Dirty-paper Coding without Channel Information at the Transmitter and Imperfect Estimation at the Receiver

Pablo Piantanida; Pierre Duhamel

Home/Archive/arXiv

arXiv · 0706.2795

Dirty-paper Coding without Channel Information at the Transmitter and Imperfect Estimation at the Receiver

Pablo Piantanida·Pierre Duhamel

View original source ↗Read source document ↗Export metadata ↓

Abstract

In this paper, we examine the effects of imperfect channel estimation at the receiver and no channel knowledge at the transmitter on the capacity of the fading Costa's channel with channel state information non-causally known at the transmitter. We derive the optimal Dirty-paper coding (DPC) scheme and its corresponding achievable rates with the assumption of Gaussian inputs. Our results, for uncorrelated Rayleigh fading, provide intuitive insights on the impact of the channel estimate and the channel characteristics (e.g. SNR, fading process, channel training) on the achievable rates. These are useful in practical scenarios of multiuser wireless communications (e.g. Broadcast Channels) and information embedding applications (e.g. robust watermarking). We also studied optimal training design adapted to each application. We provide numerical results for a single-user fading Costa's channel with maximum-likehood (ML) channel estimation. These illustrate an interesting practical trade-off between the amount of training and its impact to the interference cancellation performance using DPC scheme.

Explore related subjects

cs.IT math.IT

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Pablo Piantanida, Pierre Duhamel. 2007-06-19. Dirty-paper Coding without Channel Information at the Transmitter and Imperfect Estimation at the Receiver. https://arxiv.org/abs/0706.2795

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

DS and AMDS Symbol-Pair Codes from Three Families of Simple Root Cyclic Codes

Symbol-pair distances of simple-root cyclic codes are important for evaluating error correction performance under overlapping pair read channels. In this paper, we construct three families of simple-root cyclic codes over finite fields of odd characteristic via generator polynomials. For length $2q+2$, the minimum symbol-pair distance is $10$ for every odd prime power $q\ge 7$, established by excluding invalid support configurations. For length $4q-4$, when $q\equiv 3\pmod 4$ and finite field constraints hold, it is $9$ or $10$. For length $2(q-1)$, reciprocal-polynomial analysis shows it is $11$ or $12$. All obtained codes are MDS or AMDS symbol-pair codes.

cs.IT↗

arXiv2026

DeepTelecom: A Differentiable Multimodal Framework for Channel Twinning in Dynamic Heterogeneous Wireless Environments

Large-scale multimodal data are a key enabler for integrating large generative artificial intelligence models into wireless networks. However, existing wireless datasets offer limited support for heterogeneous environments, particularly in terms of reusable high-fidelity scenes, scalable differentiable ray tracing, and synchronized multimodal data. These limitations are more pronounced in mobile and aerial scenarios with time-varying environments and channels. To address these limitations, we propose DeepTelecom, a material-aware, high-fidelity multimodal channel dataset and reproducible generation framework for networks involving unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces. DeepTelecom refines coarse indoor/outdoor reconstructions and uses a large language model to map segmented surfaces to frequency-dependent electromagnetic parameters, yielding material-aware Level of Detail 3 digital twins. Then, a differentiable GPU-accelerated Sionna ray tracer simulates propagation across the sub-6 GHz/millimeter-wave/sub-terahertz bands, and specifically captures rotor-induced micro-Doppler signatures of stationary or moving UAVs at different pitch angles and translational velocities. The framework synchronously produces channel tensors, coverage maps, transmission videos, multi-view images, and weather-conditioned renderings, and supports differentiable material calibration from sparse channel anchors. The released dataset contains 10 material-aware indoor/outdoor scenes and 50,000 synchronized UAV samples; the framework supports scalable generation of additional data for user-defined scenes and system configurations. In 3D UAV localization, DeepTelecom reduces the 90th-percentile error from 31.05 m to 2.43 m compared with schemes considering only channel state information, demonstrating its significance for artificial-intelligence-enabled wireless systems.

cs.IT↗

arXiv2026

Limitations on Autoregressive Token Generation via Sequential Sampling

We study autoregressive token generation through the notion of sequential samplers: A circuit that samples the next symbol only given the previous ones and possibly a small amount of state. We show that (1) a sequential sampler with $k$ bits of state can be simulated by a stateless one at a roughly $O(2^{k})$ size overhead; (2) efficient insertional sampling (Stern, Chan, Kiros, Uszkoreit, ICML 2019) is more powerful than efficient sequential sampling in any fixed order; (3) sequential samplers with small state cannot establish certain forms of covert cryptographic channels. Result (1), together with the pseudoentropy vs. sequential sampleability duality of Vadhan and Zheng (STOC 2012), characterizes efficient sequential sampleability with small state. Result (3) provides a potential safety mechanism against subliminal coordination among malicious LLM agents.

cs.IT↗