained spaceborne nodes. In addition, the proposed design by exploiting the domain knowledge, improves parameter efficiency by $27\%$ compared with state-of-the-art AI models and requires approximately $29\times$ fewer floating-point operations than conventional methods while achieving superior MSE performance.

Fast End-to-End Simulation and Exploration of Many-RISCV-Core Baseband Transceivers for Software-Defined Radio-Access Networks

The fast-rising demand for wireless bandwidth requires rapid evolution of high-performance baseband processing infrastructure. Programmable many-core processors for software-defined radio (SDR) have emerged as high-performance baseband processing engines, offering the flexibility required to capture evolving wireless standards and technologies. This trend must be supported by a design framework enabling functional validation and end-to-end performance analysis of SDR hardware within realistic radio environment models. We propose a static binary translation based simulator augmented with a fast, approximate timing model of the hardware and coupled to wireless channel models to simulate the most performance-critical physical layer functions implemented in software on a many (1024) RISC-V cores cluster customized for SDR. Our framework simulates the detection of a 5G OFDM-symbol on a server-class processor in 9.5s-3min, on a single thread, depending on the input MIMO size (three orders of magnitude faster than RTL simulation). The simulation is easily parallelized to 128 threads with 73-121x speedup compared to a single thread.