arXiv subjects

SaiKrishna Saketh Yellapragada

Publications and source records attributed to SaiKrishna Saketh Yellapragada.

4 recordsLinked to original sources

arXiv2026

Floating-Point Microformat Quantization and Pruning for Efficient MU-MIMO Neural Receivers

Neural receivers outperform conventional 5G NR processing chains, but their compute and memory demands hinder real-time deployment. For a standard-compliant multi-user MIMO neural receiver, the 4-bit number format, not merely the bit width, determines whether compression preserves the gain over classical receivers. We apply weight and activation quantization-aware training (QAT) and, separately, 50% magnitude pruning, comparing INT8/INT4 and FP8 (E4M3)/FP4 (E2M1) weights with INT8 post-ReLU activations. Trained on 3GPP UMi channels and evaluated on TDL-B and TDL-C, 8-bit weight-activation models remain within 0.05 dB of FP32 at 10% and 1% block error rate (BLER). At 4 bits, uniform INT4 loses 3.3-3.7 dB and falls below LS-LMMSE, whereas FP4 more than halves this loss (1.3-1.4 dB) and still outperforms it by about 0.5 dB, even after pruning. FP4's denser near-zero grid matches the trained weight distribution, and FP4 avoids the residual-path over-pruning seen with INT4. An analytic cost model projects 66x fewer bit-operations and 8.8x less weight storage for pruned 4-bit-weight inference.

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arXiv2026

Computationally Efficient Neural Receivers via Axial Self-Attention

Deep learning-based neural receivers offer promising physical-layer solutions for next-generation wireless systems. We propose an axial self-attention transformer neural receiver that achieves state-of-the-art Block Error Rate (BLER) performance with significantly improved computational efficiency during inference and large-scale training. By factorizing attention operations along temporal and spectral axes, the proposed architecture reduces computational complexity from $O((TF)^2)$ to $O(T^2F+TF^2)$, yielding substantially fewer floating-point operations and attention matrix multiplications per transformer block. Experimental validation under 3GPP Clustered Delay Line (CDL) channels demonstrates consistent performance gains across varying mobility scenarios. Under non-line-of-sight conditions, our proposed axial neural receiver outperforms global self-attention and convolutional neural receiver baselines at 10% BLER and 1% BLER respectively, with reduced computational complexity.

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arXiv2026

Efficient Quantization-Aware Neural Receivers: Beyond Post-Training Quantization

As wireless communication systems advance toward Sixth Generation (6G) Radio Access Networks (RAN), Deep Learning (DL)-based neural receivers are emerging as transformative solutions for Physical Layer (PHY) processing, delivering superior Block Error Rate (BLER) performance compared to traditional model-based approaches. Practical deployment on resource-constrained hardware, however, requires efficient quantization to reduce latency, energy, and memory without sacrificing reliability. In this paper, we extend Post-Training Quantization (PTQ) by focusing on Quantization-Aware Training (QAT), which incorporates low-precision simulation during training for robustness at ultra-low bitwidths. In particular, we develop a QAT methodology for a neural receiver architecture and benchmark it against a PTQ approach across diverse 3GPP Clustered Delay Line (CDL) channel profiles under both Line-of-Sight (LoS) and Non-LoS (NLoS) conditions, with user velocities up to 40 m/s. Results show that 4-bit and 8-bit QAT models achieve BLERs comparable to FP32 models at a 10% target BLER. Moreover, QAT models succeed in NLoS scenarios where PTQ models fail to reach the 10% BLER target, while also yielding an 8x compression. These results with respect to full-precision demonstrate that QAT is a key enabler of low-complexity and latency-constrained inference at the PHY layer, facilitating real-time processing in 6G edge devices.

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arXiv2025

Efficient Deep Neural Receiver with Post-Training Quantization

Deep learning has recently garnered significant interest in wireless communications due to its superior performance compared to traditional model-based algorithms. Deep convolutional neural networks (CNNs) have demonstrated notable improvements in block error rate (BLER) under various channel models and mobility scenarios. However, the high computational complexity and resource demands of deep CNNs pose challenges for deployment in resource-constrained edge systems. The 3rd Generation Partnership Project (3GPP) Release 20 highlights the pivotal role of artificial intelligence (AI) integration in enabling advanced radio-access networks for 6G systems. The hard real-time processing demands of 5G and 6G require efficient techniques such as post-training quantization (PTQ), quantization-aware training (QAT), pruning, and hybrid approaches to meet latency requirements. In this paper, we focus on PTQ to reduce model complexity by lowering the bit-width of weights, thereby enhancing computational efficiency. Our analysis employs symmetric uniform quantization, applying both per-tensor and per-channel PTQ to a neural receiver achieving performance comparable to full-precision models. Specifically, 8-bit per-channel quantization maintains BLER performance with minimal degradation, while 4-bit quantization shows great promise but requires further optimization to achieve target BLER levels. These results highlight the potential of ultra-low bitwidth PTQ for efficient neural receiver deployment in 6G systems.

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