arXiv subjects

Yuanning Yu

Publications and source records attributed to Yuanning Yu.

3 recordsLinked to original sources

arXiv2025

ReQuestNet: A Foundational Learning model for Channel Estimation

In this paper, we present a novel neural architecture for channel estimation (CE) in 5G and beyond, the Recurrent Equivariant UERS Estimation Network (ReQuestNet). It incorporates several practical considerations in wireless communication systems, such as ability to handle variable number of resource block (RB), dynamic number of transmit layers, physical resource block groups (PRGs) bundling size (BS), demodulation reference signal (DMRS) patterns with a single unified model, thereby, drastically simplifying the CE pipeline. Besides it addresses several limitations of the legacy linear MMSE solutions, for example, by being independent of other reference signals and particularly by jointly processing MIMO layers and differently precoded channels with unknown precoding at the receiver. ReQuestNet comprises of two sub-units, CoarseNet followed by RefinementNet. CoarseNet performs per PRG, per transmit-receive (Tx-Rx) stream channel estimation, while RefinementNet refines the CoarseNet channel estimate by incorporating correlations across differently precoded PRGs, and correlation across multiple input multiple output (MIMO) channel spatial dimensions (cross-MIMO). Simulation results demonstrate that ReQuestNet significantly outperforms genie minimum mean squared error (MMSE) CE across a wide range of channel conditions, delay-Doppler profiles, achieving up to 10dB gain at high SNRs. Notably, ReQuestNet generalizes effectively to unseen channel profiles, efficiently exploiting inter-PRG and cross-MIMO correlations under dynamic PRG BS and varying transmit layer allocations.

eess.SP↗

arXiv2007

Blind Estimation of Multiple Carrier Frequency Offsets

Multiple carrier-frequency offsets (CFO) arise in a distributed antenna system, where data are transmitted simultaneously from multiple antennas. In such systems the received signal contains multiple CFOs due to mismatch between the local oscillators of transmitters and receiver. This results in a time-varying rotation of the data constellation, which needs to be compensated for at the receiver before symbol recovery. This paper proposes a new approach for blind CFO estimation and symbol recovery. The received base-band signal is over-sampled, and its polyphase components are used to formulate a virtual Multiple-Input Multiple-Output (MIMO) problem. By applying blind MIMO system estimation techniques, the system response is estimated and used to subsequently transform the multiple CFOs estimation problem into many independent single CFO estimation problems. Furthermore, an initial estimate of the CFO is obtained from the phase of the MIMO system response. The Cramer-Rao Lower bound is also derived, and the large sample performance of the proposed estimator is compared to the bound.

cs.IT↗

arXiv2007

Blind Identification of Distributed Antenna Systems with Multiple Carrier Frequency Offsets

In spatially distributed multiuser antenna systems, the received signal contains multiple carrier-frequency offsets (CFOs) arising from mismatch between the oscillators of transmitters and receivers. This results in a time-varying rotation of the data constellation, which needs to be compensated at the receiver before symbol recovery. In this paper, a new approach for blind CFO estimation and symbol recovery is proposed. The received base-band signal is over-sampled, and its polyphase components are used to formulate a virtual Multiple-Input Multiple-Output (MIMO) problem. By applying blind MIMO system estimation techniques, the system response can be estimated and decoupled versions of the user symbols can be recovered, each one of which contains a distinct CFO. By applying a decision feedback Phase Lock Loop (PLL), the CFO can be mitigated and the transmitted symbols can be recovered. The estimated MIMO system response provides information about the CFOs that can be used to initialize the PLL, speed up its convergence, and avoid ambiguities usually linked with PLL.

cs.IT↗